{"repo_id":"OpenAgents","entity_id":"py:backend.memory","uri":"program://OpenAgents/module/backend.memory#L1-L296","kind":"module","name":"backend.memory","path":"backend/memory.py","language":"python","start_line":1,"end_line":296,"context_start_line":1,"context_end_line":296,"code":"from typing import Any, Dict, List, Union\nfrom loguru import logger\nimport json\n\nfrom backend.app import app\nfrom backend.utils.running_time_storage import get_running_time_storage\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom real_agents.adapters.memory import BaseChatMemory\n\nHUMAN_MESSAGE_KEY = \"human_message\"\nAI_MESSAGE_KEY = \"ai_message\"\n\nLOCAL = \"local\"\nDATABASE = \"database\"\n\n\nclass UserMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources,\n etc. on user level\"\"\"\n\n # api_key_pool:\n # {\n # \"user_id\": [{\n # \"tool_id\": the id of the tool,\n # \"tool_name\": the name of the tool,\n # \"api_key\": the api_key of the tool,\n # }]\n # }\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):\n self.backend = backend\n self.name = name\n if self.backend == LOCAL:\n if memory_pool is None:\n memory_pool = {}\n self.memory_pool = memory_pool\n elif self.backend == DATABASE:\n with app.app_context():\n self.redis_client = get_running_time_storage()\n self.db_client = get_user_conversation_storage()\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def get_pool_info_with_id(\n self,\n user_id: str,\n default_value: Union[List, Dict],\n ) -> Any:\n \"\"\"Gets the information with user_id and chat_id from the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id in pool:\n return pool[user_id]\n else:\n return default_value\n elif self.backend == DATABASE:\n memory_pool_name = f\"{self.name}:{user_id}\"\n if self.redis_client.exists(memory_pool_name):\n # In cache\n info = json.loads(self.redis_client.get(memory_pool_name))\n else:\n # Cache miss\n try:\n # api_keys are not stored in database\n if self.name == \"api_key_pool\":\n info = default_value\n else:\n raise NotImplementedError(f\"Currently only support message pool in database, not {self.name}\")\n except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, info in self.memory_pool.items():\n yield user_id, info\n elif self.backend == DATABASE:\n raise NotImplementedError(\"Currently not support UserMemoryManager iteration in database mode.\")\n\n\nclass ChatMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources, etc. on chat level\"\"\"\n\n # memory_pool:\n # {user_id: {chat_id: [\n # {\"message_id\": the id of this pair of messages,\n # \"parent_message_id\": the message of the parent message,\n # \"message_type\": type of the message, possible values: human_message / ai_message\n # \"message_content\": content of the message\n # }\n # ]\n # }\n # }\n # grounding_source_pool:\n # {user_id: {chat_id: {\"filenames\": List of filenames,\n # \"activated_filenames\": List of user-selected activated names}}\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):\n \"\"\"\n This ChatMemoryManager can not be applied to grounding_source_pool in database mode.\n \"\"\"\n self.backend = backend\n self.name = name\n if self.backend == LOCAL:\n if memory_pool is None:\n memory_pool = {}\n self.memory_pool = memory_pool\n elif self.backend == DATABASE:\n with app.app_context():\n self.redis_client = get_running_time_storage()\n self.db_client = get_user_conversation_storage()\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def get_pool_info_with_id(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n ) -> Any:\n \"\"\"Gets the information with user_id and chat_id from the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id in pool and chat_id in pool[user_id]:\n return pool[user_id][chat_id]\n else:\n return default_value\n elif self.backend == DATABASE:\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n if self.redis_client.exists(memory_pool_name):\n # In cache\n info = json.loads(self.redis_client.get(memory_pool_name))\n else:\n # Cache miss\n try:\n # Found in database\n if self.name == \"message_pool\":\n info = []\n response = self.db_client.message.find({\"conversation_id\": chat_id})\n if response is None:\n # Not in database (new chat)\n info = default_value\n else:\n # In database\n for message in response:\n if message[\"role\"] == \"user\":\n message_type = HUMAN_MESSAGE_KEY\n elif message[\"role\"] == \"assistant\":\n message_type = AI_MESSAGE_KEY\n else:\n raise ValueError(\"Unknown role: {}\".format(message[\"role\"]))\n info.append(\n {\n \"message_id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"message_type\": message_type,\n \"message_content\": message[\"data_for_llm\"],\n }\n )\n self.redis_client.set(memory_pool_name, json.dumps(info))\n elif self.name == \"jupyter_kernel_pool\":\n info = default_value\n else:\n raise NotImplementedError(f\"Currently only support message pool in database, not {self.name}\")\n except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, chat_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = {}\n pool[user_id][chat_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, chat_id_info in self.memory_pool.items():\n for chat_id, info in chat_id_info.items():\n yield user_id, chat_id, info\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n iterator = self.redis_client.scan_iter(\"jupyter_kernel_pool:*\")\n for key in iterator:\n user_id, chat_id = key.split(\":\")[1:]\n yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:\n self.memory_pool[user_id].pop([chat_id], None)\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n self.redis_client.delete(f\"{self.name}:{user_id}:{chat_id}\")\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool delete in database mode.\")\n\n\nclass MessageMemoryManager(ChatMemoryManager):\n \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()\n for message in message_list:\n if message.get(\"message_type\", None) == HUMAN_MESSAGE_KEY:\n agent_memory.chat_memory.add_user_message(message[\"message_content\"])\n elif message.get(\"message_type\", None) == AI_MESSAGE_KEY:\n agent_memory.chat_memory.add_ai_message(message[\"message_content\"])\n try:\n agent_memory.fit_max_token_limit()\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n pass\n\n @staticmethod\n def save_agent_memory_to_list(agent_memory: BaseChatMemory) -> List[Dict[str, str]]:\n \"\"\"Saves agent's memory to a list\"\"\"\n messages = agent_memory.chat_memory.messages\n message_list = []\n for message in messages:\n if message.type == \"human\":\n message_list.append(\n {\n \"message_type\": \"human_message\",\n \"message_content\": message.content,\n }\n )\n elif message.type == \"ai\":\n message_list.append(\n {\n \"message_type\": \"ai_message\",\n \"message_content\": message.content,\n }\n )\n return message_list\n\n def get_activated_message_list(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n parent_message_id: Union[int, str],\n ) -> List:\n \"\"\"Gets the activated message list from leaf to root.\"\"\"\n # ONLY work for messages\n message_list = self.get_pool_info_with_id(user_id, chat_id, default_value)\n activated_message_list = []\n end_point = parent_message_id\n while len(message_list) > 0 and end_point != -1:\n flag = False\n for msg in message_list:\n if msg[\"message_id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break\n activated_message_list = [msg] + activated_message_list\n end_point = msg[\"parent_message_id\"]\n flag = True\n break\n if not flag:\n break\n logger.bind(msg_head=f\"get_activated_message_list\").debug(activated_message_list)\n return activated_message_list","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.UserMemoryManager","uri":"program://OpenAgents/class/backend.memory.UserMemoryManager#L17-L94","kind":"class","name":"UserMemoryManager","path":"backend/memory.py","language":"python","start_line":17,"end_line":94,"context_start_line":1,"context_end_line":114,"code":"from typing import Any, Dict, List, Union\nfrom loguru import logger\nimport json\n\nfrom backend.app import app\nfrom backend.utils.running_time_storage import get_running_time_storage\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom real_agents.adapters.memory import BaseChatMemory\n\nHUMAN_MESSAGE_KEY = \"human_message\"\nAI_MESSAGE_KEY = \"ai_message\"\n\nLOCAL = \"local\"\nDATABASE = \"database\"\n\n\nclass UserMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources,\n etc. on user level\"\"\"\n\n # api_key_pool:\n # {\n # \"user_id\": [{\n # \"tool_id\": the id of the tool,\n # \"tool_name\": the name of the tool,\n # \"api_key\": the api_key of the tool,\n # }]\n # }\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):\n self.backend = backend\n self.name = name\n if self.backend == LOCAL:\n if memory_pool is None:\n memory_pool = {}\n self.memory_pool = memory_pool\n elif self.backend == DATABASE:\n with app.app_context():\n self.redis_client = get_running_time_storage()\n self.db_client = get_user_conversation_storage()\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def get_pool_info_with_id(\n self,\n user_id: str,\n default_value: Union[List, Dict],\n ) -> Any:\n \"\"\"Gets the information with user_id and chat_id from the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id in pool:\n return pool[user_id]\n else:\n return default_value\n elif self.backend == DATABASE:\n memory_pool_name = f\"{self.name}:{user_id}\"\n if self.redis_client.exists(memory_pool_name):\n # In cache\n info = json.loads(self.redis_client.get(memory_pool_name))\n else:\n # Cache miss\n try:\n # api_keys are not stored in database\n if self.name == \"api_key_pool\":\n info = default_value\n else:\n raise NotImplementedError(f\"Currently only support message pool in database, not {self.name}\")\n except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, info in self.memory_pool.items():\n yield user_id, info\n elif self.backend == DATABASE:\n raise NotImplementedError(\"Currently not support UserMemoryManager iteration in database mode.\")\n\n\nclass ChatMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources, etc. on chat level\"\"\"\n\n # memory_pool:\n # {user_id: {chat_id: [\n # {\"message_id\": the id of this pair of messages,\n # \"parent_message_id\": the message of the parent message,\n # \"message_type\": type of the message, possible values: human_message / ai_message\n # \"message_content\": content of the message\n # }\n # ]\n # }\n # }\n # grounding_source_pool:\n # {user_id: {chat_id: {\"filenames\": List of filenames,\n # \"activated_filenames\": List of user-selected activated names}}\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.ChatMemoryManager","uri":"program://OpenAgents/class/backend.memory.ChatMemoryManager#L97-L225","kind":"class","name":"ChatMemoryManager","path":"backend/memory.py","language":"python","start_line":97,"end_line":225,"context_start_line":77,"context_end_line":245,"code":" def set_pool_info_with_id(self, user_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, info in self.memory_pool.items():\n yield user_id, info\n elif self.backend == DATABASE:\n raise NotImplementedError(\"Currently not support UserMemoryManager iteration in database mode.\")\n\n\nclass ChatMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources, etc. on chat level\"\"\"\n\n # memory_pool:\n # {user_id: {chat_id: [\n # {\"message_id\": the id of this pair of messages,\n # \"parent_message_id\": the message of the parent message,\n # \"message_type\": type of the message, possible values: human_message / ai_message\n # \"message_content\": content of the message\n # }\n # ]\n # }\n # }\n # grounding_source_pool:\n # {user_id: {chat_id: {\"filenames\": List of filenames,\n # \"activated_filenames\": List of user-selected activated names}}\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):\n \"\"\"\n This ChatMemoryManager can not be applied to grounding_source_pool in database mode.\n \"\"\"\n self.backend = backend\n self.name = name\n if self.backend == LOCAL:\n if memory_pool is None:\n memory_pool = {}\n self.memory_pool = memory_pool\n elif self.backend == DATABASE:\n with app.app_context():\n self.redis_client = get_running_time_storage()\n self.db_client = get_user_conversation_storage()\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def get_pool_info_with_id(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n ) -> Any:\n \"\"\"Gets the information with user_id and chat_id from the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id in pool and chat_id in pool[user_id]:\n return pool[user_id][chat_id]\n else:\n return default_value\n elif self.backend == DATABASE:\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n if self.redis_client.exists(memory_pool_name):\n # In cache\n info = json.loads(self.redis_client.get(memory_pool_name))\n else:\n # Cache miss\n try:\n # Found in database\n if self.name == \"message_pool\":\n info = []\n response = self.db_client.message.find({\"conversation_id\": chat_id})\n if response is None:\n # Not in database (new chat)\n info = default_value\n else:\n # In database\n for message in response:\n if message[\"role\"] == \"user\":\n message_type = HUMAN_MESSAGE_KEY\n elif message[\"role\"] == \"assistant\":\n message_type = AI_MESSAGE_KEY\n else:\n raise ValueError(\"Unknown role: {}\".format(message[\"role\"]))\n info.append(\n {\n \"message_id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"message_type\": message_type,\n \"message_content\": message[\"data_for_llm\"],\n }\n )\n self.redis_client.set(memory_pool_name, json.dumps(info))\n elif self.name == \"jupyter_kernel_pool\":\n info = default_value\n else:\n raise NotImplementedError(f\"Currently only support message pool in database, not {self.name}\")\n except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, chat_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = {}\n pool[user_id][chat_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, chat_id_info in self.memory_pool.items():\n for chat_id, info in chat_id_info.items():\n yield user_id, chat_id, info\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n iterator = self.redis_client.scan_iter(\"jupyter_kernel_pool:*\")\n for key in iterator:\n user_id, chat_id = key.split(\":\")[1:]\n yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:\n self.memory_pool[user_id].pop([chat_id], None)\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n self.redis_client.delete(f\"{self.name}:{user_id}:{chat_id}\")\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool delete in database mode.\")\n\n\nclass MessageMemoryManager(ChatMemoryManager):\n \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()\n for message in message_list:\n if message.get(\"message_type\", None) == HUMAN_MESSAGE_KEY:\n agent_memory.chat_memory.add_user_message(message[\"message_content\"])\n elif message.get(\"message_type\", None) == AI_MESSAGE_KEY:\n agent_memory.chat_memory.add_ai_message(message[\"message_content\"])\n try:\n agent_memory.fit_max_token_limit()\n except Exception as e:\n import traceback\n\n traceback.print_exc()","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.MessageMemoryManager","uri":"program://OpenAgents/class/backend.memory.MessageMemoryManager#L228-L296","kind":"class","name":"MessageMemoryManager","path":"backend/memory.py","language":"python","start_line":228,"end_line":296,"context_start_line":208,"context_end_line":296,"code":" if self.name == \"jupyter_kernel_pool\":\n iterator = self.redis_client.scan_iter(\"jupyter_kernel_pool:*\")\n for key in iterator:\n user_id, chat_id = key.split(\":\")[1:]\n yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:\n self.memory_pool[user_id].pop([chat_id], None)\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n self.redis_client.delete(f\"{self.name}:{user_id}:{chat_id}\")\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool delete in database mode.\")\n\n\nclass MessageMemoryManager(ChatMemoryManager):\n \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()\n for message in message_list:\n if message.get(\"message_type\", None) == HUMAN_MESSAGE_KEY:\n agent_memory.chat_memory.add_user_message(message[\"message_content\"])\n elif message.get(\"message_type\", None) == AI_MESSAGE_KEY:\n agent_memory.chat_memory.add_ai_message(message[\"message_content\"])\n try:\n agent_memory.fit_max_token_limit()\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n pass\n\n @staticmethod\n def save_agent_memory_to_list(agent_memory: BaseChatMemory) -> List[Dict[str, str]]:\n \"\"\"Saves agent's memory to a list\"\"\"\n messages = agent_memory.chat_memory.messages\n message_list = []\n for message in messages:\n if message.type == \"human\":\n message_list.append(\n {\n \"message_type\": \"human_message\",\n \"message_content\": message.content,\n }\n )\n elif message.type == \"ai\":\n message_list.append(\n {\n \"message_type\": \"ai_message\",\n \"message_content\": message.content,\n }\n )\n return message_list\n\n def get_activated_message_list(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n parent_message_id: Union[int, str],\n ) -> List:\n \"\"\"Gets the activated message list from leaf to root.\"\"\"\n # ONLY work for messages\n message_list = self.get_pool_info_with_id(user_id, chat_id, default_value)\n activated_message_list = []\n end_point = parent_message_id\n while len(message_list) > 0 and end_point != -1:\n flag = False\n for msg in message_list:\n if msg[\"message_id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break\n activated_message_list = [msg] + activated_message_list\n end_point = msg[\"parent_message_id\"]\n flag = True\n break\n if not flag:\n break\n logger.bind(msg_head=f\"get_activated_message_list\").debug(activated_message_list)\n return activated_message_list","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.__init__","uri":"program://OpenAgents/function/backend.memory.__init__#L114-L129","kind":"function","name":"__init__","path":"backend/memory.py","language":"python","start_line":114,"end_line":129,"context_start_line":94,"context_end_line":149,"code":" raise NotImplementedError(\"Currently not support UserMemoryManager iteration in database mode.\")\n\n\nclass ChatMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources, etc. on chat level\"\"\"\n\n # memory_pool:\n # {user_id: {chat_id: [\n # {\"message_id\": the id of this pair of messages,\n # \"parent_message_id\": the message of the parent message,\n # \"message_type\": type of the message, possible values: human_message / ai_message\n # \"message_content\": content of the message\n # }\n # ]\n # }\n # }\n # grounding_source_pool:\n # {user_id: {chat_id: {\"filenames\": List of filenames,\n # \"activated_filenames\": List of user-selected activated names}}\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):\n \"\"\"\n This ChatMemoryManager can not be applied to grounding_source_pool in database mode.\n \"\"\"\n self.backend = backend\n self.name = name\n if self.backend == LOCAL:\n if memory_pool is None:\n memory_pool = {}\n self.memory_pool = memory_pool\n elif self.backend == DATABASE:\n with app.app_context():\n self.redis_client = get_running_time_storage()\n self.db_client = get_user_conversation_storage()\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def get_pool_info_with_id(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n ) -> Any:\n \"\"\"Gets the information with user_id and chat_id from the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id in pool and chat_id in pool[user_id]:\n return pool[user_id][chat_id]\n else:\n return default_value\n elif self.backend == DATABASE:\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n if self.redis_client.exists(memory_pool_name):\n # In cache\n info = json.loads(self.redis_client.get(memory_pool_name))\n else:","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.get_pool_info_with_id","uri":"program://OpenAgents/function/backend.memory.get_pool_info_with_id#L131-L187","kind":"function","name":"get_pool_info_with_id","path":"backend/memory.py","language":"python","start_line":131,"end_line":187,"context_start_line":111,"context_end_line":207,"code":" # {user_id: {chat_id: {\"filenames\": List of filenames,\n # \"activated_filenames\": List of user-selected activated names}}\n\n def __init__(self, name: str = None, backend: str = LOCAL, memory_pool: Dict = None):\n \"\"\"\n This ChatMemoryManager can not be applied to grounding_source_pool in database mode.\n \"\"\"\n self.backend = backend\n self.name = name\n if self.backend == LOCAL:\n if memory_pool is None:\n memory_pool = {}\n self.memory_pool = memory_pool\n elif self.backend == DATABASE:\n with app.app_context():\n self.redis_client = get_running_time_storage()\n self.db_client = get_user_conversation_storage()\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def get_pool_info_with_id(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n ) -> Any:\n \"\"\"Gets the information with user_id and chat_id from the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id in pool and chat_id in pool[user_id]:\n return pool[user_id][chat_id]\n else:\n return default_value\n elif self.backend == DATABASE:\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n if self.redis_client.exists(memory_pool_name):\n # In cache\n info = json.loads(self.redis_client.get(memory_pool_name))\n else:\n # Cache miss\n try:\n # Found in database\n if self.name == \"message_pool\":\n info = []\n response = self.db_client.message.find({\"conversation_id\": chat_id})\n if response is None:\n # Not in database (new chat)\n info = default_value\n else:\n # In database\n for message in response:\n if message[\"role\"] == \"user\":\n message_type = HUMAN_MESSAGE_KEY\n elif message[\"role\"] == \"assistant\":\n message_type = AI_MESSAGE_KEY\n else:\n raise ValueError(\"Unknown role: {}\".format(message[\"role\"]))\n info.append(\n {\n \"message_id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"message_type\": message_type,\n \"message_content\": message[\"data_for_llm\"],\n }\n )\n self.redis_client.set(memory_pool_name, json.dumps(info))\n elif self.name == \"jupyter_kernel_pool\":\n info = default_value\n else:\n raise NotImplementedError(f\"Currently only support message pool in database, not {self.name}\")\n except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, chat_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = {}\n pool[user_id][chat_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, chat_id_info in self.memory_pool.items():\n for chat_id, info in chat_id_info.items():\n yield user_id, chat_id, info\n elif self.backend == DATABASE:","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.set_pool_info_with_id","uri":"program://OpenAgents/function/backend.memory.set_pool_info_with_id#L189-L199","kind":"function","name":"set_pool_info_with_id","path":"backend/memory.py","language":"python","start_line":189,"end_line":199,"context_start_line":169,"context_end_line":219,"code":" {\n \"message_id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"message_type\": message_type,\n \"message_content\": message[\"data_for_llm\"],\n }\n )\n self.redis_client.set(memory_pool_name, json.dumps(info))\n elif self.name == \"jupyter_kernel_pool\":\n info = default_value\n else:\n raise NotImplementedError(f\"Currently only support message pool in database, not {self.name}\")\n except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, chat_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = {}\n pool[user_id][chat_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, chat_id_info in self.memory_pool.items():\n for chat_id, info in chat_id_info.items():\n yield user_id, chat_id, info\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n iterator = self.redis_client.scan_iter(\"jupyter_kernel_pool:*\")\n for key in iterator:\n user_id, chat_id = key.split(\":\")[1:]\n yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.__iter__","uri":"program://OpenAgents/function/backend.memory.__iter__#L201-L214","kind":"function","name":"__iter__","path":"backend/memory.py","language":"python","start_line":201,"end_line":214,"context_start_line":181,"context_end_line":234,"code":" except Exception as e:\n # Not in database\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Cache miss but not in database\").warning(\n \"Failed to get pool info from database: {}\".format(e)\n )\n info = default_value\n return info\n\n def set_pool_info_with_id(self, user_id: str, chat_id: str, info: Any) -> None:\n \"\"\"Sets the information with user_id and chat_id to the pool.\"\"\"\n if self.backend == LOCAL:\n pool = self.memory_pool\n if user_id not in pool:\n pool[user_id] = {}\n pool[user_id][chat_id] = info\n elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, chat_id_info in self.memory_pool.items():\n for chat_id, info in chat_id_info.items():\n yield user_id, chat_id, info\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n iterator = self.redis_client.scan_iter(\"jupyter_kernel_pool:*\")\n for key in iterator:\n user_id, chat_id = key.split(\":\")[1:]\n yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:\n self.memory_pool[user_id].pop([chat_id], None)\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n self.redis_client.delete(f\"{self.name}:{user_id}:{chat_id}\")\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool delete in database mode.\")\n\n\nclass MessageMemoryManager(ChatMemoryManager):\n \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.drop_item_with_id","uri":"program://OpenAgents/function/backend.memory.drop_item_with_id#L216-L225","kind":"function","name":"drop_item_with_id","path":"backend/memory.py","language":"python","start_line":216,"end_line":225,"context_start_line":196,"context_end_line":245,"code":" elif self.backend == DATABASE:\n # As db has its own updating logic, we only need to update the cache here (write-through).\n memory_pool_name = f\"{self.name}:{user_id}:{chat_id}\"\n self.redis_client.set(memory_pool_name, json.dumps(info))\n\n def __iter__(self):\n \"\"\"Iterates over the memory pool.\"\"\"\n if self.backend == LOCAL:\n for user_id, chat_id_info in self.memory_pool.items():\n for chat_id, info in chat_id_info.items():\n yield user_id, chat_id, info\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n iterator = self.redis_client.scan_iter(\"jupyter_kernel_pool:*\")\n for key in iterator:\n user_id, chat_id = key.split(\":\")[1:]\n yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:\n self.memory_pool[user_id].pop([chat_id], None)\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n self.redis_client.delete(f\"{self.name}:{user_id}:{chat_id}\")\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool delete in database mode.\")\n\n\nclass MessageMemoryManager(ChatMemoryManager):\n \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()\n for message in message_list:\n if message.get(\"message_type\", None) == HUMAN_MESSAGE_KEY:\n agent_memory.chat_memory.add_user_message(message[\"message_content\"])\n elif message.get(\"message_type\", None) == AI_MESSAGE_KEY:\n agent_memory.chat_memory.add_ai_message(message[\"message_content\"])\n try:\n agent_memory.fit_max_token_limit()\n except Exception as e:\n import traceback\n\n traceback.print_exc()","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.load_agent_memory_from_list","uri":"program://OpenAgents/function/backend.memory.load_agent_memory_from_list#L232-L246","kind":"function","name":"load_agent_memory_from_list","path":"backend/memory.py","language":"python","start_line":232,"end_line":246,"context_start_line":212,"context_end_line":266,"code":" yield user_id, chat_id, self.get_pool_info_with_id(user_id, chat_id, {})\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool iteration in database mode.\")\n\n def drop_item_with_id(self, user_id: str, chat_id: str):\n if self.backend == LOCAL:\n # drop item under one user\n if user_id in self.memory_pool:\n self.memory_pool[user_id].pop([chat_id], None)\n elif self.backend == DATABASE:\n if self.name == \"jupyter_kernel_pool\":\n self.redis_client.delete(f\"{self.name}:{user_id}:{chat_id}\")\n else:\n raise NotImplementedError(\"Currently only support jupyter kernel pool delete in database mode.\")\n\n\nclass MessageMemoryManager(ChatMemoryManager):\n \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()\n for message in message_list:\n if message.get(\"message_type\", None) == HUMAN_MESSAGE_KEY:\n agent_memory.chat_memory.add_user_message(message[\"message_content\"])\n elif message.get(\"message_type\", None) == AI_MESSAGE_KEY:\n agent_memory.chat_memory.add_ai_message(message[\"message_content\"])\n try:\n agent_memory.fit_max_token_limit()\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n pass\n\n @staticmethod\n def save_agent_memory_to_list(agent_memory: BaseChatMemory) -> List[Dict[str, str]]:\n \"\"\"Saves agent's memory to a list\"\"\"\n messages = agent_memory.chat_memory.messages\n message_list = []\n for message in messages:\n if message.type == \"human\":\n message_list.append(\n {\n \"message_type\": \"human_message\",\n \"message_content\": message.content,\n }\n )\n elif message.type == \"ai\":\n message_list.append(\n {\n \"message_type\": \"ai_message\",\n \"message_content\": message.content,\n }","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.save_agent_memory_to_list","uri":"program://OpenAgents/function/backend.memory.save_agent_memory_to_list#L249-L268","kind":"function","name":"save_agent_memory_to_list","path":"backend/memory.py","language":"python","start_line":249,"end_line":268,"context_start_line":229,"context_end_line":288,"code":" \"\"\"A class to manage the memory of messages.\"\"\"\n\n @staticmethod\n def load_agent_memory_from_list(agent_memory: BaseChatMemory, message_list: List[Dict[str, str]]) -> None:\n \"\"\"Load agent's memory from a list.\"\"\"\n agent_memory.clear()\n for message in message_list:\n if message.get(\"message_type\", None) == HUMAN_MESSAGE_KEY:\n agent_memory.chat_memory.add_user_message(message[\"message_content\"])\n elif message.get(\"message_type\", None) == AI_MESSAGE_KEY:\n agent_memory.chat_memory.add_ai_message(message[\"message_content\"])\n try:\n agent_memory.fit_max_token_limit()\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n pass\n\n @staticmethod\n def save_agent_memory_to_list(agent_memory: BaseChatMemory) -> List[Dict[str, str]]:\n \"\"\"Saves agent's memory to a list\"\"\"\n messages = agent_memory.chat_memory.messages\n message_list = []\n for message in messages:\n if message.type == \"human\":\n message_list.append(\n {\n \"message_type\": \"human_message\",\n \"message_content\": message.content,\n }\n )\n elif message.type == \"ai\":\n message_list.append(\n {\n \"message_type\": \"ai_message\",\n \"message_content\": message.content,\n }\n )\n return message_list\n\n def get_activated_message_list(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n parent_message_id: Union[int, str],\n ) -> List:\n \"\"\"Gets the activated message list from leaf to root.\"\"\"\n # ONLY work for messages\n message_list = self.get_pool_info_with_id(user_id, chat_id, default_value)\n activated_message_list = []\n end_point = parent_message_id\n while len(message_list) > 0 and end_point != -1:\n flag = False\n for msg in message_list:\n if msg[\"message_id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.memory.get_activated_message_list","uri":"program://OpenAgents/function/backend.memory.get_activated_message_list#L270-L296","kind":"function","name":"get_activated_message_list","path":"backend/memory.py","language":"python","start_line":270,"end_line":296,"context_start_line":250,"context_end_line":296,"code":" \"\"\"Saves agent's memory to a list\"\"\"\n messages = agent_memory.chat_memory.messages\n message_list = []\n for message in messages:\n if message.type == \"human\":\n message_list.append(\n {\n \"message_type\": \"human_message\",\n \"message_content\": message.content,\n }\n )\n elif message.type == \"ai\":\n message_list.append(\n {\n \"message_type\": \"ai_message\",\n \"message_content\": message.content,\n }\n )\n return message_list\n\n def get_activated_message_list(\n self,\n user_id: str,\n chat_id: str,\n default_value: Union[List, Dict],\n parent_message_id: Union[int, str],\n ) -> List:\n \"\"\"Gets the activated message list from leaf to root.\"\"\"\n # ONLY work for messages\n message_list = self.get_pool_info_with_id(user_id, chat_id, default_value)\n activated_message_list = []\n end_point = parent_message_id\n while len(message_list) > 0 and end_point != -1:\n flag = False\n for msg in message_list:\n if msg[\"message_id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break\n activated_message_list = [msg] + activated_message_list\n end_point = msg[\"parent_message_id\"]\n flag = True\n break\n if not flag:\n break\n logger.bind(msg_head=f\"get_activated_message_list\").debug(activated_message_list)\n return activated_message_list","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.app","uri":"program://OpenAgents/module/backend.app#L1-L12","kind":"module","name":"backend.app","path":"backend/app.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"import os\n\nfrom flask import Flask\nfrom flask_cors import CORS\n\napp = Flask(__name__)\ncurrent_path = os.path.abspath(__file__)\napp.config[\"UPLOAD_FOLDER\"] = os.path.dirname(current_path) + \"/data\"\nos.makedirs(app.config[\"UPLOAD_FOLDER\"], exist_ok=True)\n# Execute code locally or remotely on docker\napp.config[\"CODE_EXECUTION_MODE\"] = os.getenv(\"CODE_EXECUTION_MODE\", \"local\")\nCORS(app)","source_hash":"74af841d92b325bc36ea5bd4fa5d27588e3e8b6c329275bdb773a9dd4ec680d4","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.schemas","uri":"program://OpenAgents/module/backend.schemas#L1-L19","kind":"module","name":"backend.schemas","path":"backend/schemas.py","language":"python","start_line":1,"end_line":19,"context_start_line":1,"context_end_line":19,"code":"APP_TYPES = [\"copilot\", \"plugins\", \"webot\"]\nTIME_STEP = 0.035\nTIME_OUT_MAP = {\"copilot\": 90, \"plugins\": 300, \"webot\": 600}\nSTREAM_BLOCK_TYPES = [\"image\", \"echarts\"]\nSTREAM_TOKEN_TYPES = [\"tool\", \"transition\", \"execution_result\", \"error\", \"kaggle_search\", \"kaggle_connect\", \"plain\"]\nEXECUTION_RESULT_MAX_TOKENS_MAP = {\"copilot\": 1000, \"plugins\": 2000, \"webot\": 20000}\n\nHEARTBEAT_INTERVAL = 10\n\n# define error code\nUNAUTH = 401\nUNFOUND = 404\nOVERLOAD = 503\nINTERNAL = 500\nUNSUPPORTED = 403\n\n# define models which need extra continue flag\nNEED_CONTINUE_MODEL = {\"claude-v1\", \"claude-2\"}\nDEFAULT_USER_ID = \"DefaultUser\"","source_hash":"1def68d2c8a3ad4f2d1427fd0c86bae0e432420f46727729c006913977de767c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.main","uri":"program://OpenAgents/module/backend.main#L1-L47","kind":"module","name":"backend.main","path":"backend/main.py","language":"python","start_line":1,"end_line":47,"context_start_line":1,"context_end_line":47,"code":"import os\nimport warnings\nimport threading\n\nfrom backend.app import app\nfrom backend.kernel_publisher import start_kernel_publisher\nfrom backend.utils.threading import ThreadManager\nfrom backend.utils.utils import VariableRegister, init_log\nfrom backend.memory import (\n ChatMemoryManager,\n MessageMemoryManager,\n UserMemoryManager,\n)\n\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\nlogger = init_log(\n error=os.path.join(\".logging\", \"error.log\"),\n runtime=os.path.join(\".logging\", \"runtime.log\"),\n serialize=os.path.join(\".logging\", \"serialize.log\"),\n trace=os.path.join(\".logging\", \"trace.log\"),\n)\n\nVARIABLE_REGISTER_BACKEND = os.environ.get(\"VARIABLE_REGISTER_BACKEND\", \"local\")\nMESSAGE_MEMORY_MANAGER_BACKEND = os.environ.get(\"MESSAGE_MEMORY_MANAGER_BACKEND\", \"local\")\nAPI_KEY_MEMORY_MANAGER_BACKEND = os.environ.get(\"API_KEY_MEMORY_MANAGER_BACKEND\", \"local\")\nJUPYTER_KERNEL_MEMORY_MANAGER_BACKEND = os.environ.get(\"JUPYTER_KERNEL_MEMORY_MANAGER_BACKEND\", \"local\")\n\nmessage_pool: MessageMemoryManager = MessageMemoryManager(name=\"message_pool\", backend=MESSAGE_MEMORY_MANAGER_BACKEND)\ngrounding_source_pool: ChatMemoryManager = ChatMemoryManager()\napi_key_pool: UserMemoryManager = UserMemoryManager(name=\"api_key_pool\", backend=API_KEY_MEMORY_MANAGER_BACKEND)\njupyter_kernel_pool: ChatMemoryManager = ChatMemoryManager(\n name=\"jupyter_kernel_pool\", backend=JUPYTER_KERNEL_MEMORY_MANAGER_BACKEND\n)\nthreading_pool: ThreadManager = ThreadManager()\n\nmessage_id_register = VariableRegister(name=\"message_id_register\", backend=VARIABLE_REGISTER_BACKEND)\n\n# Monitor kernel and kill long running kernels\nif app.config[\"CODE_EXECUTION_MODE\"] == \"docker\":\n threading.Thread(target=start_kernel_publisher, args=(), daemon=True).start()\n\nif __name__ == \"__main__\":\n import multiprocess\n\n multiprocess.set_start_method(\"spawn\", True)\n app.run()","source_hash":"cb78f6504771eb49fa7fc33a1e4a191be4382072fd45140bb74025ccee3fc9fc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.kernel_publisher","uri":"program://OpenAgents/module/backend.kernel_publisher#L1-L70","kind":"module","name":"backend.kernel_publisher","path":"backend/kernel_publisher.py","language":"python","start_line":1,"end_line":70,"context_start_line":1,"context_end_line":70,"code":"import redis\nfrom typing import Any\n\nfrom backend.utils.utils import logger\nimport os\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n\n# Set the queue and pending key\nQUEUE_RUNNING = \"kernel_running_queue\"\nQUEUE_PENDING = \"kernel_pending_queue\"\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n\nMAX_CONCURRENT_KERNELS = 300\n\n\ndef add_job_to_pending(job: Any) -> None:\n # always add the jobn to pending\n r.rpush(QUEUE_PENDING, job)\n\n\ndef move_pending_to_running() -> None:\n \"\"\"Move pending jobs to the running queue.\"\"\"\n # Get the first pending job\n job = r.lindex(QUEUE_PENDING, 0)\n if job is not None:\n logger.bind(msg_head=\"Job running\").debug(job)\n # Move the job from pending queue to running queue\n r.rpush(QUEUE_RUNNING, job)\n # Notify the running channel\n r.publish(RUNNING_EVENT, job)\n # Remove the job from pending queue\n r.lpop(QUEUE_PENDING)\n\n\n# Subscribe to job completion events\ndef handle_job_completion(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job completed\").debug(chat_id)\n # here we only care about the capacity, not caring about which one is poped now\n logger.bind(msg_head=\"Queue running\").debug(r.lrange(QUEUE_RUNNING, 0, -1))\n r.lrem(QUEUE_RUNNING, 0, chat_id)\n move_pending_to_running()\n\n\ndef handle_new_job(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job submitted\").debug(chat_id)\n # all submitted jobs into pending queue\n add_job_to_pending(chat_id)\n # push the id to the pending queue\n logger.bind(msg_head=\"Queue pending\").debug(r.lrange(QUEUE_PENDING, 0, -1))\n if r.llen(QUEUE_RUNNING) < MAX_CONCURRENT_KERNELS:\n move_pending_to_running()\n\n\ndef start_kernel_publisher() -> None:\n # Connect to Redis\n r.delete(QUEUE_RUNNING)\n r.delete(QUEUE_PENDING)\n # Start the publisher & subscriber\n p = r.pubsub()\n p.subscribe(**{COMPLETE_EVENT: handle_job_completion, SUBMIT_EVENT: handle_new_job})\n\n p.run_in_thread(sleep_time=0.1)","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.kernel_publisher.add_job_to_pending","uri":"program://OpenAgents/function/backend.kernel_publisher.add_job_to_pending#L20-L22","kind":"function","name":"add_job_to_pending","path":"backend/kernel_publisher.py","language":"python","start_line":20,"end_line":22,"context_start_line":1,"context_end_line":42,"code":"import redis\nfrom typing import Any\n\nfrom backend.utils.utils import logger\nimport os\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n\n# Set the queue and pending key\nQUEUE_RUNNING = \"kernel_running_queue\"\nQUEUE_PENDING = \"kernel_pending_queue\"\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n\nMAX_CONCURRENT_KERNELS = 300\n\n\ndef add_job_to_pending(job: Any) -> None:\n # always add the jobn to pending\n r.rpush(QUEUE_PENDING, job)\n\n\ndef move_pending_to_running() -> None:\n \"\"\"Move pending jobs to the running queue.\"\"\"\n # Get the first pending job\n job = r.lindex(QUEUE_PENDING, 0)\n if job is not None:\n logger.bind(msg_head=\"Job running\").debug(job)\n # Move the job from pending queue to running queue\n r.rpush(QUEUE_RUNNING, job)\n # Notify the running channel\n r.publish(RUNNING_EVENT, job)\n # Remove the job from pending queue\n r.lpop(QUEUE_PENDING)\n\n\n# Subscribe to job completion events\ndef handle_job_completion(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.kernel_publisher.move_pending_to_running","uri":"program://OpenAgents/function/backend.kernel_publisher.move_pending_to_running#L25-L36","kind":"function","name":"move_pending_to_running","path":"backend/kernel_publisher.py","language":"python","start_line":25,"end_line":36,"context_start_line":5,"context_end_line":56,"code":"import os\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n\n# Set the queue and pending key\nQUEUE_RUNNING = \"kernel_running_queue\"\nQUEUE_PENDING = \"kernel_pending_queue\"\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n\nMAX_CONCURRENT_KERNELS = 300\n\n\ndef add_job_to_pending(job: Any) -> None:\n # always add the jobn to pending\n r.rpush(QUEUE_PENDING, job)\n\n\ndef move_pending_to_running() -> None:\n \"\"\"Move pending jobs to the running queue.\"\"\"\n # Get the first pending job\n job = r.lindex(QUEUE_PENDING, 0)\n if job is not None:\n logger.bind(msg_head=\"Job running\").debug(job)\n # Move the job from pending queue to running queue\n r.rpush(QUEUE_RUNNING, job)\n # Notify the running channel\n r.publish(RUNNING_EVENT, job)\n # Remove the job from pending queue\n r.lpop(QUEUE_PENDING)\n\n\n# Subscribe to job completion events\ndef handle_job_completion(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job completed\").debug(chat_id)\n # here we only care about the capacity, not caring about which one is poped now\n logger.bind(msg_head=\"Queue running\").debug(r.lrange(QUEUE_RUNNING, 0, -1))\n r.lrem(QUEUE_RUNNING, 0, chat_id)\n move_pending_to_running()\n\n\ndef handle_new_job(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job submitted\").debug(chat_id)\n # all submitted jobs into pending queue\n add_job_to_pending(chat_id)\n # push the id to the pending queue","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.kernel_publisher.handle_job_completion","uri":"program://OpenAgents/function/backend.kernel_publisher.handle_job_completion#L40-L47","kind":"function","name":"handle_job_completion","path":"backend/kernel_publisher.py","language":"python","start_line":40,"end_line":47,"context_start_line":20,"context_end_line":67,"code":"def add_job_to_pending(job: Any) -> None:\n # always add the jobn to pending\n r.rpush(QUEUE_PENDING, job)\n\n\ndef move_pending_to_running() -> None:\n \"\"\"Move pending jobs to the running queue.\"\"\"\n # Get the first pending job\n job = r.lindex(QUEUE_PENDING, 0)\n if job is not None:\n logger.bind(msg_head=\"Job running\").debug(job)\n # Move the job from pending queue to running queue\n r.rpush(QUEUE_RUNNING, job)\n # Notify the running channel\n r.publish(RUNNING_EVENT, job)\n # Remove the job from pending queue\n r.lpop(QUEUE_PENDING)\n\n\n# Subscribe to job completion events\ndef handle_job_completion(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job completed\").debug(chat_id)\n # here we only care about the capacity, not caring about which one is poped now\n logger.bind(msg_head=\"Queue running\").debug(r.lrange(QUEUE_RUNNING, 0, -1))\n r.lrem(QUEUE_RUNNING, 0, chat_id)\n move_pending_to_running()\n\n\ndef handle_new_job(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job submitted\").debug(chat_id)\n # all submitted jobs into pending queue\n add_job_to_pending(chat_id)\n # push the id to the pending queue\n logger.bind(msg_head=\"Queue pending\").debug(r.lrange(QUEUE_PENDING, 0, -1))\n if r.llen(QUEUE_RUNNING) < MAX_CONCURRENT_KERNELS:\n move_pending_to_running()\n\n\ndef start_kernel_publisher() -> None:\n # Connect to Redis\n r.delete(QUEUE_RUNNING)\n r.delete(QUEUE_PENDING)\n # Start the publisher & subscriber\n p = r.pubsub()","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.kernel_publisher.handle_new_job","uri":"program://OpenAgents/function/backend.kernel_publisher.handle_new_job#L50-L59","kind":"function","name":"handle_new_job","path":"backend/kernel_publisher.py","language":"python","start_line":50,"end_line":59,"context_start_line":30,"context_end_line":70,"code":" logger.bind(msg_head=\"Job running\").debug(job)\n # Move the job from pending queue to running queue\n r.rpush(QUEUE_RUNNING, job)\n # Notify the running channel\n r.publish(RUNNING_EVENT, job)\n # Remove the job from pending queue\n r.lpop(QUEUE_PENDING)\n\n\n# Subscribe to job completion events\ndef handle_job_completion(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job completed\").debug(chat_id)\n # here we only care about the capacity, not caring about which one is poped now\n logger.bind(msg_head=\"Queue running\").debug(r.lrange(QUEUE_RUNNING, 0, -1))\n r.lrem(QUEUE_RUNNING, 0, chat_id)\n move_pending_to_running()\n\n\ndef handle_new_job(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job submitted\").debug(chat_id)\n # all submitted jobs into pending queue\n add_job_to_pending(chat_id)\n # push the id to the pending queue\n logger.bind(msg_head=\"Queue pending\").debug(r.lrange(QUEUE_PENDING, 0, -1))\n if r.llen(QUEUE_RUNNING) < MAX_CONCURRENT_KERNELS:\n move_pending_to_running()\n\n\ndef start_kernel_publisher() -> None:\n # Connect to Redis\n r.delete(QUEUE_RUNNING)\n r.delete(QUEUE_PENDING)\n # Start the publisher & subscriber\n p = r.pubsub()\n p.subscribe(**{COMPLETE_EVENT: handle_job_completion, SUBMIT_EVENT: handle_new_job})\n\n p.run_in_thread(sleep_time=0.1)","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.kernel_publisher.start_kernel_publisher","uri":"program://OpenAgents/function/backend.kernel_publisher.start_kernel_publisher#L62-L70","kind":"function","name":"start_kernel_publisher","path":"backend/kernel_publisher.py","language":"python","start_line":62,"end_line":70,"context_start_line":42,"context_end_line":70,"code":" chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job completed\").debug(chat_id)\n # here we only care about the capacity, not caring about which one is poped now\n logger.bind(msg_head=\"Queue running\").debug(r.lrange(QUEUE_RUNNING, 0, -1))\n r.lrem(QUEUE_RUNNING, 0, chat_id)\n move_pending_to_running()\n\n\ndef handle_new_job(message: dict) -> None:\n # the data should be the chat id\n chat_id = message[\"data\"]\n logger.bind(msg_head=\"Job submitted\").debug(chat_id)\n # all submitted jobs into pending queue\n add_job_to_pending(chat_id)\n # push the id to the pending queue\n logger.bind(msg_head=\"Queue pending\").debug(r.lrange(QUEUE_PENDING, 0, -1))\n if r.llen(QUEUE_RUNNING) < MAX_CONCURRENT_KERNELS:\n move_pending_to_running()\n\n\ndef start_kernel_publisher() -> None:\n # Connect to Redis\n r.delete(QUEUE_RUNNING)\n r.delete(QUEUE_PENDING)\n # Start the publisher & subscriber\n p = r.pubsub()\n p.subscribe(**{COMPLETE_EVENT: handle_job_completion, SUBMIT_EVENT: handle_new_job})\n\n p.run_in_thread(sleep_time=0.1)","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming","uri":"program://OpenAgents/module/backend.display_streaming#L1-L216","kind":"module","name":"backend.display_streaming","path":"backend/display_streaming.py","language":"python","start_line":1,"end_line":216,"context_start_line":1,"context_end_line":216,"code":"from typing import Dict, Optional, List\nimport json\nimport base64\nimport re\nimport ast\n\nimport mo_sql_parsing\nfrom pydantic import BaseModel\n\nfrom real_agents.adapters.data_model import MessageDataModel, DataModel\n\n\ndef is_json(text: str) -> bool:\n try:\n json.loads(text)\n return True\n except json.JSONDecodeError:\n return False\n\n\ndef split_text_and_code(text: str) -> List:\n pattern = r\"(```[\\s\\S]+?```)\"\n result = [x for x in re.split(pattern, text) if x.strip()]\n\n return result\n\n\ndef detect_code_type(code) -> str:\n # Attempt Python parsing\n try:\n ast.parse(code)\n return \"python\"\n except SyntaxError:\n pass\n\n # Attempt SQL parsing\n try:\n mo_sql_parsing.parse(code)\n return \"sql\"\n except:\n pass\n\n # If all else fails, it's probably plain text\n return \"text\"\n\n\ndef add_backticks(text: str) -> str:\n \"\"\"Add backticks to code blocks.\"\"\"\n text_type = detect_code_type(text)\n if is_json(text):\n text = \"```json\\n\" + text + \"\\n```\"\n elif text_type == \"python\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```python\\n\" + text + \"\\n```\"\n elif text_type == \"sql\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```sql\\n\" + text + \"\\n```\"\n return text\n\n\nclass DisplayStream(BaseModel):\n \"\"\"The display stream to parse and render tokens and blocks\"\"\"\n\n streaming_mode: str = \"plain\"\n action: str = \"\"\n action_cache: str = \"\"\n execution_result_max_tokens: int = 1000\n escape: bool = False\n escape_cache: str = \"\"\n llm_call_id: int = -1\n\n def reset(self):\n self.streaming_mode = \"plain\"\n self.action = \"\"\n self.action_cache = \"\"\n self.escape = False\n self.escape_cache = \"\"\n\n def display(self, token: Dict) -> Optional[List[Dict]]:\n # Reset if the llm_call_id has changed\n if token[\"llm_call_id\"] != self.llm_call_id:\n self.llm_call_id = token[\"llm_call_id\"]\n self.reset()\n # Handle escape characters\n import codecs\n\n if token[\"text\"] == \"\\\\\":\n self.escape = True\n self.escape_cache = \"\\\\\"\n return None\n else:\n if self.escape:\n try:\n token[\"text\"] = codecs.decode(self.escape_cache + token[\"text\"], \"unicode_escape\")\n self.escape = False\n self.escape_cache = \"\"\n except Exception as e:\n self.escape_cache += token[\"text\"]\n # Tool selection\n if self.action != \"\" and token[\"type\"] != \"action\":\n # An action has been generated\n if self.action != \"Final Answer\":\n _pretty_name = self.action\n self.action_cache = self.action\n self.action = \"\"\n return [{\"text\": _pretty_name, \"type\": \"tool\", \"final\": False}]\n if token[\"type\"] == \"plain\":\n # Display plain text\n if self.streaming_mode == \"identifier\":\n return None\n else:\n self.streaming_mode = \"plain\"\n return [{\"text\": token[\"text\"], \"type\": \"transition\", \"final\": False}]\n elif token[\"type\"] == \"identifier\":\n self.streaming_mode = \"identifier\"\n return None\n elif token[\"type\"] == \"key\":\n self.streaming_mode = \"key\"\n return None\n elif token[\"type\"] == \"action\":\n self.streaming_mode = \"action\"\n self.action += token[\"text\"]\n return None\n elif token[\"type\"] == \"action_input\":\n self.streaming_mode = \"action_input\"\n if self.action == \"Final Answer\":\n return [{\"text\": token[\"text\"], \"type\": \"plain\", \"final\": True}]\n elif token[\"type\"] == \"block\":\n observation = token[\"text\"]\n result = self._display_observation(observation=observation)\n return result\n else:\n raise ValueError(\"Unknown token type: {}\".format(token[\"type\"]))\n\n def _display_observation(self, observation: Dict) -> Optional[List]:\n \"\"\"Display the observation, i.e., the response from the tool\n\n Args:\n observation: Tool response block\n\n Returns:\n A list of display blocks to the frontend\n \"\"\"\n tool_response_list = []\n if isinstance(observation, str):\n # Observation is a plain text (not used)\n tool_response_list.append({\"text\": observation, \"type\": \"plain\", \"final\": False})\n return tool_response_list\n\n assert isinstance(observation, DataModel), \"Observation must be a DataModel object\"\n observation = observation.get_human_side_data()\n\n assert isinstance(observation, Dict), \"Observation must be a Dict object\"\n\n result = observation.get(\"result\", \"\")\n result_str = str(result)\n # Code & Plugin block\n if \"intermediate_steps\" in observation:\n intermediate_steps = observation[\"intermediate_steps\"]\n if self.action_cache == \"PythonCodeBuilder\":\n intermediate_steps = \"```python\\n\" + intermediate_steps + \"\\n```\"\n elif self.action_cache == \"SQLCodeBuilder\":\n intermediate_steps = \"```sql\\n\" + intermediate_steps + \"\\n```\"\n else:\n intermediate_steps = add_backticks(intermediate_steps)\n tool_response_list.append({\"text\": intermediate_steps, \"type\": \"plain\", \"final\": False})\n\n # Execution result\n if not observation[\"success\"]:\n tool_response_list.append({\"text\": result_str, \"type\": \"error\", \"final\": False})\n else:\n result_str = MessageDataModel.truncate_text(result_str, max_token=self.execution_result_max_tokens)\n\n tool_response_list.append(\n {\n \"text\": f\"\"\"```console\\n{result_str.strip(' ').strip(\"```\")}\\n```\"\"\"\n if result_str.strip(\"```\")\n else \"\",\n \"type\": \"execution_result\",\n \"final\": False,\n }\n )\n # Kaggle search and connect\n if \"kaggle_action\" in observation:\n kaggle_action = observation[\"kaggle_action\"]\n tool_response_list.append(\n {\n \"text\": json.dumps(observation[\"kaggle_output_info\"]),\n \"type\": f\"kaggle_{kaggle_action}\",\n \"final\": False,\n }\n )\n # Image result, e.g., matplotlib\n if \"images\" in observation:\n try:\n for image in observation[\"images\"]:\n if isinstance(image, str):\n continue\n image_data_64 = \"data:image/png;base64,\" + base64.b64encode(\n base64.b64decode(image.data[\"image/png\"])\n ).decode(\"utf-8\")\n tool_response_list.append({\"text\": image_data_64, \"type\": \"image\", \"final\": False})\n except:\n tool_response_list.append(\n {\"text\": \"[ERROR]: error rendering image/png\", \"type\": \"error\", \"final\": False}\n )\n # Echarts\n if \"echarts\" in observation:\n chart_json = observation[\"echarts\"]\n\n if is_json(chart_json):\n tool_response_list.append({\"text\": chart_json, \"type\": \"echarts\", \"final\": False})\n else:\n tool_response_list.append({\"text\": f\"\"\"```json{chart_json}```\"\"\", \"type\": \"plain\", \"final\": False})\n\n return tool_response_list","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.is_json","uri":"program://OpenAgents/function/backend.display_streaming.is_json#L13-L18","kind":"function","name":"is_json","path":"backend/display_streaming.py","language":"python","start_line":13,"end_line":18,"context_start_line":1,"context_end_line":38,"code":"from typing import Dict, Optional, List\nimport json\nimport base64\nimport re\nimport ast\n\nimport mo_sql_parsing\nfrom pydantic import BaseModel\n\nfrom real_agents.adapters.data_model import MessageDataModel, DataModel\n\n\ndef is_json(text: str) -> bool:\n try:\n json.loads(text)\n return True\n except json.JSONDecodeError:\n return False\n\n\ndef split_text_and_code(text: str) -> List:\n pattern = r\"(```[\\s\\S]+?```)\"\n result = [x for x in re.split(pattern, text) if x.strip()]\n\n return result\n\n\ndef detect_code_type(code) -> str:\n # Attempt Python parsing\n try:\n ast.parse(code)\n return \"python\"\n except SyntaxError:\n pass\n\n # Attempt SQL parsing\n try:\n mo_sql_parsing.parse(code)","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.split_text_and_code","uri":"program://OpenAgents/function/backend.display_streaming.split_text_and_code#L21-L25","kind":"function","name":"split_text_and_code","path":"backend/display_streaming.py","language":"python","start_line":21,"end_line":25,"context_start_line":1,"context_end_line":45,"code":"from typing import Dict, Optional, List\nimport json\nimport base64\nimport re\nimport ast\n\nimport mo_sql_parsing\nfrom pydantic import BaseModel\n\nfrom real_agents.adapters.data_model import MessageDataModel, DataModel\n\n\ndef is_json(text: str) -> bool:\n try:\n json.loads(text)\n return True\n except json.JSONDecodeError:\n return False\n\n\ndef split_text_and_code(text: str) -> List:\n pattern = r\"(```[\\s\\S]+?```)\"\n result = [x for x in re.split(pattern, text) if x.strip()]\n\n return result\n\n\ndef detect_code_type(code) -> str:\n # Attempt Python parsing\n try:\n ast.parse(code)\n return \"python\"\n except SyntaxError:\n pass\n\n # Attempt SQL parsing\n try:\n mo_sql_parsing.parse(code)\n return \"sql\"\n except:\n pass\n\n # If all else fails, it's probably plain text\n return \"text\"\n","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.detect_code_type","uri":"program://OpenAgents/function/backend.display_streaming.detect_code_type#L28-L44","kind":"function","name":"detect_code_type","path":"backend/display_streaming.py","language":"python","start_line":28,"end_line":44,"context_start_line":8,"context_end_line":64,"code":"from pydantic import BaseModel\n\nfrom real_agents.adapters.data_model import MessageDataModel, DataModel\n\n\ndef is_json(text: str) -> bool:\n try:\n json.loads(text)\n return True\n except json.JSONDecodeError:\n return False\n\n\ndef split_text_and_code(text: str) -> List:\n pattern = r\"(```[\\s\\S]+?```)\"\n result = [x for x in re.split(pattern, text) if x.strip()]\n\n return result\n\n\ndef detect_code_type(code) -> str:\n # Attempt Python parsing\n try:\n ast.parse(code)\n return \"python\"\n except SyntaxError:\n pass\n\n # Attempt SQL parsing\n try:\n mo_sql_parsing.parse(code)\n return \"sql\"\n except:\n pass\n\n # If all else fails, it's probably plain text\n return \"text\"\n\n\ndef add_backticks(text: str) -> str:\n \"\"\"Add backticks to code blocks.\"\"\"\n text_type = detect_code_type(text)\n if is_json(text):\n text = \"```json\\n\" + text + \"\\n```\"\n elif text_type == \"python\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```python\\n\" + text + \"\\n```\"\n elif text_type == \"sql\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```sql\\n\" + text + \"\\n```\"\n return text\n\n\nclass DisplayStream(BaseModel):\n \"\"\"The display stream to parse and render tokens and blocks\"\"\"\n\n streaming_mode: str = \"plain\"","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.add_backticks","uri":"program://OpenAgents/function/backend.display_streaming.add_backticks#L47-L58","kind":"function","name":"add_backticks","path":"backend/display_streaming.py","language":"python","start_line":47,"end_line":58,"context_start_line":27,"context_end_line":78,"code":"\ndef detect_code_type(code) -> str:\n # Attempt Python parsing\n try:\n ast.parse(code)\n return \"python\"\n except SyntaxError:\n pass\n\n # Attempt SQL parsing\n try:\n mo_sql_parsing.parse(code)\n return \"sql\"\n except:\n pass\n\n # If all else fails, it's probably plain text\n return \"text\"\n\n\ndef add_backticks(text: str) -> str:\n \"\"\"Add backticks to code blocks.\"\"\"\n text_type = detect_code_type(text)\n if is_json(text):\n text = \"```json\\n\" + text + \"\\n```\"\n elif text_type == \"python\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```python\\n\" + text + \"\\n```\"\n elif text_type == \"sql\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```sql\\n\" + text + \"\\n```\"\n return text\n\n\nclass DisplayStream(BaseModel):\n \"\"\"The display stream to parse and render tokens and blocks\"\"\"\n\n streaming_mode: str = \"plain\"\n action: str = \"\"\n action_cache: str = \"\"\n execution_result_max_tokens: int = 1000\n escape: bool = False\n escape_cache: str = \"\"\n llm_call_id: int = -1\n\n def reset(self):\n self.streaming_mode = \"plain\"\n self.action = \"\"\n self.action_cache = \"\"\n self.escape = False\n self.escape_cache = \"\"\n","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.DisplayStream","uri":"program://OpenAgents/class/backend.display_streaming.DisplayStream#L61-L216","kind":"class","name":"DisplayStream","path":"backend/display_streaming.py","language":"python","start_line":61,"end_line":216,"context_start_line":41,"context_end_line":216,"code":" pass\n\n # If all else fails, it's probably plain text\n return \"text\"\n\n\ndef add_backticks(text: str) -> str:\n \"\"\"Add backticks to code blocks.\"\"\"\n text_type = detect_code_type(text)\n if is_json(text):\n text = \"```json\\n\" + text + \"\\n```\"\n elif text_type == \"python\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```python\\n\" + text + \"\\n```\"\n elif text_type == \"sql\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```sql\\n\" + text + \"\\n```\"\n return text\n\n\nclass DisplayStream(BaseModel):\n \"\"\"The display stream to parse and render tokens and blocks\"\"\"\n\n streaming_mode: str = \"plain\"\n action: str = \"\"\n action_cache: str = \"\"\n execution_result_max_tokens: int = 1000\n escape: bool = False\n escape_cache: str = \"\"\n llm_call_id: int = -1\n\n def reset(self):\n self.streaming_mode = \"plain\"\n self.action = \"\"\n self.action_cache = \"\"\n self.escape = False\n self.escape_cache = \"\"\n\n def display(self, token: Dict) -> Optional[List[Dict]]:\n # Reset if the llm_call_id has changed\n if token[\"llm_call_id\"] != self.llm_call_id:\n self.llm_call_id = token[\"llm_call_id\"]\n self.reset()\n # Handle escape characters\n import codecs\n\n if token[\"text\"] == \"\\\\\":\n self.escape = True\n self.escape_cache = \"\\\\\"\n return None\n else:\n if self.escape:\n try:\n token[\"text\"] = codecs.decode(self.escape_cache + token[\"text\"], \"unicode_escape\")\n self.escape = False\n self.escape_cache = \"\"\n except Exception as e:\n self.escape_cache += token[\"text\"]\n # Tool selection\n if self.action != \"\" and token[\"type\"] != \"action\":\n # An action has been generated\n if self.action != \"Final Answer\":\n _pretty_name = self.action\n self.action_cache = self.action\n self.action = \"\"\n return [{\"text\": _pretty_name, \"type\": \"tool\", \"final\": False}]\n if token[\"type\"] == \"plain\":\n # Display plain text\n if self.streaming_mode == \"identifier\":\n return None\n else:\n self.streaming_mode = \"plain\"\n return [{\"text\": token[\"text\"], \"type\": \"transition\", \"final\": False}]\n elif token[\"type\"] == \"identifier\":\n self.streaming_mode = \"identifier\"\n return None\n elif token[\"type\"] == \"key\":\n self.streaming_mode = \"key\"\n return None\n elif token[\"type\"] == \"action\":\n self.streaming_mode = \"action\"\n self.action += token[\"text\"]\n return None\n elif token[\"type\"] == \"action_input\":\n self.streaming_mode = \"action_input\"\n if self.action == \"Final Answer\":\n return [{\"text\": token[\"text\"], \"type\": \"plain\", \"final\": True}]\n elif token[\"type\"] == \"block\":\n observation = token[\"text\"]\n result = self._display_observation(observation=observation)\n return result\n else:\n raise ValueError(\"Unknown token type: {}\".format(token[\"type\"]))\n\n def _display_observation(self, observation: Dict) -> Optional[List]:\n \"\"\"Display the observation, i.e., the response from the tool\n\n Args:\n observation: Tool response block\n\n Returns:\n A list of display blocks to the frontend\n \"\"\"\n tool_response_list = []\n if isinstance(observation, str):\n # Observation is a plain text (not used)\n tool_response_list.append({\"text\": observation, \"type\": \"plain\", \"final\": False})\n return tool_response_list\n\n assert isinstance(observation, DataModel), \"Observation must be a DataModel object\"\n observation = observation.get_human_side_data()\n\n assert isinstance(observation, Dict), \"Observation must be a Dict object\"\n\n result = observation.get(\"result\", \"\")\n result_str = str(result)\n # Code & Plugin block\n if \"intermediate_steps\" in observation:\n intermediate_steps = observation[\"intermediate_steps\"]\n if self.action_cache == \"PythonCodeBuilder\":\n intermediate_steps = \"```python\\n\" + intermediate_steps + \"\\n```\"\n elif self.action_cache == \"SQLCodeBuilder\":\n intermediate_steps = \"```sql\\n\" + intermediate_steps + \"\\n```\"\n else:\n intermediate_steps = add_backticks(intermediate_steps)\n tool_response_list.append({\"text\": intermediate_steps, \"type\": \"plain\", \"final\": False})\n\n # Execution result\n if not observation[\"success\"]:\n tool_response_list.append({\"text\": result_str, \"type\": \"error\", \"final\": False})\n else:\n result_str = MessageDataModel.truncate_text(result_str, max_token=self.execution_result_max_tokens)\n\n tool_response_list.append(\n {\n \"text\": f\"\"\"```console\\n{result_str.strip(' ').strip(\"```\")}\\n```\"\"\"\n if result_str.strip(\"```\")\n else \"\",\n \"type\": \"execution_result\",\n \"final\": False,\n }\n )\n # Kaggle search and connect\n if \"kaggle_action\" in observation:\n kaggle_action = observation[\"kaggle_action\"]\n tool_response_list.append(\n {\n \"text\": json.dumps(observation[\"kaggle_output_info\"]),\n \"type\": f\"kaggle_{kaggle_action}\",\n \"final\": False,\n }\n )\n # Image result, e.g., matplotlib\n if \"images\" in observation:\n try:\n for image in observation[\"images\"]:\n if isinstance(image, str):\n continue\n image_data_64 = \"data:image/png;base64,\" + base64.b64encode(\n base64.b64decode(image.data[\"image/png\"])\n ).decode(\"utf-8\")\n tool_response_list.append({\"text\": image_data_64, \"type\": \"image\", \"final\": False})\n except:\n tool_response_list.append(\n {\"text\": \"[ERROR]: error rendering image/png\", \"type\": \"error\", \"final\": False}\n )\n # Echarts\n if \"echarts\" in observation:\n chart_json = observation[\"echarts\"]\n\n if is_json(chart_json):\n tool_response_list.append({\"text\": chart_json, \"type\": \"echarts\", \"final\": False})\n else:\n tool_response_list.append({\"text\": f\"\"\"```json{chart_json}```\"\"\", \"type\": \"plain\", \"final\": False})\n\n return tool_response_list","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.reset","uri":"program://OpenAgents/function/backend.display_streaming.reset#L72-L77","kind":"function","name":"reset","path":"backend/display_streaming.py","language":"python","start_line":72,"end_line":77,"context_start_line":52,"context_end_line":97,"code":" elif text_type == \"python\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```python\\n\" + text + \"\\n```\"\n elif text_type == \"sql\":\n if not text.startswith(\"```\") and not text.endswith(\"```\"):\n text = \"```sql\\n\" + text + \"\\n```\"\n return text\n\n\nclass DisplayStream(BaseModel):\n \"\"\"The display stream to parse and render tokens and blocks\"\"\"\n\n streaming_mode: str = \"plain\"\n action: str = \"\"\n action_cache: str = \"\"\n execution_result_max_tokens: int = 1000\n escape: bool = False\n escape_cache: str = \"\"\n llm_call_id: int = -1\n\n def reset(self):\n self.streaming_mode = \"plain\"\n self.action = \"\"\n self.action_cache = \"\"\n self.escape = False\n self.escape_cache = \"\"\n\n def display(self, token: Dict) -> Optional[List[Dict]]:\n # Reset if the llm_call_id has changed\n if token[\"llm_call_id\"] != self.llm_call_id:\n self.llm_call_id = token[\"llm_call_id\"]\n self.reset()\n # Handle escape characters\n import codecs\n\n if token[\"text\"] == \"\\\\\":\n self.escape = True\n self.escape_cache = \"\\\\\"\n return None\n else:\n if self.escape:\n try:\n token[\"text\"] = codecs.decode(self.escape_cache + token[\"text\"], \"unicode_escape\")\n self.escape = False\n self.escape_cache = \"\"\n except Exception as e:","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming.display","uri":"program://OpenAgents/function/backend.display_streaming.display#L79-L133","kind":"function","name":"display","path":"backend/display_streaming.py","language":"python","start_line":79,"end_line":133,"context_start_line":59,"context_end_line":153,"code":"\n\nclass DisplayStream(BaseModel):\n \"\"\"The display stream to parse and render tokens and blocks\"\"\"\n\n streaming_mode: str = \"plain\"\n action: str = \"\"\n action_cache: str = \"\"\n execution_result_max_tokens: int = 1000\n escape: bool = False\n escape_cache: str = \"\"\n llm_call_id: int = -1\n\n def reset(self):\n self.streaming_mode = \"plain\"\n self.action = \"\"\n self.action_cache = \"\"\n self.escape = False\n self.escape_cache = \"\"\n\n def display(self, token: Dict) -> Optional[List[Dict]]:\n # Reset if the llm_call_id has changed\n if token[\"llm_call_id\"] != self.llm_call_id:\n self.llm_call_id = token[\"llm_call_id\"]\n self.reset()\n # Handle escape characters\n import codecs\n\n if token[\"text\"] == \"\\\\\":\n self.escape = True\n self.escape_cache = \"\\\\\"\n return None\n else:\n if self.escape:\n try:\n token[\"text\"] = codecs.decode(self.escape_cache + token[\"text\"], \"unicode_escape\")\n self.escape = False\n self.escape_cache = \"\"\n except Exception as e:\n self.escape_cache += token[\"text\"]\n # Tool selection\n if self.action != \"\" and token[\"type\"] != \"action\":\n # An action has been generated\n if self.action != \"Final Answer\":\n _pretty_name = self.action\n self.action_cache = self.action\n self.action = \"\"\n return [{\"text\": _pretty_name, \"type\": \"tool\", \"final\": False}]\n if token[\"type\"] == \"plain\":\n # Display plain text\n if self.streaming_mode == \"identifier\":\n return None\n else:\n self.streaming_mode = \"plain\"\n return [{\"text\": token[\"text\"], \"type\": \"transition\", \"final\": False}]\n elif token[\"type\"] == \"identifier\":\n self.streaming_mode = \"identifier\"\n return None\n elif token[\"type\"] == \"key\":\n self.streaming_mode = \"key\"\n return None\n elif token[\"type\"] == \"action\":\n self.streaming_mode = \"action\"\n self.action += token[\"text\"]\n return None\n elif token[\"type\"] == \"action_input\":\n self.streaming_mode = \"action_input\"\n if self.action == \"Final Answer\":\n return [{\"text\": token[\"text\"], \"type\": \"plain\", \"final\": True}]\n elif token[\"type\"] == \"block\":\n observation = token[\"text\"]\n result = self._display_observation(observation=observation)\n return result\n else:\n raise ValueError(\"Unknown token type: {}\".format(token[\"type\"]))\n\n def _display_observation(self, observation: Dict) -> Optional[List]:\n \"\"\"Display the observation, i.e., the response from the tool\n\n Args:\n observation: Tool response block\n\n Returns:\n A list of display blocks to the frontend\n \"\"\"\n tool_response_list = []\n if isinstance(observation, str):\n # Observation is a plain text (not used)\n tool_response_list.append({\"text\": observation, \"type\": \"plain\", \"final\": False})\n return tool_response_list\n\n assert isinstance(observation, DataModel), \"Observation must be a DataModel object\"\n observation = observation.get_human_side_data()\n\n assert isinstance(observation, Dict), \"Observation must be a Dict object\"","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.display_streaming._display_observation","uri":"program://OpenAgents/function/backend.display_streaming._display_observation#L135-L216","kind":"function","name":"_display_observation","path":"backend/display_streaming.py","language":"python","start_line":135,"end_line":216,"context_start_line":115,"context_end_line":216,"code":" self.streaming_mode = \"identifier\"\n return None\n elif token[\"type\"] == \"key\":\n self.streaming_mode = \"key\"\n return None\n elif token[\"type\"] == \"action\":\n self.streaming_mode = \"action\"\n self.action += token[\"text\"]\n return None\n elif token[\"type\"] == \"action_input\":\n self.streaming_mode = \"action_input\"\n if self.action == \"Final Answer\":\n return [{\"text\": token[\"text\"], \"type\": \"plain\", \"final\": True}]\n elif token[\"type\"] == \"block\":\n observation = token[\"text\"]\n result = self._display_observation(observation=observation)\n return result\n else:\n raise ValueError(\"Unknown token type: {}\".format(token[\"type\"]))\n\n def _display_observation(self, observation: Dict) -> Optional[List]:\n \"\"\"Display the observation, i.e., the response from the tool\n\n Args:\n observation: Tool response block\n\n Returns:\n A list of display blocks to the frontend\n \"\"\"\n tool_response_list = []\n if isinstance(observation, str):\n # Observation is a plain text (not used)\n tool_response_list.append({\"text\": observation, \"type\": \"plain\", \"final\": False})\n return tool_response_list\n\n assert isinstance(observation, DataModel), \"Observation must be a DataModel object\"\n observation = observation.get_human_side_data()\n\n assert isinstance(observation, Dict), \"Observation must be a Dict object\"\n\n result = observation.get(\"result\", \"\")\n result_str = str(result)\n # Code & Plugin block\n if \"intermediate_steps\" in observation:\n intermediate_steps = observation[\"intermediate_steps\"]\n if self.action_cache == \"PythonCodeBuilder\":\n intermediate_steps = \"```python\\n\" + intermediate_steps + \"\\n```\"\n elif self.action_cache == \"SQLCodeBuilder\":\n intermediate_steps = \"```sql\\n\" + intermediate_steps + \"\\n```\"\n else:\n intermediate_steps = add_backticks(intermediate_steps)\n tool_response_list.append({\"text\": intermediate_steps, \"type\": \"plain\", \"final\": False})\n\n # Execution result\n if not observation[\"success\"]:\n tool_response_list.append({\"text\": result_str, \"type\": \"error\", \"final\": False})\n else:\n result_str = MessageDataModel.truncate_text(result_str, max_token=self.execution_result_max_tokens)\n\n tool_response_list.append(\n {\n \"text\": f\"\"\"```console\\n{result_str.strip(' ').strip(\"```\")}\\n```\"\"\"\n if result_str.strip(\"```\")\n else \"\",\n \"type\": \"execution_result\",\n \"final\": False,\n }\n )\n # Kaggle search and connect\n if \"kaggle_action\" in observation:\n kaggle_action = observation[\"kaggle_action\"]\n tool_response_list.append(\n {\n \"text\": json.dumps(observation[\"kaggle_output_info\"]),\n \"type\": f\"kaggle_{kaggle_action}\",\n \"final\": False,\n }\n )\n # Image result, e.g., matplotlib\n if \"images\" in observation:\n try:\n for image in observation[\"images\"]:\n if isinstance(image, str):\n continue\n image_data_64 = \"data:image/png;base64,\" + base64.b64encode(\n base64.b64decode(image.data[\"image/png\"])\n ).decode(\"utf-8\")\n tool_response_list.append({\"text\": image_data_64, \"type\": \"image\", \"final\": False})\n except:\n tool_response_list.append(\n {\"text\": \"[ERROR]: error rendering image/png\", \"type\": \"error\", \"final\": False}\n )\n # Echarts\n if \"echarts\" in observation:\n chart_json = observation[\"echarts\"]\n\n if is_json(chart_json):\n tool_response_list.append({\"text\": chart_json, \"type\": \"echarts\", \"final\": False})\n else:\n tool_response_list.append({\"text\": f\"\"\"```json{chart_json}```\"\"\", \"type\": \"plain\", \"final\": False})\n\n return tool_response_list","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.running_time_storage","uri":"program://OpenAgents/module/backend.utils.running_time_storage#L1-L14","kind":"module","name":"backend.utils.running_time_storage","path":"backend/utils/running_time_storage.py","language":"python","start_line":1,"end_line":14,"context_start_line":1,"context_end_line":14,"code":"import redis\nfrom flask import g\nimport os\n\n\ndef get_running_time_storage():\n \"\"\"Connects to redis.\"\"\"\n if \"running_time_storage\" not in g:\n g.running_time_storage = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n # Set maxmemory to 200MB (value is in bytes)\n g.running_time_storage.config_set(\"maxmemory\", \"500000000\")\n # Set maxmemory policy to allkeys-lru (Least Recently Used)\n g.running_time_storage.config_set(\"maxmemory-policy\", \"allkeys-lru\")\n return g.running_time_storage","source_hash":"0350157e598fdbd5d0e3e1be5d47529c59915d4c4e69ad198a2b11aa996f0501","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.running_time_storage.get_running_time_storage","uri":"program://OpenAgents/function/backend.utils.running_time_storage.get_running_time_storage#L6-L14","kind":"function","name":"get_running_time_storage","path":"backend/utils/running_time_storage.py","language":"python","start_line":6,"end_line":14,"context_start_line":1,"context_end_line":14,"code":"import redis\nfrom flask import g\nimport os\n\n\ndef get_running_time_storage():\n \"\"\"Connects to redis.\"\"\"\n if \"running_time_storage\" not in g:\n g.running_time_storage = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n # Set maxmemory to 200MB (value is in bytes)\n g.running_time_storage.config_set(\"maxmemory\", \"500000000\")\n # Set maxmemory policy to allkeys-lru (Least Recently Used)\n g.running_time_storage.config_set(\"maxmemory-policy\", \"allkeys-lru\")\n return g.running_time_storage","source_hash":"0350157e598fdbd5d0e3e1be5d47529c59915d4c4e69ad198a2b11aa996f0501","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.user_conversation_storage","uri":"program://OpenAgents/module/backend.utils.user_conversation_storage#L1-L16","kind":"module","name":"backend.utils.user_conversation_storage","path":"backend/utils/user_conversation_storage.py","language":"python","start_line":1,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"import pymongo\nfrom flask import g\nimport os\n\ndef get_user_conversation_storage():\n \"\"\"Connects to mongodb.\"\"\"\n if \"user_conversation_storage\" not in g:\n g.user_conversation_storage = pymongo.MongoClient(\"mongodb://{0}:27017/\".format(os.getenv(\"MONGO_SERVER\")))\n return g.user_conversation_storage[\"xlang\"]\n\n\ndef close_user_conversation_storage():\n \"\"\"Closes mongodb.\"\"\"\n user_conversation_storage = g.pop(\"user_conversation_storage\", None)\n if user_conversation_storage is not None:\n user_conversation_storage[\"xlang\"].close()","source_hash":"1bd093929108a82996140d5fa6f043df654d2bc6b6981e68648026ef57eb268c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.user_conversation_storage.get_user_conversation_storage","uri":"program://OpenAgents/function/backend.utils.user_conversation_storage.get_user_conversation_storage#L5-L9","kind":"function","name":"get_user_conversation_storage","path":"backend/utils/user_conversation_storage.py","language":"python","start_line":5,"end_line":9,"context_start_line":1,"context_end_line":16,"code":"import pymongo\nfrom flask import g\nimport os\n\ndef get_user_conversation_storage():\n \"\"\"Connects to mongodb.\"\"\"\n if \"user_conversation_storage\" not in g:\n g.user_conversation_storage = pymongo.MongoClient(\"mongodb://{0}:27017/\".format(os.getenv(\"MONGO_SERVER\")))\n return g.user_conversation_storage[\"xlang\"]\n\n\ndef close_user_conversation_storage():\n \"\"\"Closes mongodb.\"\"\"\n user_conversation_storage = g.pop(\"user_conversation_storage\", None)\n if user_conversation_storage is not None:\n user_conversation_storage[\"xlang\"].close()","source_hash":"1bd093929108a82996140d5fa6f043df654d2bc6b6981e68648026ef57eb268c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.user_conversation_storage.close_user_conversation_storage","uri":"program://OpenAgents/function/backend.utils.user_conversation_storage.close_user_conversation_storage#L12-L16","kind":"function","name":"close_user_conversation_storage","path":"backend/utils/user_conversation_storage.py","language":"python","start_line":12,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"import pymongo\nfrom flask import g\nimport os\n\ndef get_user_conversation_storage():\n \"\"\"Connects to mongodb.\"\"\"\n if \"user_conversation_storage\" not in g:\n g.user_conversation_storage = pymongo.MongoClient(\"mongodb://{0}:27017/\".format(os.getenv(\"MONGO_SERVER\")))\n return g.user_conversation_storage[\"xlang\"]\n\n\ndef close_user_conversation_storage():\n \"\"\"Closes mongodb.\"\"\"\n user_conversation_storage = g.pop(\"user_conversation_storage\", None)\n if user_conversation_storage is not None:\n user_conversation_storage[\"xlang\"].close()","source_hash":"1bd093929108a82996140d5fa6f043df654d2bc6b6981e68648026ef57eb268c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming","uri":"program://OpenAgents/module/backend.utils.streaming#L1-L608","kind":"module","name":"backend.utils.streaming","path":"backend/utils/streaming.py","language":"python","start_line":1,"end_line":608,"context_start_line":1,"context_end_line":608,"code":"import json\nimport re\nimport struct\nimport time\nfrom typing import Any, Dict, List, Optional, Literal\nimport multiprocess\nimport requests\nfrom bs4 import BeautifulSoup\n\nfrom backend.display_streaming import DisplayStream\nfrom backend.main import logger, message_pool, threading_pool\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.utils.utils import error_rendering\nfrom backend.memory import MessageMemoryManager\nfrom backend.schemas import (\n APP_TYPES,\n TIME_OUT_MAP,\n HEARTBEAT_INTERVAL,\n STREAM_BLOCK_TYPES,\n STREAM_TOKEN_TYPES,\n EXECUTION_RESULT_MAX_TOKENS_MAP,\n)\nfrom real_agents.data_agent import DataSummaryExecutor\nfrom real_agents.adapters.callbacks.agent_streaming import AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.agent_helpers import Agent, AgentExecutor\nfrom real_agents.adapters.llm import BaseLanguageModel\n\n\ndef check_url_exist(text: str) -> bool:\n \"\"\"check in a text whether there is a url\"\"\"\n # this regex extracts the http(s) with whitespace or () in the beginning and end, since usually the url is surrounded by whitespace or ()\n # e.g. \" https://google.com \" or \"(https://google.com)\"\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n\n links = re.findall(url_regex, text)\n return len(links) > 0\n\n\n# function to extract links from text\ndef extract_links(text: str) -> list[Any]:\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n links = re.findall(url_regex, text)\n return links\n\n\n# function to extract image links from a webpage\ndef extract_title_and_image_links(url: str) -> (tuple[Literal[''], list] | tuple[Any, list]):\n try:\n res = requests.get(url, timeout=3)\n if res.status_code != 200:\n return \"\", []\n soup = BeautifulSoup(res.text, \"html.parser\")\n title_tag = soup.find_all(\"title\")[0].text\n img_tags = soup.find_all(\"img\")\n # List to store image links with large width and height\n large_img_links = []\n # List to store all image links\n all_img_links = []\n for img in img_tags:\n if \"src\" in img.attrs:\n all_img_links.append(img[\"src\"])\n # Check if width and height attributes exist and add to the large list\n if \"width\" in img.attrs and \"height\" in img.attrs:\n # Ensure the width and height attributes can be converted to integers\n if int(img[\"width\"]) > 100 and int(img[\"height\"]) > 100:\n large_img_links.append(img[\"src\"])\n else:\n continue\n # If large images were found, return those, otherwise return all images\n img_links = large_img_links if large_img_links else []\n # fixme: handle the case there are no such tags\n\n return title_tag, img_links\n except requests.exceptions.Timeout:\n print(\"Request timed out!\")\n return \"\", []\n except Exception as e:\n print(f\"Error processing {url}: {e}\")\n return \"\", []\n\n\ndef extract_card_info_from_text(message: str) -> list:\n links = extract_links(message)\n rt = []\n for link in links:\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n return rt\n\n\ndef extract_card_info_from_links(links: List[str]) -> list[dict[str, Any]]:\n rt = []\n for link in links:\n if check_url_exist(link):\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n else:\n continue\n return rt\n\n\ndef pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" bytes:\n \"\"\"Stream a block to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: [\n {\n \"type\": fancy_block[\"type\"],\n \"text\": fancy_block[\"text\"],\n }\n ],\n \"is_block_first\": True,\n \"streaming_method\": \"block\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _streaming_token(token: Dict, is_final: bool, user_id: str, chat_id: str, is_block_first: bool) -> bytes:\n \"\"\"Streams a token to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: {\n \"type\": token[\"type\"],\n \"text\": token[\"text\"],\n },\n \"is_block_first\": is_block_first,\n \"streaming_method\": \"char\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _wrap_agent_caller(\n interaction_executor: Any,\n inputs: Dict[str, Any],\n chat_id: str,\n err_pool: Dict[str, Any],\n memory_pool: Dict[str, Any],\n callbacks: List,\n) -> None:\n try:\n _ = interaction_executor(inputs, callbacks=callbacks)\n message_list_from_memory = MessageMemoryManager.save_agent_memory_to_list(interaction_executor.memory)\n memory_pool.update({chat_id: message_list_from_memory})\n del interaction_executor\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef _combine_streaming(stream_list: List) -> List:\n \"\"\"Combine the streaming tokens/blocks to be saved in database.\"\"\"\n stream_list_combined = []\n current_type, current_text = None, \"\"\n for idx, item in enumerate(stream_list):\n if current_type in STREAM_TOKEN_TYPES and (item[\"type\"] != current_type) or idx == len(stream_list) - 1:\n stream_list_combined.append(\n {\n \"type\": current_type,\n \"text\": current_text,\n }\n )\n current_text = \"\"\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n stream_list_combined.append(item)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n current_text += item[\"text\"]\n current_type = item[\"type\"]\n return stream_list_combined\n\n\ndef _render_preprocess(string: Optional[str] = None) -> str:\n \"\"\"Preprocess the string to be rendered in frontend.\"\"\"\n if string is None: # this is due to openai stop policy or other stream issue\n return \"\"\n string = string.replace(\"$\", \"\\$\")\n return string\n\n\ndef single_round_chat_with_agent_streaming(\n stream_handler: AgentStreamingStdOutCallbackHandler,\n interaction_executor: AgentExecutor,\n user_intent: str,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,\n llm_name: str,\n app_type: str = \"plugins\",\n) -> Any:\n \"\"\"Streams the response of the agent to the frontend.\"\"\"\n assert app_type in APP_TYPES, f\"app_type should be one of {APP_TYPES}\"\n\n with multiprocess.Manager() as share_manager:\n err_pool: Dict[str, Any] = share_manager.dict()\n memory_pool: Dict[str, Any] = share_manager.dict()\n share_list = share_manager.list()\n memory_pool[chat_id] = []\n\n stream_handler.for_display = share_list\n\n chat_thread = multiprocess.Process(\n target=_wrap_agent_caller,\n args=(\n interaction_executor,\n {\n \"input\": user_intent,\n },\n chat_id,\n err_pool,\n memory_pool,\n [stream_handler],\n ),\n )\n\n threading_pool.register_thread(chat_id, chat_thread)\n chat_thread.start()\n empty_s_time: float = -1\n last_heartbeat_time: float = -1\n timeout = TIME_OUT_MAP[app_type]\n LEFT_SIGN = \"(\"\n RIGHT_SIGN = \")\"\n start_buffer = False\n streamed_transition_text_buffer = \"\"\n streamed_links = []\n converted_card_info_list = []\n yield pack_json(\n {\n \"human_message_id\": human_message_id,\n \"ai_message_id\": ai_message_id,\n }\n )\n # Display streaming to frontend\n display_stream = DisplayStream(execution_result_max_tokens=EXECUTION_RESULT_MAX_TOKENS_MAP[app_type])\n is_block_first, current_block_type = False, None\n intermediate_list, final_list = [], [] # Only for database storage\n try:\n while chat_thread.is_alive() or len(stream_handler.for_display) > 0:\n # print(memory_pool, err_pool, \"out\")\n if stream_handler.is_end:\n # The ending of the streaming is marked by the is_end variable from AgentStreamingStdOutCallbackHandler in agent_streaming.py\n break\n\n if len(stream_handler.for_display) == 0:\n # first time display list is empty\n if empty_s_time == -1:\n empty_s_time = time.time()\n # already empty for some time\n else:\n if time.time() - empty_s_time > timeout and chat_thread.is_alive():\n threading_pool.timeout_thread(chat_id)\n break\n\n if last_heartbeat_time == -1:\n last_heartbeat_time = time.time()\n else:\n if time.time() - last_heartbeat_time > HEARTBEAT_INTERVAL and chat_thread.is_alive():\n last_heartbeat_time = -1\n yield _streaming_token(\n {\"text\": \"🫀\", \"type\": \"heartbeat\", \"final\": False}, False, user_id, chat_id, False\n )\n\n else:\n empty_s_time = -1\n last_heartbeat_time = -1\n\n while len(stream_handler.for_display) > 0:\n token = stream_handler.for_display.pop(0)\n items_to_display = display_stream.display(token)\n\n # Skip the \"identifier\" and \"key\" token\n if items_to_display is None:\n continue\n\n for item in items_to_display:\n # Check if the block type is changed\n if item[\"type\"] != current_block_type:\n current_block_type = item[\"type\"]\n is_block_first = True\n else:\n is_block_first = False\n is_final = item.get(\"final\", False)\n\n # Render the item(s)\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n # Render image and echarts as block\n yield _streaming_block(item, is_final, user_id, chat_id)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n # Render the rest as plain text\n item[\"text\"] = _render_preprocess(item[\"text\"])\n yield _streaming_token(item, is_final, user_id, chat_id, is_block_first)\n # Save the intermediate steps and final answer\n if is_final:\n final_list.append(item)\n else:\n intermediate_list.append(item)\n\n if item[\"type\"] == \"transition\" and item[\"text\"] == RIGHT_SIGN:\n start_buffer = False\n link = streamed_transition_text_buffer\n streamed_transition_text_buffer = \"\"\n card_info_list = extract_card_info_from_text(link)\n # empty the buffer after extracting card info\n streamed_transition_text_buffer = \"\"\n if len(card_info_list) > 0:\n streaming_card_info_list: list[dict[str, Any]] = [\n {\n \"final_answer\": {\n \"text\": json.dumps(card_info),\n \"type\": \"card_info\",\n },\n \"is_block_first\": False,\n \"streaming_method\": \"card_info\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n for card_info in card_info_list\n ]\n streamed_links.extend([card_info[\"web_link\"] for card_info in card_info_list])\n converted_card_info_list.extend(\n [\n {\n \"text\": stream_card_info[\"final_answer\"][\"text\"],\n \"type\": stream_card_info[\"final_answer\"][\"type\"],\n }\n for stream_card_info in streaming_card_info_list\n ]\n )\n for streaming_card_info in streaming_card_info_list:\n yield pack_json(streaming_card_info)\n\n if start_buffer == True:\n streamed_transition_text_buffer += item[\"text\"]\n\n if item[\"type\"] == \"transition\" and item[\"text\"] == LEFT_SIGN:\n start_buffer = True\n\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n # Wait for the chat thread to finish\n chat_thread.join()\n stop_flag, timeout_flag, error_msg = threading_pool.flush_thread(chat_id)\n error_msg = err_pool.pop(chat_id, None)\n # Response Error!!\n if stop_flag:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n return\n elif timeout_flag:\n yield pack_json({\"success\": False, \"error\": \"timeout\"})\n return\n elif error_msg is not None:\n error_msg_to_render = error_rendering(error_msg)\n yield pack_json({\"success\": False, \"error\": \"internal\", \"error_msg\": error_msg_to_render})\n return\n elif len(memory_pool[chat_id]) == 0:\n yield pack_json({\"success\": False, \"error\": \"internal\"})\n return\n # Response Success!!\n message_list_from_memory = memory_pool[chat_id]\n del stream_handler\n # share_manager.shutdown()\n del memory_pool, err_pool, share_list, share_manager, interaction_executor\n\n # Save conversation to memory\n new_human_message = message_list_from_memory[-2]\n new_ai_message = message_list_from_memory[-1]\n new_human_message.update({\"message_id\": human_message_id, \"parent_message_id\": parent_message_id})\n new_ai_message.update({\"message_id\": ai_message_id, \"parent_message_id\": human_message_id})\n message_list.extend([new_human_message, new_ai_message])\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\", msg_head=\"New human message\").debug(new_human_message)\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\", msg_head=\"New ai message\").debug(new_ai_message)\n\n MessageMemoryManager.set_pool_info_with_id(message_pool, user_id, chat_id, message_list)\n\n # Save conversation to database\n db = get_user_conversation_storage()\n # Combine the streaming tokens/blocks\n intermediate_list_combined = _combine_streaming(intermediate_list)\n final_list_combined = _combine_streaming(final_list)\n if len(converted_card_info_list) > 0:\n final_list_combined.extend(converted_card_info_list)\n # Insert User Message, if regenerate there is no need to insert again\n db.message.insert_one(\n {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"version_id\": 0,\n \"role\": \"user\",\n \"data_for_human\": user_intent,\n \"data_for_llm\": message_list[-2][\"message_content\"],\n \"raw_data\": None,\n }\n )\n # Insert AI Message\n db.message.insert_one(\n {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": ai_message_id,\n \"parent_message_id\": human_message_id,\n \"version_id\": 0,\n \"role\": \"assistant\",\n \"data_for_human\": {\n \"intermediate_steps\": intermediate_list_combined,\n \"final_answer\": final_list_combined,\n },\n \"data_for_llm\": message_list[-1][\"message_content\"],\n \"raw_data\": None,\n }\n )\n\n\ndef _wrap_executor_caller(\n executor: Any, inputs: Any, llm: Any, chat_id: str, err_pool: Dict[str, Any], memory_pool: Dict[str, Any]\n) -> None:\n try:\n results = executor.run(inputs, llm)\n message_list_from_memory = results\n memory_pool.update({chat_id: message_list_from_memory})\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef single_round_chat_with_executor(\n executor: Any,\n user_intent: Any,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,\n llm: BaseLanguageModel,\n app_type: str = \"copilot\",\n) -> Any:\n \"\"\"Streams the response of the executor to the frontend.\"\"\"\n stream_handler = executor.stream_handler\n share_manager = multiprocess.Manager()\n err_pool: Dict[str, Any] = share_manager.dict()\n memory_pool: Dict[str, Any] = share_manager.dict()\n share_list = share_manager.list()\n stream_handler._all = share_list\n memory_pool[chat_id] = []\n chat_thread = multiprocess.Process(\n target=_wrap_executor_caller,\n args=(\n executor,\n user_intent,\n llm,\n chat_id,\n err_pool,\n memory_pool,\n ),\n )\n threading_pool.register_thread(chat_id, chat_thread)\n\n empty_s_time: float = -1\n timeout = TIME_OUT_MAP[app_type]\n chat_thread.start()\n yield pack_json(\n {\n \"human_message_id\": human_message_id,\n \"ai_message_id\": ai_message_id,\n }\n )\n # FIXME: treat data summary as a special tool\n STREAM_TOOL_TYPE = \"tool\"\n data_summary_tool_item = {\n \"text\": executor.tool_name,\n \"type\": STREAM_TOOL_TYPE,\n }\n yield _streaming_block(data_summary_tool_item, is_final=False, user_id=user_id, chat_id=chat_id)\n is_block_first = True\n final_answer = []\n while chat_thread.is_alive() or len(stream_handler._all) > 0:\n if stream_handler.is_end:\n break\n if len(stream_handler._all) == 0:\n # first time display list is\n# ... truncated ...","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":true} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.check_url_exist","uri":"program://OpenAgents/function/backend.utils.streaming.check_url_exist#L29-L36","kind":"function","name":"check_url_exist","path":"backend/utils/streaming.py","language":"python","start_line":29,"end_line":36,"context_start_line":9,"context_end_line":56,"code":"\nfrom backend.display_streaming import DisplayStream\nfrom backend.main import logger, message_pool, threading_pool\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.utils.utils import error_rendering\nfrom backend.memory import MessageMemoryManager\nfrom backend.schemas import (\n APP_TYPES,\n TIME_OUT_MAP,\n HEARTBEAT_INTERVAL,\n STREAM_BLOCK_TYPES,\n STREAM_TOKEN_TYPES,\n EXECUTION_RESULT_MAX_TOKENS_MAP,\n)\nfrom real_agents.data_agent import DataSummaryExecutor\nfrom real_agents.adapters.callbacks.agent_streaming import AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.agent_helpers import Agent, AgentExecutor\nfrom real_agents.adapters.llm import BaseLanguageModel\n\n\ndef check_url_exist(text: str) -> bool:\n \"\"\"check in a text whether there is a url\"\"\"\n # this regex extracts the http(s) with whitespace or () in the beginning and end, since usually the url is surrounded by whitespace or ()\n # e.g. \" https://google.com \" or \"(https://google.com)\"\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n\n links = re.findall(url_regex, text)\n return len(links) > 0\n\n\n# function to extract links from text\ndef extract_links(text: str) -> list[Any]:\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n links = re.findall(url_regex, text)\n return links\n\n\n# function to extract image links from a webpage\ndef extract_title_and_image_links(url: str) -> (tuple[Literal[''], list] | tuple[Any, list]):\n try:\n res = requests.get(url, timeout=3)\n if res.status_code != 200:\n return \"\", []\n soup = BeautifulSoup(res.text, \"html.parser\")\n title_tag = soup.find_all(\"title\")[0].text\n img_tags = soup.find_all(\"img\")\n # List to store image links with large width and height\n large_img_links = []","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.extract_links","uri":"program://OpenAgents/function/backend.utils.streaming.extract_links#L40-L43","kind":"function","name":"extract_links","path":"backend/utils/streaming.py","language":"python","start_line":40,"end_line":43,"context_start_line":20,"context_end_line":63,"code":" STREAM_TOKEN_TYPES,\n EXECUTION_RESULT_MAX_TOKENS_MAP,\n)\nfrom real_agents.data_agent import DataSummaryExecutor\nfrom real_agents.adapters.callbacks.agent_streaming import AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.agent_helpers import Agent, AgentExecutor\nfrom real_agents.adapters.llm import BaseLanguageModel\n\n\ndef check_url_exist(text: str) -> bool:\n \"\"\"check in a text whether there is a url\"\"\"\n # this regex extracts the http(s) with whitespace or () in the beginning and end, since usually the url is surrounded by whitespace or ()\n # e.g. \" https://google.com \" or \"(https://google.com)\"\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n\n links = re.findall(url_regex, text)\n return len(links) > 0\n\n\n# function to extract links from text\ndef extract_links(text: str) -> list[Any]:\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n links = re.findall(url_regex, text)\n return links\n\n\n# function to extract image links from a webpage\ndef extract_title_and_image_links(url: str) -> (tuple[Literal[''], list] | tuple[Any, list]):\n try:\n res = requests.get(url, timeout=3)\n if res.status_code != 200:\n return \"\", []\n soup = BeautifulSoup(res.text, \"html.parser\")\n title_tag = soup.find_all(\"title\")[0].text\n img_tags = soup.find_all(\"img\")\n # List to store image links with large width and height\n large_img_links = []\n # List to store all image links\n all_img_links = []\n for img in img_tags:\n if \"src\" in img.attrs:\n all_img_links.append(img[\"src\"])\n # Check if width and height attributes exist and add to the large list\n if \"width\" in img.attrs and \"height\" in img.attrs:","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.extract_title_and_image_links","uri":"program://OpenAgents/function/backend.utils.streaming.extract_title_and_image_links#L47-L79","kind":"function","name":"extract_title_and_image_links","path":"backend/utils/streaming.py","language":"python","start_line":47,"end_line":79,"context_start_line":27,"context_end_line":99,"code":"\n\ndef check_url_exist(text: str) -> bool:\n \"\"\"check in a text whether there is a url\"\"\"\n # this regex extracts the http(s) with whitespace or () in the beginning and end, since usually the url is surrounded by whitespace or ()\n # e.g. \" https://google.com \" or \"(https://google.com)\"\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n\n links = re.findall(url_regex, text)\n return len(links) > 0\n\n\n# function to extract links from text\ndef extract_links(text: str) -> list[Any]:\n url_regex = r\"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)\"\n links = re.findall(url_regex, text)\n return links\n\n\n# function to extract image links from a webpage\ndef extract_title_and_image_links(url: str) -> (tuple[Literal[''], list] | tuple[Any, list]):\n try:\n res = requests.get(url, timeout=3)\n if res.status_code != 200:\n return \"\", []\n soup = BeautifulSoup(res.text, \"html.parser\")\n title_tag = soup.find_all(\"title\")[0].text\n img_tags = soup.find_all(\"img\")\n # List to store image links with large width and height\n large_img_links = []\n # List to store all image links\n all_img_links = []\n for img in img_tags:\n if \"src\" in img.attrs:\n all_img_links.append(img[\"src\"])\n # Check if width and height attributes exist and add to the large list\n if \"width\" in img.attrs and \"height\" in img.attrs:\n # Ensure the width and height attributes can be converted to integers\n if int(img[\"width\"]) > 100 and int(img[\"height\"]) > 100:\n large_img_links.append(img[\"src\"])\n else:\n continue\n # If large images were found, return those, otherwise return all images\n img_links = large_img_links if large_img_links else []\n # fixme: handle the case there are no such tags\n\n return title_tag, img_links\n except requests.exceptions.Timeout:\n print(\"Request timed out!\")\n return \"\", []\n except Exception as e:\n print(f\"Error processing {url}: {e}\")\n return \"\", []\n\n\ndef extract_card_info_from_text(message: str) -> list:\n links = extract_links(message)\n rt = []\n for link in links:\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n return rt\n\n\ndef extract_card_info_from_links(links: List[str]) -> list[dict[str, Any]]:\n rt = []\n for link in links:\n if check_url_exist(link):\n title, image_links = extract_title_and_image_links(link)","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.extract_card_info_from_text","uri":"program://OpenAgents/function/backend.utils.streaming.extract_card_info_from_text#L82-L92","kind":"function","name":"extract_card_info_from_text","path":"backend/utils/streaming.py","language":"python","start_line":82,"end_line":92,"context_start_line":62,"context_end_line":112,"code":" # Check if width and height attributes exist and add to the large list\n if \"width\" in img.attrs and \"height\" in img.attrs:\n # Ensure the width and height attributes can be converted to integers\n if int(img[\"width\"]) > 100 and int(img[\"height\"]) > 100:\n large_img_links.append(img[\"src\"])\n else:\n continue\n # If large images were found, return those, otherwise return all images\n img_links = large_img_links if large_img_links else []\n # fixme: handle the case there are no such tags\n\n return title_tag, img_links\n except requests.exceptions.Timeout:\n print(\"Request timed out!\")\n return \"\", []\n except Exception as e:\n print(f\"Error processing {url}: {e}\")\n return \"\", []\n\n\ndef extract_card_info_from_text(message: str) -> list:\n links = extract_links(message)\n rt = []\n for link in links:\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n return rt\n\n\ndef extract_card_info_from_links(links: List[str]) -> list[dict[str, Any]]:\n rt = []\n for link in links:\n if check_url_exist(link):\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n else:\n continue\n return rt\n\n\ndef pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" list:\n links = extract_links(message)\n rt = []\n for link in links:\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n return rt\n\n\ndef extract_card_info_from_links(links: List[str]) -> list[dict[str, Any]]:\n rt = []\n for link in links:\n if check_url_exist(link):\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n else:\n continue\n return rt\n\n\ndef pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" bytes:\n \"\"\"Stream a block to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: [\n {\n \"type\": fancy_block[\"type\"],","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.pack_json","uri":"program://OpenAgents/function/backend.utils.streaming.pack_json#L110-L112","kind":"function","name":"pack_json","path":"backend/utils/streaming.py","language":"python","start_line":110,"end_line":112,"context_start_line":90,"context_end_line":132,"code":" selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n return rt\n\n\ndef extract_card_info_from_links(links: List[str]) -> list[dict[str, Any]]:\n rt = []\n for link in links:\n if check_url_exist(link):\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n else:\n continue\n return rt\n\n\ndef pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" bytes:\n \"\"\"Stream a block to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: [\n {\n \"type\": fancy_block[\"type\"],\n \"text\": fancy_block[\"text\"],\n }\n ],\n \"is_block_first\": True,\n \"streaming_method\": \"block\",","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming._streaming_block","uri":"program://OpenAgents/function/backend.utils.streaming._streaming_block#L115-L136","kind":"function","name":"_streaming_block","path":"backend/utils/streaming.py","language":"python","start_line":115,"end_line":136,"context_start_line":95,"context_end_line":156,"code":"def extract_card_info_from_links(links: List[str]) -> list[dict[str, Any]]:\n rt = []\n for link in links:\n if check_url_exist(link):\n title, image_links = extract_title_and_image_links(link)\n if len(image_links) > 0:\n selected_image_link = image_links[0]\n else:\n selected_image_link = \"\" # no image in this website\n rt.append({\"title\": title, \"web_link\": link, \"image_link\": selected_image_link})\n else:\n continue\n return rt\n\n\ndef pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" bytes:\n \"\"\"Stream a block to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: [\n {\n \"type\": fancy_block[\"type\"],\n \"text\": fancy_block[\"text\"],\n }\n ],\n \"is_block_first\": True,\n \"streaming_method\": \"block\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _streaming_token(token: Dict, is_final: bool, user_id: str, chat_id: str, is_block_first: bool) -> bytes:\n \"\"\"Streams a token to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: {\n \"type\": token[\"type\"],\n \"text\": token[\"text\"],\n },\n \"is_block_first\": is_block_first,\n \"streaming_method\": \"char\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _wrap_agent_caller(","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming._streaming_token","uri":"program://OpenAgents/function/backend.utils.streaming._streaming_token#L139-L153","kind":"function","name":"_streaming_token","path":"backend/utils/streaming.py","language":"python","start_line":139,"end_line":153,"context_start_line":119,"context_end_line":173,"code":" chat_id: str,\n) -> bytes:\n \"\"\"Stream a block to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: [\n {\n \"type\": fancy_block[\"type\"],\n \"text\": fancy_block[\"text\"],\n }\n ],\n \"is_block_first\": True,\n \"streaming_method\": \"block\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _streaming_token(token: Dict, is_final: bool, user_id: str, chat_id: str, is_block_first: bool) -> bytes:\n \"\"\"Streams a token to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: {\n \"type\": token[\"type\"],\n \"text\": token[\"text\"],\n },\n \"is_block_first\": is_block_first,\n \"streaming_method\": \"char\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _wrap_agent_caller(\n interaction_executor: Any,\n inputs: Dict[str, Any],\n chat_id: str,\n err_pool: Dict[str, Any],\n memory_pool: Dict[str, Any],\n callbacks: List,\n) -> None:\n try:\n _ = interaction_executor(inputs, callbacks=callbacks)\n message_list_from_memory = MessageMemoryManager.save_agent_memory_to_list(interaction_executor.memory)\n memory_pool.update({chat_id: message_list_from_memory})\n del interaction_executor\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming._wrap_agent_caller","uri":"program://OpenAgents/function/backend.utils.streaming._wrap_agent_caller#L156-L174","kind":"function","name":"_wrap_agent_caller","path":"backend/utils/streaming.py","language":"python","start_line":156,"end_line":174,"context_start_line":136,"context_end_line":194,"code":" )\n\n\ndef _streaming_token(token: Dict, is_final: bool, user_id: str, chat_id: str, is_block_first: bool) -> bytes:\n \"\"\"Streams a token to the frontend.\"\"\"\n render_position = \"intermediate_steps\" if not is_final else \"final_answer\"\n return pack_json(\n {\n render_position: {\n \"type\": token[\"type\"],\n \"text\": token[\"text\"],\n },\n \"is_block_first\": is_block_first,\n \"streaming_method\": \"char\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n\n\ndef _wrap_agent_caller(\n interaction_executor: Any,\n inputs: Dict[str, Any],\n chat_id: str,\n err_pool: Dict[str, Any],\n memory_pool: Dict[str, Any],\n callbacks: List,\n) -> None:\n try:\n _ = interaction_executor(inputs, callbacks=callbacks)\n message_list_from_memory = MessageMemoryManager.save_agent_memory_to_list(interaction_executor.memory)\n memory_pool.update({chat_id: message_list_from_memory})\n del interaction_executor\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef _combine_streaming(stream_list: List) -> List:\n \"\"\"Combine the streaming tokens/blocks to be saved in database.\"\"\"\n stream_list_combined = []\n current_type, current_text = None, \"\"\n for idx, item in enumerate(stream_list):\n if current_type in STREAM_TOKEN_TYPES and (item[\"type\"] != current_type) or idx == len(stream_list) - 1:\n stream_list_combined.append(\n {\n \"type\": current_type,\n \"text\": current_text,\n }\n )\n current_text = \"\"\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n stream_list_combined.append(item)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n current_text += item[\"text\"]\n current_type = item[\"type\"]","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming._combine_streaming","uri":"program://OpenAgents/function/backend.utils.streaming._combine_streaming#L177-L195","kind":"function","name":"_combine_streaming","path":"backend/utils/streaming.py","language":"python","start_line":177,"end_line":195,"context_start_line":157,"context_end_line":215,"code":" interaction_executor: Any,\n inputs: Dict[str, Any],\n chat_id: str,\n err_pool: Dict[str, Any],\n memory_pool: Dict[str, Any],\n callbacks: List,\n) -> None:\n try:\n _ = interaction_executor(inputs, callbacks=callbacks)\n message_list_from_memory = MessageMemoryManager.save_agent_memory_to_list(interaction_executor.memory)\n memory_pool.update({chat_id: message_list_from_memory})\n del interaction_executor\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef _combine_streaming(stream_list: List) -> List:\n \"\"\"Combine the streaming tokens/blocks to be saved in database.\"\"\"\n stream_list_combined = []\n current_type, current_text = None, \"\"\n for idx, item in enumerate(stream_list):\n if current_type in STREAM_TOKEN_TYPES and (item[\"type\"] != current_type) or idx == len(stream_list) - 1:\n stream_list_combined.append(\n {\n \"type\": current_type,\n \"text\": current_text,\n }\n )\n current_text = \"\"\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n stream_list_combined.append(item)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n current_text += item[\"text\"]\n current_type = item[\"type\"]\n return stream_list_combined\n\n\ndef _render_preprocess(string: Optional[str] = None) -> str:\n \"\"\"Preprocess the string to be rendered in frontend.\"\"\"\n if string is None: # this is due to openai stop policy or other stream issue\n return \"\"\n string = string.replace(\"$\", \"\\$\")\n return string\n\n\ndef single_round_chat_with_agent_streaming(\n stream_handler: AgentStreamingStdOutCallbackHandler,\n interaction_executor: AgentExecutor,\n user_intent: str,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming._render_preprocess","uri":"program://OpenAgents/function/backend.utils.streaming._render_preprocess#L198-L203","kind":"function","name":"_render_preprocess","path":"backend/utils/streaming.py","language":"python","start_line":198,"end_line":203,"context_start_line":178,"context_end_line":223,"code":" \"\"\"Combine the streaming tokens/blocks to be saved in database.\"\"\"\n stream_list_combined = []\n current_type, current_text = None, \"\"\n for idx, item in enumerate(stream_list):\n if current_type in STREAM_TOKEN_TYPES and (item[\"type\"] != current_type) or idx == len(stream_list) - 1:\n stream_list_combined.append(\n {\n \"type\": current_type,\n \"text\": current_text,\n }\n )\n current_text = \"\"\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n stream_list_combined.append(item)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n current_text += item[\"text\"]\n current_type = item[\"type\"]\n return stream_list_combined\n\n\ndef _render_preprocess(string: Optional[str] = None) -> str:\n \"\"\"Preprocess the string to be rendered in frontend.\"\"\"\n if string is None: # this is due to openai stop policy or other stream issue\n return \"\"\n string = string.replace(\"$\", \"\\$\")\n return string\n\n\ndef single_round_chat_with_agent_streaming(\n stream_handler: AgentStreamingStdOutCallbackHandler,\n interaction_executor: AgentExecutor,\n user_intent: str,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,\n llm_name: str,\n app_type: str = \"plugins\",\n) -> Any:\n \"\"\"Streams the response of the agent to the frontend.\"\"\"\n assert app_type in APP_TYPES, f\"app_type should be one of {APP_TYPES}\"\n\n with multiprocess.Manager() as share_manager:\n err_pool: Dict[str, Any] = share_manager.dict()","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.single_round_chat_with_agent_streaming","uri":"program://OpenAgents/function/backend.utils.streaming.single_round_chat_with_agent_streaming#L206-L443","kind":"function","name":"single_round_chat_with_agent_streaming","path":"backend/utils/streaming.py","language":"python","start_line":206,"end_line":443,"context_start_line":186,"context_end_line":463,"code":" \"text\": current_text,\n }\n )\n current_text = \"\"\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n stream_list_combined.append(item)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n current_text += item[\"text\"]\n current_type = item[\"type\"]\n return stream_list_combined\n\n\ndef _render_preprocess(string: Optional[str] = None) -> str:\n \"\"\"Preprocess the string to be rendered in frontend.\"\"\"\n if string is None: # this is due to openai stop policy or other stream issue\n return \"\"\n string = string.replace(\"$\", \"\\$\")\n return string\n\n\ndef single_round_chat_with_agent_streaming(\n stream_handler: AgentStreamingStdOutCallbackHandler,\n interaction_executor: AgentExecutor,\n user_intent: str,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,\n llm_name: str,\n app_type: str = \"plugins\",\n) -> Any:\n \"\"\"Streams the response of the agent to the frontend.\"\"\"\n assert app_type in APP_TYPES, f\"app_type should be one of {APP_TYPES}\"\n\n with multiprocess.Manager() as share_manager:\n err_pool: Dict[str, Any] = share_manager.dict()\n memory_pool: Dict[str, Any] = share_manager.dict()\n share_list = share_manager.list()\n memory_pool[chat_id] = []\n\n stream_handler.for_display = share_list\n\n chat_thread = multiprocess.Process(\n target=_wrap_agent_caller,\n args=(\n interaction_executor,\n {\n \"input\": user_intent,\n },\n chat_id,\n err_pool,\n memory_pool,\n [stream_handler],\n ),\n )\n\n threading_pool.register_thread(chat_id, chat_thread)\n chat_thread.start()\n empty_s_time: float = -1\n last_heartbeat_time: float = -1\n timeout = TIME_OUT_MAP[app_type]\n LEFT_SIGN = \"(\"\n RIGHT_SIGN = \")\"\n start_buffer = False\n streamed_transition_text_buffer = \"\"\n streamed_links = []\n converted_card_info_list = []\n yield pack_json(\n {\n \"human_message_id\": human_message_id,\n \"ai_message_id\": ai_message_id,\n }\n )\n # Display streaming to frontend\n display_stream = DisplayStream(execution_result_max_tokens=EXECUTION_RESULT_MAX_TOKENS_MAP[app_type])\n is_block_first, current_block_type = False, None\n intermediate_list, final_list = [], [] # Only for database storage\n try:\n while chat_thread.is_alive() or len(stream_handler.for_display) > 0:\n # print(memory_pool, err_pool, \"out\")\n if stream_handler.is_end:\n # The ending of the streaming is marked by the is_end variable from AgentStreamingStdOutCallbackHandler in agent_streaming.py\n break\n\n if len(stream_handler.for_display) == 0:\n # first time display list is empty\n if empty_s_time == -1:\n empty_s_time = time.time()\n # already empty for some time\n else:\n if time.time() - empty_s_time > timeout and chat_thread.is_alive():\n threading_pool.timeout_thread(chat_id)\n break\n\n if last_heartbeat_time == -1:\n last_heartbeat_time = time.time()\n else:\n if time.time() - last_heartbeat_time > HEARTBEAT_INTERVAL and chat_thread.is_alive():\n last_heartbeat_time = -1\n yield _streaming_token(\n {\"text\": \"🫀\", \"type\": \"heartbeat\", \"final\": False}, False, user_id, chat_id, False\n )\n\n else:\n empty_s_time = -1\n last_heartbeat_time = -1\n\n while len(stream_handler.for_display) > 0:\n token = stream_handler.for_display.pop(0)\n items_to_display = display_stream.display(token)\n\n # Skip the \"identifier\" and \"key\" token\n if items_to_display is None:\n continue\n\n for item in items_to_display:\n # Check if the block type is changed\n if item[\"type\"] != current_block_type:\n current_block_type = item[\"type\"]\n is_block_first = True\n else:\n is_block_first = False\n is_final = item.get(\"final\", False)\n\n # Render the item(s)\n if item[\"type\"] in STREAM_BLOCK_TYPES:\n # Render image and echarts as block\n yield _streaming_block(item, is_final, user_id, chat_id)\n elif item[\"type\"] in STREAM_TOKEN_TYPES:\n # Render the rest as plain text\n item[\"text\"] = _render_preprocess(item[\"text\"])\n yield _streaming_token(item, is_final, user_id, chat_id, is_block_first)\n # Save the intermediate steps and final answer\n if is_final:\n final_list.append(item)\n else:\n intermediate_list.append(item)\n\n if item[\"type\"] == \"transition\" and item[\"text\"] == RIGHT_SIGN:\n start_buffer = False\n link = streamed_transition_text_buffer\n streamed_transition_text_buffer = \"\"\n card_info_list = extract_card_info_from_text(link)\n # empty the buffer after extracting card info\n streamed_transition_text_buffer = \"\"\n if len(card_info_list) > 0:\n streaming_card_info_list: list[dict[str, Any]] = [\n {\n \"final_answer\": {\n \"text\": json.dumps(card_info),\n \"type\": \"card_info\",\n },\n \"is_block_first\": False,\n \"streaming_method\": \"card_info\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n for card_info in card_info_list\n ]\n streamed_links.extend([card_info[\"web_link\"] for card_info in card_info_list])\n converted_card_info_list.extend(\n [\n {\n \"text\": stream_card_info[\"final_answer\"][\"text\"],\n \"type\": stream_card_info[\"final_answer\"][\"type\"],\n }\n for stream_card_info in streaming_card_info_list\n ]\n )\n for streaming_card_info in streaming_card_info_list:\n yield pack_json(streaming_card_info)\n\n if start_buffer == True:\n streamed_transition_text_buffer += item[\"text\"]\n\n if item[\"type\"] == \"transition\" and item[\"text\"] == LEFT_SIGN:\n start_buffer = True\n\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n # Wait for the chat thread to finish\n chat_thread.join()\n stop_flag, timeout_flag, error_msg = threading_pool.flush_thread(chat_id)\n error_msg = err_pool.pop(chat_id, None)\n # Response Error!!\n if stop_flag:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n return\n elif timeout_flag:\n yield pack_json({\"success\": False, \"error\": \"timeout\"})\n return\n elif error_msg is not None:\n error_msg_to_render = error_rendering(error_msg)\n yield pack_json({\"success\": False, \"error\": \"internal\", \"error_msg\": error_msg_to_render})\n return\n elif len(memory_pool[chat_id]) == 0:\n yield pack_json({\"success\": False, \"error\": \"internal\"})\n return\n # Response Success!!\n message_list_from_memory = memory_pool[chat_id]\n del stream_handler\n # share_manager.shutdown()\n del memory_pool, err_pool, share_list, share_manager, interaction_executor\n\n # Save conversation to memory\n new_human_message = message_list_from_memory[-2]\n new_ai_message = message_list_from_memory[-1]\n new_human_message.update({\"message_id\": human_message_id, \"parent_message_id\": parent_message_id})\n new_ai_message.update({\"message_id\": ai_message_id, \"parent_message_id\": human_message_id})\n message_list.extend([new_human_message, new_ai_message])\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\", msg_head=\"New human message\").debug(new_human_message)\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\", msg_head=\"New ai message\").debug(new_ai_message)\n\n MessageMemoryManager.set_pool_info_with_id(message_pool, user_id, chat_id, message_list)\n\n # Save conversation to database\n db = get_user_conversation_storage()\n # Combine the streaming tokens/blocks\n intermediate_list_combined = _combine_streaming(intermediate_list)\n final_list_combined = _combine_streaming(final_list)\n if len(converted_card_info_list) > 0:\n final_list_combined.extend(converted_card_info_list)\n # Insert User Message, if regenerate there is no need to insert again\n db.message.insert_one(\n {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"version_id\": 0,\n \"role\": \"user\",\n \"data_for_human\": user_intent,\n \"data_for_llm\": message_list[-2][\"message_content\"],\n \"raw_data\": None,\n }\n )\n # Insert AI Message\n db.message.insert_one(\n {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": ai_message_id,\n \"parent_message_id\": human_message_id,\n \"version_id\": 0,\n \"role\": \"assistant\",\n \"data_for_human\": {\n \"intermediate_steps\": intermediate_list_combined,\n \"final_answer\": final_list_combined,\n },\n \"data_for_llm\": message_list[-1][\"message_content\"],\n \"raw_data\": None,\n }\n )\n\n\ndef _wrap_executor_caller(\n executor: Any, inputs: Any, llm: Any, chat_id: str, err_pool: Dict[str, Any], memory_pool: Dict[str, Any]\n) -> None:\n try:\n results = executor.run(inputs, llm)\n message_list_from_memory = results\n memory_pool.update({chat_id: message_list_from_memory})\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef single_round_chat_with_executor(\n executor: Any,\n user_intent: Any,","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming._wrap_executor_caller","uri":"program://OpenAgents/function/backend.utils.streaming._wrap_executor_caller#L446-L458","kind":"function","name":"_wrap_executor_caller","path":"backend/utils/streaming.py","language":"python","start_line":446,"end_line":458,"context_start_line":426,"context_end_line":478,"code":" )\n # Insert AI Message\n db.message.insert_one(\n {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": ai_message_id,\n \"parent_message_id\": human_message_id,\n \"version_id\": 0,\n \"role\": \"assistant\",\n \"data_for_human\": {\n \"intermediate_steps\": intermediate_list_combined,\n \"final_answer\": final_list_combined,\n },\n \"data_for_llm\": message_list[-1][\"message_content\"],\n \"raw_data\": None,\n }\n )\n\n\ndef _wrap_executor_caller(\n executor: Any, inputs: Any, llm: Any, chat_id: str, err_pool: Dict[str, Any], memory_pool: Dict[str, Any]\n) -> None:\n try:\n results = executor.run(inputs, llm)\n message_list_from_memory = results\n memory_pool.update({chat_id: message_list_from_memory})\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef single_round_chat_with_executor(\n executor: Any,\n user_intent: Any,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,\n llm: BaseLanguageModel,\n app_type: str = \"copilot\",\n) -> Any:\n \"\"\"Streams the response of the executor to the frontend.\"\"\"\n stream_handler = executor.stream_handler\n share_manager = multiprocess.Manager()\n err_pool: Dict[str, Any] = share_manager.dict()\n memory_pool: Dict[str, Any] = share_manager.dict()\n share_list = share_manager.list()","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.streaming.single_round_chat_with_executor","uri":"program://OpenAgents/function/backend.utils.streaming.single_round_chat_with_executor#L461-L608","kind":"function","name":"single_round_chat_with_executor","path":"backend/utils/streaming.py","language":"python","start_line":461,"end_line":608,"context_start_line":441,"context_end_line":608,"code":" \"raw_data\": None,\n }\n )\n\n\ndef _wrap_executor_caller(\n executor: Any, inputs: Any, llm: Any, chat_id: str, err_pool: Dict[str, Any], memory_pool: Dict[str, Any]\n) -> None:\n try:\n results = executor.run(inputs, llm)\n message_list_from_memory = results\n memory_pool.update({chat_id: message_list_from_memory})\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n\n err_pool[chat_id] = f\"{type(e).__name__}: {str(e)}\"\n\n\ndef single_round_chat_with_executor(\n executor: Any,\n user_intent: Any,\n human_message_id: int,\n ai_message_id: int,\n user_id: str,\n chat_id: str,\n message_list: List[Dict[str, Any]],\n parent_message_id: int,\n llm: BaseLanguageModel,\n app_type: str = \"copilot\",\n) -> Any:\n \"\"\"Streams the response of the executor to the frontend.\"\"\"\n stream_handler = executor.stream_handler\n share_manager = multiprocess.Manager()\n err_pool: Dict[str, Any] = share_manager.dict()\n memory_pool: Dict[str, Any] = share_manager.dict()\n share_list = share_manager.list()\n stream_handler._all = share_list\n memory_pool[chat_id] = []\n chat_thread = multiprocess.Process(\n target=_wrap_executor_caller,\n args=(\n executor,\n user_intent,\n llm,\n chat_id,\n err_pool,\n memory_pool,\n ),\n )\n threading_pool.register_thread(chat_id, chat_thread)\n\n empty_s_time: float = -1\n timeout = TIME_OUT_MAP[app_type]\n chat_thread.start()\n yield pack_json(\n {\n \"human_message_id\": human_message_id,\n \"ai_message_id\": ai_message_id,\n }\n )\n # FIXME: treat data summary as a special tool\n STREAM_TOOL_TYPE = \"tool\"\n data_summary_tool_item = {\n \"text\": executor.tool_name,\n \"type\": STREAM_TOOL_TYPE,\n }\n yield _streaming_block(data_summary_tool_item, is_final=False, user_id=user_id, chat_id=chat_id)\n is_block_first = True\n final_answer = []\n while chat_thread.is_alive() or len(stream_handler._all) > 0:\n if stream_handler.is_end:\n break\n if len(stream_handler._all) == 0:\n # first time display list is empty\n if empty_s_time == -1:\n empty_s_time = time.time()\n # already empty for some time\n else:\n if time.time() - empty_s_time > timeout and chat_thread.is_alive():\n threading_pool.timeout_thread(chat_id)\n break\n else:\n empty_s_time = -1\n\n while len(stream_handler._all) > 0:\n text = stream_handler._all.pop(0)\n final_answer.append(text)\n if is_block_first:\n is_block_first_ = True\n is_block_first = False\n else:\n is_block_first_ = False\n yield pack_json(\n {\n \"final_answer\": {\n \"type\": \"text\",\n \"text\": text + \" \",\n },\n \"is_block_first\": is_block_first_,\n \"streaming_method\": \"char\",\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }\n )\n time.sleep(0.035)\n chat_thread.join()\n stop_flag, timeout_flag, error_msg = threading_pool.flush_thread(chat_id)\n error_msg = err_pool.pop(chat_id, None)\n if stop_flag:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n return\n elif timeout_flag:\n yield pack_json({\"success\": False, \"error\": \"timeout\"})\n return\n elif error_msg is not None:\n error_msg_to_render = error_rendering(error_msg)\n yield pack_json({\"success\": False, \"error\": \"internal\", \"error_msg\": error_msg_to_render})\n return\n elif len(memory_pool[chat_id]) == 0 or len(final_answer) == 0:\n yield pack_json({\"success\": False, \"error\": \"internal\"})\n return\n # Response Success!!\n del share_list, stream_handler\n del memory_pool, err_pool, share_manager, executor\n\n # Save conversation to memory\n final_answer_str = \" \".join(final_answer)\n message_list.append(\n {\n \"message_id\": ai_message_id,\n \"parent_message_id\": parent_message_id,\n \"message_type\": \"ai_message\",\n \"message_content\": final_answer_str,\n }\n )\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"chat/\", msg_head=\"New data summary message\").debug(\n message_list[-1]\n )\n\n MessageMemoryManager.set_pool_info_with_id(message_pool, user_id, chat_id, message_list)\n\n # Database Operations\n db = get_user_conversation_storage()\n db.message.insert_one(\n {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": ai_message_id,\n \"parent_message_id\": parent_message_id,\n \"version_id\": 0,\n \"role\": \"assistant\",\n \"data_for_human\": {\n \"intermediate_steps\": [\n data_summary_tool_item,\n ],\n \"final_answer\": [\n {\n \"text\": final_answer,\n \"type\": \"plain\",\n }\n ],\n },\n \"data_for_llm\": message_list[-1][\"message_content\"],\n \"raw_data\": None,\n }\n )","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils","uri":"program://OpenAgents/module/backend.utils.utils#L1-L328","kind":"module","name":"backend.utils.utils","path":"backend/utils/utils.py","language":"python","start_line":1,"end_line":328,"context_start_line":1,"context_end_line":328,"code":"import os\nimport sys\nimport base64\nfrom pathlib import Path\nfrom typing import Any, Dict, Tuple, Union\n\nimport pandas as pd\nimport tiktoken\nfrom flask import Request\nfrom sqlalchemy import create_engine\nfrom PIL import Image\nfrom loguru import logger\n\nfrom real_agents.adapters.data_model import (\n DatabaseDataModel,\n DataModel,\n ImageDataModel,\n TableDataModel,\n KaggleDataModel,\n)\nfrom real_agents.data_agent import (\n DataSummaryExecutor,\n TableSummaryExecutor,\n ImageSummaryExecutor,\n)\nfrom real_agents.adapters.schema import SQLDatabase\nfrom backend.utils.running_time_storage import get_running_time_storage\nfrom backend.app import app\nfrom backend.schemas import DEFAULT_USER_ID\n\nTABLE_EXTENSIONS = {\"csv\", \"xls\", \"xlsx\", \"tsv\"}\nDOCUMENT_EXTENSIONS = {\"pdf\", \"doc\", \"docx\", \"txt\"}\nDATABASE_EXTENSIONS = {\"sqlite\", \"db\"}\nIMAGE_EXTENSIONS = {\"jpg\", \"png\", \"jpeg\"}\nALLOW_EXTENSIONS = TABLE_EXTENSIONS | DOCUMENT_EXTENSIONS | DATABASE_EXTENSIONS | IMAGE_EXTENSIONS\n\nLOCAL = \"local\"\nREDIS = \"redis\"\n\n\nclass VariableRegister:\n def __init__(self, name=None, backend=LOCAL) -> None:\n self.backend = backend\n if self.backend == LOCAL:\n self.variables: Dict[int, Any] = {}\n self.counter = 1\n elif self.backend == REDIS:\n assert name is not None\n self.name = name\n self.counter_name = f\"{self.name}:counter\"\n self.variables_name = f\"{self.name}:variables\"\n with app.app_context():\n self.redis_client = get_running_time_storage()\n if not self.redis_client.exists(self.counter_name):\n self.redis_client.set(self.counter_name, 0)\n else:\n logger.bind(msg_head=\"VariableRegister\").debug(\n f\"Reuse the {self.counter_name}({self.redis_client.get(self.counter_name)}) and {self.variables_name}.\"\n )\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def add_variable(self, variable: Any) -> int:\n if self.backend == LOCAL:\n variable_id = self.counter\n self.variables[variable_id] = variable\n self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n\ndef load_grounding_source(file_path: str) -> Any:\n # TODO: Maybe convert to DataModel here\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n # Assume it is a collection of csv files, usually downloaded from kaggle.\n grounding_source = {}\n for file in Path(file_path).iterdir():\n if file.suffix == \".csv\":\n grounding_source[file.as_posix()] = pd.read_csv(file, index_col=False)\n else:\n raise ValueError(\"Only csv files are allowed in the directory\")\n elif suffix == \".csv\":\n grounding_source = pd.read_csv(file_path, index_col=False)\n elif suffix == \".tsv\" or suffix == \".txt\":\n grounding_source = pd.read_csv(file_path, sep=\"\\t\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n grounding_source = pd.read_excel(file_path)\n elif suffix == \".db\" or suffix == \".sqlite\":\n engine = create_engine(f\"sqlite:///{file_path}\")\n grounding_source = SQLDatabase(engine)\n return grounding_source\n elif suffix == \".png\" or suffix == \".jpg\" or suffix == \".jpeg\":\n img = Image.open(file_path)\n with open(file_path, \"rb\") as image2string:\n converted_string = \"data:image/png;base64,\" + base64.b64encode(image2string.read()).decode(\"utf-8\")\n grounding_source = {\n \"base64_string\": converted_string,\n \"format\": img.format,\n \"size\": img.size,\n \"mode\": img.mode,\n }\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return grounding_source\n\n\ndef get_data_model_cls(file_path: str) -> DataModel:\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n data_model_cls = KaggleDataModel\n elif suffix == \".csv\":\n data_model_cls = TableDataModel\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_model_cls = TableDataModel\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_model_cls = DatabaseDataModel\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_model_cls = ImageDataModel\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_model_cls\n\n\ndef get_data_summary_cls(file_path: str) -> DataSummaryExecutor:\n suffix = Path(file_path).suffix\n if suffix == \".csv\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_summary_cls = ImageSummaryExecutor\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_summary_cls\n\n\ndef allowed_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in ALLOW_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_table_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in TABLE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_document_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DOCUMENT_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_sqlite_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DATABASE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_image_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in IMAGE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef remove_nan(file_path: str) -> None:\n \"\"\"\n We only support csv file in the current version\n By default, we remove columns that contain only nan values\n For columns that have both nan values and non-nan values, we replace nan values with the mean (number type)\n or the mode (other type)\n \"\"\"\n if file_path.endswith(\"csv\"):\n df = pd.read_csv(file_path)\n columns = list(df.columns)\n nan_columns = []\n for c in columns:\n if all(list(df[c].isnull())):\n nan_columns.append(c)\n df.drop(columns=nan_columns, inplace=True)\n columns = list(df.columns)\n for c in columns:\n try:\n fillin_value = df[c].mean()\n except Exception:\n fillin_value = df[c].mode()\n df[c].fillna(value=fillin_value, inplace=True)\n df.to_csv(file_path)\n\n\ndef is_valid_input(user_intent: str, max_token_limit: int = 2000) -> bool:\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(user_intent))\n return tokens <= max_token_limit\n\n\ndef error_rendering(error_message: str) -> str:\n \"\"\"Map (certain) error message to frontend rendering form, otherwise show\n 'internal backend error'. Currently, only handle OpenAI error message.\n \"\"\"\n if \"openai\" in error_message:\n if \"Timeout\" in error_message:\n return \"OpenAI timeout error. Please try again.\"\n elif \"RateLimitError\" in error_message:\n return \"OpenAI rate limit error. Please try again.\"\n elif \"APIConnectionError\" in error_message:\n return \"OpenAI API connection error. Please try again.\"\n elif \"InvalidRequestError\" in error_message:\n return \"OpenAI invalid request error. Please try again.\"\n elif \"AuthenticationError\" in error_message:\n return \"OpenAI authentication error. Please try again.\"\n elif \"ServiceUnavailableError\" in error_message:\n return \"OpenAI service unavailable error. Please try again.\"\n else:\n return \"Internal backend error. Please try again.\"\n\n\ndef init_log(**sink_channel):\n \"\"\"Initialize loguru log information\"\"\"\n\n # Just for sys.stdout log message\n format_stdout = (\n \"{time:YYYY-MM-DD HH:mm:ss} | {level} - {extra[user_id]}++{extra[chat_id]}->{extra[api]} \"\n \"{extra[msg_head]}:{message}\"\n )\n\n # Avoid unexpected KeyError\n # Do not unpack key-value pairs, but save all records.\n format_full_extra = (\n \"{time:YYYY-MM-DD HH:mm:ss} | {level} - {name} | {message} - {extra}\"\n )\n\n logger.remove()\n\n logger.configure(\n handlers=[\n dict(sink=sys.stdout, format=format_stdout, level=\"TRACE\"),\n dict(\n sink=sink_channel.get(\"error\"),\n format=format_full_extra,\n level=\"ERROR\",\n diagnose=False,\n rotation=\"1 week\",\n ),\n dict(\n sink=sink_channel.get(\"runtime\"),\n format=format_full_extra,\n level=\"DEBUG\",\n diagnose=False,\n rotation=\"20 MB\",\n retention=\"20 days\",\n ),\n dict(\n sink=sink_channel.get(\"serialize\"),\n level=\"DEBUG\",\n diagnose=False,\n serialize=True,\n ),\n ],\n extra={\"user_id\": \"\", \"chat_id\": \"\", \"api\": \"\", \"msg_head\": \"\"},\n )\n\n return logger\n\n\ndef create_personal_folder(user_id: str) -> str:\n # mkdir user folder\n from backend.main import app\n\n user_folder = os.path.join(app.config[\"UPLOAD_FOLDER\"], user_id)\n os.makedirs(user_folder, exist_ok=True)\n # mkdir chat folder under user folder\n return user_folder","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.VariableRegister","uri":"program://OpenAgents/class/backend.utils.utils.VariableRegister#L41-L84","kind":"class","name":"VariableRegister","path":"backend/utils/utils.py","language":"python","start_line":41,"end_line":84,"context_start_line":21,"context_end_line":104,"code":"from real_agents.data_agent import (\n DataSummaryExecutor,\n TableSummaryExecutor,\n ImageSummaryExecutor,\n)\nfrom real_agents.adapters.schema import SQLDatabase\nfrom backend.utils.running_time_storage import get_running_time_storage\nfrom backend.app import app\nfrom backend.schemas import DEFAULT_USER_ID\n\nTABLE_EXTENSIONS = {\"csv\", \"xls\", \"xlsx\", \"tsv\"}\nDOCUMENT_EXTENSIONS = {\"pdf\", \"doc\", \"docx\", \"txt\"}\nDATABASE_EXTENSIONS = {\"sqlite\", \"db\"}\nIMAGE_EXTENSIONS = {\"jpg\", \"png\", \"jpeg\"}\nALLOW_EXTENSIONS = TABLE_EXTENSIONS | DOCUMENT_EXTENSIONS | DATABASE_EXTENSIONS | IMAGE_EXTENSIONS\n\nLOCAL = \"local\"\nREDIS = \"redis\"\n\n\nclass VariableRegister:\n def __init__(self, name=None, backend=LOCAL) -> None:\n self.backend = backend\n if self.backend == LOCAL:\n self.variables: Dict[int, Any] = {}\n self.counter = 1\n elif self.backend == REDIS:\n assert name is not None\n self.name = name\n self.counter_name = f\"{self.name}:counter\"\n self.variables_name = f\"{self.name}:variables\"\n with app.app_context():\n self.redis_client = get_running_time_storage()\n if not self.redis_client.exists(self.counter_name):\n self.redis_client.set(self.counter_name, 0)\n else:\n logger.bind(msg_head=\"VariableRegister\").debug(\n f\"Reuse the {self.counter_name}({self.redis_client.get(self.counter_name)}) and {self.variables_name}.\"\n )\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def add_variable(self, variable: Any) -> int:\n if self.backend == LOCAL:\n variable_id = self.counter\n self.variables[variable_id] = variable\n self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n\ndef load_grounding_source(file_path: str) -> Any:\n # TODO: Maybe convert to DataModel here\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n # Assume it is a collection of csv files, usually downloaded from kaggle.\n grounding_source = {}","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.get_user_and_chat_id_from_request_json","uri":"program://OpenAgents/function/backend.utils.utils.get_user_and_chat_id_from_request_json#L87-L90","kind":"function","name":"get_user_and_chat_id_from_request_json","path":"backend/utils/utils.py","language":"python","start_line":87,"end_line":90,"context_start_line":67,"context_end_line":110,"code":" self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n\ndef load_grounding_source(file_path: str) -> Any:\n # TODO: Maybe convert to DataModel here\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n # Assume it is a collection of csv files, usually downloaded from kaggle.\n grounding_source = {}\n for file in Path(file_path).iterdir():\n if file.suffix == \".csv\":\n grounding_source[file.as_posix()] = pd.read_csv(file, index_col=False)\n else:\n raise ValueError(\"Only csv files are allowed in the directory\")\n elif suffix == \".csv\":","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.get_user_and_chat_id_from_request","uri":"program://OpenAgents/function/backend.utils.utils.get_user_and_chat_id_from_request#L93-L96","kind":"function","name":"get_user_and_chat_id_from_request","path":"backend/utils/utils.py","language":"python","start_line":93,"end_line":96,"context_start_line":73,"context_end_line":116,"code":"\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n\ndef load_grounding_source(file_path: str) -> Any:\n # TODO: Maybe convert to DataModel here\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n # Assume it is a collection of csv files, usually downloaded from kaggle.\n grounding_source = {}\n for file in Path(file_path).iterdir():\n if file.suffix == \".csv\":\n grounding_source[file.as_posix()] = pd.read_csv(file, index_col=False)\n else:\n raise ValueError(\"Only csv files are allowed in the directory\")\n elif suffix == \".csv\":\n grounding_source = pd.read_csv(file_path, index_col=False)\n elif suffix == \".tsv\" or suffix == \".txt\":\n grounding_source = pd.read_csv(file_path, sep=\"\\t\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n grounding_source = pd.read_excel(file_path)\n elif suffix == \".db\" or suffix == \".sqlite\":","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.load_grounding_source","uri":"program://OpenAgents/function/backend.utils.utils.load_grounding_source#L99-L132","kind":"function","name":"load_grounding_source","path":"backend/utils/utils.py","language":"python","start_line":99,"end_line":132,"context_start_line":79,"context_end_line":152,"code":"\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n\ndef load_grounding_source(file_path: str) -> Any:\n # TODO: Maybe convert to DataModel here\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n # Assume it is a collection of csv files, usually downloaded from kaggle.\n grounding_source = {}\n for file in Path(file_path).iterdir():\n if file.suffix == \".csv\":\n grounding_source[file.as_posix()] = pd.read_csv(file, index_col=False)\n else:\n raise ValueError(\"Only csv files are allowed in the directory\")\n elif suffix == \".csv\":\n grounding_source = pd.read_csv(file_path, index_col=False)\n elif suffix == \".tsv\" or suffix == \".txt\":\n grounding_source = pd.read_csv(file_path, sep=\"\\t\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n grounding_source = pd.read_excel(file_path)\n elif suffix == \".db\" or suffix == \".sqlite\":\n engine = create_engine(f\"sqlite:///{file_path}\")\n grounding_source = SQLDatabase(engine)\n return grounding_source\n elif suffix == \".png\" or suffix == \".jpg\" or suffix == \".jpeg\":\n img = Image.open(file_path)\n with open(file_path, \"rb\") as image2string:\n converted_string = \"data:image/png;base64,\" + base64.b64encode(image2string.read()).decode(\"utf-8\")\n grounding_source = {\n \"base64_string\": converted_string,\n \"format\": img.format,\n \"size\": img.size,\n \"mode\": img.mode,\n }\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return grounding_source\n\n\ndef get_data_model_cls(file_path: str) -> DataModel:\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n data_model_cls = KaggleDataModel\n elif suffix == \".csv\":\n data_model_cls = TableDataModel\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_model_cls = TableDataModel\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_model_cls = DatabaseDataModel\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_model_cls = ImageDataModel\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_model_cls\n","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.get_data_model_cls","uri":"program://OpenAgents/function/backend.utils.utils.get_data_model_cls#L135-L151","kind":"function","name":"get_data_model_cls","path":"backend/utils/utils.py","language":"python","start_line":135,"end_line":151,"context_start_line":115,"context_end_line":171,"code":" grounding_source = pd.read_excel(file_path)\n elif suffix == \".db\" or suffix == \".sqlite\":\n engine = create_engine(f\"sqlite:///{file_path}\")\n grounding_source = SQLDatabase(engine)\n return grounding_source\n elif suffix == \".png\" or suffix == \".jpg\" or suffix == \".jpeg\":\n img = Image.open(file_path)\n with open(file_path, \"rb\") as image2string:\n converted_string = \"data:image/png;base64,\" + base64.b64encode(image2string.read()).decode(\"utf-8\")\n grounding_source = {\n \"base64_string\": converted_string,\n \"format\": img.format,\n \"size\": img.size,\n \"mode\": img.mode,\n }\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return grounding_source\n\n\ndef get_data_model_cls(file_path: str) -> DataModel:\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n data_model_cls = KaggleDataModel\n elif suffix == \".csv\":\n data_model_cls = TableDataModel\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_model_cls = TableDataModel\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_model_cls = DatabaseDataModel\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_model_cls = ImageDataModel\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_model_cls\n\n\ndef get_data_summary_cls(file_path: str) -> DataSummaryExecutor:\n suffix = Path(file_path).suffix\n if suffix == \".csv\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_summary_cls = ImageSummaryExecutor\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_summary_cls\n\n\ndef allowed_file(filename: Union[str, Path]) -> bool:","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.get_data_summary_cls","uri":"program://OpenAgents/function/backend.utils.utils.get_data_summary_cls#L154-L168","kind":"function","name":"get_data_summary_cls","path":"backend/utils/utils.py","language":"python","start_line":154,"end_line":168,"context_start_line":134,"context_end_line":188,"code":"\ndef get_data_model_cls(file_path: str) -> DataModel:\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n data_model_cls = KaggleDataModel\n elif suffix == \".csv\":\n data_model_cls = TableDataModel\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_model_cls = TableDataModel\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_model_cls = DatabaseDataModel\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_model_cls = ImageDataModel\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_model_cls\n\n\ndef get_data_summary_cls(file_path: str) -> DataSummaryExecutor:\n suffix = Path(file_path).suffix\n if suffix == \".csv\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_summary_cls = ImageSummaryExecutor\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_summary_cls\n\n\ndef allowed_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in ALLOW_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_table_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in TABLE_EXTENSIONS:\n return True\n else:\n return False","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.allowed_file","uri":"program://OpenAgents/function/backend.utils.utils.allowed_file#L171-L178","kind":"function","name":"allowed_file","path":"backend/utils/utils.py","language":"python","start_line":171,"end_line":178,"context_start_line":151,"context_end_line":198,"code":" return data_model_cls\n\n\ndef get_data_summary_cls(file_path: str) -> DataSummaryExecutor:\n suffix = Path(file_path).suffix\n if suffix == \".csv\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".tsv\" or suffix == \".txt\":\n raise NotImplementedError(\"Not implemented yet\")\n elif suffix == \".xlsx\" or suffix == \".xls\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_summary_cls = ImageSummaryExecutor\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_summary_cls\n\n\ndef allowed_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in ALLOW_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_table_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in TABLE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_document_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DOCUMENT_EXTENSIONS:\n return True\n else:\n return False","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.is_table_file","uri":"program://OpenAgents/function/backend.utils.utils.is_table_file#L181-L188","kind":"function","name":"is_table_file","path":"backend/utils/utils.py","language":"python","start_line":181,"end_line":188,"context_start_line":161,"context_end_line":208,"code":" data_summary_cls = TableSummaryExecutor\n elif suffix == \".sqlite\" or suffix == \".db\":\n data_summary_cls = TableSummaryExecutor\n elif suffix == \".jpeg\" or suffix == \".png\" or suffix == \".jpg\":\n data_summary_cls = ImageSummaryExecutor\n else:\n raise ValueError(\"File type not allowed to be set as grounding source\")\n return data_summary_cls\n\n\ndef allowed_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in ALLOW_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_table_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in TABLE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_document_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DOCUMENT_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_sqlite_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DATABASE_EXTENSIONS:\n return True\n else:\n return False","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.is_document_file","uri":"program://OpenAgents/function/backend.utils.utils.is_document_file#L191-L198","kind":"function","name":"is_document_file","path":"backend/utils/utils.py","language":"python","start_line":191,"end_line":198,"context_start_line":171,"context_end_line":218,"code":"def allowed_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in ALLOW_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_table_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in TABLE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_document_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DOCUMENT_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_sqlite_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DATABASE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_image_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in IMAGE_EXTENSIONS:\n return True\n else:\n return False","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.is_sqlite_file","uri":"program://OpenAgents/function/backend.utils.utils.is_sqlite_file#L201-L208","kind":"function","name":"is_sqlite_file","path":"backend/utils/utils.py","language":"python","start_line":201,"end_line":208,"context_start_line":181,"context_end_line":228,"code":"def is_table_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in TABLE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_document_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DOCUMENT_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_sqlite_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DATABASE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_image_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in IMAGE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef remove_nan(file_path: str) -> None:\n \"\"\"\n We only support csv file in the current version\n By default, we remove columns that contain only nan values\n For columns that have both nan values and non-nan values, we replace nan values with the mean (number type)\n or the mode (other type)\n \"\"\"\n if file_path.endswith(\"csv\"):","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.is_image_file","uri":"program://OpenAgents/function/backend.utils.utils.is_image_file#L211-L218","kind":"function","name":"is_image_file","path":"backend/utils/utils.py","language":"python","start_line":211,"end_line":218,"context_start_line":191,"context_end_line":238,"code":"def is_document_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DOCUMENT_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_sqlite_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DATABASE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_image_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in IMAGE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef remove_nan(file_path: str) -> None:\n \"\"\"\n We only support csv file in the current version\n By default, we remove columns that contain only nan values\n For columns that have both nan values and non-nan values, we replace nan values with the mean (number type)\n or the mode (other type)\n \"\"\"\n if file_path.endswith(\"csv\"):\n df = pd.read_csv(file_path)\n columns = list(df.columns)\n nan_columns = []\n for c in columns:\n if all(list(df[c].isnull())):\n nan_columns.append(c)\n df.drop(columns=nan_columns, inplace=True)\n columns = list(df.columns)\n for c in columns:\n try:","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.remove_nan","uri":"program://OpenAgents/function/backend.utils.utils.remove_nan#L221-L243","kind":"function","name":"remove_nan","path":"backend/utils/utils.py","language":"python","start_line":221,"end_line":243,"context_start_line":201,"context_end_line":263,"code":"def is_sqlite_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in DATABASE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef is_image_file(filename: Union[str, Path]) -> bool:\n if isinstance(filename, str):\n filename = Path(filename)\n suffix = filename.suffix[1:]\n if suffix in IMAGE_EXTENSIONS:\n return True\n else:\n return False\n\n\ndef remove_nan(file_path: str) -> None:\n \"\"\"\n We only support csv file in the current version\n By default, we remove columns that contain only nan values\n For columns that have both nan values and non-nan values, we replace nan values with the mean (number type)\n or the mode (other type)\n \"\"\"\n if file_path.endswith(\"csv\"):\n df = pd.read_csv(file_path)\n columns = list(df.columns)\n nan_columns = []\n for c in columns:\n if all(list(df[c].isnull())):\n nan_columns.append(c)\n df.drop(columns=nan_columns, inplace=True)\n columns = list(df.columns)\n for c in columns:\n try:\n fillin_value = df[c].mean()\n except Exception:\n fillin_value = df[c].mode()\n df[c].fillna(value=fillin_value, inplace=True)\n df.to_csv(file_path)\n\n\ndef is_valid_input(user_intent: str, max_token_limit: int = 2000) -> bool:\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(user_intent))\n return tokens <= max_token_limit\n\n\ndef error_rendering(error_message: str) -> str:\n \"\"\"Map (certain) error message to frontend rendering form, otherwise show\n 'internal backend error'. Currently, only handle OpenAI error message.\n \"\"\"\n if \"openai\" in error_message:\n if \"Timeout\" in error_message:\n return \"OpenAI timeout error. Please try again.\"\n elif \"RateLimitError\" in error_message:\n return \"OpenAI rate limit error. Please try again.\"\n elif \"APIConnectionError\" in error_message:\n return \"OpenAI API connection error. Please try again.\"\n elif \"InvalidRequestError\" in error_message:","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.is_valid_input","uri":"program://OpenAgents/function/backend.utils.utils.is_valid_input#L246-L249","kind":"function","name":"is_valid_input","path":"backend/utils/utils.py","language":"python","start_line":246,"end_line":249,"context_start_line":226,"context_end_line":269,"code":" or the mode (other type)\n \"\"\"\n if file_path.endswith(\"csv\"):\n df = pd.read_csv(file_path)\n columns = list(df.columns)\n nan_columns = []\n for c in columns:\n if all(list(df[c].isnull())):\n nan_columns.append(c)\n df.drop(columns=nan_columns, inplace=True)\n columns = list(df.columns)\n for c in columns:\n try:\n fillin_value = df[c].mean()\n except Exception:\n fillin_value = df[c].mode()\n df[c].fillna(value=fillin_value, inplace=True)\n df.to_csv(file_path)\n\n\ndef is_valid_input(user_intent: str, max_token_limit: int = 2000) -> bool:\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(user_intent))\n return tokens <= max_token_limit\n\n\ndef error_rendering(error_message: str) -> str:\n \"\"\"Map (certain) error message to frontend rendering form, otherwise show\n 'internal backend error'. Currently, only handle OpenAI error message.\n \"\"\"\n if \"openai\" in error_message:\n if \"Timeout\" in error_message:\n return \"OpenAI timeout error. Please try again.\"\n elif \"RateLimitError\" in error_message:\n return \"OpenAI rate limit error. Please try again.\"\n elif \"APIConnectionError\" in error_message:\n return \"OpenAI API connection error. Please try again.\"\n elif \"InvalidRequestError\" in error_message:\n return \"OpenAI invalid request error. Please try again.\"\n elif \"AuthenticationError\" in error_message:\n return \"OpenAI authentication error. Please try again.\"\n elif \"ServiceUnavailableError\" in error_message:\n return \"OpenAI service unavailable error. Please try again.\"\n else:","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.error_rendering","uri":"program://OpenAgents/function/backend.utils.utils.error_rendering#L252-L270","kind":"function","name":"error_rendering","path":"backend/utils/utils.py","language":"python","start_line":252,"end_line":270,"context_start_line":232,"context_end_line":290,"code":" for c in columns:\n if all(list(df[c].isnull())):\n nan_columns.append(c)\n df.drop(columns=nan_columns, inplace=True)\n columns = list(df.columns)\n for c in columns:\n try:\n fillin_value = df[c].mean()\n except Exception:\n fillin_value = df[c].mode()\n df[c].fillna(value=fillin_value, inplace=True)\n df.to_csv(file_path)\n\n\ndef is_valid_input(user_intent: str, max_token_limit: int = 2000) -> bool:\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(user_intent))\n return tokens <= max_token_limit\n\n\ndef error_rendering(error_message: str) -> str:\n \"\"\"Map (certain) error message to frontend rendering form, otherwise show\n 'internal backend error'. Currently, only handle OpenAI error message.\n \"\"\"\n if \"openai\" in error_message:\n if \"Timeout\" in error_message:\n return \"OpenAI timeout error. Please try again.\"\n elif \"RateLimitError\" in error_message:\n return \"OpenAI rate limit error. Please try again.\"\n elif \"APIConnectionError\" in error_message:\n return \"OpenAI API connection error. Please try again.\"\n elif \"InvalidRequestError\" in error_message:\n return \"OpenAI invalid request error. Please try again.\"\n elif \"AuthenticationError\" in error_message:\n return \"OpenAI authentication error. Please try again.\"\n elif \"ServiceUnavailableError\" in error_message:\n return \"OpenAI service unavailable error. Please try again.\"\n else:\n return \"Internal backend error. Please try again.\"\n\n\ndef init_log(**sink_channel):\n \"\"\"Initialize loguru log information\"\"\"\n\n # Just for sys.stdout log message\n format_stdout = (\n \"{time:YYYY-MM-DD HH:mm:ss} | {level} - {extra[user_id]}++{extra[chat_id]}->{extra[api]} \"\n \"{extra[msg_head]}:{message}\"\n )\n\n # Avoid unexpected KeyError\n # Do not unpack key-value pairs, but save all records.\n format_full_extra = (\n \"{time:YYYY-MM-DD HH:mm:ss} | {level} - {name} | {message} - {extra}\"\n )\n\n logger.remove()\n\n logger.configure(","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.init_log","uri":"program://OpenAgents/function/backend.utils.utils.init_log#L273-L318","kind":"function","name":"init_log","path":"backend/utils/utils.py","language":"python","start_line":273,"end_line":318,"context_start_line":253,"context_end_line":328,"code":" \"\"\"Map (certain) error message to frontend rendering form, otherwise show\n 'internal backend error'. Currently, only handle OpenAI error message.\n \"\"\"\n if \"openai\" in error_message:\n if \"Timeout\" in error_message:\n return \"OpenAI timeout error. Please try again.\"\n elif \"RateLimitError\" in error_message:\n return \"OpenAI rate limit error. Please try again.\"\n elif \"APIConnectionError\" in error_message:\n return \"OpenAI API connection error. Please try again.\"\n elif \"InvalidRequestError\" in error_message:\n return \"OpenAI invalid request error. Please try again.\"\n elif \"AuthenticationError\" in error_message:\n return \"OpenAI authentication error. Please try again.\"\n elif \"ServiceUnavailableError\" in error_message:\n return \"OpenAI service unavailable error. Please try again.\"\n else:\n return \"Internal backend error. Please try again.\"\n\n\ndef init_log(**sink_channel):\n \"\"\"Initialize loguru log information\"\"\"\n\n # Just for sys.stdout log message\n format_stdout = (\n \"{time:YYYY-MM-DD HH:mm:ss} | {level} - {extra[user_id]}++{extra[chat_id]}->{extra[api]} \"\n \"{extra[msg_head]}:{message}\"\n )\n\n # Avoid unexpected KeyError\n # Do not unpack key-value pairs, but save all records.\n format_full_extra = (\n \"{time:YYYY-MM-DD HH:mm:ss} | {level} - {name} | {message} - {extra}\"\n )\n\n logger.remove()\n\n logger.configure(\n handlers=[\n dict(sink=sys.stdout, format=format_stdout, level=\"TRACE\"),\n dict(\n sink=sink_channel.get(\"error\"),\n format=format_full_extra,\n level=\"ERROR\",\n diagnose=False,\n rotation=\"1 week\",\n ),\n dict(\n sink=sink_channel.get(\"runtime\"),\n format=format_full_extra,\n level=\"DEBUG\",\n diagnose=False,\n rotation=\"20 MB\",\n retention=\"20 days\",\n ),\n dict(\n sink=sink_channel.get(\"serialize\"),\n level=\"DEBUG\",\n diagnose=False,\n serialize=True,\n ),\n ],\n extra={\"user_id\": \"\", \"chat_id\": \"\", \"api\": \"\", \"msg_head\": \"\"},\n )\n\n return logger\n\n\ndef create_personal_folder(user_id: str) -> str:\n # mkdir user folder\n from backend.main import app\n\n user_folder = os.path.join(app.config[\"UPLOAD_FOLDER\"], user_id)\n os.makedirs(user_folder, exist_ok=True)\n # mkdir chat folder under user folder\n return user_folder","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.create_personal_folder","uri":"program://OpenAgents/function/backend.utils.utils.create_personal_folder#L321-L328","kind":"function","name":"create_personal_folder","path":"backend/utils/utils.py","language":"python","start_line":321,"end_line":328,"context_start_line":301,"context_end_line":328,"code":" sink=sink_channel.get(\"runtime\"),\n format=format_full_extra,\n level=\"DEBUG\",\n diagnose=False,\n rotation=\"20 MB\",\n retention=\"20 days\",\n ),\n dict(\n sink=sink_channel.get(\"serialize\"),\n level=\"DEBUG\",\n diagnose=False,\n serialize=True,\n ),\n ],\n extra={\"user_id\": \"\", \"chat_id\": \"\", \"api\": \"\", \"msg_head\": \"\"},\n )\n\n return logger\n\n\ndef create_personal_folder(user_id: str) -> str:\n # mkdir user folder\n from backend.main import app\n\n user_folder = os.path.join(app.config[\"UPLOAD_FOLDER\"], user_id)\n os.makedirs(user_folder, exist_ok=True)\n # mkdir chat folder under user folder\n return user_folder","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.__init__","uri":"program://OpenAgents/function/backend.utils.utils.__init__#L42-L61","kind":"function","name":"__init__","path":"backend/utils/utils.py","language":"python","start_line":42,"end_line":61,"context_start_line":22,"context_end_line":81,"code":" DataSummaryExecutor,\n TableSummaryExecutor,\n ImageSummaryExecutor,\n)\nfrom real_agents.adapters.schema import SQLDatabase\nfrom backend.utils.running_time_storage import get_running_time_storage\nfrom backend.app import app\nfrom backend.schemas import DEFAULT_USER_ID\n\nTABLE_EXTENSIONS = {\"csv\", \"xls\", \"xlsx\", \"tsv\"}\nDOCUMENT_EXTENSIONS = {\"pdf\", \"doc\", \"docx\", \"txt\"}\nDATABASE_EXTENSIONS = {\"sqlite\", \"db\"}\nIMAGE_EXTENSIONS = {\"jpg\", \"png\", \"jpeg\"}\nALLOW_EXTENSIONS = TABLE_EXTENSIONS | DOCUMENT_EXTENSIONS | DATABASE_EXTENSIONS | IMAGE_EXTENSIONS\n\nLOCAL = \"local\"\nREDIS = \"redis\"\n\n\nclass VariableRegister:\n def __init__(self, name=None, backend=LOCAL) -> None:\n self.backend = backend\n if self.backend == LOCAL:\n self.variables: Dict[int, Any] = {}\n self.counter = 1\n elif self.backend == REDIS:\n assert name is not None\n self.name = name\n self.counter_name = f\"{self.name}:counter\"\n self.variables_name = f\"{self.name}:variables\"\n with app.app_context():\n self.redis_client = get_running_time_storage()\n if not self.redis_client.exists(self.counter_name):\n self.redis_client.set(self.counter_name, 0)\n else:\n logger.bind(msg_head=\"VariableRegister\").debug(\n f\"Reuse the {self.counter_name}({self.redis_client.get(self.counter_name)}) and {self.variables_name}.\"\n )\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def add_variable(self, variable: Any) -> int:\n if self.backend == LOCAL:\n variable_id = self.counter\n self.variables[variable_id] = variable\n self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.add_variable","uri":"program://OpenAgents/function/backend.utils.utils.add_variable#L63-L72","kind":"function","name":"add_variable","path":"backend/utils/utils.py","language":"python","start_line":63,"end_line":72,"context_start_line":43,"context_end_line":92,"code":" self.backend = backend\n if self.backend == LOCAL:\n self.variables: Dict[int, Any] = {}\n self.counter = 1\n elif self.backend == REDIS:\n assert name is not None\n self.name = name\n self.counter_name = f\"{self.name}:counter\"\n self.variables_name = f\"{self.name}:variables\"\n with app.app_context():\n self.redis_client = get_running_time_storage()\n if not self.redis_client.exists(self.counter_name):\n self.redis_client.set(self.counter_name, 0)\n else:\n logger.bind(msg_head=\"VariableRegister\").debug(\n f\"Reuse the {self.counter_name}({self.redis_client.get(self.counter_name)}) and {self.variables_name}.\"\n )\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def add_variable(self, variable: Any) -> int:\n if self.backend == LOCAL:\n variable_id = self.counter\n self.variables[variable_id] = variable\n self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.get_variable","uri":"program://OpenAgents/function/backend.utils.utils.get_variable#L74-L78","kind":"function","name":"get_variable","path":"backend/utils/utils.py","language":"python","start_line":74,"end_line":78,"context_start_line":54,"context_end_line":98,"code":" if not self.redis_client.exists(self.counter_name):\n self.redis_client.set(self.counter_name, 0)\n else:\n logger.bind(msg_head=\"VariableRegister\").debug(\n f\"Reuse the {self.counter_name}({self.redis_client.get(self.counter_name)}) and {self.variables_name}.\"\n )\n else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def add_variable(self, variable: Any) -> int:\n if self.backend == LOCAL:\n variable_id = self.counter\n self.variables[variable_id] = variable\n self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.utils.get_variables","uri":"program://OpenAgents/function/backend.utils.utils.get_variables#L80-L84","kind":"function","name":"get_variables","path":"backend/utils/utils.py","language":"python","start_line":80,"end_line":84,"context_start_line":60,"context_end_line":104,"code":" else:\n raise ValueError(\"Unknown backend option: {}\".format(self.backend))\n\n def add_variable(self, variable: Any) -> int:\n if self.backend == LOCAL:\n variable_id = self.counter\n self.variables[variable_id] = variable\n self.counter += 1\n return variable_id\n elif self.backend == REDIS:\n variable_id = self.redis_client.incrby(self.counter_name, 1)\n self.redis_client.hset(self.variables_name, variable_id, variable)\n return variable_id\n\n def get_variable(self, variable_id: int) -> Any:\n if self.backend == LOCAL:\n return self.variables.get(variable_id, None)\n elif self.backend == REDIS:\n return self.redis_client.hget(self.variables_name, variable_id)\n\n def get_variables(self) -> Dict[int, Any]:\n if self.backend == LOCAL:\n return self.variables\n elif self.backend == REDIS:\n return self.redis_client.hgetall(self.variables_name)\n\n\ndef get_user_and_chat_id_from_request_json(request_json: Dict) -> Tuple[str, str]:\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n return user_id, chat_id\n\n\ndef get_user_and_chat_id_from_request(request: Request) -> Tuple[str, str]:\n user_id = request.form.get(\"user_id\", DEFAULT_USER_ID)\n chat_id = request.form.get(\"chat_id\")\n return user_id, chat_id\n\n\ndef load_grounding_source(file_path: str) -> Any:\n # TODO: Maybe convert to DataModel here\n suffix = Path(file_path).suffix\n if Path(file_path).is_dir():\n # Assume it is a collection of csv files, usually downloaded from kaggle.\n grounding_source = {}","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading","uri":"program://OpenAgents/module/backend.utils.threading#L1-L62","kind":"module","name":"backend.utils.threading","path":"backend/utils/threading.py","language":"python","start_line":1,"end_line":62,"context_start_line":1,"context_end_line":62,"code":"# Python program using\n# traces to kill threads\nfrom typing import Dict, Tuple, Optional\nfrom multiprocess import Process\n\n\nclass ThreadManager:\n \"\"\"Manager class of all user chat threads.\"\"\"\n\n def __init__(self) -> None:\n self.thread_pool: Dict[str, Process] = {}\n self.stop_pool: Dict[str, bool] = {}\n self.timeout_pool: Dict[str, bool] = {}\n self.run_error_pool: Dict[str, Optional[str]] = {}\n\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n\n def flush_thread(self, chat_id) -> Tuple[bool, bool, str]:\n # self.thread_pool[chat_id] = None\n stop_flag = self.stop_pool[chat_id]\n timeout_flag = self.timeout_pool[chat_id]\n run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n\n def kill_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.stop_pool[chat_id] = True\n pass\n\n def timeout_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.timeout_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.timeout_pool[chat_id] = True\n pass\n\n def error_thread(self, chat_id, e_msg: str) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.run_error_pool[chat_id] = e_msg\n except:\n pass","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.ThreadManager","uri":"program://OpenAgents/class/backend.utils.threading.ThreadManager#L7-L62","kind":"class","name":"ThreadManager","path":"backend/utils/threading.py","language":"python","start_line":7,"end_line":62,"context_start_line":1,"context_end_line":62,"code":"# Python program using\n# traces to kill threads\nfrom typing import Dict, Tuple, Optional\nfrom multiprocess import Process\n\n\nclass ThreadManager:\n \"\"\"Manager class of all user chat threads.\"\"\"\n\n def __init__(self) -> None:\n self.thread_pool: Dict[str, Process] = {}\n self.stop_pool: Dict[str, bool] = {}\n self.timeout_pool: Dict[str, bool] = {}\n self.run_error_pool: Dict[str, Optional[str]] = {}\n\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n\n def flush_thread(self, chat_id) -> Tuple[bool, bool, str]:\n # self.thread_pool[chat_id] = None\n stop_flag = self.stop_pool[chat_id]\n timeout_flag = self.timeout_pool[chat_id]\n run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n\n def kill_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.stop_pool[chat_id] = True\n pass\n\n def timeout_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.timeout_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.timeout_pool[chat_id] = True\n pass\n\n def error_thread(self, chat_id, e_msg: str) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.run_error_pool[chat_id] = e_msg\n except:\n pass","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.__init__","uri":"program://OpenAgents/function/backend.utils.threading.__init__#L10-L14","kind":"function","name":"__init__","path":"backend/utils/threading.py","language":"python","start_line":10,"end_line":14,"context_start_line":1,"context_end_line":34,"code":"# Python program using\n# traces to kill threads\nfrom typing import Dict, Tuple, Optional\nfrom multiprocess import Process\n\n\nclass ThreadManager:\n \"\"\"Manager class of all user chat threads.\"\"\"\n\n def __init__(self) -> None:\n self.thread_pool: Dict[str, Process] = {}\n self.stop_pool: Dict[str, bool] = {}\n self.timeout_pool: Dict[str, bool] = {}\n self.run_error_pool: Dict[str, Optional[str]] = {}\n\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n\n def flush_thread(self, chat_id) -> Tuple[bool, bool, str]:\n # self.thread_pool[chat_id] = None\n stop_flag = self.stop_pool[chat_id]\n timeout_flag = self.timeout_pool[chat_id]\n run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.register_thread","uri":"program://OpenAgents/function/backend.utils.threading.register_thread#L16-L20","kind":"function","name":"register_thread","path":"backend/utils/threading.py","language":"python","start_line":16,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"# Python program using\n# traces to kill threads\nfrom typing import Dict, Tuple, Optional\nfrom multiprocess import Process\n\n\nclass ThreadManager:\n \"\"\"Manager class of all user chat threads.\"\"\"\n\n def __init__(self) -> None:\n self.thread_pool: Dict[str, Process] = {}\n self.stop_pool: Dict[str, bool] = {}\n self.timeout_pool: Dict[str, bool] = {}\n self.run_error_pool: Dict[str, Optional[str]] = {}\n\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n\n def flush_thread(self, chat_id) -> Tuple[bool, bool, str]:\n # self.thread_pool[chat_id] = None\n stop_flag = self.stop_pool[chat_id]\n timeout_flag = self.timeout_pool[chat_id]\n run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n\n def kill_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.flush_thread","uri":"program://OpenAgents/function/backend.utils.threading.flush_thread#L22-L33","kind":"function","name":"flush_thread","path":"backend/utils/threading.py","language":"python","start_line":22,"end_line":33,"context_start_line":2,"context_end_line":53,"code":"# traces to kill threads\nfrom typing import Dict, Tuple, Optional\nfrom multiprocess import Process\n\n\nclass ThreadManager:\n \"\"\"Manager class of all user chat threads.\"\"\"\n\n def __init__(self) -> None:\n self.thread_pool: Dict[str, Process] = {}\n self.stop_pool: Dict[str, bool] = {}\n self.timeout_pool: Dict[str, bool] = {}\n self.run_error_pool: Dict[str, Optional[str]] = {}\n\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n\n def flush_thread(self, chat_id) -> Tuple[bool, bool, str]:\n # self.thread_pool[chat_id] = None\n stop_flag = self.stop_pool[chat_id]\n timeout_flag = self.timeout_pool[chat_id]\n run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n\n def kill_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.stop_pool[chat_id] = True\n pass\n\n def timeout_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.timeout_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.kill_thread","uri":"program://OpenAgents/function/backend.utils.threading.kill_thread#L35-L44","kind":"function","name":"kill_thread","path":"backend/utils/threading.py","language":"python","start_line":35,"end_line":44,"context_start_line":15,"context_end_line":62,"code":"\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n\n def flush_thread(self, chat_id) -> Tuple[bool, bool, str]:\n # self.thread_pool[chat_id] = None\n stop_flag = self.stop_pool[chat_id]\n timeout_flag = self.timeout_pool[chat_id]\n run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n\n def kill_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.stop_pool[chat_id] = True\n pass\n\n def timeout_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.timeout_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.timeout_pool[chat_id] = True\n pass\n\n def error_thread(self, chat_id, e_msg: str) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.run_error_pool[chat_id] = e_msg\n except:\n pass","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.timeout_thread","uri":"program://OpenAgents/function/backend.utils.threading.timeout_thread#L46-L55","kind":"function","name":"timeout_thread","path":"backend/utils/threading.py","language":"python","start_line":46,"end_line":55,"context_start_line":26,"context_end_line":62,"code":" run_error = self.run_error_pool[chat_id]\n _ = self.thread_pool.pop(chat_id)\n _.terminate()\n del _\n self.stop_pool.pop(chat_id)\n self.timeout_pool.pop(chat_id)\n self.run_error_pool.pop(chat_id)\n return stop_flag, timeout_flag, run_error\n\n def kill_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.stop_pool[chat_id] = True\n pass\n\n def timeout_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.timeout_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.timeout_pool[chat_id] = True\n pass\n\n def error_thread(self, chat_id, e_msg: str) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.run_error_pool[chat_id] = e_msg\n except:\n pass","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.threading.error_thread","uri":"program://OpenAgents/function/backend.utils.threading.error_thread#L57-L62","kind":"function","name":"error_thread","path":"backend/utils/threading.py","language":"python","start_line":57,"end_line":62,"context_start_line":37,"context_end_line":62,"code":" try:\n self.stop_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.stop_pool[chat_id] = True\n pass\n\n def timeout_thread(self, chat_id) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.timeout_pool[chat_id] = True\n while self.thread_pool[chat_id].is_alive():\n self.thread_pool[chat_id].terminate()\n except Exception as e:\n if not self.thread_pool[chat_id].is_alive():\n self.timeout_pool[chat_id] = True\n pass\n\n def error_thread(self, chat_id, e_msg: str) -> None:\n if chat_id in self.thread_pool and self.thread_pool[chat_id] is not None:\n try:\n self.run_error_pool[chat_id] = e_msg\n except:\n pass","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.charts","uri":"program://OpenAgents/module/backend.utils.charts#L1-L32","kind":"module","name":"backend.utils.charts","path":"backend/utils/charts.py","language":"python","start_line":1,"end_line":32,"context_start_line":1,"context_end_line":32,"code":"import json\n\n\ndef polish_echarts(echarts_str):\n \"\"\"Polishes the echarts output into prettier format.\"\"\"\n try:\n option = json.loads(echarts_str)\n\n # turn numeric axis into str\n category_flag = False\n for idx, series_data in enumerate(option[\"series\"]):\n if series_data[\"type\"] in [\"bar\", \"line\"]:\n category_flag = True\n break\n if category_flag:\n option[\"xAxis\"][0][\"data\"] = [str(_) for _ in option[\"xAxis\"][0][\"data\"]]\n for idx, series_data in enumerate(option[\"series\"]):\n try:\n option[\"series\"][idx][\"data\"] = [[str(_[0]), _[1]] for _ in series_data[\"data\"]]\n except:\n continue\n for idx, series_data in enumerate(option[\"series\"]):\n option[\"series\"][idx][\"label\"][\"show\"] = False\n # set title position\n option[\"title\"][0][\"bottom\"] = \"bottom\"\n option[\"title\"][0][\"left\"] = \"center\"\n option[\"tooltip\"][\"alwaysShowContent\"] = False\n\n return json.dumps(option)\n except Exception as e:\n print(e)\n return echarts_str","source_hash":"91e6c4a6004302f90171eae2c027a1e0a3ff6a40eb67620d08c0e38b3caf6c0f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.utils.charts.polish_echarts","uri":"program://OpenAgents/function/backend.utils.charts.polish_echarts#L4-L32","kind":"function","name":"polish_echarts","path":"backend/utils/charts.py","language":"python","start_line":4,"end_line":32,"context_start_line":1,"context_end_line":32,"code":"import json\n\n\ndef polish_echarts(echarts_str):\n \"\"\"Polishes the echarts output into prettier format.\"\"\"\n try:\n option = json.loads(echarts_str)\n\n # turn numeric axis into str\n category_flag = False\n for idx, series_data in enumerate(option[\"series\"]):\n if series_data[\"type\"] in [\"bar\", \"line\"]:\n category_flag = True\n break\n if category_flag:\n option[\"xAxis\"][0][\"data\"] = [str(_) for _ in option[\"xAxis\"][0][\"data\"]]\n for idx, series_data in enumerate(option[\"series\"]):\n try:\n option[\"series\"][idx][\"data\"] = [[str(_[0]), _[1]] for _ in series_data[\"data\"]]\n except:\n continue\n for idx, series_data in enumerate(option[\"series\"]):\n option[\"series\"][idx][\"label\"][\"show\"] = False\n # set title position\n option[\"title\"][0][\"bottom\"] = \"bottom\"\n option[\"title\"][0][\"left\"] = \"center\"\n option[\"tooltip\"][\"alwaysShowContent\"] = False\n\n return json.dumps(option)\n except Exception as e:\n print(e)\n return echarts_str","source_hash":"91e6c4a6004302f90171eae2c027a1e0a3ff6a40eb67620d08c0e38b3caf6c0f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot","uri":"program://OpenAgents/module/backend.api.chat_webot#L1-L347","kind":"module","name":"backend.api.chat_webot","path":"backend/api/chat_webot.py","language":"python","start_line":1,"end_line":347,"context_start_line":1,"context_end_line":347,"code":"from time import sleep\nimport copy\nimport redis\nimport json\nimport pickle\nimport traceback\nfrom flask import Response, request, stream_with_context\nfrom typing import Dict, Union\nimport os\n\nfrom langchain.schema import HumanMessage, SystemMessage\n\nfrom backend.api.language_model import get_llm\nfrom backend.main import app, message_id_register, message_pool, logger\nfrom backend.utils.streaming import single_round_chat_with_agent_streaming\nfrom backend.schemas import OVERLOAD, NEED_CONTINUE_MODEL\nfrom backend.schemas import DEFAULT_USER_ID\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks.agent_streaming import \\\n AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.models import ChatOpenAI\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.adapters.data_model import DataModel, JsonDataModel\nfrom real_agents.adapters.interactive_executor import initialize_webot_agent\nfrom real_agents.web_agent import WebBrowsingExecutor, WebotExecutor\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, db=0) # adjust host/port/db as needed\n\n\n# here webot and webot_status are stored in redis since the two global variable can not be modified and accessed normally in multiprocess\n# fixme:now webot is stored without message_id or chat_id info, so it can only be used for one chat at a time\n# fixme:now webot_status is stored with chat_id info, if the status is not reset after a message ended abnormally e.g. the message is interrupted, it will be reused wrongly for the next chat\ndef get_webot_from_redis(user_id: str, chat_id: str, ) -> WebBrowsingExecutor:\n data = r.get(f'webot_{user_id}_{chat_id}')\n if data is not None:\n webot = pickle.loads(data)\n else:\n # initialize a webot with None instrucition if webot does not exist\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n return webot\n\n\ndef save_webot_to_redis(user_id: str, chat_id: str, webot: WebBrowsingExecutor, ):\n r.set(f'webot_{user_id}_{chat_id}', pickle.dumps(webot))\n\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n\ndef reset_webot_status(user_id: str, chat_id: str):\n webot_status = {\"webot_status\": \"idle\", \"url\": None}\n save_webot_status_to_redis(user_id, chat_id, webot_status)\n\n\n# this function has been deprecated\ndef get_plan(instruction: str, start_url: str, chat_llm: ChatOpenAI):\n # fixme: Move this into a separate chain or executors to decompose the LLMs\n system_message = f\"\"\"\nYou are a planner to assist another browser automation assistant.\n\nHere is the instruction for the other assistant:\n```\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n1. click(element): Clicks on an element\n2. setValue(element, value: string): Focuses on and sets the value of an input element\n3. finish(): Indicates the task is finished\n4. fail(): Indicates that you are unable to complete the task\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n\nI should click the add to cart button\nclick(223)\n\nYou MUST always include the and open/close tags or else your response will be marked as invalid.\n\nRules you MUST follow:\n1. You must only take one step at a time. You cannot take multiple actions in a single response.\n2. You should not consider the action to present the result to the user. You only need to do available actions. If info in current page is enough for the user to solve the problem, you should finish.\n```\nNow your responsibility is to give a step-by-step plan according to user's instruction. This plan will be given to the assistant as a reference when it is performing tasks.\n\"\"\".strip()\n\n human_message = f\"\"\"\nThe user requests the following task:\n\n{instruction}\n\nNow you are at {start_url}\n\nProvide a plan to do this (you can use pseudo description as below to describe the item).\n\nHere is an example case:\n\nrequest: Go to google calendar to schedule a meeting\n\ncurrent url: \"https://google.com\"\n\nexample plan:\n\n1. setValue(searchBar, \"google calendar\")\n2. click(search)\n3. click(the item with title of google calendar)\n4.1 if user has loginned \n do nothing \n4.2 if user hasn't loginned \n do login \n5. click(create event button) \n6. setValue(event title input bar, \"meeting\") \n7. click(save event button)\n8. finish()\n\"\"\".strip()\n\n messages = [SystemMessage(content=system_message),\n HumanMessage(content=human_message)]\n response = chat_llm(messages).content\n return response\n\n\ndef create_webot_interaction_executor(\n llm: BaseLanguageModel,\n llm_name: str,\n user_id: str,\n chat_id: str\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, max_token_limit=10000)\n\n class RunWebot:\n def __init__(self, webot: WebotExecutor, llm: BaseLanguageModel, user_id: str,\n chat_id: str):\n self.llm = llm\n self.webot = webot\n self.user_id = user_id\n self.chat_id = chat_id\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n user_id = self.user_id\n chat_id = self.chat_id\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n raw_observation = self.webot.run(user_intent=term, llm=self.llm)\n instruction, start_url = raw_observation[\"instruction\"], \\\n raw_observation[\"start_url\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = instruction\n # webot.plan = get_plan(instruction, start_url)\n webot.plan = \"\"\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n webot_status = {\n \"webot_status\": \"running\",\n \"url\": start_url\n }\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status=webot_status)\n while True:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n if webot.finish or webot.interrupt or webot.error or webot.fail:\n break\n else:\n sleep(0.5)\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status={\"webot_status\": \"idle\",\n \"url\": None})\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = None\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n if webot.finish:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n action_history = webot.action_history\n last_page = webot.pages_viewed[-1]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps({\"action_history\": action_history,\n \"last_page\": last_page}, indent=4),\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.fail:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"The webot failed to execute the instruction.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.interrupt:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"The web browsing is interrupted by user.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.error:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"Error occurs during web browsing.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n except Exception as e:\n print(traceback.format_exc())\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": f\"Failed in web browsing with the input: {term}, please try again later.\",\n \"intermediate_steps\": json.dumps({\"error\": str(e)})\n }\n )\n return observation\n\n webot = WebotExecutor.from_webot()\n llm = copy.deepcopy(llm)\n run_webot = RunWebot(webot, llm, chat_id=chat_id, user_id=user_id)\n tools = [Tool(name=webot.name, func=run_webot.run, description=webot.description)]\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_webot_agent(\n tools, llm, continue_model, memory=memory, verbose=True\n )\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_webot\", methods=[\"POST\"])\ndef chat_xlang_webot() -> Dict:\n \"\"\"Returns the chat response of web agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = request_json[\"parent_message_id\"]\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.4)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n\n # Get language model\n llm = get_llm(llm_name, **kwargs)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Request json\").debug(request_json)\n\n human_message_id = message_id_register.add_variable(user_intent)\n ai_message_id = message_id_register.add_variable(\"\")\n\n stream_handler = AgentStreamingStdOutCallbackHandler()\n # Build executor and run chat\n\n # reset webot and status\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n\n interaction_executor = create_webot_interaction_executor(\n llm=llm,\n llm_name=llm_name,\n chat_id=chat_id,\n user_id=user_id\n )\n\n activated_message_list = message_pool.get_activated_message_list(user_id,\n chat_id,\n list(),\n parent_message_id)\n message_pool.load_agent_memory_from_list(interaction_executor.memory,\n activated_message_list)\n return stream_with_context(\n Response(\n single_round_chat_with_agent_streaming(\n interaction_executor=interaction_executor,\n user_intent=user_intent,\n human_message_id=human_message_id,\n ai_message_id=ai_message_id,\n user_id=user_id,\n chat_id=chat_id,\n message_list=activated_message_list,\n parent_message_id=parent_message_id,\n stream_handler=stream_handler,\n llm_name=llm_name,\n app_type=\"webot\",\n ),\n content_type=\"application/json\",\n )\n )\n\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n return Response(response=None,\n status=f\"{OVERLOAD} backend is currently overloaded\")","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.get_webot_from_redis","uri":"program://OpenAgents/function/backend.api.chat_webot.get_webot_from_redis#L34-L42","kind":"function","name":"get_webot_from_redis","path":"backend/api/chat_webot.py","language":"python","start_line":34,"end_line":42,"context_start_line":14,"context_end_line":62,"code":"from backend.main import app, message_id_register, message_pool, logger\nfrom backend.utils.streaming import single_round_chat_with_agent_streaming\nfrom backend.schemas import OVERLOAD, NEED_CONTINUE_MODEL\nfrom backend.schemas import DEFAULT_USER_ID\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks.agent_streaming import \\\n AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.models import ChatOpenAI\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.adapters.data_model import DataModel, JsonDataModel\nfrom real_agents.adapters.interactive_executor import initialize_webot_agent\nfrom real_agents.web_agent import WebBrowsingExecutor, WebotExecutor\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, db=0) # adjust host/port/db as needed\n\n\n# here webot and webot_status are stored in redis since the two global variable can not be modified and accessed normally in multiprocess\n# fixme:now webot is stored without message_id or chat_id info, so it can only be used for one chat at a time\n# fixme:now webot_status is stored with chat_id info, if the status is not reset after a message ended abnormally e.g. the message is interrupted, it will be reused wrongly for the next chat\ndef get_webot_from_redis(user_id: str, chat_id: str, ) -> WebBrowsingExecutor:\n data = r.get(f'webot_{user_id}_{chat_id}')\n if data is not None:\n webot = pickle.loads(data)\n else:\n # initialize a webot with None instrucition if webot does not exist\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n return webot\n\n\ndef save_webot_to_redis(user_id: str, chat_id: str, webot: WebBrowsingExecutor, ):\n r.set(f'webot_{user_id}_{chat_id}', pickle.dumps(webot))\n\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.save_webot_to_redis","uri":"program://OpenAgents/function/backend.api.chat_webot.save_webot_to_redis#L45-L46","kind":"function","name":"save_webot_to_redis","path":"backend/api/chat_webot.py","language":"python","start_line":45,"end_line":46,"context_start_line":25,"context_end_line":66,"code":"from real_agents.adapters.interactive_executor import initialize_webot_agent\nfrom real_agents.web_agent import WebBrowsingExecutor, WebotExecutor\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, db=0) # adjust host/port/db as needed\n\n\n# here webot and webot_status are stored in redis since the two global variable can not be modified and accessed normally in multiprocess\n# fixme:now webot is stored without message_id or chat_id info, so it can only be used for one chat at a time\n# fixme:now webot_status is stored with chat_id info, if the status is not reset after a message ended abnormally e.g. the message is interrupted, it will be reused wrongly for the next chat\ndef get_webot_from_redis(user_id: str, chat_id: str, ) -> WebBrowsingExecutor:\n data = r.get(f'webot_{user_id}_{chat_id}')\n if data is not None:\n webot = pickle.loads(data)\n else:\n # initialize a webot with None instrucition if webot does not exist\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n return webot\n\n\ndef save_webot_to_redis(user_id: str, chat_id: str, webot: WebBrowsingExecutor, ):\n r.set(f'webot_{user_id}_{chat_id}', pickle.dumps(webot))\n\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.get_webot_status_from_redis","uri":"program://OpenAgents/function/backend.api.chat_webot.get_webot_status_from_redis#L49-L55","kind":"function","name":"get_webot_status_from_redis","path":"backend/api/chat_webot.py","language":"python","start_line":49,"end_line":55,"context_start_line":29,"context_end_line":75,"code":"\n\n# here webot and webot_status are stored in redis since the two global variable can not be modified and accessed normally in multiprocess\n# fixme:now webot is stored without message_id or chat_id info, so it can only be used for one chat at a time\n# fixme:now webot_status is stored with chat_id info, if the status is not reset after a message ended abnormally e.g. the message is interrupted, it will be reused wrongly for the next chat\ndef get_webot_from_redis(user_id: str, chat_id: str, ) -> WebBrowsingExecutor:\n data = r.get(f'webot_{user_id}_{chat_id}')\n if data is not None:\n webot = pickle.loads(data)\n else:\n # initialize a webot with None instrucition if webot does not exist\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n return webot\n\n\ndef save_webot_to_redis(user_id: str, chat_id: str, webot: WebBrowsingExecutor, ):\n r.set(f'webot_{user_id}_{chat_id}', pickle.dumps(webot))\n\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n\ndef reset_webot_status(user_id: str, chat_id: str):\n webot_status = {\"webot_status\": \"idle\", \"url\": None}\n save_webot_status_to_redis(user_id, chat_id, webot_status)\n\n\n# this function has been deprecated\ndef get_plan(instruction: str, start_url: str, chat_llm: ChatOpenAI):\n # fixme: Move this into a separate chain or executors to decompose the LLMs\n system_message = f\"\"\"","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.save_webot_status_to_redis","uri":"program://OpenAgents/function/backend.api.chat_webot.save_webot_status_to_redis#L58-L59","kind":"function","name":"save_webot_status_to_redis","path":"backend/api/chat_webot.py","language":"python","start_line":58,"end_line":59,"context_start_line":38,"context_end_line":79,"code":" else:\n # initialize a webot with None instrucition if webot does not exist\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n return webot\n\n\ndef save_webot_to_redis(user_id: str, chat_id: str, webot: WebBrowsingExecutor, ):\n r.set(f'webot_{user_id}_{chat_id}', pickle.dumps(webot))\n\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n\ndef reset_webot_status(user_id: str, chat_id: str):\n webot_status = {\"webot_status\": \"idle\", \"url\": None}\n save_webot_status_to_redis(user_id, chat_id, webot_status)\n\n\n# this function has been deprecated\ndef get_plan(instruction: str, start_url: str, chat_llm: ChatOpenAI):\n # fixme: Move this into a separate chain or executors to decompose the LLMs\n system_message = f\"\"\"\nYou are a planner to assist another browser automation assistant.\n\nHere is the instruction for the other assistant:\n```","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.reset_webot","uri":"program://OpenAgents/function/backend.api.chat_webot.reset_webot#L62-L64","kind":"function","name":"reset_webot","path":"backend/api/chat_webot.py","language":"python","start_line":62,"end_line":64,"context_start_line":42,"context_end_line":84,"code":" return webot\n\n\ndef save_webot_to_redis(user_id: str, chat_id: str, webot: WebBrowsingExecutor, ):\n r.set(f'webot_{user_id}_{chat_id}', pickle.dumps(webot))\n\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n\ndef reset_webot_status(user_id: str, chat_id: str):\n webot_status = {\"webot_status\": \"idle\", \"url\": None}\n save_webot_status_to_redis(user_id, chat_id, webot_status)\n\n\n# this function has been deprecated\ndef get_plan(instruction: str, start_url: str, chat_llm: ChatOpenAI):\n # fixme: Move this into a separate chain or executors to decompose the LLMs\n system_message = f\"\"\"\nYou are a planner to assist another browser automation assistant.\n\nHere is the instruction for the other assistant:\n```\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n1. click(element): Clicks on an element\n2. setValue(element, value: string): Focuses on and sets the value of an input element\n3. finish(): Indicates the task is finished","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.reset_webot_status","uri":"program://OpenAgents/function/backend.api.chat_webot.reset_webot_status#L67-L69","kind":"function","name":"reset_webot_status","path":"backend/api/chat_webot.py","language":"python","start_line":67,"end_line":69,"context_start_line":47,"context_end_line":89,"code":"\n\ndef get_webot_status_from_redis(user_id: str, chat_id: str):\n webot_status_json = r.get(f'webot_status_{user_id}_{chat_id}')\n if webot_status_json is not None:\n webot_status = json.loads(webot_status_json)\n return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n\ndef reset_webot_status(user_id: str, chat_id: str):\n webot_status = {\"webot_status\": \"idle\", \"url\": None}\n save_webot_status_to_redis(user_id, chat_id, webot_status)\n\n\n# this function has been deprecated\ndef get_plan(instruction: str, start_url: str, chat_llm: ChatOpenAI):\n # fixme: Move this into a separate chain or executors to decompose the LLMs\n system_message = f\"\"\"\nYou are a planner to assist another browser automation assistant.\n\nHere is the instruction for the other assistant:\n```\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n1. click(element): Clicks on an element\n2. setValue(element, value: string): Focuses on and sets the value of an input element\n3. finish(): Indicates the task is finished\n4. fail(): Indicates that you are unable to complete the task\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.get_plan","uri":"program://OpenAgents/function/backend.api.chat_webot.get_plan#L73-L135","kind":"function","name":"get_plan","path":"backend/api/chat_webot.py","language":"python","start_line":73,"end_line":135,"context_start_line":53,"context_end_line":155,"code":" return webot_status\n else:\n return {}\n\n\ndef save_webot_status_to_redis(user_id: str, chat_id: str, webot_status: Dict):\n r.set(f'webot_status_{user_id}_{chat_id}', json.dumps(webot_status))\n\n\ndef reset_webot(user_id: str, chat_id: str):\n webot = WebBrowsingExecutor(None)\n save_webot_to_redis(user_id, chat_id, webot)\n\n\ndef reset_webot_status(user_id: str, chat_id: str):\n webot_status = {\"webot_status\": \"idle\", \"url\": None}\n save_webot_status_to_redis(user_id, chat_id, webot_status)\n\n\n# this function has been deprecated\ndef get_plan(instruction: str, start_url: str, chat_llm: ChatOpenAI):\n # fixme: Move this into a separate chain or executors to decompose the LLMs\n system_message = f\"\"\"\nYou are a planner to assist another browser automation assistant.\n\nHere is the instruction for the other assistant:\n```\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n1. click(element): Clicks on an element\n2. setValue(element, value: string): Focuses on and sets the value of an input element\n3. finish(): Indicates the task is finished\n4. fail(): Indicates that you are unable to complete the task\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n\nI should click the add to cart button\nclick(223)\n\nYou MUST always include the and open/close tags or else your response will be marked as invalid.\n\nRules you MUST follow:\n1. You must only take one step at a time. You cannot take multiple actions in a single response.\n2. You should not consider the action to present the result to the user. You only need to do available actions. If info in current page is enough for the user to solve the problem, you should finish.\n```\nNow your responsibility is to give a step-by-step plan according to user's instruction. This plan will be given to the assistant as a reference when it is performing tasks.\n\"\"\".strip()\n\n human_message = f\"\"\"\nThe user requests the following task:\n\n{instruction}\n\nNow you are at {start_url}\n\nProvide a plan to do this (you can use pseudo description as below to describe the item).\n\nHere is an example case:\n\nrequest: Go to google calendar to schedule a meeting\n\ncurrent url: \"https://google.com\"\n\nexample plan:\n\n1. setValue(searchBar, \"google calendar\")\n2. click(search)\n3. click(the item with title of google calendar)\n4.1 if user has loginned \n do nothing \n4.2 if user hasn't loginned \n do login \n5. click(create event button) \n6. setValue(event title input bar, \"meeting\") \n7. click(save event button)\n8. finish()\n\"\"\".strip()\n\n messages = [SystemMessage(content=system_message),\n HumanMessage(content=human_message)]\n response = chat_llm(messages).content\n return response\n\n\ndef create_webot_interaction_executor(\n llm: BaseLanguageModel,\n llm_name: str,\n user_id: str,\n chat_id: str\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n\n Returns:\n An agent executor.\n\n \"\"\"","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.create_webot_interaction_executor","uri":"program://OpenAgents/function/backend.api.chat_webot.create_webot_interaction_executor#L138-L273","kind":"function","name":"create_webot_interaction_executor","path":"backend/api/chat_webot.py","language":"python","start_line":138,"end_line":273,"context_start_line":118,"context_end_line":293,"code":"\n1. setValue(searchBar, \"google calendar\")\n2. click(search)\n3. click(the item with title of google calendar)\n4.1 if user has loginned \n do nothing \n4.2 if user hasn't loginned \n do login \n5. click(create event button) \n6. setValue(event title input bar, \"meeting\") \n7. click(save event button)\n8. finish()\n\"\"\".strip()\n\n messages = [SystemMessage(content=system_message),\n HumanMessage(content=human_message)]\n response = chat_llm(messages).content\n return response\n\n\ndef create_webot_interaction_executor(\n llm: BaseLanguageModel,\n llm_name: str,\n user_id: str,\n chat_id: str\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, max_token_limit=10000)\n\n class RunWebot:\n def __init__(self, webot: WebotExecutor, llm: BaseLanguageModel, user_id: str,\n chat_id: str):\n self.llm = llm\n self.webot = webot\n self.user_id = user_id\n self.chat_id = chat_id\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n user_id = self.user_id\n chat_id = self.chat_id\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n raw_observation = self.webot.run(user_intent=term, llm=self.llm)\n instruction, start_url = raw_observation[\"instruction\"], \\\n raw_observation[\"start_url\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = instruction\n # webot.plan = get_plan(instruction, start_url)\n webot.plan = \"\"\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n webot_status = {\n \"webot_status\": \"running\",\n \"url\": start_url\n }\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status=webot_status)\n while True:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n if webot.finish or webot.interrupt or webot.error or webot.fail:\n break\n else:\n sleep(0.5)\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status={\"webot_status\": \"idle\",\n \"url\": None})\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = None\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n if webot.finish:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n action_history = webot.action_history\n last_page = webot.pages_viewed[-1]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps({\"action_history\": action_history,\n \"last_page\": last_page}, indent=4),\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.fail:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"The webot failed to execute the instruction.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.interrupt:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"The web browsing is interrupted by user.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.error:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"Error occurs during web browsing.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n except Exception as e:\n print(traceback.format_exc())\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": f\"Failed in web browsing with the input: {term}, please try again later.\",\n \"intermediate_steps\": json.dumps({\"error\": str(e)})\n }\n )\n return observation\n\n webot = WebotExecutor.from_webot()\n llm = copy.deepcopy(llm)\n run_webot = RunWebot(webot, llm, chat_id=chat_id, user_id=user_id)\n tools = [Tool(name=webot.name, func=run_webot.run, description=webot.description)]\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_webot_agent(\n tools, llm, continue_model, memory=memory, verbose=True\n )\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_webot\", methods=[\"POST\"])\ndef chat_xlang_webot() -> Dict:\n \"\"\"Returns the chat response of web agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = request_json[\"parent_message_id\"]\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.4)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.chat_xlang_webot","uri":"program://OpenAgents/function/backend.api.chat_webot.chat_xlang_webot#L277-L347","kind":"function","name":"chat_xlang_webot","path":"backend/api/chat_webot.py","language":"python","start_line":277,"end_line":347,"context_start_line":257,"context_end_line":347,"code":" \"success\": False,\n \"result\": f\"Failed in web browsing with the input: {term}, please try again later.\",\n \"intermediate_steps\": json.dumps({\"error\": str(e)})\n }\n )\n return observation\n\n webot = WebotExecutor.from_webot()\n llm = copy.deepcopy(llm)\n run_webot = RunWebot(webot, llm, chat_id=chat_id, user_id=user_id)\n tools = [Tool(name=webot.name, func=run_webot.run, description=webot.description)]\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_webot_agent(\n tools, llm, continue_model, memory=memory, verbose=True\n )\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_webot\", methods=[\"POST\"])\ndef chat_xlang_webot() -> Dict:\n \"\"\"Returns the chat response of web agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = request_json[\"parent_message_id\"]\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.4)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n\n # Get language model\n llm = get_llm(llm_name, **kwargs)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Request json\").debug(request_json)\n\n human_message_id = message_id_register.add_variable(user_intent)\n ai_message_id = message_id_register.add_variable(\"\")\n\n stream_handler = AgentStreamingStdOutCallbackHandler()\n # Build executor and run chat\n\n # reset webot and status\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n\n interaction_executor = create_webot_interaction_executor(\n llm=llm,\n llm_name=llm_name,\n chat_id=chat_id,\n user_id=user_id\n )\n\n activated_message_list = message_pool.get_activated_message_list(user_id,\n chat_id,\n list(),\n parent_message_id)\n message_pool.load_agent_memory_from_list(interaction_executor.memory,\n activated_message_list)\n return stream_with_context(\n Response(\n single_round_chat_with_agent_streaming(\n interaction_executor=interaction_executor,\n user_intent=user_intent,\n human_message_id=human_message_id,\n ai_message_id=ai_message_id,\n user_id=user_id,\n chat_id=chat_id,\n message_list=activated_message_list,\n parent_message_id=parent_message_id,\n stream_handler=stream_handler,\n llm_name=llm_name,\n app_type=\"webot\",\n ),\n content_type=\"application/json\",\n )\n )\n\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n return Response(response=None,\n status=f\"{OVERLOAD} backend is currently overloaded\")","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.RunWebot","uri":"program://OpenAgents/class/backend.api.chat_webot.RunWebot#L160-L262","kind":"class","name":"RunWebot","path":"backend/api/chat_webot.py","language":"python","start_line":160,"end_line":262,"context_start_line":140,"context_end_line":282,"code":" llm_name: str,\n user_id: str,\n chat_id: str\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, max_token_limit=10000)\n\n class RunWebot:\n def __init__(self, webot: WebotExecutor, llm: BaseLanguageModel, user_id: str,\n chat_id: str):\n self.llm = llm\n self.webot = webot\n self.user_id = user_id\n self.chat_id = chat_id\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n user_id = self.user_id\n chat_id = self.chat_id\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n raw_observation = self.webot.run(user_intent=term, llm=self.llm)\n instruction, start_url = raw_observation[\"instruction\"], \\\n raw_observation[\"start_url\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = instruction\n # webot.plan = get_plan(instruction, start_url)\n webot.plan = \"\"\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n webot_status = {\n \"webot_status\": \"running\",\n \"url\": start_url\n }\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status=webot_status)\n while True:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n if webot.finish or webot.interrupt or webot.error or webot.fail:\n break\n else:\n sleep(0.5)\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status={\"webot_status\": \"idle\",\n \"url\": None})\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = None\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n if webot.finish:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n action_history = webot.action_history\n last_page = webot.pages_viewed[-1]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps({\"action_history\": action_history,\n \"last_page\": last_page}, indent=4),\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.fail:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"The webot failed to execute the instruction.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.interrupt:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"The web browsing is interrupted by user.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.error:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"Error occurs during web browsing.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n except Exception as e:\n print(traceback.format_exc())\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": f\"Failed in web browsing with the input: {term}, please try again later.\",\n \"intermediate_steps\": json.dumps({\"error\": str(e)})\n }\n )\n return observation\n\n webot = WebotExecutor.from_webot()\n llm = copy.deepcopy(llm)\n run_webot = RunWebot(webot, llm, chat_id=chat_id, user_id=user_id)\n tools = [Tool(name=webot.name, func=run_webot.run, description=webot.description)]\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_webot_agent(\n tools, llm, continue_model, memory=memory, verbose=True\n )\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_webot\", methods=[\"POST\"])\ndef chat_xlang_webot() -> Dict:\n \"\"\"Returns the chat response of web agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.__init__","uri":"program://OpenAgents/function/backend.api.chat_webot.__init__#L161-L166","kind":"function","name":"__init__","path":"backend/api/chat_webot.py","language":"python","start_line":161,"end_line":166,"context_start_line":141,"context_end_line":186,"code":" user_id: str,\n chat_id: str\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, max_token_limit=10000)\n\n class RunWebot:\n def __init__(self, webot: WebotExecutor, llm: BaseLanguageModel, user_id: str,\n chat_id: str):\n self.llm = llm\n self.webot = webot\n self.user_id = user_id\n self.chat_id = chat_id\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n user_id = self.user_id\n chat_id = self.chat_id\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n raw_observation = self.webot.run(user_intent=term, llm=self.llm)\n instruction, start_url = raw_observation[\"instruction\"], \\\n raw_observation[\"start_url\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = instruction\n # webot.plan = get_plan(instruction, start_url)\n webot.plan = \"\"\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n webot_status = {\n \"webot_status\": \"running\",\n \"url\": start_url\n }\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_webot.run","uri":"program://OpenAgents/function/backend.api.chat_webot.run#L168-L262","kind":"function","name":"run","path":"backend/api/chat_webot.py","language":"python","start_line":168,"end_line":262,"context_start_line":148,"context_end_line":282,"code":" llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, max_token_limit=10000)\n\n class RunWebot:\n def __init__(self, webot: WebotExecutor, llm: BaseLanguageModel, user_id: str,\n chat_id: str):\n self.llm = llm\n self.webot = webot\n self.user_id = user_id\n self.chat_id = chat_id\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n user_id = self.user_id\n chat_id = self.chat_id\n reset_webot(user_id=user_id, chat_id=chat_id)\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n raw_observation = self.webot.run(user_intent=term, llm=self.llm)\n instruction, start_url = raw_observation[\"instruction\"], \\\n raw_observation[\"start_url\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = instruction\n # webot.plan = get_plan(instruction, start_url)\n webot.plan = \"\"\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n webot_status = {\n \"webot_status\": \"running\",\n \"url\": start_url\n }\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status=webot_status)\n while True:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n if webot.finish or webot.interrupt or webot.error or webot.fail:\n break\n else:\n sleep(0.5)\n save_webot_status_to_redis(user_id=user_id, chat_id=chat_id,\n webot_status={\"webot_status\": \"idle\",\n \"url\": None})\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.instruction = None\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n if webot.finish:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n action_history = webot.action_history\n last_page = webot.pages_viewed[-1]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps({\"action_history\": action_history,\n \"last_page\": last_page}, indent=4),\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.fail:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"The webot failed to execute the instruction.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.interrupt:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"The web browsing is interrupted by user.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n if webot.error:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": \"Error occurs during web browsing.\",\n \"intermediate_steps\": json.dumps(\n {\"instruction\": instruction, \"start_url\": start_url},\n indent=4)\n }\n )\n return observation\n\n except Exception as e:\n print(traceback.format_exc())\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": f\"Failed in web browsing with the input: {term}, please try again later.\",\n \"intermediate_steps\": json.dumps({\"error\": str(e)})\n }\n )\n return observation\n\n webot = WebotExecutor.from_webot()\n llm = copy.deepcopy(llm)\n run_webot = RunWebot(webot, llm, chat_id=chat_id, user_id=user_id)\n tools = [Tool(name=webot.name, func=run_webot.run, description=webot.description)]\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_webot_agent(\n tools, llm, continue_model, memory=memory, verbose=True\n )\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_webot\", methods=[\"POST\"])\ndef chat_xlang_webot() -> Dict:\n \"\"\"Returns the chat response of web agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin","uri":"program://OpenAgents/module/backend.api.chat_plugin#L1-L277","kind":"module","name":"backend.api.chat_plugin","path":"backend/api/chat_plugin.py","language":"python","start_line":1,"end_line":277,"context_start_line":1,"context_end_line":277,"code":"import base64\nimport copy\nimport json\nimport os\nimport random\nimport traceback\nfrom typing import Dict, List, Union\n\nimport requests\nfrom flask import Response, request, stream_with_context\nfrom retrying import retry\n\nfrom backend.api.language_model import get_llm\nfrom backend.app import app\nfrom backend.main import message_id_register, message_pool, logger\nfrom backend.utils.streaming import single_round_chat_with_agent_streaming\nfrom backend.schemas import OVERLOAD, NEED_CONTINUE_MODEL, DEFAULT_USER_ID\nfrom backend.main import api_key_pool\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks.agent_streaming import \\\n AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.data_model import DataModel, JsonDataModel\nfrom real_agents.adapters.interactive_executor import initialize_plugin_agent\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.plugins_agent.plugins.utils import load_all_plugins_elements\nfrom real_agents.plugins_agent.plugins.tool_selector import ToolSelector\nfrom real_agents.plugins_agent import PluginExecutor\n\n# The plugins list\nglobal plugins\nplugins = []\n\n# Set up the tool selector for automatically selecting plugins\ntry:\n tool_selector = ToolSelector(tools_list=plugins, mode=\"embedding\", api_key_pool=api_key_pool)\nexcept Exception as e:\n print(e, \"The auto selection feature of plugins agent will return random elements.\")\n tool_selector = None\n\n# Load plugin info and icon image\nfor plugin_type, plugin_info in load_all_plugins_elements().items():\n @retry(stop_max_attempt_number=10,\n wait_fixed=2000) # Retry 3 times with a 2-second delay between retries\n def make_request(_image_url) -> Response:\n response = requests.get(_image_url) # Replace with your actual request code\n response.raise_for_status() # Raise an exception for unsuccessful response status codes\n return response\n\n\n # Load icon image\n image_url = plugin_info[\"meta_info\"][\"manifest\"][\"logo_url\"]\n\n # If image is base64 encoded\n if image_url.startswith(\"data:image\"):\n plugins.append(\n {\n \"id\": plugin_type,\n \"name\": plugin_type,\n \"name_for_human\": plugin_info[\"meta_info\"][\"manifest\"][\n \"name_for_human\"],\n \"description\": plugin_info[\"description\"],\n \"icon\": image_url,\n \"require_api_key\": plugin_info[\"need_auth\"],\n }\n )\n continue\n\n image_format = image_url.split(\".\")[-1]\n\n try:\n # Check if in cache\n os.makedirs(\"backend/static/images\", exist_ok=True)\n if os.path.exists(f\"backend/static/images/{plugin_type}.cache\"):\n with open(f\"backend/static/images/{plugin_type}.cache\", \"rb\") as f:\n image_content = f.read()\n else:\n response = make_request(image_url)\n image_content = response.content\n # Make a .cache file for the image\n with open(f\"backend/static/images/{plugin_type}.cache\", \"wb\") as f:\n f.write(image_content)\n except requests.exceptions.RequestException as e:\n print(f\"Error: Failed to make the request {plugin_type}:\", e)\n continue\n\n if image_format == \"svg\":\n encoded_image = \"data:image/svg+xml;base64, \".format(\n image_format) + base64.b64encode(image_content).decode(\n \"utf-8\"\n )\n else:\n encoded_image = \"data:image/{};base64, \".format(\n image_format) + base64.b64encode(image_content).decode(\n \"utf-8\"\n )\n\n plugins.append(\n {\n \"id\": plugin_type,\n \"name\": plugin_type,\n \"name_for_human\": plugin_info[\"meta_info\"][\"manifest\"][\"name_for_human\"],\n \"description\": plugin_info[\"description\"],\n \"icon\": encoded_image,\n \"require_api_key\": plugin_info[\"need_auth\"],\n }\n )\n\n\ndef create_plugins_interaction_executor(\n selected_plugins: List[str],\n api_key_info: List[Dict],\n llm: BaseLanguageModel,\n llm_name: str,\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n selected_plugins: A list of selected plugins.\n api_key_info: A list of plugin api keys.\n llm: A llm model.\n llm_name: A string llm name.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, style=\"plugin\",\n max_token_limit=10000)\n\n class RunPlugin:\n def __init__(self, plugin: PluginExecutor, llm: BaseLanguageModel):\n self.plugin = plugin\n self.llm = llm\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n raw_observation = self.plugin.run(user_intent=term, llm=self.llm)\n input_json, output = raw_observation[\"input_json\"], raw_observation[\n \"api_output\"]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps(output, indent=4) if isinstance(output,\n dict) else output,\n \"intermediate_steps\": json.dumps(input_json, indent=4),\n }\n )\n return observation\n\n except Exception as e:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": str(e),\n }\n )\n print(traceback.format_exc())\n return observation\n\n # Load plugins from selected names\n _plugins = []\n for selected_plugin in selected_plugins:\n plugin = PluginExecutor.from_plugin_name(selected_plugin)\n llm = copy.deepcopy(llm)\n\n if len([i for i in api_key_info if i[\"tool_name\"] == plugin.name]) != 0:\n plugin.api_key = \\\n [i for i in api_key_info if i[\"tool_name\"] == plugin.name][0][\"api_key\"]\n # For some plugins, we need to reload the plugin to update personal data\n plugin.load_personnel_info() # warning: this will change the plugin object every time we make a new query\n\n run_plugin = RunPlugin(plugin, llm)\n\n _plugins.append(Tool(name=plugin.name, func=run_plugin.run,\n description=plugin.full_description))\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_plugin_agent(\n _plugins, llm, continue_model, memory=memory, verbose=True\n )\n\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_plugin\", methods=[\"POST\"])\ndef chat_xlang_plugin() -> Dict:\n \"\"\"Returns the chat response of plugins agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = request_json[\"parent_message_id\"]\n selected_plugins = request_json[\"selected_plugins\"]\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.4)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n\n # pass user id and chat id to tool selector\n if tool_selector:\n tool_selector.user_id = user_id\n tool_selector.chat_id = chat_id\n\n # Get language model\n llm = get_llm(llm_name, **kwargs)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Request json\").debug(request_json)\n\n # Get API key for plugins\n api_key_info = api_key_pool.get_pool_info_with_id(user_id,\n default_value=[]) # fixme: mock user_id: 1\n\n activated_message_list = message_pool.get_activated_message_list(user_id,\n chat_id,\n list(),\n parent_message_id)\n\n # Flag for auto retrieving plugins\n if len(selected_plugins) == 1 and selected_plugins[0].lower() == \"auto\":\n\n if tool_selector:\n # this will return a list of plugin names sorted by relevance (lower case and the same as their dir name)\n query = tool_selector.load_query_from_message_list(activated_message_list,\n user_intent)\n selected_plugins = tool_selector.select_tools(query=query, top_k=5)\n else:\n selected_plugins = random.sample(plugins, 5)\n\n # Build executor and run chat\n stream_handler = AgentStreamingStdOutCallbackHandler()\n interaction_executor = create_plugins_interaction_executor(\n selected_plugins=selected_plugins,\n api_key_info=api_key_info,\n llm=llm,\n llm_name=llm_name,\n )\n\n message_pool.load_agent_memory_from_list(interaction_executor.memory,\n activated_message_list)\n\n human_message_id = message_id_register.add_variable(user_intent)\n ai_message_id = message_id_register.add_variable(\"\")\n\n return stream_with_context(\n Response(\n single_round_chat_with_agent_streaming(\n interaction_executor=interaction_executor,\n user_intent=user_intent,\n human_message_id=human_message_id,\n ai_message_id=ai_message_id,\n user_id=user_id,\n chat_id=chat_id,\n message_list=activated_message_list,\n parent_message_id=parent_message_id,\n stream_handler=stream_handler,\n llm_name=llm_name,\n app_type=\"plugins\",\n ),\n content_type=\"application/json\",\n )\n )\n\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n return Response(response=None,\n status=f\"{OVERLOAD} backend is currently overloaded\")","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin.create_plugins_interaction_executor","uri":"program://OpenAgents/function/backend.api.chat_plugin.create_plugins_interaction_executor#L110-L185","kind":"function","name":"create_plugins_interaction_executor","path":"backend/api/chat_plugin.py","language":"python","start_line":110,"end_line":185,"context_start_line":90,"context_end_line":205,"code":" \"utf-8\"\n )\n else:\n encoded_image = \"data:image/{};base64, \".format(\n image_format) + base64.b64encode(image_content).decode(\n \"utf-8\"\n )\n\n plugins.append(\n {\n \"id\": plugin_type,\n \"name\": plugin_type,\n \"name_for_human\": plugin_info[\"meta_info\"][\"manifest\"][\"name_for_human\"],\n \"description\": plugin_info[\"description\"],\n \"icon\": encoded_image,\n \"require_api_key\": plugin_info[\"need_auth\"],\n }\n )\n\n\ndef create_plugins_interaction_executor(\n selected_plugins: List[str],\n api_key_info: List[Dict],\n llm: BaseLanguageModel,\n llm_name: str,\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n selected_plugins: A list of selected plugins.\n api_key_info: A list of plugin api keys.\n llm: A llm model.\n llm_name: A string llm name.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, style=\"plugin\",\n max_token_limit=10000)\n\n class RunPlugin:\n def __init__(self, plugin: PluginExecutor, llm: BaseLanguageModel):\n self.plugin = plugin\n self.llm = llm\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n raw_observation = self.plugin.run(user_intent=term, llm=self.llm)\n input_json, output = raw_observation[\"input_json\"], raw_observation[\n \"api_output\"]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps(output, indent=4) if isinstance(output,\n dict) else output,\n \"intermediate_steps\": json.dumps(input_json, indent=4),\n }\n )\n return observation\n\n except Exception as e:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": str(e),\n }\n )\n print(traceback.format_exc())\n return observation\n\n # Load plugins from selected names\n _plugins = []\n for selected_plugin in selected_plugins:\n plugin = PluginExecutor.from_plugin_name(selected_plugin)\n llm = copy.deepcopy(llm)\n\n if len([i for i in api_key_info if i[\"tool_name\"] == plugin.name]) != 0:\n plugin.api_key = \\\n [i for i in api_key_info if i[\"tool_name\"] == plugin.name][0][\"api_key\"]\n # For some plugins, we need to reload the plugin to update personal data\n plugin.load_personnel_info() # warning: this will change the plugin object every time we make a new query\n\n run_plugin = RunPlugin(plugin, llm)\n\n _plugins.append(Tool(name=plugin.name, func=run_plugin.run,\n description=plugin.full_description))\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_plugin_agent(\n _plugins, llm, continue_model, memory=memory, verbose=True\n )\n\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_plugin\", methods=[\"POST\"])\ndef chat_xlang_plugin() -> Dict:\n \"\"\"Returns the chat response of plugins agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = request_json[\"parent_message_id\"]\n selected_plugins = request_json[\"selected_plugins\"]\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.4)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin.chat_xlang_plugin","uri":"program://OpenAgents/function/backend.api.chat_plugin.chat_xlang_plugin#L189-L277","kind":"function","name":"chat_xlang_plugin","path":"backend/api/chat_plugin.py","language":"python","start_line":189,"end_line":277,"context_start_line":169,"context_end_line":277,"code":" if len([i for i in api_key_info if i[\"tool_name\"] == plugin.name]) != 0:\n plugin.api_key = \\\n [i for i in api_key_info if i[\"tool_name\"] == plugin.name][0][\"api_key\"]\n # For some plugins, we need to reload the plugin to update personal data\n plugin.load_personnel_info() # warning: this will change the plugin object every time we make a new query\n\n run_plugin = RunPlugin(plugin, llm)\n\n _plugins.append(Tool(name=plugin.name, func=run_plugin.run,\n description=plugin.full_description))\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_plugin_agent(\n _plugins, llm, continue_model, memory=memory, verbose=True\n )\n\n return interaction_executor\n\n\n@app.route(\"/api/chat_xlang_plugin\", methods=[\"POST\"])\ndef chat_xlang_plugin() -> Dict:\n \"\"\"Returns the chat response of plugins agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = request_json[\"parent_message_id\"]\n selected_plugins = request_json[\"selected_plugins\"]\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.4)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n\n # pass user id and chat id to tool selector\n if tool_selector:\n tool_selector.user_id = user_id\n tool_selector.chat_id = chat_id\n\n # Get language model\n llm = get_llm(llm_name, **kwargs)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Request json\").debug(request_json)\n\n # Get API key for plugins\n api_key_info = api_key_pool.get_pool_info_with_id(user_id,\n default_value=[]) # fixme: mock user_id: 1\n\n activated_message_list = message_pool.get_activated_message_list(user_id,\n chat_id,\n list(),\n parent_message_id)\n\n # Flag for auto retrieving plugins\n if len(selected_plugins) == 1 and selected_plugins[0].lower() == \"auto\":\n\n if tool_selector:\n # this will return a list of plugin names sorted by relevance (lower case and the same as their dir name)\n query = tool_selector.load_query_from_message_list(activated_message_list,\n user_intent)\n selected_plugins = tool_selector.select_tools(query=query, top_k=5)\n else:\n selected_plugins = random.sample(plugins, 5)\n\n # Build executor and run chat\n stream_handler = AgentStreamingStdOutCallbackHandler()\n interaction_executor = create_plugins_interaction_executor(\n selected_plugins=selected_plugins,\n api_key_info=api_key_info,\n llm=llm,\n llm_name=llm_name,\n )\n\n message_pool.load_agent_memory_from_list(interaction_executor.memory,\n activated_message_list)\n\n human_message_id = message_id_register.add_variable(user_intent)\n ai_message_id = message_id_register.add_variable(\"\")\n\n return stream_with_context(\n Response(\n single_round_chat_with_agent_streaming(\n interaction_executor=interaction_executor,\n user_intent=user_intent,\n human_message_id=human_message_id,\n ai_message_id=ai_message_id,\n user_id=user_id,\n chat_id=chat_id,\n message_list=activated_message_list,\n parent_message_id=parent_message_id,\n stream_handler=stream_handler,\n llm_name=llm_name,\n app_type=\"plugins\",\n ),\n content_type=\"application/json\",\n )\n )\n\n except Exception as e:\n import traceback\n\n traceback.print_exc()\n return Response(response=None,\n status=f\"{OVERLOAD} backend is currently overloaded\")","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin.make_request","uri":"program://OpenAgents/function/backend.api.chat_plugin.make_request#L45-L48","kind":"function","name":"make_request","path":"backend/api/chat_plugin.py","language":"python","start_line":45,"end_line":48,"context_start_line":25,"context_end_line":68,"code":"from real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.plugins_agent.plugins.utils import load_all_plugins_elements\nfrom real_agents.plugins_agent.plugins.tool_selector import ToolSelector\nfrom real_agents.plugins_agent import PluginExecutor\n\n# The plugins list\nglobal plugins\nplugins = []\n\n# Set up the tool selector for automatically selecting plugins\ntry:\n tool_selector = ToolSelector(tools_list=plugins, mode=\"embedding\", api_key_pool=api_key_pool)\nexcept Exception as e:\n print(e, \"The auto selection feature of plugins agent will return random elements.\")\n tool_selector = None\n\n# Load plugin info and icon image\nfor plugin_type, plugin_info in load_all_plugins_elements().items():\n @retry(stop_max_attempt_number=10,\n wait_fixed=2000) # Retry 3 times with a 2-second delay between retries\n def make_request(_image_url) -> Response:\n response = requests.get(_image_url) # Replace with your actual request code\n response.raise_for_status() # Raise an exception for unsuccessful response status codes\n return response\n\n\n # Load icon image\n image_url = plugin_info[\"meta_info\"][\"manifest\"][\"logo_url\"]\n\n # If image is base64 encoded\n if image_url.startswith(\"data:image\"):\n plugins.append(\n {\n \"id\": plugin_type,\n \"name\": plugin_type,\n \"name_for_human\": plugin_info[\"meta_info\"][\"manifest\"][\n \"name_for_human\"],\n \"description\": plugin_info[\"description\"],\n \"icon\": image_url,\n \"require_api_key\": plugin_info[\"need_auth\"],\n }\n )\n continue\n","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin.RunPlugin","uri":"program://OpenAgents/class/backend.api.chat_plugin.RunPlugin#L133-L161","kind":"class","name":"RunPlugin","path":"backend/api/chat_plugin.py","language":"python","start_line":133,"end_line":161,"context_start_line":113,"context_end_line":181,"code":" llm: BaseLanguageModel,\n llm_name: str,\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n selected_plugins: A list of selected plugins.\n api_key_info: A list of plugin api keys.\n llm: A llm model.\n llm_name: A string llm name.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, style=\"plugin\",\n max_token_limit=10000)\n\n class RunPlugin:\n def __init__(self, plugin: PluginExecutor, llm: BaseLanguageModel):\n self.plugin = plugin\n self.llm = llm\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n raw_observation = self.plugin.run(user_intent=term, llm=self.llm)\n input_json, output = raw_observation[\"input_json\"], raw_observation[\n \"api_output\"]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps(output, indent=4) if isinstance(output,\n dict) else output,\n \"intermediate_steps\": json.dumps(input_json, indent=4),\n }\n )\n return observation\n\n except Exception as e:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": str(e),\n }\n )\n print(traceback.format_exc())\n return observation\n\n # Load plugins from selected names\n _plugins = []\n for selected_plugin in selected_plugins:\n plugin = PluginExecutor.from_plugin_name(selected_plugin)\n llm = copy.deepcopy(llm)\n\n if len([i for i in api_key_info if i[\"tool_name\"] == plugin.name]) != 0:\n plugin.api_key = \\\n [i for i in api_key_info if i[\"tool_name\"] == plugin.name][0][\"api_key\"]\n # For some plugins, we need to reload the plugin to update personal data\n plugin.load_personnel_info() # warning: this will change the plugin object every time we make a new query\n\n run_plugin = RunPlugin(plugin, llm)\n\n _plugins.append(Tool(name=plugin.name, func=run_plugin.run,\n description=plugin.full_description))\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_plugin_agent(","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin.__init__","uri":"program://OpenAgents/function/backend.api.chat_plugin.__init__#L134-L136","kind":"function","name":"__init__","path":"backend/api/chat_plugin.py","language":"python","start_line":134,"end_line":136,"context_start_line":114,"context_end_line":156,"code":" llm_name: str,\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n selected_plugins: A list of selected plugins.\n api_key_info: A list of plugin api keys.\n llm: A llm model.\n llm_name: A string llm name.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, style=\"plugin\",\n max_token_limit=10000)\n\n class RunPlugin:\n def __init__(self, plugin: PluginExecutor, llm: BaseLanguageModel):\n self.plugin = plugin\n self.llm = llm\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n raw_observation = self.plugin.run(user_intent=term, llm=self.llm)\n input_json, output = raw_observation[\"input_json\"], raw_observation[\n \"api_output\"]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps(output, indent=4) if isinstance(output,\n dict) else output,\n \"intermediate_steps\": json.dumps(input_json, indent=4),\n }\n )\n return observation\n\n except Exception as e:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_plugin.run","uri":"program://OpenAgents/function/backend.api.chat_plugin.run#L138-L161","kind":"function","name":"run","path":"backend/api/chat_plugin.py","language":"python","start_line":138,"end_line":161,"context_start_line":118,"context_end_line":181,"code":" Args:\n selected_plugins: A list of selected plugins.\n api_key_info: A list of plugin api keys.\n llm: A llm model.\n llm_name: A string llm name.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize memory\n memory = ConversationReActBufferMemory(memory_key=\"chat_history\",\n return_messages=True, style=\"plugin\",\n max_token_limit=10000)\n\n class RunPlugin:\n def __init__(self, plugin: PluginExecutor, llm: BaseLanguageModel):\n self.plugin = plugin\n self.llm = llm\n\n def run(self, term: str) -> Union[str, Dict, DataModel]:\n try:\n raw_observation = self.plugin.run(user_intent=term, llm=self.llm)\n input_json, output = raw_observation[\"input_json\"], raw_observation[\n \"api_output\"]\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": json.dumps(output, indent=4) if isinstance(output,\n dict) else output,\n \"intermediate_steps\": json.dumps(input_json, indent=4),\n }\n )\n return observation\n\n except Exception as e:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": str(e),\n }\n )\n print(traceback.format_exc())\n return observation\n\n # Load plugins from selected names\n _plugins = []\n for selected_plugin in selected_plugins:\n plugin = PluginExecutor.from_plugin_name(selected_plugin)\n llm = copy.deepcopy(llm)\n\n if len([i for i in api_key_info if i[\"tool_name\"] == plugin.name]) != 0:\n plugin.api_key = \\\n [i for i in api_key_info if i[\"tool_name\"] == plugin.name][0][\"api_key\"]\n # For some plugins, we need to reload the plugin to update personal data\n plugin.load_personnel_info() # warning: this will change the plugin object every time we make a new query\n\n run_plugin = RunPlugin(plugin, llm)\n\n _plugins.append(Tool(name=plugin.name, func=run_plugin.run,\n description=plugin.full_description))\n\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_plugin_agent(","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation","uri":"program://OpenAgents/module/backend.api.conversation#L1-L346","kind":"module","name":"backend.api.conversation","path":"backend/api/conversation.py","language":"python","start_line":1,"end_line":346,"context_start_line":1,"context_end_line":346,"code":"import struct\nimport json\nimport datetime\nfrom typing import Any, Generator\nfrom bson.objectid import ObjectId\nfrom flask import jsonify, request, Response\n\nfrom backend.app import app\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.main import threading_pool, logger\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.schemas import INTERNAL, UNFOUND\n\n\n@app.route(\"/api/conversations/get_conversation_list\", methods=[\"POST\"])\ndef get_conversation_list() -> Response:\n \"\"\"Gets the history conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n conversations = []\n try:\n # Login with API Key, then retrieve the user history based\n # on the hashed API key.\n db = get_user_conversation_storage()\n conversation_list = db.conversation.find({\"user_id\": user_id})\n for conversation in conversation_list:\n conversations.append(\n {\n \"id\": str(conversation[\"_id\"]),\n \"name\": conversation[\"name\"],\n \"folderId\": conversation[\"folder_id\"],\n }\n )\n except Exception as e:\n return Response(response=None,\n status=f'{INTERNAL} error fetch conversation list')\n return jsonify(conversations)\n\n\n@app.route(\"/api/conversations/get_folder_list\", methods=[\"POST\"])\ndef get_folder_list() -> Response:\n \"\"\"Gets the folder list.\"\"\"\n user_id = DEFAULT_USER_ID\n folders = []\n try:\n db = get_user_conversation_storage()\n folder_list = db.folder.find({\"user_id\": user_id})\n for folder in folder_list:\n folders.append(\n {\n \"id\": str(folder[\"_id\"]),\n \"name\": folder[\"name\"],\n \"type\": \"chat\",\n }\n )\n return jsonify({\"success\": True, \"data\": folders})\n except Exception as e:\n return Response(response=None, status=f'{INTERNAL} error fetch folder list')\n\n\ndef process_rich_content_item(data: dict, message_id: str) -> dict:\n \"\"\"Processes the rich content from db format into frontend renderable format.\"\"\"\n processed_items: dict = {\"intermediateSteps\": [], \"finalAnswer\": []}\n if \"intermediate_steps\" in data:\n for item in data[\"intermediate_steps\"]:\n processed_items[\"intermediateSteps\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n if \"final_answer\" in data:\n for item in data[\"final_answer\"]:\n processed_items[\"finalAnswer\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n return processed_items\n\n\n@app.route(\"/api/conversation\", methods=[\"POST\"])\ndef get_conversation_content() -> Response:\n \"\"\"Gets the conversation content for one assigned conversation.\"\"\"\n request_json = request.get_json()\n conversation_id = request_json.get(\"chat_id\", None)\n if conversation_id is not None:\n try:\n db = get_user_conversation_storage()\n conversation = db.conversation.find_one({\"_id\": ObjectId(conversation_id)})\n message_list = db.message.find({\"conversation_id\": conversation_id}).sort(\n \"_id\", -1)\n messages = [\n {\n \"id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"role\": message[\"role\"],\n \"content\": message[\"data_for_human\"] if message[\n \"role\"] == \"user\" else None,\n \"type\": \"rich_message\" if isinstance(message[\"data_for_human\"],\n dict) else \"\",\n \"richContent\": process_rich_content_item(message[\"data_for_human\"],\n message[\"message_id\"])\n if isinstance(message[\"data_for_human\"], dict)\n else None,\n }\n for message in message_list\n ]\n\n def _get_activated_conversation_branch(messages: list) -> list:\n # By default, the latest message is the end point, e.g., the current branch of messages.\n activated_messages: list = []\n end_point = messages[0][\"id\"]\n while len(messages) > 0 and end_point != -1:\n flag = False\n for msg in messages:\n if msg[\"id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break\n activated_messages = [msg] + activated_messages\n end_point = msg[\"parent_message_id\"]\n flag = True\n break\n if not flag:\n break\n return activated_messages\n\n # Find the current activated branch of messages as frontend only shows one branch\n\n if messages:\n messages = _get_activated_conversation_branch(messages)\n\n logger.bind(msg_head=f\"get_activated_message_list\").debug(messages)\n\n conversation = {\n \"id\": conversation_id,\n \"name\": conversation[\"name\"],\n \"messages\": messages,\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folderId\": conversation[\"folder_id\"],\n \"bookmarkedMessagesIds\": conversation[\"bookmarked_message_ids\"],\n \"selectedCodeInterpreterPlugins\": conversation[\n \"selected_code_interpreter_plugins\"],\n \"selectedPlugins\": conversation[\"selected_plugins\"],\n\n }\n return jsonify(conversation)\n except Exception as e:\n import traceback\n traceback.print_exc()\n return Response(response=None,\n status=f'{INTERNAL} error fetch conversation')\n else:\n return Response(response=None, status=f'{INTERNAL} error fetch conversation')\n\n\n@app.route(\"/api/conversations/update_conversation\", methods=[\"POST\"])\ndef update_conversation() -> Response:\n \"\"\"Updates a conversation name.\"\"\"\n try:\n request_json = request.get_json()\n conversations = request_json[\"conversations\"]\n db = get_user_conversation_storage()\n messages = []\n success = True\n update_key_dict = {\"name\": \"name\", \"folder_id\": \"folderId\"}\n for conversation_to_update in conversations:\n conversation_id = conversation_to_update[\"id\"]\n name = conversation_to_update[\"name\"]\n updates = {}\n for key in update_key_dict.keys():\n if update_key_dict[key] in conversation_to_update:\n updates[key] = conversation_to_update[update_key_dict[key]]\n if conversation_id is not None:\n try:\n db.conversation.update_one({\"_id\": ObjectId(conversation_id)},\n {\"$set\": updates})\n messages.append(\"Conversation name updated to {}.\".format(name))\n except Exception as e:\n messages.append(str(e))\n success = False\n else:\n success = False\n messages.append(\"Missing conversation id or title.\")\n return jsonify({\"success\": success, \"message\": \" \".join(messages)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error fetch conversation\")\n\n\n@app.route(\"/api/conversations/update_folder\", methods=[\"POST\"])\ndef update_folder() -> Response:\n \"\"\"Update a folder name.\"\"\"\n request_json = request.get_json()\n folder_id = request_json[\"folder_id\"]\n folder_name = request_json[\"name\"]\n try:\n db = get_user_conversation_storage()\n db.folder.update_one({\"_id\": ObjectId(folder_id)},\n {\"$set\": {\"name\": folder_name}})\n return jsonify({\"success\": True,\n \"message\": \"Folder name updated to {}.\".format(folder_name)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error update folder\")\n\n\n@app.route(\"/api/conversations/register_folder\", methods=[\"POST\"])\ndef register_folder() -> Response:\n \"\"\"Creates a new folder.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n folder = request_json.get(\"folder\", None)\n if folder:\n try:\n db = get_user_conversation_storage()\n folder = db.folder.insert_one({\"name\": folder[\"name\"], \"user_id\": user_id})\n return jsonify({\"id\": str(folder.inserted_id),\n \"message\": \"Register folder successfully.\"})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error register folder\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing folder\")\n\n\n@app.route(\"/api/conversations/register_conversation\", methods=[\"POST\"])\ndef register_conversation() -> Response:\n \"\"\"Creates a new conversation.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n conversation = request_json.get(\"conversation\", None)\n if conversation:\n try:\n db = get_user_conversation_storage()\n conversation_id = conversation[\"id\"]\n if conversation_id is not None and db.conversation.find_one(\n {\"_id\": ObjectId(conversation_id)}):\n updates = {\n \"name\": conversation[\"name\"],\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folder_id\": conversation[\"folderId\"],\n \"bookmarked_message_ids\": conversation.get(\"bookmarkedMessagesIds\",\n None),\n \"selected_code_interpreter_plugins\": conversation[\n \"selectedCodeInterpreterPlugins\"],\n \"selected_plugins\": conversation[\"selectedPlugins\"],\n }\n db.conversation.update_one({\"_id\": ObjectId(conversation_id)},\n {\"$set\": updates})\n else:\n conversation = db.conversation.insert_one(\n {\n \"name\": conversation[\"name\"],\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folder_id\": conversation[\"folderId\"],\n \"bookmarked_message_ids\": conversation.get(\n \"bookmarkedMessagesIds\", None),\n \"hashed_api_key\": \"\",\n \"user_id\": user_id,\n \"selected_code_interpreter_plugins\": conversation[\n \"selectedCodeInterpreterPlugins\"],\n \"selected_plugins\": conversation[\"selectedPlugins\"],\n \"timestamp\": datetime.datetime.utcnow(),\n }\n )\n conversation_id = str(conversation.inserted_id)\n return jsonify({\"id\": conversation_id})\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} error register conversation\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing conversation\")\n\n\n@app.route(\"/api/conversations/delete_conversation\", methods=[\"POST\"])\ndef delete_conversation() -> Response:\n \"\"\"Deletes a conversation.\"\"\"\n request_json = request.get_json()\n chat_id = request_json.get(\"chat_id\", None)\n if chat_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_one({\"_id\": ObjectId(chat_id)})\n db.message.delete_many({\"conversation_id\": chat_id})\n return jsonify({\"success\": True, \"message\": \"Conversation is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"chat_id is missing\"})\n\n\n@app.route(\"/api/conversations/delete_folder\", methods=[\"POST\"])\ndef delete_folder() -> Response:\n \"\"\"Deletes a folder.\"\"\"\n request_json = request.get_json()\n folder_id = request_json.get(\"folder_id\", None)\n if folder_id:\n try:\n db = get_user_conversation_storage()\n db.folder.delete_one({\"_id\": ObjectId(folder_id)})\n return jsonify({\"success\": True, \"message\": \"Folder is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"folder_id is missing\"})\n\n\n@app.route(\"/api/conversations/clear\", methods=[\"POST\"])\ndef clear_all_conversation() -> Response:\n \"\"\"Clears all previous conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_many({\"user_id\": user_id})\n db.folder.delete_many({\"user_id\": user_id})\n db.message.delete_many({\"user_id\": user_id})\n return jsonify({\"success\": True, \"message\": \"Clear All Conversations.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"user_id is missing\"})\n\n\n@app.route(\"/api/conversations/stop_conversation\", methods=[\"POST\"])\ndef stop_generation() -> Response:\n \"\"\"Stops the current generation, cut on streaming.\"\"\"\n try:\n request_json = request.get_json()\n chat_id = request_json[\"chat_id\"]\n threading_pool.kill_thread(chat_id)\n except Exception as e:\n print(e)\n return Response(response={}, status=f\"{INTERNAL} error stopping\")\n\n def pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" Generator[bytes, Any, None]:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n\n return Response(response={})","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.get_conversation_list","uri":"program://OpenAgents/function/backend.api.conversation.get_conversation_list#L16-L37","kind":"function","name":"get_conversation_list","path":"backend/api/conversation.py","language":"python","start_line":16,"end_line":37,"context_start_line":1,"context_end_line":57,"code":"import struct\nimport json\nimport datetime\nfrom typing import Any, Generator\nfrom bson.objectid import ObjectId\nfrom flask import jsonify, request, Response\n\nfrom backend.app import app\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.main import threading_pool, logger\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.schemas import INTERNAL, UNFOUND\n\n\n@app.route(\"/api/conversations/get_conversation_list\", methods=[\"POST\"])\ndef get_conversation_list() -> Response:\n \"\"\"Gets the history conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n conversations = []\n try:\n # Login with API Key, then retrieve the user history based\n # on the hashed API key.\n db = get_user_conversation_storage()\n conversation_list = db.conversation.find({\"user_id\": user_id})\n for conversation in conversation_list:\n conversations.append(\n {\n \"id\": str(conversation[\"_id\"]),\n \"name\": conversation[\"name\"],\n \"folderId\": conversation[\"folder_id\"],\n }\n )\n except Exception as e:\n return Response(response=None,\n status=f'{INTERNAL} error fetch conversation list')\n return jsonify(conversations)\n\n\n@app.route(\"/api/conversations/get_folder_list\", methods=[\"POST\"])\ndef get_folder_list() -> Response:\n \"\"\"Gets the folder list.\"\"\"\n user_id = DEFAULT_USER_ID\n folders = []\n try:\n db = get_user_conversation_storage()\n folder_list = db.folder.find({\"user_id\": user_id})\n for folder in folder_list:\n folders.append(\n {\n \"id\": str(folder[\"_id\"]),\n \"name\": folder[\"name\"],\n \"type\": \"chat\",\n }\n )\n return jsonify({\"success\": True, \"data\": folders})\n except Exception as e:","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.get_folder_list","uri":"program://OpenAgents/function/backend.api.conversation.get_folder_list#L41-L58","kind":"function","name":"get_folder_list","path":"backend/api/conversation.py","language":"python","start_line":41,"end_line":58,"context_start_line":21,"context_end_line":78,"code":" try:\n # Login with API Key, then retrieve the user history based\n # on the hashed API key.\n db = get_user_conversation_storage()\n conversation_list = db.conversation.find({\"user_id\": user_id})\n for conversation in conversation_list:\n conversations.append(\n {\n \"id\": str(conversation[\"_id\"]),\n \"name\": conversation[\"name\"],\n \"folderId\": conversation[\"folder_id\"],\n }\n )\n except Exception as e:\n return Response(response=None,\n status=f'{INTERNAL} error fetch conversation list')\n return jsonify(conversations)\n\n\n@app.route(\"/api/conversations/get_folder_list\", methods=[\"POST\"])\ndef get_folder_list() -> Response:\n \"\"\"Gets the folder list.\"\"\"\n user_id = DEFAULT_USER_ID\n folders = []\n try:\n db = get_user_conversation_storage()\n folder_list = db.folder.find({\"user_id\": user_id})\n for folder in folder_list:\n folders.append(\n {\n \"id\": str(folder[\"_id\"]),\n \"name\": folder[\"name\"],\n \"type\": \"chat\",\n }\n )\n return jsonify({\"success\": True, \"data\": folders})\n except Exception as e:\n return Response(response=None, status=f'{INTERNAL} error fetch folder list')\n\n\ndef process_rich_content_item(data: dict, message_id: str) -> dict:\n \"\"\"Processes the rich content from db format into frontend renderable format.\"\"\"\n processed_items: dict = {\"intermediateSteps\": [], \"finalAnswer\": []}\n if \"intermediate_steps\" in data:\n for item in data[\"intermediate_steps\"]:\n processed_items[\"intermediateSteps\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n if \"final_answer\" in data:\n for item in data[\"final_answer\"]:\n processed_items[\"finalAnswer\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n return processed_items\n\n","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.process_rich_content_item","uri":"program://OpenAgents/function/backend.api.conversation.process_rich_content_item#L61-L76","kind":"function","name":"process_rich_content_item","path":"backend/api/conversation.py","language":"python","start_line":61,"end_line":76,"context_start_line":41,"context_end_line":96,"code":"def get_folder_list() -> Response:\n \"\"\"Gets the folder list.\"\"\"\n user_id = DEFAULT_USER_ID\n folders = []\n try:\n db = get_user_conversation_storage()\n folder_list = db.folder.find({\"user_id\": user_id})\n for folder in folder_list:\n folders.append(\n {\n \"id\": str(folder[\"_id\"]),\n \"name\": folder[\"name\"],\n \"type\": \"chat\",\n }\n )\n return jsonify({\"success\": True, \"data\": folders})\n except Exception as e:\n return Response(response=None, status=f'{INTERNAL} error fetch folder list')\n\n\ndef process_rich_content_item(data: dict, message_id: str) -> dict:\n \"\"\"Processes the rich content from db format into frontend renderable format.\"\"\"\n processed_items: dict = {\"intermediateSteps\": [], \"finalAnswer\": []}\n if \"intermediate_steps\" in data:\n for item in data[\"intermediate_steps\"]:\n processed_items[\"intermediateSteps\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n if \"final_answer\" in data:\n for item in data[\"final_answer\"]:\n processed_items[\"finalAnswer\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n return processed_items\n\n\n@app.route(\"/api/conversation\", methods=[\"POST\"])\ndef get_conversation_content() -> Response:\n \"\"\"Gets the conversation content for one assigned conversation.\"\"\"\n request_json = request.get_json()\n conversation_id = request_json.get(\"chat_id\", None)\n if conversation_id is not None:\n try:\n db = get_user_conversation_storage()\n conversation = db.conversation.find_one({\"_id\": ObjectId(conversation_id)})\n message_list = db.message.find({\"conversation_id\": conversation_id}).sort(\n \"_id\", -1)\n messages = [\n {\n \"id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"role\": message[\"role\"],\n \"content\": message[\"data_for_human\"] if message[\n \"role\"] == \"user\" else None,","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.get_conversation_content","uri":"program://OpenAgents/function/backend.api.conversation.get_conversation_content#L80-L154","kind":"function","name":"get_conversation_content","path":"backend/api/conversation.py","language":"python","start_line":80,"end_line":154,"context_start_line":60,"context_end_line":174,"code":"\ndef process_rich_content_item(data: dict, message_id: str) -> dict:\n \"\"\"Processes the rich content from db format into frontend renderable format.\"\"\"\n processed_items: dict = {\"intermediateSteps\": [], \"finalAnswer\": []}\n if \"intermediate_steps\" in data:\n for item in data[\"intermediate_steps\"]:\n processed_items[\"intermediateSteps\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n if \"final_answer\" in data:\n for item in data[\"final_answer\"]:\n processed_items[\"finalAnswer\"].append(\n {\"message_id\": message_id, \"content\": item[\"text\"],\n \"type\": item[\"type\"]}\n )\n return processed_items\n\n\n@app.route(\"/api/conversation\", methods=[\"POST\"])\ndef get_conversation_content() -> Response:\n \"\"\"Gets the conversation content for one assigned conversation.\"\"\"\n request_json = request.get_json()\n conversation_id = request_json.get(\"chat_id\", None)\n if conversation_id is not None:\n try:\n db = get_user_conversation_storage()\n conversation = db.conversation.find_one({\"_id\": ObjectId(conversation_id)})\n message_list = db.message.find({\"conversation_id\": conversation_id}).sort(\n \"_id\", -1)\n messages = [\n {\n \"id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"role\": message[\"role\"],\n \"content\": message[\"data_for_human\"] if message[\n \"role\"] == \"user\" else None,\n \"type\": \"rich_message\" if isinstance(message[\"data_for_human\"],\n dict) else \"\",\n \"richContent\": process_rich_content_item(message[\"data_for_human\"],\n message[\"message_id\"])\n if isinstance(message[\"data_for_human\"], dict)\n else None,\n }\n for message in message_list\n ]\n\n def _get_activated_conversation_branch(messages: list) -> list:\n # By default, the latest message is the end point, e.g., the current branch of messages.\n activated_messages: list = []\n end_point = messages[0][\"id\"]\n while len(messages) > 0 and end_point != -1:\n flag = False\n for msg in messages:\n if msg[\"id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break\n activated_messages = [msg] + activated_messages\n end_point = msg[\"parent_message_id\"]\n flag = True\n break\n if not flag:\n break\n return activated_messages\n\n # Find the current activated branch of messages as frontend only shows one branch\n\n if messages:\n messages = _get_activated_conversation_branch(messages)\n\n logger.bind(msg_head=f\"get_activated_message_list\").debug(messages)\n\n conversation = {\n \"id\": conversation_id,\n \"name\": conversation[\"name\"],\n \"messages\": messages,\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folderId\": conversation[\"folder_id\"],\n \"bookmarkedMessagesIds\": conversation[\"bookmarked_message_ids\"],\n \"selectedCodeInterpreterPlugins\": conversation[\n \"selected_code_interpreter_plugins\"],\n \"selectedPlugins\": conversation[\"selected_plugins\"],\n\n }\n return jsonify(conversation)\n except Exception as e:\n import traceback\n traceback.print_exc()\n return Response(response=None,\n status=f'{INTERNAL} error fetch conversation')\n else:\n return Response(response=None, status=f'{INTERNAL} error fetch conversation')\n\n\n@app.route(\"/api/conversations/update_conversation\", methods=[\"POST\"])\ndef update_conversation() -> Response:\n \"\"\"Updates a conversation name.\"\"\"\n try:\n request_json = request.get_json()\n conversations = request_json[\"conversations\"]\n db = get_user_conversation_storage()\n messages = []\n success = True\n update_key_dict = {\"name\": \"name\", \"folder_id\": \"folderId\"}\n for conversation_to_update in conversations:\n conversation_id = conversation_to_update[\"id\"]\n name = conversation_to_update[\"name\"]\n updates = {}\n for key in update_key_dict.keys():\n if update_key_dict[key] in conversation_to_update:\n updates[key] = conversation_to_update[update_key_dict[key]]\n if conversation_id is not None:","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.update_conversation","uri":"program://OpenAgents/function/backend.api.conversation.update_conversation#L158-L187","kind":"function","name":"update_conversation","path":"backend/api/conversation.py","language":"python","start_line":158,"end_line":187,"context_start_line":138,"context_end_line":207,"code":" \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folderId\": conversation[\"folder_id\"],\n \"bookmarkedMessagesIds\": conversation[\"bookmarked_message_ids\"],\n \"selectedCodeInterpreterPlugins\": conversation[\n \"selected_code_interpreter_plugins\"],\n \"selectedPlugins\": conversation[\"selected_plugins\"],\n\n }\n return jsonify(conversation)\n except Exception as e:\n import traceback\n traceback.print_exc()\n return Response(response=None,\n status=f'{INTERNAL} error fetch conversation')\n else:\n return Response(response=None, status=f'{INTERNAL} error fetch conversation')\n\n\n@app.route(\"/api/conversations/update_conversation\", methods=[\"POST\"])\ndef update_conversation() -> Response:\n \"\"\"Updates a conversation name.\"\"\"\n try:\n request_json = request.get_json()\n conversations = request_json[\"conversations\"]\n db = get_user_conversation_storage()\n messages = []\n success = True\n update_key_dict = {\"name\": \"name\", \"folder_id\": \"folderId\"}\n for conversation_to_update in conversations:\n conversation_id = conversation_to_update[\"id\"]\n name = conversation_to_update[\"name\"]\n updates = {}\n for key in update_key_dict.keys():\n if update_key_dict[key] in conversation_to_update:\n updates[key] = conversation_to_update[update_key_dict[key]]\n if conversation_id is not None:\n try:\n db.conversation.update_one({\"_id\": ObjectId(conversation_id)},\n {\"$set\": updates})\n messages.append(\"Conversation name updated to {}.\".format(name))\n except Exception as e:\n messages.append(str(e))\n success = False\n else:\n success = False\n messages.append(\"Missing conversation id or title.\")\n return jsonify({\"success\": success, \"message\": \" \".join(messages)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error fetch conversation\")\n\n\n@app.route(\"/api/conversations/update_folder\", methods=[\"POST\"])\ndef update_folder() -> Response:\n \"\"\"Update a folder name.\"\"\"\n request_json = request.get_json()\n folder_id = request_json[\"folder_id\"]\n folder_name = request_json[\"name\"]\n try:\n db = get_user_conversation_storage()\n db.folder.update_one({\"_id\": ObjectId(folder_id)},\n {\"$set\": {\"name\": folder_name}})\n return jsonify({\"success\": True,\n \"message\": \"Folder name updated to {}.\".format(folder_name)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error update folder\")\n\n\n@app.route(\"/api/conversations/register_folder\", methods=[\"POST\"])\ndef register_folder() -> Response:","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.update_folder","uri":"program://OpenAgents/function/backend.api.conversation.update_folder#L191-L203","kind":"function","name":"update_folder","path":"backend/api/conversation.py","language":"python","start_line":191,"end_line":203,"context_start_line":171,"context_end_line":223,"code":" for key in update_key_dict.keys():\n if update_key_dict[key] in conversation_to_update:\n updates[key] = conversation_to_update[update_key_dict[key]]\n if conversation_id is not None:\n try:\n db.conversation.update_one({\"_id\": ObjectId(conversation_id)},\n {\"$set\": updates})\n messages.append(\"Conversation name updated to {}.\".format(name))\n except Exception as e:\n messages.append(str(e))\n success = False\n else:\n success = False\n messages.append(\"Missing conversation id or title.\")\n return jsonify({\"success\": success, \"message\": \" \".join(messages)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error fetch conversation\")\n\n\n@app.route(\"/api/conversations/update_folder\", methods=[\"POST\"])\ndef update_folder() -> Response:\n \"\"\"Update a folder name.\"\"\"\n request_json = request.get_json()\n folder_id = request_json[\"folder_id\"]\n folder_name = request_json[\"name\"]\n try:\n db = get_user_conversation_storage()\n db.folder.update_one({\"_id\": ObjectId(folder_id)},\n {\"$set\": {\"name\": folder_name}})\n return jsonify({\"success\": True,\n \"message\": \"Folder name updated to {}.\".format(folder_name)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error update folder\")\n\n\n@app.route(\"/api/conversations/register_folder\", methods=[\"POST\"])\ndef register_folder() -> Response:\n \"\"\"Creates a new folder.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n folder = request_json.get(\"folder\", None)\n if folder:\n try:\n db = get_user_conversation_storage()\n folder = db.folder.insert_one({\"name\": folder[\"name\"], \"user_id\": user_id})\n return jsonify({\"id\": str(folder.inserted_id),\n \"message\": \"Register folder successfully.\"})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error register folder\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing folder\")\n\n","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.register_folder","uri":"program://OpenAgents/function/backend.api.conversation.register_folder#L207-L221","kind":"function","name":"register_folder","path":"backend/api/conversation.py","language":"python","start_line":207,"end_line":221,"context_start_line":187,"context_end_line":241,"code":" return Response(response=None, status=f\"{INTERNAL} error fetch conversation\")\n\n\n@app.route(\"/api/conversations/update_folder\", methods=[\"POST\"])\ndef update_folder() -> Response:\n \"\"\"Update a folder name.\"\"\"\n request_json = request.get_json()\n folder_id = request_json[\"folder_id\"]\n folder_name = request_json[\"name\"]\n try:\n db = get_user_conversation_storage()\n db.folder.update_one({\"_id\": ObjectId(folder_id)},\n {\"$set\": {\"name\": folder_name}})\n return jsonify({\"success\": True,\n \"message\": \"Folder name updated to {}.\".format(folder_name)})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error update folder\")\n\n\n@app.route(\"/api/conversations/register_folder\", methods=[\"POST\"])\ndef register_folder() -> Response:\n \"\"\"Creates a new folder.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n folder = request_json.get(\"folder\", None)\n if folder:\n try:\n db = get_user_conversation_storage()\n folder = db.folder.insert_one({\"name\": folder[\"name\"], \"user_id\": user_id})\n return jsonify({\"id\": str(folder.inserted_id),\n \"message\": \"Register folder successfully.\"})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error register folder\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing folder\")\n\n\n@app.route(\"/api/conversations/register_conversation\", methods=[\"POST\"])\ndef register_conversation() -> Response:\n \"\"\"Creates a new conversation.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n conversation = request_json.get(\"conversation\", None)\n if conversation:\n try:\n db = get_user_conversation_storage()\n conversation_id = conversation[\"id\"]\n if conversation_id is not None and db.conversation.find_one(\n {\"_id\": ObjectId(conversation_id)}):\n updates = {\n \"name\": conversation[\"name\"],\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folder_id\": conversation[\"folderId\"],","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.register_conversation","uri":"program://OpenAgents/function/backend.api.conversation.register_conversation#L225-L274","kind":"function","name":"register_conversation","path":"backend/api/conversation.py","language":"python","start_line":225,"end_line":274,"context_start_line":205,"context_end_line":294,"code":"\n@app.route(\"/api/conversations/register_folder\", methods=[\"POST\"])\ndef register_folder() -> Response:\n \"\"\"Creates a new folder.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n folder = request_json.get(\"folder\", None)\n if folder:\n try:\n db = get_user_conversation_storage()\n folder = db.folder.insert_one({\"name\": folder[\"name\"], \"user_id\": user_id})\n return jsonify({\"id\": str(folder.inserted_id),\n \"message\": \"Register folder successfully.\"})\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} error register folder\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing folder\")\n\n\n@app.route(\"/api/conversations/register_conversation\", methods=[\"POST\"])\ndef register_conversation() -> Response:\n \"\"\"Creates a new conversation.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n conversation = request_json.get(\"conversation\", None)\n if conversation:\n try:\n db = get_user_conversation_storage()\n conversation_id = conversation[\"id\"]\n if conversation_id is not None and db.conversation.find_one(\n {\"_id\": ObjectId(conversation_id)}):\n updates = {\n \"name\": conversation[\"name\"],\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folder_id\": conversation[\"folderId\"],\n \"bookmarked_message_ids\": conversation.get(\"bookmarkedMessagesIds\",\n None),\n \"selected_code_interpreter_plugins\": conversation[\n \"selectedCodeInterpreterPlugins\"],\n \"selected_plugins\": conversation[\"selectedPlugins\"],\n }\n db.conversation.update_one({\"_id\": ObjectId(conversation_id)},\n {\"$set\": updates})\n else:\n conversation = db.conversation.insert_one(\n {\n \"name\": conversation[\"name\"],\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folder_id\": conversation[\"folderId\"],\n \"bookmarked_message_ids\": conversation.get(\n \"bookmarkedMessagesIds\", None),\n \"hashed_api_key\": \"\",\n \"user_id\": user_id,\n \"selected_code_interpreter_plugins\": conversation[\n \"selectedCodeInterpreterPlugins\"],\n \"selected_plugins\": conversation[\"selectedPlugins\"],\n \"timestamp\": datetime.datetime.utcnow(),\n }\n )\n conversation_id = str(conversation.inserted_id)\n return jsonify({\"id\": conversation_id})\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} error register conversation\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing conversation\")\n\n\n@app.route(\"/api/conversations/delete_conversation\", methods=[\"POST\"])\ndef delete_conversation() -> Response:\n \"\"\"Deletes a conversation.\"\"\"\n request_json = request.get_json()\n chat_id = request_json.get(\"chat_id\", None)\n if chat_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_one({\"_id\": ObjectId(chat_id)})\n db.message.delete_many({\"conversation_id\": chat_id})\n return jsonify({\"success\": True, \"message\": \"Conversation is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"chat_id is missing\"})\n\n\n@app.route(\"/api/conversations/delete_folder\", methods=[\"POST\"])","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.delete_conversation","uri":"program://OpenAgents/function/backend.api.conversation.delete_conversation#L278-L291","kind":"function","name":"delete_conversation","path":"backend/api/conversation.py","language":"python","start_line":278,"end_line":291,"context_start_line":258,"context_end_line":311,"code":" \"bookmarked_message_ids\": conversation.get(\n \"bookmarkedMessagesIds\", None),\n \"hashed_api_key\": \"\",\n \"user_id\": user_id,\n \"selected_code_interpreter_plugins\": conversation[\n \"selectedCodeInterpreterPlugins\"],\n \"selected_plugins\": conversation[\"selectedPlugins\"],\n \"timestamp\": datetime.datetime.utcnow(),\n }\n )\n conversation_id = str(conversation.inserted_id)\n return jsonify({\"id\": conversation_id})\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} error register conversation\")\n else:\n return Response(response=None, status=f\"{UNFOUND} missing conversation\")\n\n\n@app.route(\"/api/conversations/delete_conversation\", methods=[\"POST\"])\ndef delete_conversation() -> Response:\n \"\"\"Deletes a conversation.\"\"\"\n request_json = request.get_json()\n chat_id = request_json.get(\"chat_id\", None)\n if chat_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_one({\"_id\": ObjectId(chat_id)})\n db.message.delete_many({\"conversation_id\": chat_id})\n return jsonify({\"success\": True, \"message\": \"Conversation is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"chat_id is missing\"})\n\n\n@app.route(\"/api/conversations/delete_folder\", methods=[\"POST\"])\ndef delete_folder() -> Response:\n \"\"\"Deletes a folder.\"\"\"\n request_json = request.get_json()\n folder_id = request_json.get(\"folder_id\", None)\n if folder_id:\n try:\n db = get_user_conversation_storage()\n db.folder.delete_one({\"_id\": ObjectId(folder_id)})\n return jsonify({\"success\": True, \"message\": \"Folder is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"folder_id is missing\"})\n\n\n@app.route(\"/api/conversations/clear\", methods=[\"POST\"])\ndef clear_all_conversation() -> Response:","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.delete_folder","uri":"program://OpenAgents/function/backend.api.conversation.delete_folder#L295-L307","kind":"function","name":"delete_folder","path":"backend/api/conversation.py","language":"python","start_line":295,"end_line":307,"context_start_line":275,"context_end_line":327,"code":"\n\n@app.route(\"/api/conversations/delete_conversation\", methods=[\"POST\"])\ndef delete_conversation() -> Response:\n \"\"\"Deletes a conversation.\"\"\"\n request_json = request.get_json()\n chat_id = request_json.get(\"chat_id\", None)\n if chat_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_one({\"_id\": ObjectId(chat_id)})\n db.message.delete_many({\"conversation_id\": chat_id})\n return jsonify({\"success\": True, \"message\": \"Conversation is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"chat_id is missing\"})\n\n\n@app.route(\"/api/conversations/delete_folder\", methods=[\"POST\"])\ndef delete_folder() -> Response:\n \"\"\"Deletes a folder.\"\"\"\n request_json = request.get_json()\n folder_id = request_json.get(\"folder_id\", None)\n if folder_id:\n try:\n db = get_user_conversation_storage()\n db.folder.delete_one({\"_id\": ObjectId(folder_id)})\n return jsonify({\"success\": True, \"message\": \"Folder is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"folder_id is missing\"})\n\n\n@app.route(\"/api/conversations/clear\", methods=[\"POST\"])\ndef clear_all_conversation() -> Response:\n \"\"\"Clears all previous conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_many({\"user_id\": user_id})\n db.folder.delete_many({\"user_id\": user_id})\n db.message.delete_many({\"user_id\": user_id})\n return jsonify({\"success\": True, \"message\": \"Clear All Conversations.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"user_id is missing\"})\n\n","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.clear_all_conversation","uri":"program://OpenAgents/function/backend.api.conversation.clear_all_conversation#L311-L325","kind":"function","name":"clear_all_conversation","path":"backend/api/conversation.py","language":"python","start_line":311,"end_line":325,"context_start_line":291,"context_end_line":345,"code":" return jsonify({\"success\": False, \"message\": \"chat_id is missing\"})\n\n\n@app.route(\"/api/conversations/delete_folder\", methods=[\"POST\"])\ndef delete_folder() -> Response:\n \"\"\"Deletes a folder.\"\"\"\n request_json = request.get_json()\n folder_id = request_json.get(\"folder_id\", None)\n if folder_id:\n try:\n db = get_user_conversation_storage()\n db.folder.delete_one({\"_id\": ObjectId(folder_id)})\n return jsonify({\"success\": True, \"message\": \"Folder is deleted.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"folder_id is missing\"})\n\n\n@app.route(\"/api/conversations/clear\", methods=[\"POST\"])\ndef clear_all_conversation() -> Response:\n \"\"\"Clears all previous conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_many({\"user_id\": user_id})\n db.folder.delete_many({\"user_id\": user_id})\n db.message.delete_many({\"user_id\": user_id})\n return jsonify({\"success\": True, \"message\": \"Clear All Conversations.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"user_id is missing\"})\n\n\n@app.route(\"/api/conversations/stop_conversation\", methods=[\"POST\"])\ndef stop_generation() -> Response:\n \"\"\"Stops the current generation, cut on streaming.\"\"\"\n try:\n request_json = request.get_json()\n chat_id = request_json[\"chat_id\"]\n threading_pool.kill_thread(chat_id)\n except Exception as e:\n print(e)\n return Response(response={}, status=f\"{INTERNAL} error stopping\")\n\n def pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" Generator[bytes, Any, None]:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.stop_generation","uri":"program://OpenAgents/function/backend.api.conversation.stop_generation#L329-L346","kind":"function","name":"stop_generation","path":"backend/api/conversation.py","language":"python","start_line":329,"end_line":346,"context_start_line":309,"context_end_line":346,"code":"\n@app.route(\"/api/conversations/clear\", methods=[\"POST\"])\ndef clear_all_conversation() -> Response:\n \"\"\"Clears all previous conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id:\n try:\n db = get_user_conversation_storage()\n db.conversation.delete_many({\"user_id\": user_id})\n db.folder.delete_many({\"user_id\": user_id})\n db.message.delete_many({\"user_id\": user_id})\n return jsonify({\"success\": True, \"message\": \"Clear All Conversations.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"user_id is missing\"})\n\n\n@app.route(\"/api/conversations/stop_conversation\", methods=[\"POST\"])\ndef stop_generation() -> Response:\n \"\"\"Stops the current generation, cut on streaming.\"\"\"\n try:\n request_json = request.get_json()\n chat_id = request_json[\"chat_id\"]\n threading_pool.kill_thread(chat_id)\n except Exception as e:\n print(e)\n return Response(response={}, status=f\"{INTERNAL} error stopping\")\n\n def pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" Generator[bytes, Any, None]:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n\n return Response(response={})","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.pack_json","uri":"program://OpenAgents/function/backend.api.conversation.pack_json#L339-L341","kind":"function","name":"pack_json","path":"backend/api/conversation.py","language":"python","start_line":339,"end_line":341,"context_start_line":319,"context_end_line":346,"code":" db.folder.delete_many({\"user_id\": user_id})\n db.message.delete_many({\"user_id\": user_id})\n return jsonify({\"success\": True, \"message\": \"Clear All Conversations.\"})\n except Exception as e:\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"user_id is missing\"})\n\n\n@app.route(\"/api/conversations/stop_conversation\", methods=[\"POST\"])\ndef stop_generation() -> Response:\n \"\"\"Stops the current generation, cut on streaming.\"\"\"\n try:\n request_json = request.get_json()\n chat_id = request_json[\"chat_id\"]\n threading_pool.kill_thread(chat_id)\n except Exception as e:\n print(e)\n return Response(response={}, status=f\"{INTERNAL} error stopping\")\n\n def pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" Generator[bytes, Any, None]:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n\n return Response(response={})","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation.yield_stop","uri":"program://OpenAgents/function/backend.api.conversation.yield_stop#L343-L344","kind":"function","name":"yield_stop","path":"backend/api/conversation.py","language":"python","start_line":343,"end_line":344,"context_start_line":323,"context_end_line":346,"code":" return jsonify({\"success\": False, \"message\": str(e)})\n else:\n return jsonify({\"success\": False, \"message\": \"user_id is missing\"})\n\n\n@app.route(\"/api/conversations/stop_conversation\", methods=[\"POST\"])\ndef stop_generation() -> Response:\n \"\"\"Stops the current generation, cut on streaming.\"\"\"\n try:\n request_json = request.get_json()\n chat_id = request_json[\"chat_id\"]\n threading_pool.kill_thread(chat_id)\n except Exception as e:\n print(e)\n return Response(response={}, status=f\"{INTERNAL} error stopping\")\n\n def pack_json(object: Any) -> bytes:\n json_text = json.dumps(object)\n return struct.pack(\" Generator[bytes, Any, None]:\n yield pack_json({\"success\": False, \"error\": \"stop\"})\n\n return Response(response={})","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.conversation._get_activated_conversation_branch","uri":"program://OpenAgents/function/backend.api.conversation._get_activated_conversation_branch#L107-L124","kind":"function","name":"_get_activated_conversation_branch","path":"backend/api/conversation.py","language":"python","start_line":107,"end_line":124,"context_start_line":87,"context_end_line":144,"code":" conversation = db.conversation.find_one({\"_id\": ObjectId(conversation_id)})\n message_list = db.message.find({\"conversation_id\": conversation_id}).sort(\n \"_id\", -1)\n messages = [\n {\n \"id\": message[\"message_id\"],\n \"parent_message_id\": message[\"parent_message_id\"],\n \"role\": message[\"role\"],\n \"content\": message[\"data_for_human\"] if message[\n \"role\"] == \"user\" else None,\n \"type\": \"rich_message\" if isinstance(message[\"data_for_human\"],\n dict) else \"\",\n \"richContent\": process_rich_content_item(message[\"data_for_human\"],\n message[\"message_id\"])\n if isinstance(message[\"data_for_human\"], dict)\n else None,\n }\n for message in message_list\n ]\n\n def _get_activated_conversation_branch(messages: list) -> list:\n # By default, the latest message is the end point, e.g., the current branch of messages.\n activated_messages: list = []\n end_point = messages[0][\"id\"]\n while len(messages) > 0 and end_point != -1:\n flag = False\n for msg in messages:\n if msg[\"id\"] == end_point:\n if end_point == msg[\"parent_message_id\"]:\n flag = False\n break\n activated_messages = [msg] + activated_messages\n end_point = msg[\"parent_message_id\"]\n flag = True\n break\n if not flag:\n break\n return activated_messages\n\n # Find the current activated branch of messages as frontend only shows one branch\n\n if messages:\n messages = _get_activated_conversation_branch(messages)\n\n logger.bind(msg_head=f\"get_activated_message_list\").debug(messages)\n\n conversation = {\n \"id\": conversation_id,\n \"name\": conversation[\"name\"],\n \"messages\": messages,\n \"agent\": conversation[\"agent\"],\n \"prompt\": conversation[\"prompt\"],\n \"temperature\": conversation[\"temperature\"],\n \"folderId\": conversation[\"folder_id\"],\n \"bookmarkedMessagesIds\": conversation[\"bookmarked_message_ids\"],\n \"selectedCodeInterpreterPlugins\": conversation[\n \"selected_code_interpreter_plugins\"],\n \"selectedPlugins\": conversation[\"selected_plugins\"],","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.data_connector","uri":"program://OpenAgents/module/backend.api.data_connector#L1-L36","kind":"module","name":"backend.api.data_connector","path":"backend/api/data_connector.py","language":"python","start_line":1,"end_line":36,"context_start_line":1,"context_end_line":36,"code":"import os\nfrom flask import request, Response\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n\nfrom backend.app import app\nfrom backend.utils.utils import create_personal_folder\nfrom backend.schemas import UNFOUND, INTERNAL, DEFAULT_USER_ID\n\napi = KaggleApi()\napi.authenticate()\n\n\n@app.route(\"/api/kaggle/download_dataset\", methods=[\"POST\"])\ndef kaggle_dataset_download() -> dict | Response:\n \"\"\"Use Kaggle-api to connect. \"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n url = request_json[\"url\"]\n if url.startswith(\"http\"):\n return {\"success\": False,\n \"message\": \"Please remove the http in your submitted URL.\"}\n kaggle_dataset_id = url.replace(\"www.kaggle.com/datasets/\", \"\")\n if not kaggle_dataset_id:\n return {\"success\": False, \"message\": \"Please input a valid Kaggle dataset URL.\"}\n root_path = create_personal_folder(user_id)\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n path = os.path.join(root_path, kaggle_dataset_id)\n api.dataset_download_files(kaggle_dataset_id, path=path, unzip=True)\n return {\"success\": True, \"message\": \"Download {} successfully.\",\n \"data_path\": path}\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} Error Downloading, please try another datasets\")\n else:\n return Response(response=None, status=f\"{UNFOUND} Missing User folder\")","source_hash":"ce22f7578d02a5fdba3330b6a37a70536b751353f6f20dbff2a520c796dd7067","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.data_connector.kaggle_dataset_download","uri":"program://OpenAgents/function/backend.api.data_connector.kaggle_dataset_download#L14-L36","kind":"function","name":"kaggle_dataset_download","path":"backend/api/data_connector.py","language":"python","start_line":14,"end_line":36,"context_start_line":1,"context_end_line":36,"code":"import os\nfrom flask import request, Response\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n\nfrom backend.app import app\nfrom backend.utils.utils import create_personal_folder\nfrom backend.schemas import UNFOUND, INTERNAL, DEFAULT_USER_ID\n\napi = KaggleApi()\napi.authenticate()\n\n\n@app.route(\"/api/kaggle/download_dataset\", methods=[\"POST\"])\ndef kaggle_dataset_download() -> dict | Response:\n \"\"\"Use Kaggle-api to connect. \"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n url = request_json[\"url\"]\n if url.startswith(\"http\"):\n return {\"success\": False,\n \"message\": \"Please remove the http in your submitted URL.\"}\n kaggle_dataset_id = url.replace(\"www.kaggle.com/datasets/\", \"\")\n if not kaggle_dataset_id:\n return {\"success\": False, \"message\": \"Please input a valid Kaggle dataset URL.\"}\n root_path = create_personal_folder(user_id)\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n path = os.path.join(root_path, kaggle_dataset_id)\n api.dataset_download_files(kaggle_dataset_id, path=path, unzip=True)\n return {\"success\": True, \"message\": \"Download {} successfully.\",\n \"data_path\": path}\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} Error Downloading, please try another datasets\")\n else:\n return Response(response=None, status=f\"{UNFOUND} Missing User folder\")","source_hash":"ce22f7578d02a5fdba3330b6a37a70536b751353f6f20dbff2a520c796dd7067","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot","uri":"program://OpenAgents/module/backend.api.chat_copilot#L1-L435","kind":"module","name":"backend.api.chat_copilot","path":"backend/api/chat_copilot.py","language":"python","start_line":1,"end_line":435,"context_start_line":1,"context_end_line":435,"code":"import traceback\nfrom typing import Dict, List, Union\nfrom flask import Response, request, stream_with_context, Response\n\nfrom backend.api.file import _get_file_path_from_node\nfrom backend.api.language_model import get_llm\nfrom backend.app import app\nfrom backend.main import (\n grounding_source_pool,\n jupyter_kernel_pool,\n logger,\n message_id_register,\n message_pool,\n)\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.utils.utils import create_personal_folder\nfrom backend.utils.charts import polish_echarts\nfrom backend.utils.streaming import (\n single_round_chat_with_executor,\n single_round_chat_with_agent_streaming,\n)\nfrom backend.utils.utils import get_data_summary_cls\nfrom backend.schemas import OVERLOAD, UNAUTH, NEED_CONTINUE_MODEL\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks import AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.data_model import DatabaseDataModel, DataModel, JsonDataModel, \\\n TableDataModel\nfrom real_agents.adapters.executors import ChatExecutor\nfrom real_agents.adapters.interactive_executor import initialize_agent\nfrom real_agents.data_agent import CodeGenerationExecutor, KaggleDataLoadingExecutor\nfrom real_agents.adapters.memory import ConversationReActBufferMemory, \\\n ReadOnlySharedStringMemory\n\n\ndef create_interaction_executor(\n grounding_source_dict: Dict[str, DataModel],\n code_interpreter_languages: List[str],\n code_interpreter_tools: List[str],\n llm: BaseLanguageModel,\n llm_name: str,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n grounding_source_dict: A dict of grounding source filepath and data.\n code_interpreter_languages: A string to indicate the programming language to use.\n code_interpreter_tools: A list of augmented data tools.\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n code_execution_mode: A string indicating where code is executed.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize Memory\n memory = ConversationReActBufferMemory(\n memory_key=\"chat_history\", return_messages=True, llm=llm, max_token_limit=3500\n )\n read_only_memory = ReadOnlySharedStringMemory(memory=memory)\n\n # Initialize tools(executors)\n basic_chat_executor = ChatExecutor()\n python_code_generation_executor = CodeGenerationExecutor(\n programming_language=\"python\", memory=read_only_memory)\n sql_code_generation_executor = CodeGenerationExecutor(programming_language=\"sql\",\n memory=read_only_memory)\n echart_code_generation_executor = CodeGenerationExecutor(\n programming_language=\"python\", memory=read_only_memory, usage=\"echarts\"\n )\n kaggle_data_loading_executor = KaggleDataLoadingExecutor()\n\n def run_python_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n # Only TableDataModel are allowed as input to python\n # input_grounding_source = [gs for _, gs in grounding_source_dict.items()\n # if isinstance(gs, TableDataModel)]\n input_grounding_source = [gs for gs in grounding_source_dict.values()]\n # Get the result\n results = python_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PythonCodeBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n if results[\"result\"][\"result\"] is not None:\n raw_output = results[\"result\"][\"result\"]\n elif results[\"result\"][\"stdout\"] != \"\":\n raw_output = results[\"result\"][\"stdout\"]\n else:\n raw_output = \"\"\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": raw_output,\n \"images\": results[\"result\"][\"outputs\"] if \".show()\" in results[\n \"intermediate_steps\"] else [],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"images\"],\n )\n else:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"PythonCodeBuilder error({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_sql_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n\n def convert_grounding_source_as_db(\n grounding_source_dict: Dict[str, DataModel]\n ) -> Union[List[TableDataModel], DatabaseDataModel]:\n db_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, DatabaseDataModel)\n ]\n table_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)\n ]\n assert len(db_grounding_source) <= 1\n if len(table_grounding_source) == 0:\n # Only DatabaseDataModel. Assume there is at least one grounding\n # source\n return db_grounding_source[0]\n else:\n for t_gs in table_grounding_source:\n if len(db_grounding_source) == 0:\n # No DatabaseDataModel, then convert the first TableModel\n # into DatabaseDataModel.\n if t_gs.db_view is None:\n t_gs.set_db_view(\n DatabaseDataModel.from_table_data_model(t_gs))\n db_gs = t_gs.db_view\n db_grounding_source.append(db_gs)\n else:\n # Insert TableDataModel into the existing DatabaseDataModel\n db_gs = db_grounding_source[0]\n db_gs.insert_table_data_model(t_gs)\n return db_gs\n\n input_grounding_source = convert_grounding_source_as_db(\n grounding_source_dict)\n results = sql_code_generation_executor.run(\n user_intent=term,\n grounding_source=input_grounding_source,\n llm=llm,\n )\n\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n observation = JsonDataModel.from_raw_data({\n \"success\": True,\n \"result\": results[\"result\"][\"result\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n else:\n observation = JsonDataModel.from_raw_data({\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_echarts_interactive_plotter(term: str) -> Union[Dict, DataModel]:\n try:\n input_grounding_source = [gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)]\n results = echart_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PlotInteractivePlotter results({llm})\").debug(\n results)\n\n if results[\"result\"][\"success\"]:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"\",\n \"echarts\": polish_echarts(results[\"result\"][\"stdout\"]),\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"result\", \"echarts\"],\n )\n else:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"PlotInteractivePlotter error({llm})\").error(str(e))\n\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_kaggle_data_loader(term: str) -> Union[Dict, DataModel]:\n try:\n results = kaggle_data_loading_executor.run(\n user_intent=term,\n llm=llm,\n )\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").debug(results)\n\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"kaggle_action\": results[\"kaggle_action\"],\n \"kaggle_output_info\": results[\"kaggle_output_info\"],\n },\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n tool_dict = {\n \"PythonCodeBuilder\": Tool(\n name=\"PythonCodeBuilder\",\n func=run_python_code_builder,\n description=\"\"\"\nDescription: This tool adeptly turns your textual problem or query into Python code & execute it to get results. It shines when dealing with mathematics, data manipulation tasks, general computational problems and basic visualization like matplotlib. Please note it does not generate database queries.\nInput: A natural language problem or question.\nOutput: A Python program + its execution result to solve the presented problem or answer the question.\nNote: The tool MUST be used whenever you want to generate & execute Python code.\n \"\"\",\n ),\n \"SQLQueryBuilder\": Tool(\n name=\"SQLQueryBuilder\",\n func=run_sql_code_builder,\n description=\"\"\"\nDescription: Specialized for database tasks, this tool converts your natural language query into SQL code & execute it to get results. It's particularly suited for creating database queries, but it doesn't solve mathematical problems or perform data manipulations outside the SQL context. Be sure to specify the table name for successful operation.\nInput: A natural language query related to database operations, along with the name of the table on which the query will operate.\nOutput: A SQL program, ready to execute on the specified database table, and its execution result.\nNote: It is ALWAYS preferable to use the tool whenever you want to generate SQL query & execute the SQL query.\n \"\"\",\n ),\n \"Echarts\": Tool(\n name=\"Echarts\",\n func=run_echarts_interactive_plotter,\n description=\"\"\"\nDescription: Dive into the world of data visualization with this specialized Echarts tool. It takes your data table and creates Echarts code & show Echarts for four distinct chart types: scatter, bar, line, and pie, selecting the most appropriate labels and titles.\nInput: A natural language query detailing your desired visualization, no other words.\nOutput: An Echarts script, specifically tailored for your data, that generates an interactive chart upon execution.\nNote: Currently, this tool supports only the listed chart types. Please ensure your desired visualization aligns with these options to experience the full capabilities of this dynamic Echarts tool.\"\"\",\n ),\n \"KaggleDataLoader\": Tool(\n name=\"KaggleDataLoader\",\n func=run_kaggle_data_loader,\n description=\"\"\"\nDescription: The KaggleDataLoader tool allows you to seamlessly connect to Kaggle datasets. It allows you to load specific datasets by providing the exact dataset path, or it can aid in the search and retrieval of datasets based on the information given in your user input, providing you with a vast array of data sources for your projects.\nInput: A natural language intent that may mention path of the Kaggle dataset, or some keyword or other relevant information about the dataset you are interested in.\nOutput: The action you want to perform, and the extracted path or searched relevant datasets depending on your input.\n\"\"\",\n ),\n }\n # Data profiling is not activated in agent\n IGNORE_TOOLS = [\"DataProfiling\"]\n # Activate tools according to the user selection\n tools = [tool_dict[lang[\"name\"]] for lang in code_interpreter_languages]\n for tool in code_interpreter_tools:\n if tool[\"name\"] not in IGNORE_TOOLS:\n tools.append(tool_dict[tool[\"name\"]])\n\n # Build the chat agent with LLM and tools\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_agent(tools, llm, continue_model, memory=memory,\n verbose=True)\n return interaction_executor\n\n\n@app.route(\"/api/chat\", methods=[\"POST\"])\ndef chat() -> Response | Dict:\n \"\"\"Returns the chat response of data agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = int(request_json[\"parent_message_id\"])\n code_interpreter_languages = request_json.get(\"code_interpreter_languages\", [])\n code_interpreter_tools = request_json.get(\"code_interpreter_tools\", [])\n api_call = request_json.get(\"api_call\", None)\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.7)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n\n # Get language model\n stream_handler = AgentStreamingStdOutCallbackHandler()\n llm = get_llm(llm_name, **kwargs)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Request json\").debug(request_json)\n\n if api_call:\n # Load/init grounding source\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n\n # Find the mainstay message list from leaf to root\n activated_message_list = message_pool.get_activated_message_list(\n user_id, chat_id, default_value=list(),\n parent_message_id=parent_message_id\n )\n assert api_call[\"api_name\"] == \"DataProfiling\"\n ai_message_id = message_id_register.add_variable(\"\")\n file_node = api_call[\"args\"][\"activated_file\"]\n\n folder = create_personal_folder(user_id)\n file_path = _get_file_path_from_node(folder, file_node)\n executor = get_data_summary_cls(file_path)()\n gs = grounding_source_dict[file_path]\n return stream_with_context(\n Response(\n single_round_chat_with_executor(\n executor,\n user_intent=gs,\n human_message_id=None,\n ai_message_id=ai_message_id,\n user_id=DEFAULT_USER_ID,\n chat_id=api_call[\"args\"][\"chat_id\"],\n message_list=activated_message_list,\n parent_message_id=api_call[\"args\"][\"parent_message_id\"],\n llm=llm,\n app_type=\"copilot\",\n ),\n content_type=\"application/json\",\n )\n )\n else:\n # Load/init grounding source\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n # Build executor and run chat\n interaction_executor = create_interaction_executor(\n grounding_source_dict=grounding_source_dict,\n code_interpreter_languages=code_interpreter_languages,\n code_interpreter_tools=code_interpreter_tools,\n llm=llm,\n llm_name=llm_name,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=app.config[\"CODE_EXECUTION_MODE\"],\n )\n # Find the mainstay message list from leaf to root\n activated_message_list = message_pool.get_activated_message_list(\n user_id, chat_id, default_value=list(),\n parent_message_id=parent_message_id\n )\n message_pool.load_agent_memory_from_list(interaction_executor.memory,\n activated_message_list)\n human_message_id = message_id_register.add_variable(user_intent)\n ai_message_id = message_id_register.add_variable(\"\")\n return stream_with_context(\n Response(\n single_round_chat_with_agent_streaming(\n interaction_executor=interaction_executor,\n user_intent=user_intent,\n human_message_id=human_message_id,\n ai_message_id=ai_message_id,\n user_id=user_id,\n chat_id=chat_id,\n message_list=activated_message_list,\n parent_message_id=parent_message_id,\n llm_name=llm_name,\n stream_handler=stream_handler,\n app_type=\"copilot\"\n ),\n content_type=\"application/json\",\n )\n )\n\n except Exception as e:\n# ... truncated ...","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":true} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.create_interaction_executor","uri":"program://OpenAgents/function/backend.api.chat_copilot.create_interaction_executor#L36-L313","kind":"function","name":"create_interaction_executor","path":"backend/api/chat_copilot.py","language":"python","start_line":36,"end_line":313,"context_start_line":16,"context_end_line":333,"code":"from backend.utils.utils import create_personal_folder\nfrom backend.utils.charts import polish_echarts\nfrom backend.utils.streaming import (\n single_round_chat_with_executor,\n single_round_chat_with_agent_streaming,\n)\nfrom backend.utils.utils import get_data_summary_cls\nfrom backend.schemas import OVERLOAD, UNAUTH, NEED_CONTINUE_MODEL\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks import AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.data_model import DatabaseDataModel, DataModel, JsonDataModel, \\\n TableDataModel\nfrom real_agents.adapters.executors import ChatExecutor\nfrom real_agents.adapters.interactive_executor import initialize_agent\nfrom real_agents.data_agent import CodeGenerationExecutor, KaggleDataLoadingExecutor\nfrom real_agents.adapters.memory import ConversationReActBufferMemory, \\\n ReadOnlySharedStringMemory\n\n\ndef create_interaction_executor(\n grounding_source_dict: Dict[str, DataModel],\n code_interpreter_languages: List[str],\n code_interpreter_tools: List[str],\n llm: BaseLanguageModel,\n llm_name: str,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n) -> AgentExecutor:\n \"\"\"Creates an agent executor for interaction.\n\n Args:\n grounding_source_dict: A dict of grounding source filepath and data.\n code_interpreter_languages: A string to indicate the programming language to use.\n code_interpreter_tools: A list of augmented data tools.\n llm: A llm model.\n llm_name: A string llm name.\n user_id: A string of user id.\n chat_id: A string chat id.\n code_execution_mode: A string indicating where code is executed.\n\n Returns:\n An agent executor.\n\n \"\"\"\n # Initialize Memory\n memory = ConversationReActBufferMemory(\n memory_key=\"chat_history\", return_messages=True, llm=llm, max_token_limit=3500\n )\n read_only_memory = ReadOnlySharedStringMemory(memory=memory)\n\n # Initialize tools(executors)\n basic_chat_executor = ChatExecutor()\n python_code_generation_executor = CodeGenerationExecutor(\n programming_language=\"python\", memory=read_only_memory)\n sql_code_generation_executor = CodeGenerationExecutor(programming_language=\"sql\",\n memory=read_only_memory)\n echart_code_generation_executor = CodeGenerationExecutor(\n programming_language=\"python\", memory=read_only_memory, usage=\"echarts\"\n )\n kaggle_data_loading_executor = KaggleDataLoadingExecutor()\n\n def run_python_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n # Only TableDataModel are allowed as input to python\n # input_grounding_source = [gs for _, gs in grounding_source_dict.items()\n # if isinstance(gs, TableDataModel)]\n input_grounding_source = [gs for gs in grounding_source_dict.values()]\n # Get the result\n results = python_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PythonCodeBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n if results[\"result\"][\"result\"] is not None:\n raw_output = results[\"result\"][\"result\"]\n elif results[\"result\"][\"stdout\"] != \"\":\n raw_output = results[\"result\"][\"stdout\"]\n else:\n raw_output = \"\"\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": raw_output,\n \"images\": results[\"result\"][\"outputs\"] if \".show()\" in results[\n \"intermediate_steps\"] else [],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"images\"],\n )\n else:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"PythonCodeBuilder error({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_sql_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n\n def convert_grounding_source_as_db(\n grounding_source_dict: Dict[str, DataModel]\n ) -> Union[List[TableDataModel], DatabaseDataModel]:\n db_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, DatabaseDataModel)\n ]\n table_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)\n ]\n assert len(db_grounding_source) <= 1\n if len(table_grounding_source) == 0:\n # Only DatabaseDataModel. Assume there is at least one grounding\n # source\n return db_grounding_source[0]\n else:\n for t_gs in table_grounding_source:\n if len(db_grounding_source) == 0:\n # No DatabaseDataModel, then convert the first TableModel\n # into DatabaseDataModel.\n if t_gs.db_view is None:\n t_gs.set_db_view(\n DatabaseDataModel.from_table_data_model(t_gs))\n db_gs = t_gs.db_view\n db_grounding_source.append(db_gs)\n else:\n # Insert TableDataModel into the existing DatabaseDataModel\n db_gs = db_grounding_source[0]\n db_gs.insert_table_data_model(t_gs)\n return db_gs\n\n input_grounding_source = convert_grounding_source_as_db(\n grounding_source_dict)\n results = sql_code_generation_executor.run(\n user_intent=term,\n grounding_source=input_grounding_source,\n llm=llm,\n )\n\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n observation = JsonDataModel.from_raw_data({\n \"success\": True,\n \"result\": results[\"result\"][\"result\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n else:\n observation = JsonDataModel.from_raw_data({\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_echarts_interactive_plotter(term: str) -> Union[Dict, DataModel]:\n try:\n input_grounding_source = [gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)]\n results = echart_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PlotInteractivePlotter results({llm})\").debug(\n results)\n\n if results[\"result\"][\"success\"]:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"\",\n \"echarts\": polish_echarts(results[\"result\"][\"stdout\"]),\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"result\", \"echarts\"],\n )\n else:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"PlotInteractivePlotter error({llm})\").error(str(e))\n\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_kaggle_data_loader(term: str) -> Union[Dict, DataModel]:\n try:\n results = kaggle_data_loading_executor.run(\n user_intent=term,\n llm=llm,\n )\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").debug(results)\n\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"kaggle_action\": results[\"kaggle_action\"],\n \"kaggle_output_info\": results[\"kaggle_output_info\"],\n },\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n tool_dict = {\n \"PythonCodeBuilder\": Tool(\n name=\"PythonCodeBuilder\",\n func=run_python_code_builder,\n description=\"\"\"\nDescription: This tool adeptly turns your textual problem or query into Python code & execute it to get results. It shines when dealing with mathematics, data manipulation tasks, general computational problems and basic visualization like matplotlib. Please note it does not generate database queries.\nInput: A natural language problem or question.\nOutput: A Python program + its execution result to solve the presented problem or answer the question.\nNote: The tool MUST be used whenever you want to generate & execute Python code.\n \"\"\",\n ),\n \"SQLQueryBuilder\": Tool(\n name=\"SQLQueryBuilder\",\n func=run_sql_code_builder,\n description=\"\"\"\nDescription: Specialized for database tasks, this tool converts your natural language query into SQL code & execute it to get results. It's particularly suited for creating database queries, but it doesn't solve mathematical problems or perform data manipulations outside the SQL context. Be sure to specify the table name for successful operation.\nInput: A natural language query related to database operations, along with the name of the table on which the query will operate.\nOutput: A SQL program, ready to execute on the specified database table, and its execution result.\nNote: It is ALWAYS preferable to use the tool whenever you want to generate SQL query & execute the SQL query.\n \"\"\",\n ),\n \"Echarts\": Tool(\n name=\"Echarts\",\n func=run_echarts_interactive_plotter,\n description=\"\"\"\nDescription: Dive into the world of data visualization with this specialized Echarts tool. It takes your data table and creates Echarts code & show Echarts for four distinct chart types: scatter, bar, line, and pie, selecting the most appropriate labels and titles.\nInput: A natural language query detailing your desired visualization, no other words.\nOutput: An Echarts script, specifically tailored for your data, that generates an interactive chart upon execution.\nNote: Currently, this tool supports only the listed chart types. Please ensure your desired visualization aligns with these options to experience the full capabilities of this dynamic Echarts tool.\"\"\",\n ),\n \"KaggleDataLoader\": Tool(\n name=\"KaggleDataLoader\",\n func=run_kaggle_data_loader,\n description=\"\"\"\nDescription: The KaggleDataLoader tool allows you to seamlessly connect to Kaggle datasets. It allows you to load specific datasets by providing the exact dataset path, or it can aid in the search and retrieval of datasets based on the information given in your user input, providing you with a vast array of data sources for your projects.\nInput: A natural language intent that may mention path of the Kaggle dataset, or some keyword or other relevant information about the dataset you are interested in.\nOutput: The action you want to perform, and the extracted path or searched relevant datasets depending on your input.\n\"\"\",\n ),\n }\n # Data profiling is not activated in agent\n IGNORE_TOOLS = [\"DataProfiling\"]\n # Activate tools according to the user selection\n tools = [tool_dict[lang[\"name\"]] for lang in code_interpreter_languages]\n for tool in code_interpreter_tools:\n if tool[\"name\"] not in IGNORE_TOOLS:\n tools.append(tool_dict[tool[\"name\"]])\n\n # Build the chat agent with LLM and tools\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_agent(tools, llm, continue_model, memory=memory,\n verbose=True)\n return interaction_executor\n\n\n@app.route(\"/api/chat\", methods=[\"POST\"])\ndef chat() -> Response | Dict:\n \"\"\"Returns the chat response of data agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = int(request_json[\"parent_message_id\"])\n code_interpreter_languages = request_json.get(\"code_interpreter_languages\", [])\n code_interpreter_tools = request_json.get(\"code_interpreter_tools\", [])\n api_call = request_json.get(\"api_call\", None)\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.7)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.chat","uri":"program://OpenAgents/function/backend.api.chat_copilot.chat#L317-L435","kind":"function","name":"chat","path":"backend/api/chat_copilot.py","language":"python","start_line":317,"end_line":435,"context_start_line":297,"context_end_line":435,"code":"Output: The action you want to perform, and the extracted path or searched relevant datasets depending on your input.\n\"\"\",\n ),\n }\n # Data profiling is not activated in agent\n IGNORE_TOOLS = [\"DataProfiling\"]\n # Activate tools according to the user selection\n tools = [tool_dict[lang[\"name\"]] for lang in code_interpreter_languages]\n for tool in code_interpreter_tools:\n if tool[\"name\"] not in IGNORE_TOOLS:\n tools.append(tool_dict[tool[\"name\"]])\n\n # Build the chat agent with LLM and tools\n continue_model = llm_name if llm_name in NEED_CONTINUE_MODEL else None\n interaction_executor = initialize_agent(tools, llm, continue_model, memory=memory,\n verbose=True)\n return interaction_executor\n\n\n@app.route(\"/api/chat\", methods=[\"POST\"])\ndef chat() -> Response | Dict:\n \"\"\"Returns the chat response of data agent.\"\"\"\n try:\n # Get request parameters\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n user_intent = request_json[\"user_intent\"]\n parent_message_id = int(request_json[\"parent_message_id\"])\n code_interpreter_languages = request_json.get(\"code_interpreter_languages\", [])\n code_interpreter_tools = request_json.get(\"code_interpreter_tools\", [])\n api_call = request_json.get(\"api_call\", None)\n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.7)\n stop_words = [\"[RESPONSE_BEGIN]\", \"TOOL RESPONSE\"]\n kwargs = {\n \"temperature\": temperature,\n \"stop\": stop_words,\n }\n\n # Get language model\n stream_handler = AgentStreamingStdOutCallbackHandler()\n llm = get_llm(llm_name, **kwargs)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Request json\").debug(request_json)\n\n if api_call:\n # Load/init grounding source\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n\n # Find the mainstay message list from leaf to root\n activated_message_list = message_pool.get_activated_message_list(\n user_id, chat_id, default_value=list(),\n parent_message_id=parent_message_id\n )\n assert api_call[\"api_name\"] == \"DataProfiling\"\n ai_message_id = message_id_register.add_variable(\"\")\n file_node = api_call[\"args\"][\"activated_file\"]\n\n folder = create_personal_folder(user_id)\n file_path = _get_file_path_from_node(folder, file_node)\n executor = get_data_summary_cls(file_path)()\n gs = grounding_source_dict[file_path]\n return stream_with_context(\n Response(\n single_round_chat_with_executor(\n executor,\n user_intent=gs,\n human_message_id=None,\n ai_message_id=ai_message_id,\n user_id=DEFAULT_USER_ID,\n chat_id=api_call[\"args\"][\"chat_id\"],\n message_list=activated_message_list,\n parent_message_id=api_call[\"args\"][\"parent_message_id\"],\n llm=llm,\n app_type=\"copilot\",\n ),\n content_type=\"application/json\",\n )\n )\n else:\n # Load/init grounding source\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n # Build executor and run chat\n interaction_executor = create_interaction_executor(\n grounding_source_dict=grounding_source_dict,\n code_interpreter_languages=code_interpreter_languages,\n code_interpreter_tools=code_interpreter_tools,\n llm=llm,\n llm_name=llm_name,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=app.config[\"CODE_EXECUTION_MODE\"],\n )\n # Find the mainstay message list from leaf to root\n activated_message_list = message_pool.get_activated_message_list(\n user_id, chat_id, default_value=list(),\n parent_message_id=parent_message_id\n )\n message_pool.load_agent_memory_from_list(interaction_executor.memory,\n activated_message_list)\n human_message_id = message_id_register.add_variable(user_intent)\n ai_message_id = message_id_register.add_variable(\"\")\n return stream_with_context(\n Response(\n single_round_chat_with_agent_streaming(\n interaction_executor=interaction_executor,\n user_intent=user_intent,\n human_message_id=human_message_id,\n ai_message_id=ai_message_id,\n user_id=user_id,\n chat_id=chat_id,\n message_list=activated_message_list,\n parent_message_id=parent_message_id,\n llm_name=llm_name,\n stream_handler=stream_handler,\n app_type=\"copilot\"\n ),\n content_type=\"application/json\",\n )\n )\n\n except Exception as e:\n try:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/chat\",\n msg_head=\"Chat error\").error(str(e))\n import traceback\n\n traceback.print_exc()\n except:\n # if user_id & chat_id not found, unauth err\n return Response(response=None, status=f\"{UNAUTH} Invalid Authentication\")\n return Response(response=None,\n status=f\"{OVERLOAD} Server is currently overloaded\")","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.run_python_code_builder","uri":"program://OpenAgents/function/backend.api.chat_copilot.run_python_code_builder#L79-L129","kind":"function","name":"run_python_code_builder","path":"backend/api/chat_copilot.py","language":"python","start_line":79,"end_line":129,"context_start_line":59,"context_end_line":149,"code":" An agent executor.\n\n \"\"\"\n # Initialize Memory\n memory = ConversationReActBufferMemory(\n memory_key=\"chat_history\", return_messages=True, llm=llm, max_token_limit=3500\n )\n read_only_memory = ReadOnlySharedStringMemory(memory=memory)\n\n # Initialize tools(executors)\n basic_chat_executor = ChatExecutor()\n python_code_generation_executor = CodeGenerationExecutor(\n programming_language=\"python\", memory=read_only_memory)\n sql_code_generation_executor = CodeGenerationExecutor(programming_language=\"sql\",\n memory=read_only_memory)\n echart_code_generation_executor = CodeGenerationExecutor(\n programming_language=\"python\", memory=read_only_memory, usage=\"echarts\"\n )\n kaggle_data_loading_executor = KaggleDataLoadingExecutor()\n\n def run_python_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n # Only TableDataModel are allowed as input to python\n # input_grounding_source = [gs for _, gs in grounding_source_dict.items()\n # if isinstance(gs, TableDataModel)]\n input_grounding_source = [gs for gs in grounding_source_dict.values()]\n # Get the result\n results = python_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PythonCodeBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n if results[\"result\"][\"result\"] is not None:\n raw_output = results[\"result\"][\"result\"]\n elif results[\"result\"][\"stdout\"] != \"\":\n raw_output = results[\"result\"][\"stdout\"]\n else:\n raw_output = \"\"\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": raw_output,\n \"images\": results[\"result\"][\"outputs\"] if \".show()\" in results[\n \"intermediate_steps\"] else [],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"images\"],\n )\n else:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"PythonCodeBuilder error({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_sql_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n\n def convert_grounding_source_as_db(\n grounding_source_dict: Dict[str, DataModel]\n ) -> Union[List[TableDataModel], DatabaseDataModel]:\n db_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, DatabaseDataModel)\n ]\n table_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)\n ]\n assert len(db_grounding_source) <= 1\n if len(table_grounding_source) == 0:\n # Only DatabaseDataModel. Assume there is at least one grounding\n # source\n return db_grounding_source[0]","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.run_sql_code_builder","uri":"program://OpenAgents/function/backend.api.chat_copilot.run_sql_code_builder#L131-L194","kind":"function","name":"run_sql_code_builder","path":"backend/api/chat_copilot.py","language":"python","start_line":131,"end_line":194,"context_start_line":111,"context_end_line":214,"code":" \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"images\"],\n )\n else:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"PythonCodeBuilder error({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_sql_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n\n def convert_grounding_source_as_db(\n grounding_source_dict: Dict[str, DataModel]\n ) -> Union[List[TableDataModel], DatabaseDataModel]:\n db_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, DatabaseDataModel)\n ]\n table_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)\n ]\n assert len(db_grounding_source) <= 1\n if len(table_grounding_source) == 0:\n # Only DatabaseDataModel. Assume there is at least one grounding\n # source\n return db_grounding_source[0]\n else:\n for t_gs in table_grounding_source:\n if len(db_grounding_source) == 0:\n # No DatabaseDataModel, then convert the first TableModel\n # into DatabaseDataModel.\n if t_gs.db_view is None:\n t_gs.set_db_view(\n DatabaseDataModel.from_table_data_model(t_gs))\n db_gs = t_gs.db_view\n db_grounding_source.append(db_gs)\n else:\n # Insert TableDataModel into the existing DatabaseDataModel\n db_gs = db_grounding_source[0]\n db_gs.insert_table_data_model(t_gs)\n return db_gs\n\n input_grounding_source = convert_grounding_source_as_db(\n grounding_source_dict)\n results = sql_code_generation_executor.run(\n user_intent=term,\n grounding_source=input_grounding_source,\n llm=llm,\n )\n\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n observation = JsonDataModel.from_raw_data({\n \"success\": True,\n \"result\": results[\"result\"][\"result\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n else:\n observation = JsonDataModel.from_raw_data({\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_echarts_interactive_plotter(term: str) -> Union[Dict, DataModel]:\n try:\n input_grounding_source = [gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)]\n results = echart_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PlotInteractivePlotter results({llm})\").debug(\n results)\n\n if results[\"result\"][\"success\"]:\n results = JsonDataModel.from_raw_data(","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.run_echarts_interactive_plotter","uri":"program://OpenAgents/function/backend.api.chat_copilot.run_echarts_interactive_plotter#L196-L236","kind":"function","name":"run_echarts_interactive_plotter","path":"backend/api/chat_copilot.py","language":"python","start_line":196,"end_line":236,"context_start_line":176,"context_end_line":256,"code":" if results[\"result\"][\"success\"]:\n observation = JsonDataModel.from_raw_data({\n \"success\": True,\n \"result\": results[\"result\"][\"result\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n else:\n observation = JsonDataModel.from_raw_data({\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_echarts_interactive_plotter(term: str) -> Union[Dict, DataModel]:\n try:\n input_grounding_source = [gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)]\n results = echart_code_generation_executor.run(\n user_intent=term,\n llm=llm,\n grounding_source=input_grounding_source,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n\n logger.bind(msg_head=f\"PlotInteractivePlotter results({llm})\").debug(\n results)\n\n if results[\"result\"][\"success\"]:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"result\": \"\",\n \"echarts\": polish_echarts(results[\"result\"][\"stdout\"]),\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"result\", \"echarts\"],\n )\n else:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"PlotInteractivePlotter error({llm})\").error(str(e))\n\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_kaggle_data_loader(term: str) -> Union[Dict, DataModel]:\n try:\n results = kaggle_data_loading_executor.run(\n user_intent=term,\n llm=llm,\n )\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").debug(results)\n\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"kaggle_action\": results[\"kaggle_action\"],\n \"kaggle_output_info\": results[\"kaggle_output_info\"],\n },\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").error(str(e))\n","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.run_kaggle_data_loader","uri":"program://OpenAgents/function/backend.api.chat_copilot.run_kaggle_data_loader#L238-L259","kind":"function","name":"run_kaggle_data_loader","path":"backend/api/chat_copilot.py","language":"python","start_line":238,"end_line":259,"context_start_line":218,"context_end_line":279,"code":" \"echarts\": polish_echarts(results[\"result\"][\"stdout\"]),\n \"intermediate_steps\": results[\"intermediate_steps\"],\n },\n filter_keys=[\"result\", \"echarts\"],\n )\n else:\n results = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"PlotInteractivePlotter error({llm})\").error(str(e))\n\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_kaggle_data_loader(term: str) -> Union[Dict, DataModel]:\n try:\n results = kaggle_data_loading_executor.run(\n user_intent=term,\n llm=llm,\n )\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").debug(results)\n\n results = JsonDataModel.from_raw_data(\n {\n \"success\": True,\n \"kaggle_action\": results[\"kaggle_action\"],\n \"kaggle_output_info\": results[\"kaggle_output_info\"],\n },\n )\n return results\n except Exception as e:\n logger.bind(msg_head=f\"KaggleDataLoader results({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n tool_dict = {\n \"PythonCodeBuilder\": Tool(\n name=\"PythonCodeBuilder\",\n func=run_python_code_builder,\n description=\"\"\"\nDescription: This tool adeptly turns your textual problem or query into Python code & execute it to get results. It shines when dealing with mathematics, data manipulation tasks, general computational problems and basic visualization like matplotlib. Please note it does not generate database queries.\nInput: A natural language problem or question.\nOutput: A Python program + its execution result to solve the presented problem or answer the question.\nNote: The tool MUST be used whenever you want to generate & execute Python code.\n \"\"\",\n ),\n \"SQLQueryBuilder\": Tool(\n name=\"SQLQueryBuilder\",\n func=run_sql_code_builder,\n description=\"\"\"\nDescription: Specialized for database tasks, this tool converts your natural language query into SQL code & execute it to get results. It's particularly suited for creating database queries, but it doesn't solve mathematical problems or perform data manipulations outside the SQL context. Be sure to specify the table name for successful operation.\nInput: A natural language query related to database operations, along with the name of the table on which the query will operate.\nOutput: A SQL program, ready to execute on the specified database table, and its execution result.\nNote: It is ALWAYS preferable to use the tool whenever you want to generate SQL query & execute the SQL query.","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.chat_copilot.convert_grounding_source_as_db","uri":"program://OpenAgents/function/backend.api.chat_copilot.convert_grounding_source_as_db#L134-L164","kind":"function","name":"convert_grounding_source_as_db","path":"backend/api/chat_copilot.py","language":"python","start_line":134,"end_line":164,"context_start_line":114,"context_end_line":184,"code":" )\n else:\n observation = JsonDataModel.from_raw_data(\n {\n \"success\": False,\n \"result\": results[\"result\"][\"error_message\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n }\n )\n return observation\n except Exception as e:\n logger.bind(msg_head=f\"PythonCodeBuilder error({llm})\").error(str(e))\n\n traceback.print_exc()\n results = basic_chat_executor.run(user_intent=term, llm=llm)\n return results[\"result\"]\n\n def run_sql_code_builder(term: str) -> Union[Dict, DataModel]:\n try:\n\n def convert_grounding_source_as_db(\n grounding_source_dict: Dict[str, DataModel]\n ) -> Union[List[TableDataModel], DatabaseDataModel]:\n db_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, DatabaseDataModel)\n ]\n table_grounding_source = [\n gs for _, gs in grounding_source_dict.items() if\n isinstance(gs, TableDataModel)\n ]\n assert len(db_grounding_source) <= 1\n if len(table_grounding_source) == 0:\n # Only DatabaseDataModel. Assume there is at least one grounding\n # source\n return db_grounding_source[0]\n else:\n for t_gs in table_grounding_source:\n if len(db_grounding_source) == 0:\n # No DatabaseDataModel, then convert the first TableModel\n # into DatabaseDataModel.\n if t_gs.db_view is None:\n t_gs.set_db_view(\n DatabaseDataModel.from_table_data_model(t_gs))\n db_gs = t_gs.db_view\n db_grounding_source.append(db_gs)\n else:\n # Insert TableDataModel into the existing DatabaseDataModel\n db_gs = db_grounding_source[0]\n db_gs.insert_table_data_model(t_gs)\n return db_gs\n\n input_grounding_source = convert_grounding_source_as_db(\n grounding_source_dict)\n results = sql_code_generation_executor.run(\n user_intent=term,\n grounding_source=input_grounding_source,\n llm=llm,\n )\n\n logger.bind(msg_head=f\"SQLQueryBuilder results({llm})\").debug(results)\n\n if results[\"result\"][\"success\"]:\n observation = JsonDataModel.from_raw_data({\n \"success\": True,\n \"result\": results[\"result\"][\"result\"],\n \"intermediate_steps\": results[\"intermediate_steps\"],\n })\n else:\n observation = JsonDataModel.from_raw_data({\n \"success\": False,","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_actions","uri":"program://OpenAgents/module/backend.api.webot_actions#L1-L63","kind":"module","name":"backend.api.webot_actions","path":"backend/api/webot_actions.py","language":"python","start_line":1,"end_line":63,"context_start_line":1,"context_end_line":63,"code":"from flask import request, jsonify, Response\n\nfrom backend.api.chat_webot import get_webot_from_redis, save_webot_to_redis\nfrom backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.language_model import get_llm\n\n\n@app.route(\"/api/webot/action\", methods=[\"POST\"])\ndef get_action() -> Response:\n \"\"\"Gets the next action to take for a given the current page HTML.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n # Get request parameters\n request_json = request.get_json()\n processed_html = request_json[\"processed_html\"]\n llm = get_llm(\"gpt-4\")\n result = webot.run(processed_html, llm=llm)\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"action_response\": result,\n })\n\n\n@app.route(\"/api/webot/interrupt\", methods=[\"POST\"])\ndef interrupt() -> Response:\n \"\"\"Interrupts the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n interrupt = request_json[\"interrupt\"]\n if interrupt:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"interrupt\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No interrupt signal received.\"})\n\n\n@app.route(\"/api/webot/error\", methods=[\"POST\"])\ndef error() -> Response:\n \"\"\"Appends action 'error' to the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n error = request_json[\"error\"]\n if error:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"error\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No error signal received.\"})","source_hash":"390bd768c07e3791453ef84e5dd08122b3bbbe6c1b941db336ed6e5b08fb457d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_actions.get_action","uri":"program://OpenAgents/function/backend.api.webot_actions.get_action#L10-L27","kind":"function","name":"get_action","path":"backend/api/webot_actions.py","language":"python","start_line":10,"end_line":27,"context_start_line":1,"context_end_line":47,"code":"from flask import request, jsonify, Response\n\nfrom backend.api.chat_webot import get_webot_from_redis, save_webot_to_redis\nfrom backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.language_model import get_llm\n\n\n@app.route(\"/api/webot/action\", methods=[\"POST\"])\ndef get_action() -> Response:\n \"\"\"Gets the next action to take for a given the current page HTML.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n # Get request parameters\n request_json = request.get_json()\n processed_html = request_json[\"processed_html\"]\n llm = get_llm(\"gpt-4\")\n result = webot.run(processed_html, llm=llm)\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"action_response\": result,\n })\n\n\n@app.route(\"/api/webot/interrupt\", methods=[\"POST\"])\ndef interrupt() -> Response:\n \"\"\"Interrupts the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n interrupt = request_json[\"interrupt\"]\n if interrupt:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"interrupt\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No interrupt signal received.\"})\n\n","source_hash":"390bd768c07e3791453ef84e5dd08122b3bbbe6c1b941db336ed6e5b08fb457d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_actions.interrupt","uri":"program://OpenAgents/function/backend.api.webot_actions.interrupt#L31-L45","kind":"function","name":"interrupt","path":"backend/api/webot_actions.py","language":"python","start_line":31,"end_line":45,"context_start_line":11,"context_end_line":63,"code":" \"\"\"Gets the next action to take for a given the current page HTML.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n # Get request parameters\n request_json = request.get_json()\n processed_html = request_json[\"processed_html\"]\n llm = get_llm(\"gpt-4\")\n result = webot.run(processed_html, llm=llm)\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"action_response\": result,\n })\n\n\n@app.route(\"/api/webot/interrupt\", methods=[\"POST\"])\ndef interrupt() -> Response:\n \"\"\"Interrupts the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n interrupt = request_json[\"interrupt\"]\n if interrupt:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"interrupt\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No interrupt signal received.\"})\n\n\n@app.route(\"/api/webot/error\", methods=[\"POST\"])\ndef error() -> Response:\n \"\"\"Appends action 'error' to the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n error = request_json[\"error\"]\n if error:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"error\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No error signal received.\"})","source_hash":"390bd768c07e3791453ef84e5dd08122b3bbbe6c1b941db336ed6e5b08fb457d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_actions.error","uri":"program://OpenAgents/function/backend.api.webot_actions.error#L49-L63","kind":"function","name":"error","path":"backend/api/webot_actions.py","language":"python","start_line":49,"end_line":63,"context_start_line":29,"context_end_line":63,"code":"\n@app.route(\"/api/webot/interrupt\", methods=[\"POST\"])\ndef interrupt() -> Response:\n \"\"\"Interrupts the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n interrupt = request_json[\"interrupt\"]\n if interrupt:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"interrupt\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No interrupt signal received.\"})\n\n\n@app.route(\"/api/webot/error\", methods=[\"POST\"])\ndef error() -> Response:\n \"\"\"Appends action 'error' to the current webot.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n error = request_json[\"error\"]\n if error:\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n webot.actions_taken.append(\"error\")\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })\n return jsonify({\"message\": \"No error signal received.\"})","source_hash":"390bd768c07e3791453ef84e5dd08122b3bbbe6c1b941db336ed6e5b08fb457d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.language_model","uri":"program://OpenAgents/module/backend.api.language_model#L1-L52","kind":"module","name":"backend.api.language_model","path":"backend/api/language_model.py","language":"python","start_line":1,"end_line":52,"context_start_line":1,"context_end_line":52,"code":"import os\n\nfrom backend.app import app\nfrom real_agents.adapters.models import ChatOpenAI, ChatAnthropic\nfrom real_agents.adapters.llm import BaseLanguageModel\n\nLLAMA_DIR = \"PATH_TO_LLAMA_DIR\"\n\n\n@app.route(\"/api/llm_list\", methods=[\"POST\"])\ndef get_llm_list():\n \"\"\"Gets the whole llm list.\"\"\"\n return [\n {\"id\": llm, \"name\": llm} for llm in [\n \"gpt-3.5-turbo-16k\",\n \"gpt-4\",\n \"claude-v1\",\n \"claude-2\",\n \"lemur-chat\"\n ]\n ]\n\n\ndef get_llm(llm_name: str, **kwargs) -> BaseLanguageModel:\n \"\"\"Gets the llm model by its name.\"\"\"\n if llm_name in [\"gpt-3.5-turbo-16k\", \"gpt-4\"]:\n return ChatOpenAI(\n model_name=llm_name,\n streaming=True,\n verbose=True,\n **kwargs\n )\n elif llm_name in [\"claude-v1\", \"claude-2\"]:\n anthropic_api_key = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\n return ChatAnthropic(\n model=llm_name,\n streaming=True,\n verbose=True,\n anthropic_api_key=anthropic_api_key,\n **kwargs,\n )\n elif llm_name == \"lemur-chat\":\n return ChatOpenAI(\n model_name=\"lemur-70b-chat-v1\",\n streaming=True,\n openai_api_base=\"https://model-api.xlang.ai/v1\",\n verbose=True,\n max_tokens=2048,\n **kwargs\n )\n else:\n raise ValueError(f\"llm_name {llm_name} not found\")","source_hash":"3f74dec87a124d3e466ae8340b95db7dd011dc44070dd12603a2db80b00ecf0d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.language_model.get_llm_list","uri":"program://OpenAgents/function/backend.api.language_model.get_llm_list#L11-L21","kind":"function","name":"get_llm_list","path":"backend/api/language_model.py","language":"python","start_line":11,"end_line":21,"context_start_line":1,"context_end_line":41,"code":"import os\n\nfrom backend.app import app\nfrom real_agents.adapters.models import ChatOpenAI, ChatAnthropic\nfrom real_agents.adapters.llm import BaseLanguageModel\n\nLLAMA_DIR = \"PATH_TO_LLAMA_DIR\"\n\n\n@app.route(\"/api/llm_list\", methods=[\"POST\"])\ndef get_llm_list():\n \"\"\"Gets the whole llm list.\"\"\"\n return [\n {\"id\": llm, \"name\": llm} for llm in [\n \"gpt-3.5-turbo-16k\",\n \"gpt-4\",\n \"claude-v1\",\n \"claude-2\",\n \"lemur-chat\"\n ]\n ]\n\n\ndef get_llm(llm_name: str, **kwargs) -> BaseLanguageModel:\n \"\"\"Gets the llm model by its name.\"\"\"\n if llm_name in [\"gpt-3.5-turbo-16k\", \"gpt-4\"]:\n return ChatOpenAI(\n model_name=llm_name,\n streaming=True,\n verbose=True,\n **kwargs\n )\n elif llm_name in [\"claude-v1\", \"claude-2\"]:\n anthropic_api_key = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\n return ChatAnthropic(\n model=llm_name,\n streaming=True,\n verbose=True,\n anthropic_api_key=anthropic_api_key,\n **kwargs,\n )","source_hash":"3f74dec87a124d3e466ae8340b95db7dd011dc44070dd12603a2db80b00ecf0d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.language_model.get_llm","uri":"program://OpenAgents/function/backend.api.language_model.get_llm#L24-L52","kind":"function","name":"get_llm","path":"backend/api/language_model.py","language":"python","start_line":24,"end_line":52,"context_start_line":4,"context_end_line":52,"code":"from real_agents.adapters.models import ChatOpenAI, ChatAnthropic\nfrom real_agents.adapters.llm import BaseLanguageModel\n\nLLAMA_DIR = \"PATH_TO_LLAMA_DIR\"\n\n\n@app.route(\"/api/llm_list\", methods=[\"POST\"])\ndef get_llm_list():\n \"\"\"Gets the whole llm list.\"\"\"\n return [\n {\"id\": llm, \"name\": llm} for llm in [\n \"gpt-3.5-turbo-16k\",\n \"gpt-4\",\n \"claude-v1\",\n \"claude-2\",\n \"lemur-chat\"\n ]\n ]\n\n\ndef get_llm(llm_name: str, **kwargs) -> BaseLanguageModel:\n \"\"\"Gets the llm model by its name.\"\"\"\n if llm_name in [\"gpt-3.5-turbo-16k\", \"gpt-4\"]:\n return ChatOpenAI(\n model_name=llm_name,\n streaming=True,\n verbose=True,\n **kwargs\n )\n elif llm_name in [\"claude-v1\", \"claude-2\"]:\n anthropic_api_key = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\n return ChatAnthropic(\n model=llm_name,\n streaming=True,\n verbose=True,\n anthropic_api_key=anthropic_api_key,\n **kwargs,\n )\n elif llm_name == \"lemur-chat\":\n return ChatOpenAI(\n model_name=\"lemur-70b-chat-v1\",\n streaming=True,\n openai_api_base=\"https://model-api.xlang.ai/v1\",\n verbose=True,\n max_tokens=2048,\n **kwargs\n )\n else:\n raise ValueError(f\"llm_name {llm_name} not found\")","source_hash":"3f74dec87a124d3e466ae8340b95db7dd011dc44070dd12603a2db80b00ecf0d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_instructions","uri":"program://OpenAgents/module/backend.api.webot_instructions#L1-L41","kind":"module","name":"backend.api.webot_instructions","path":"backend/api/webot_instructions.py","language":"python","start_line":1,"end_line":41,"context_start_line":1,"context_end_line":41,"code":"from flask import request, jsonify, Response\n\nfrom backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.chat_webot import get_webot_from_redis, \\\n get_webot_status_from_redis, reset_webot_status\n\n\n@app.route(\"/api/webot/instructions\", methods=[\"POST\"])\ndef get_instruction() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"instructions\": webot.instruction\n })\n\n\n@app.route(\"/api/webot/webot_status\", methods=[\"POST\"])\ndef get_webot_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot_status_json = get_webot_status_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify(webot_status_json) if webot_status_json is not None else jsonify(\n {\"webot_status\": None, \"url\": None})\n\n\n@app.route(\"/api/webot/reset_status\", methods=[\"POST\"])\ndef reset_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })","source_hash":"ce61740baa6cd8cc7b367c48fb54842362936ff624c8002a0cbad2c0afa33042","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_instructions.get_instruction","uri":"program://OpenAgents/function/backend.api.webot_instructions.get_instruction#L10-L19","kind":"function","name":"get_instruction","path":"backend/api/webot_instructions.py","language":"python","start_line":10,"end_line":19,"context_start_line":1,"context_end_line":39,"code":"from flask import request, jsonify, Response\n\nfrom backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.chat_webot import get_webot_from_redis, \\\n get_webot_status_from_redis, reset_webot_status\n\n\n@app.route(\"/api/webot/instructions\", methods=[\"POST\"])\ndef get_instruction() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"instructions\": webot.instruction\n })\n\n\n@app.route(\"/api/webot/webot_status\", methods=[\"POST\"])\ndef get_webot_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot_status_json = get_webot_status_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify(webot_status_json) if webot_status_json is not None else jsonify(\n {\"webot_status\": None, \"url\": None})\n\n\n@app.route(\"/api/webot/reset_status\", methods=[\"POST\"])\ndef reset_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,","source_hash":"ce61740baa6cd8cc7b367c48fb54842362936ff624c8002a0cbad2c0afa33042","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_instructions.get_webot_status","uri":"program://OpenAgents/function/backend.api.webot_instructions.get_webot_status#L23-L29","kind":"function","name":"get_webot_status","path":"backend/api/webot_instructions.py","language":"python","start_line":23,"end_line":29,"context_start_line":3,"context_end_line":41,"code":"from backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.chat_webot import get_webot_from_redis, \\\n get_webot_status_from_redis, reset_webot_status\n\n\n@app.route(\"/api/webot/instructions\", methods=[\"POST\"])\ndef get_instruction() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"instructions\": webot.instruction\n })\n\n\n@app.route(\"/api/webot/webot_status\", methods=[\"POST\"])\ndef get_webot_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot_status_json = get_webot_status_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify(webot_status_json) if webot_status_json is not None else jsonify(\n {\"webot_status\": None, \"url\": None})\n\n\n@app.route(\"/api/webot/reset_status\", methods=[\"POST\"])\ndef reset_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })","source_hash":"ce61740baa6cd8cc7b367c48fb54842362936ff624c8002a0cbad2c0afa33042","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.webot_instructions.reset_status","uri":"program://OpenAgents/function/backend.api.webot_instructions.reset_status#L33-L41","kind":"function","name":"reset_status","path":"backend/api/webot_instructions.py","language":"python","start_line":33,"end_line":41,"context_start_line":13,"context_end_line":41,"code":" chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"instructions\": webot.instruction\n })\n\n\n@app.route(\"/api/webot/webot_status\", methods=[\"POST\"])\ndef get_webot_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot_status_json = get_webot_status_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify(webot_status_json) if webot_status_json is not None else jsonify(\n {\"webot_status\": None, \"url\": None})\n\n\n@app.route(\"/api/webot/reset_status\", methods=[\"POST\"])\ndef reset_status() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n reset_webot_status(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n })","source_hash":"ce61740baa6cd8cc7b367c48fb54842362936ff624c8002a0cbad2c0afa33042","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.recommend","uri":"program://OpenAgents/module/backend.api.recommend#L1-L56","kind":"module","name":"backend.api.recommend","path":"backend/api/recommend.py","language":"python","start_line":1,"end_line":56,"context_start_line":1,"context_end_line":56,"code":"from typing import Dict\nfrom flask import request, jsonify, Response\n\nfrom backend.main import message_pool\nfrom backend.app import app\nfrom backend.api.language_model import get_llm\nfrom backend.utils.utils import get_user_and_chat_id_from_request_json\nfrom real_agents.adapters.executors import QuestionSuggestionExecutor\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\n\n\n@app.route(\"/api/recommend\", methods=[\"POST\"])\ndef recommend() -> dict | Response:\n \"\"\"Recommends potential inputs for users. \"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n parent_message_id = int(request_json[\"parent_message_id\"])\n user_intent = request_json[\"user_intent\"]\n\n # Find the mainstat message list from leaf to root\n activated_message_list = message_pool.get_activated_message_list(\n user_id, chat_id, default_value=list(), parent_message_id=parent_message_id\n )\n chat_memory = ConversationReActBufferMemory(memory_key=\"chat_history\", return_messages=True)\n message_pool.load_agent_memory_from_list(chat_memory, activated_message_list)\n question_suggestion_executor = QuestionSuggestionExecutor()\n \n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.7)\n kwargs = {\n \"temperature\": temperature,\n }\n\n # Get language model\n llm = get_llm(llm_name, **kwargs)\n follow_questions = question_suggestion_executor.run(\n user_intent=user_intent,\n llm=llm,\n chat_memory=chat_memory,\n mode=\"chat_memory\",\n )\n\n return jsonify({\n \"recommend_questions\": follow_questions[\"questions\"], \n \"user_id\": user_id,\n \"chat_id\": chat_id,\n })\n except Exception as e:\n import traceback\n traceback.print_exc()\n return {\n \"recommend_questions\": [],\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }","source_hash":"263720024e29b26d363dcb715fe9664affdc43d5801020a87dbd0e34eb33c825","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.recommend.recommend","uri":"program://OpenAgents/function/backend.api.recommend.recommend#L13-L56","kind":"function","name":"recommend","path":"backend/api/recommend.py","language":"python","start_line":13,"end_line":56,"context_start_line":1,"context_end_line":56,"code":"from typing import Dict\nfrom flask import request, jsonify, Response\n\nfrom backend.main import message_pool\nfrom backend.app import app\nfrom backend.api.language_model import get_llm\nfrom backend.utils.utils import get_user_and_chat_id_from_request_json\nfrom real_agents.adapters.executors import QuestionSuggestionExecutor\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\n\n\n@app.route(\"/api/recommend\", methods=[\"POST\"])\ndef recommend() -> dict | Response:\n \"\"\"Recommends potential inputs for users. \"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n parent_message_id = int(request_json[\"parent_message_id\"])\n user_intent = request_json[\"user_intent\"]\n\n # Find the mainstat message list from leaf to root\n activated_message_list = message_pool.get_activated_message_list(\n user_id, chat_id, default_value=list(), parent_message_id=parent_message_id\n )\n chat_memory = ConversationReActBufferMemory(memory_key=\"chat_history\", return_messages=True)\n message_pool.load_agent_memory_from_list(chat_memory, activated_message_list)\n question_suggestion_executor = QuestionSuggestionExecutor()\n \n llm_name = request_json[\"llm_name\"]\n temperature = request_json.get(\"temperature\", 0.7)\n kwargs = {\n \"temperature\": temperature,\n }\n\n # Get language model\n llm = get_llm(llm_name, **kwargs)\n follow_questions = question_suggestion_executor.run(\n user_intent=user_intent,\n llm=llm,\n chat_memory=chat_memory,\n mode=\"chat_memory\",\n )\n\n return jsonify({\n \"recommend_questions\": follow_questions[\"questions\"], \n \"user_id\": user_id,\n \"chat_id\": chat_id,\n })\n except Exception as e:\n import traceback\n traceback.print_exc()\n return {\n \"recommend_questions\": [],\n \"user_id\": user_id,\n \"chat_id\": chat_id,\n }","source_hash":"263720024e29b26d363dcb715fe9664affdc43d5801020a87dbd0e34eb33c825","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.data_tools","uri":"program://OpenAgents/module/backend.api.data_tools#L1-L64","kind":"module","name":"backend.api.data_tools","path":"backend/api/data_tools.py","language":"python","start_line":1,"end_line":64,"context_start_line":1,"context_end_line":64,"code":"from typing import List\nfrom flask import jsonify\n\nfrom backend.app import app\n\nDATA_TOOLS = [\n {\n \"type\": \"language\",\n \"id\": \"1cea1f39-fe63-4b08-83d5-fa4c93db0c87\",\n \"name\": \"SQLQueryBuilder\",\n \"name_for_human\": \"SQL\",\n \"pretty_name_for_human\": \"SQL Query Generation\",\n \"icon\": \"\",\n \"description\": \"Using SQL as the programming language\",\n },\n {\n \"type\": \"language\",\n \"id\": \"0c135359-af7e-473b-8425-1393d2943b57\",\n \"name\": \"PythonCodeBuilder\",\n \"name_for_human\": \"Python\",\n \"pretty_name_for_human\": \"Python Code Generation\",\n \"icon\": \"\",\n \"description\": \"Using Python as the programming language\",\n },\n {\n \"type\": \"tool\",\n \"id\": \"a86aebe1-a780-4038-a333-fb2a9d2d25fc\",\n \"name\": \"Echarts\",\n \"name_for_human\": \"Echarts\",\n \"pretty_name_for_human\": \"Echarts\",\n \"icon\": \"\",\n \"description\": \"Enhancing the analyzing experience with interactive charts\",\n },\n {\n \"type\": \"tool\",\n \"id\": \"c7c826ba-5884-4e2b-b27c-fedea30c1749\",\n \"name\": \"KaggleDataLoader\",\n \"name_for_human\": \"Kaggle Data Search\",\n \"pretty_name_for_human\": \"Kaggle Data Search\",\n \"icon\": \"\",\n \"description\": \"Search & Connect to kaggle datasets\",\n },\n {\n \"type\": \"tool\",\n \"id\": \"8f8e8dbc-ae5b-4950-9f4f-7f5238978806\",\n \"name\": \"DataProfiling\",\n \"name_for_human\": \"Data Profiling\",\n \"pretty_name_for_human\": \"Data Profiling\",\n \"icon\": \"\",\n \"description\": \"Intelligent profiling for your data\",\n },\n]\n\n\n@app.route(\"/api/data_tool_list\", methods=[\"POST\"])\ndef get_data_tool_list() -> List[dict]:\n \"\"\"Gets the data tool list. \"\"\"\n for i, tool in enumerate(DATA_TOOLS):\n cache_path = f\"backend/static/images/{tool['name']}.cache\"\n with open(cache_path, 'r') as f:\n image_content = f.read()\n DATA_TOOLS[i][\"icon\"] = image_content\n\n return jsonify(DATA_TOOLS)","source_hash":"a4b856cdf01dded89c275e86bfc969e0d68c01d68c554697eed6594321131eb6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.data_tools.get_data_tool_list","uri":"program://OpenAgents/function/backend.api.data_tools.get_data_tool_list#L56-L64","kind":"function","name":"get_data_tool_list","path":"backend/api/data_tools.py","language":"python","start_line":56,"end_line":64,"context_start_line":36,"context_end_line":64,"code":" \"id\": \"c7c826ba-5884-4e2b-b27c-fedea30c1749\",\n \"name\": \"KaggleDataLoader\",\n \"name_for_human\": \"Kaggle Data Search\",\n \"pretty_name_for_human\": \"Kaggle Data Search\",\n \"icon\": \"\",\n \"description\": \"Search & Connect to kaggle datasets\",\n },\n {\n \"type\": \"tool\",\n \"id\": \"8f8e8dbc-ae5b-4950-9f4f-7f5238978806\",\n \"name\": \"DataProfiling\",\n \"name_for_human\": \"Data Profiling\",\n \"pretty_name_for_human\": \"Data Profiling\",\n \"icon\": \"\",\n \"description\": \"Intelligent profiling for your data\",\n },\n]\n\n\n@app.route(\"/api/data_tool_list\", methods=[\"POST\"])\ndef get_data_tool_list() -> List[dict]:\n \"\"\"Gets the data tool list. \"\"\"\n for i, tool in enumerate(DATA_TOOLS):\n cache_path = f\"backend/static/images/{tool['name']}.cache\"\n with open(cache_path, 'r') as f:\n image_content = f.read()\n DATA_TOOLS[i][\"icon\"] = image_content\n\n return jsonify(DATA_TOOLS)","source_hash":"a4b856cdf01dded89c275e86bfc969e0d68c01d68c554697eed6594321131eb6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.tool","uri":"program://OpenAgents/module/backend.api.tool#L1-L68","kind":"module","name":"backend.api.tool","path":"backend/api/tool.py","language":"python","start_line":1,"end_line":68,"context_start_line":1,"context_end_line":68,"code":"from flask import request, jsonify, Response\n\nfrom backend.api.chat_plugin import plugins\nfrom backend.main import app, api_key_pool\nfrom backend.schemas import DEFAULT_USER_ID\n\n@app.route(\"/api/tool_list\", methods=[\"POST\"])\ndef get_tool_list() -> Response:\n \"\"\"parameters:\n {\n user_id: id of the user\n }\n return value:\n [{\n id: id of a plugin,\n name: name pf a plugin,\n description: description of the plugin,\n icon: icon of the plugin,\n require_api_key: whether the plugin requires api_key,\n api_key: the api key of the plugin, None if no api key\n }]\n \"\"\"\n user_id = DEFAULT_USER_ID\n api_key_info = api_key_pool.get_pool_info_with_id(user_id, [])\n tool_list = []\n for plugin in plugins:\n plugin_info = {\n \"id\": plugin[\"id\"],\n \"name\": plugin[\"name\"],\n \"name_for_human\": plugin[\"name_for_human\"],\n \"description\": plugin[\"description\"],\n \"icon\": plugin[\"icon\"],\n \"require_api_key\": plugin[\"require_api_key\"],\n }\n search_plugin = [i for i in api_key_info if i[\"tool_id\"] == plugin[\"id\"]]\n if len(search_plugin) > 0:\n plugin_info[\"api_key\"] = search_plugin[0][\"api_key\"]\n else:\n plugin_info[\"api_key\"] = None\n tool_list.append(plugin_info)\n return jsonify(tool_list)\n\n\n@app.route(\"/api/api_key\", methods=[\"POST\"])\ndef post_tool_api_key() -> Response:\n \"\"\"parameters:\n {\n user_id: id of the user,\n tool_id: id of the tool,\n tool_name: name of the tool,\n api_key: api_key of the tool\n }\n \"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n tool_id = request_json[\"tool_id\"]\n tool_name = request_json[\"tool_name\"]\n api_key = request_json[\"api_key\"]\n api_key_info = api_key_pool.get_pool_info_with_id(user_id, [])\n flag = False\n for i in api_key_info:\n if i[\"tool_id\"] == tool_id:\n flag = True\n i[\"api_key\"] = api_key\n if not flag:\n api_key_info.append({\"tool_id\": tool_id, \"tool_name\": tool_name, \"api_key\": api_key})\n api_key_pool.set_pool_info_with_id(user_id, api_key_info)\n return Response(\"Success\", status=200)","source_hash":"1e2273167cc833b32727d1fc339d71e241ecbc63f44c6c26c7d843fb6fe6e6d0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.tool.get_tool_list","uri":"program://OpenAgents/function/backend.api.tool.get_tool_list#L8-L41","kind":"function","name":"get_tool_list","path":"backend/api/tool.py","language":"python","start_line":8,"end_line":41,"context_start_line":1,"context_end_line":61,"code":"from flask import request, jsonify, Response\n\nfrom backend.api.chat_plugin import plugins\nfrom backend.main import app, api_key_pool\nfrom backend.schemas import DEFAULT_USER_ID\n\n@app.route(\"/api/tool_list\", methods=[\"POST\"])\ndef get_tool_list() -> Response:\n \"\"\"parameters:\n {\n user_id: id of the user\n }\n return value:\n [{\n id: id of a plugin,\n name: name pf a plugin,\n description: description of the plugin,\n icon: icon of the plugin,\n require_api_key: whether the plugin requires api_key,\n api_key: the api key of the plugin, None if no api key\n }]\n \"\"\"\n user_id = DEFAULT_USER_ID\n api_key_info = api_key_pool.get_pool_info_with_id(user_id, [])\n tool_list = []\n for plugin in plugins:\n plugin_info = {\n \"id\": plugin[\"id\"],\n \"name\": plugin[\"name\"],\n \"name_for_human\": plugin[\"name_for_human\"],\n \"description\": plugin[\"description\"],\n \"icon\": plugin[\"icon\"],\n \"require_api_key\": plugin[\"require_api_key\"],\n }\n search_plugin = [i for i in api_key_info if i[\"tool_id\"] == plugin[\"id\"]]\n if len(search_plugin) > 0:\n plugin_info[\"api_key\"] = search_plugin[0][\"api_key\"]\n else:\n plugin_info[\"api_key\"] = None\n tool_list.append(plugin_info)\n return jsonify(tool_list)\n\n\n@app.route(\"/api/api_key\", methods=[\"POST\"])\ndef post_tool_api_key() -> Response:\n \"\"\"parameters:\n {\n user_id: id of the user,\n tool_id: id of the tool,\n tool_name: name of the tool,\n api_key: api_key of the tool\n }\n \"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n tool_id = request_json[\"tool_id\"]\n tool_name = request_json[\"tool_name\"]\n api_key = request_json[\"api_key\"]\n api_key_info = api_key_pool.get_pool_info_with_id(user_id, [])\n flag = False\n for i in api_key_info:","source_hash":"1e2273167cc833b32727d1fc339d71e241ecbc63f44c6c26c7d843fb6fe6e6d0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.tool.post_tool_api_key","uri":"program://OpenAgents/function/backend.api.tool.post_tool_api_key#L45-L68","kind":"function","name":"post_tool_api_key","path":"backend/api/tool.py","language":"python","start_line":45,"end_line":68,"context_start_line":25,"context_end_line":68,"code":" tool_list = []\n for plugin in plugins:\n plugin_info = {\n \"id\": plugin[\"id\"],\n \"name\": plugin[\"name\"],\n \"name_for_human\": plugin[\"name_for_human\"],\n \"description\": plugin[\"description\"],\n \"icon\": plugin[\"icon\"],\n \"require_api_key\": plugin[\"require_api_key\"],\n }\n search_plugin = [i for i in api_key_info if i[\"tool_id\"] == plugin[\"id\"]]\n if len(search_plugin) > 0:\n plugin_info[\"api_key\"] = search_plugin[0][\"api_key\"]\n else:\n plugin_info[\"api_key\"] = None\n tool_list.append(plugin_info)\n return jsonify(tool_list)\n\n\n@app.route(\"/api/api_key\", methods=[\"POST\"])\ndef post_tool_api_key() -> Response:\n \"\"\"parameters:\n {\n user_id: id of the user,\n tool_id: id of the tool,\n tool_name: name of the tool,\n api_key: api_key of the tool\n }\n \"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n tool_id = request_json[\"tool_id\"]\n tool_name = request_json[\"tool_name\"]\n api_key = request_json[\"api_key\"]\n api_key_info = api_key_pool.get_pool_info_with_id(user_id, [])\n flag = False\n for i in api_key_info:\n if i[\"tool_id\"] == tool_id:\n flag = True\n i[\"api_key\"] = api_key\n if not flag:\n api_key_info.append({\"tool_id\": tool_id, \"tool_name\": tool_name, \"api_key\": api_key})\n api_key_pool.set_pool_info_with_id(user_id, api_key_info)\n return Response(\"Success\", status=200)","source_hash":"1e2273167cc833b32727d1fc339d71e241ecbc63f44c6c26c7d843fb6fe6e6d0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file","uri":"program://OpenAgents/module/backend.api.file#L1-L473","kind":"module","name":"backend.api.file","path":"backend/api/file.py","language":"python","start_line":1,"end_line":473,"context_start_line":1,"context_end_line":473,"code":"import json\nimport os\nimport shutil\nfrom typing import Dict, Any\nfrom flask import Response, jsonify, request, send_file\n\nfrom backend.app import app\nfrom backend.main import (\n grounding_source_pool,\n logger,\n message_id_register,\n message_pool,\n)\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.utils.utils import create_personal_folder\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.utils.utils import (\n allowed_file,\n get_data_model_cls,\n get_user_and_chat_id_from_request,\n get_user_and_chat_id_from_request_json,\n is_sqlite_file,\n is_table_file,\n is_image_file,\n load_grounding_source,\n)\nfrom backend.schemas import INTERNAL, UNFOUND\n\nTABLE_HUMAN_SIDE_FORMAT = \"material-react-table\"\n\n\ndef _path_tree_for_react_dnd_treeview(tree: list, id_to_path_dict: dict, path: str,\n parent: int,\n highlighted_files: list = []) -> list:\n \"\"\"\n {\n \"id\": 1,\n \"parent\": 0,\n \"droppable\": true,\n \"text\": \"Folder 1\"\n },\n {\n \"id\": 2,\n \"parent\": 1,\n \"text\": \"File 1-1\",\n \"data\": {\n \"fileType\": \"csv\",\n \"fileSize\": \"0.5MB\"\n }\n },\n \"\"\"\n for item in os.listdir(path):\n if item.startswith(\".\"):\n continue\n item_path = os.path.join(path, item)\n droppable = os.path.isdir(item_path)\n idx = len(tree) + 1\n tree.append({\n \"id\": idx,\n \"parent\": parent,\n \"droppable\": droppable,\n \"text\": item,\n \"highlight\": True if item_path in highlighted_files else False})\n id_to_path_dict[idx] = item_path\n if os.path.isdir(item_path):\n _path_tree_for_react_dnd_treeview(tree, id_to_path_dict, item_path, idx)\n\n return []\n\ndef secure_filename(filename: str) -> str:\n keep_characters = ('.', '_')\n filename = \"\".join(\n c for c in filename if c.isalnum() or c in keep_characters).rstrip()\n return filename\n\n\n@app.route(\"/api/upload\", methods=[\"POST\"])\ndef create_upload_file() -> dict | Response:\n \"\"\"Uploads a new file.\"\"\"\n try:\n if \"file\" not in request.files:\n return {\"error\": \"No file part in the request\"}\n file = request.files[\"file\"]\n (user_id, chat_id) = get_user_and_chat_id_from_request(request)\n folder = create_personal_folder(user_id)\n\n # Check if the file is allowed\n if not allowed_file(str(file.filename)):\n return {\"error\": \"File type not allowed\"}\n\n # Save and read the file\n file.filename = secure_filename(str(file.filename))\n file_path = os.path.join(folder, file.filename)\n file.save(file_path)\n response = {\"success\": file.filename}\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/upload\",\n msg_head=\"Upload file success\").debug(file_path)\n\n return jsonify(response)\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/upload\",\n msg_head=\"Upload file error\").error(str(e))\n\n return Response(response=None, status=f\"{INTERNAL} Upload File Error: {str(e)}\")\n\n\ndef _generate_human_side_data_from_file(filename: str, data_model: Any) -> Dict:\n if is_table_file(filename):\n # Determine the format of the human side(frontend) table\n human_side_data = data_model.get_human_side_data(mode=\"FULL\")\n if TABLE_HUMAN_SIDE_FORMAT == \"markdown\":\n human_side_data = human_side_data.to_markdown(index=False)\n human_side_data_type = \"plain\"\n elif TABLE_HUMAN_SIDE_FORMAT == \"material-react-table\":\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item},\n human_side_data.columns.tolist()))\n data = human_side_data.fillna(\"\").to_dict(orient=\"records\")\n human_side_data = json.dumps({\"columns\": columns, \"data\": data})\n human_side_data_type = \"table\"\n data = {\"success\": filename, \"content\": human_side_data,\n \"type\": human_side_data_type}\n elif is_sqlite_file(filename):\n data = {\"success\": filename, \"content\": filename, \"type\": \"table\"}\n elif is_image_file(filename):\n # Determine the format of human side(frontend) image\n human_side_data = data_model.get_human_side_data()\n data = {\"success\": filename, \"content\": human_side_data, \"type\": \"image\"}\n else:\n return {\"error\": \"Document file type not supported\"}\n return data\n\n\ndef _get_file_path_from_node(folder: str, file_node: dict) -> Any:\n path_tree_list: list = []\n id_to_path_dict = {0: folder}\n _path_tree_for_react_dnd_treeview(path_tree_list, id_to_path_dict, folder, 0)\n path = id_to_path_dict[file_node[\"id\"]]\n return path\n\n\n@app.route(\"/api/file_system/apply\", methods=[\"POST\"])\ndef apply_to_conversation() -> Response:\n \"\"\"Applies data to the conversation.\"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n file_node = request_json[\"activated_file\"]\n parent_message_id = request_json[\"parent_message_id\"]\n folder = create_personal_folder(user_id)\n\n # Modify the selected grounding sources\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n file_path = _get_file_path_from_node(folder, file_node)\n filename = file_node[\"text\"]\n filename_no_ext = os.path.splitext(filename)[0]\n if file_path not in grounding_source_dict:\n data = load_grounding_source(file_path)\n data_model = get_data_model_cls(filename).from_raw_data(\n raw_data=data,\n raw_data_name=filename_no_ext,\n raw_data_path=file_path,\n )\n grounding_source_dict[file_path] = data_model\n # Add uploaded file in chat memory\n message_list = message_pool.get_pool_info_with_id(user_id, chat_id,\n default_value=list())\n llm_side_data = data_model.get_llm_side_data()\n human_message_content = \"[User uploaded a file {}]\\n{}\".format(filename,\n llm_side_data)\n human_message_id = message_id_register.add_variable(human_message_content)\n message_list.append(\n {\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"message_type\": \"human_message\",\n \"message_content\": human_message_content,\n }\n )\n data = _generate_human_side_data_from_file(filename, data_model)\n message_pool.set_pool_info_with_id(user_id, chat_id, message_list)\n grounding_source_pool.set_pool_info_with_id(user_id, chat_id,\n grounding_source_dict)\n # Dump to database\n db = get_user_conversation_storage()\n db_message = {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"version_id\": 0,\n \"role\": \"user\",\n \"data_for_human\": {\n \"intermediate_steps\": [],\n \"final_answer\": [\n {\n \"type\": data[\"type\"],\n \"text\": data[\"content\"],\n \"final\": True,\n }\n ],\n },\n \"data_for_llm\": message_list[-1][\"message_content\"],\n \"raw_data\": None,\n }\n db.message.insert_one(db_message)\n response = {\n \"success\": True,\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"message\": \"Successfully apply {} to conversation {}\".format(filename,\n chat_id),\n \"content\": {\n \"intermediate_steps\": [],\n \"final_answer\": [\n {\n \"type\": data[\"type\"],\n \"text\": data[\"content\"],\n \"final\": True,\n }\n ],\n },\n }\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file success\").debug(file_path)\n del db_message[\"data_for_human\"]\n\n return jsonify(response)\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file failed\").debug(file_path)\n\n return jsonify({\"success\": False,\n \"message\": \"You have already import {} to the conversation\".format(\n filename)})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file failed\").error(file_path)\n import traceback\n traceback.print_exc()\n\n return Response(response=None,\n status=f\"{INTERNAL} Fail to apply file to chat: {str(e)}\")\n\n\n@app.route(\"/api/file_system/move\", methods=[\"POST\"])\ndef move_files() -> Response:\n \"\"\"Moves file from source path from target source.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n nodes = request_json[\"nodes\"]\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n for node in nodes:\n old_path = id_to_path_dict[node[\"id\"]]\n new_path = id_to_path_dict[node[\"parent\"]]\n shutil.move(old_path, new_path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/move\",\n msg_head=\"Move file success\").debug(\n f\"from {old_path} to {new_path}\"\n )\n\n return jsonify({\"success\": True, \"message\": \"File moved successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/move\",\n msg_head=\"Move file failed\").error(str(e))\n\n return jsonify({\"success\": False, \"message\": str(e)})\n return Response(response=None, status=f\"{INTERNAL} Fail to move file\")\n\n\n@app.route(\"/api/file_system/delete\", methods=[\"POST\"])\ndef delete_files() -> Response:\n \"\"\"Deletes a file from the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n if os.path.isdir(path):\n shutil.rmtree(path)\n else:\n os.remove(path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/delete\",\n msg_head=\"Delete file success\").debug(path)\n\n return jsonify({\"success\": True, \"message\": \"File is deleted successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/delete\",\n msg_head=\"Delete file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Delete file failed: {str(e)}\")\n\n\n@app.route(\"/api/file_system/download\", methods=[\"POST\"])\ndef download_files() -> Response:\n \"\"\"Downloads a file to local.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n\n if os.path.exists(path):\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file success\").debug(path)\n return send_file(path, as_attachment=True)\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").debug(path)\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: file not correctlt sent\")\n\n except Exception as e:\n print(str(e))\n import traceback\n traceback.print_exc()\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: {str(e)}\")\n\n\ndef _generate_directory_name(name: str, x:int=0) -> Any:\n dir_name = (name + (\"_\" + str(x) if x != 0 else \"\")).strip()\n if not os.path.exists(dir_name):\n return dir_name\n else:\n return _generate_directory_name(name, x + 1)\n\n\n@app.route(\"/api/file_system/create_folder\", methods=[\"POST\"])\ndef create_folders() -> Response:\n \"\"\"Creates a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n new_path = _generate_directory_name(os.path.join(root_path, \"Folder\"))\n os.makedirs(new_path, exist_ok=False)\n\n logger.bind(\n user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder success\"\n ).debug(new_path)\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder failed\").error(\n str(e)\n )\n\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder failed\").error(\n \"Root path does not exist.\"\n )\n\n return Response(response=None, status=f\"{INTERNAL} Root path does not exist\")\n\n\n@app.route(\"/api/file_system/update\", methods=[\"POST\"])\ndef rename_folder() -> Response:\n \"\"\"Renames a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n rename_value = request_json[\"rename_value\"]\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n new_path = os.path.join(os.path.dirname(path), rename_value)\n shutil.move(path, new_path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder success\").debug(\n f\"{path} to {new_path}\"\n )\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder failed\").error(str(e))\n\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder failed\").error(\n \"Root path does not exist.\"\n )\n\n return Response(response=None, status=f\"{INTERNAL} Root path does not exist\")\n\n\n@app.route(\"/api/file_system/get_path_tree\", methods=[\"POST\"])\ndef get_path_tree() -> Response:\n \"\"\"Gets a file path tree of one file.\"\"\"\n try:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id == \"\": # front-end may enter empty user_id\n return jsonify([])\n root_path = create_personal_folder(user_id)\n highlighted_files = request_json.get(\"highlighted_files\", [])\n if root_path is None:\n return {\"error\": \"root_path parameter is required\", \"error_code\": 404}\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0,\n highlighted_files=highlighted_files)\n return jsonify(current_path_tree_list)\n else:\n return Response(response=None, status=f\"{UNFOUND} Directory not found\")\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} Directory not found\")\n\n\n@app.route(\"/api/set_default_examples\", methods=[\"POST\"])\ndef set_default_examples() -> Response:\n \"\"\"Sets default files for each user.\"\"\"\n try:\n # Should be called after auth is verified\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n root_path = create_personal_folder(user_id)\n example_dir = os.path.dirname(os.path.dirname(app.config[\"UPLOAD_FOLDER\"]))\n example_path = os.path.join(example_dir, \"data/examples/\")\n if os.path.exists(example_path):\n shutil.copytree(example_path, root_path, dirs_exist_ok=True)\n return jsonify(\n {\"success\": True, \"message\": \"Default examples are set successfully\"})\n else:\n return Response(response=None,\n status=f\"{UNFOUND} Directory not found at {example_dir}\")\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} Fail to Set Default Examples\")","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file._path_tree_for_react_dnd_treeview","uri":"program://OpenAgents/function/backend.api.file._path_tree_for_react_dnd_treeview#L32-L68","kind":"function","name":"_path_tree_for_react_dnd_treeview","path":"backend/api/file.py","language":"python","start_line":32,"end_line":68,"context_start_line":12,"context_end_line":88,"code":" message_pool,\n)\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.utils.utils import create_personal_folder\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.utils.utils import (\n allowed_file,\n get_data_model_cls,\n get_user_and_chat_id_from_request,\n get_user_and_chat_id_from_request_json,\n is_sqlite_file,\n is_table_file,\n is_image_file,\n load_grounding_source,\n)\nfrom backend.schemas import INTERNAL, UNFOUND\n\nTABLE_HUMAN_SIDE_FORMAT = \"material-react-table\"\n\n\ndef _path_tree_for_react_dnd_treeview(tree: list, id_to_path_dict: dict, path: str,\n parent: int,\n highlighted_files: list = []) -> list:\n \"\"\"\n {\n \"id\": 1,\n \"parent\": 0,\n \"droppable\": true,\n \"text\": \"Folder 1\"\n },\n {\n \"id\": 2,\n \"parent\": 1,\n \"text\": \"File 1-1\",\n \"data\": {\n \"fileType\": \"csv\",\n \"fileSize\": \"0.5MB\"\n }\n },\n \"\"\"\n for item in os.listdir(path):\n if item.startswith(\".\"):\n continue\n item_path = os.path.join(path, item)\n droppable = os.path.isdir(item_path)\n idx = len(tree) + 1\n tree.append({\n \"id\": idx,\n \"parent\": parent,\n \"droppable\": droppable,\n \"text\": item,\n \"highlight\": True if item_path in highlighted_files else False})\n id_to_path_dict[idx] = item_path\n if os.path.isdir(item_path):\n _path_tree_for_react_dnd_treeview(tree, id_to_path_dict, item_path, idx)\n\n return []\n\ndef secure_filename(filename: str) -> str:\n keep_characters = ('.', '_')\n filename = \"\".join(\n c for c in filename if c.isalnum() or c in keep_characters).rstrip()\n return filename\n\n\n@app.route(\"/api/upload\", methods=[\"POST\"])\ndef create_upload_file() -> dict | Response:\n \"\"\"Uploads a new file.\"\"\"\n try:\n if \"file\" not in request.files:\n return {\"error\": \"No file part in the request\"}\n file = request.files[\"file\"]\n (user_id, chat_id) = get_user_and_chat_id_from_request(request)\n folder = create_personal_folder(user_id)\n\n # Check if the file is allowed\n if not allowed_file(str(file.filename)):","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.secure_filename","uri":"program://OpenAgents/function/backend.api.file.secure_filename#L70-L74","kind":"function","name":"secure_filename","path":"backend/api/file.py","language":"python","start_line":70,"end_line":74,"context_start_line":50,"context_end_line":94,"code":" },\n \"\"\"\n for item in os.listdir(path):\n if item.startswith(\".\"):\n continue\n item_path = os.path.join(path, item)\n droppable = os.path.isdir(item_path)\n idx = len(tree) + 1\n tree.append({\n \"id\": idx,\n \"parent\": parent,\n \"droppable\": droppable,\n \"text\": item,\n \"highlight\": True if item_path in highlighted_files else False})\n id_to_path_dict[idx] = item_path\n if os.path.isdir(item_path):\n _path_tree_for_react_dnd_treeview(tree, id_to_path_dict, item_path, idx)\n\n return []\n\ndef secure_filename(filename: str) -> str:\n keep_characters = ('.', '_')\n filename = \"\".join(\n c for c in filename if c.isalnum() or c in keep_characters).rstrip()\n return filename\n\n\n@app.route(\"/api/upload\", methods=[\"POST\"])\ndef create_upload_file() -> dict | Response:\n \"\"\"Uploads a new file.\"\"\"\n try:\n if \"file\" not in request.files:\n return {\"error\": \"No file part in the request\"}\n file = request.files[\"file\"]\n (user_id, chat_id) = get_user_and_chat_id_from_request(request)\n folder = create_personal_folder(user_id)\n\n # Check if the file is allowed\n if not allowed_file(str(file.filename)):\n return {\"error\": \"File type not allowed\"}\n\n # Save and read the file\n file.filename = secure_filename(str(file.filename))\n file_path = os.path.join(folder, file.filename)\n file.save(file_path)","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.create_upload_file","uri":"program://OpenAgents/function/backend.api.file.create_upload_file#L78-L105","kind":"function","name":"create_upload_file","path":"backend/api/file.py","language":"python","start_line":78,"end_line":105,"context_start_line":58,"context_end_line":125,"code":" tree.append({\n \"id\": idx,\n \"parent\": parent,\n \"droppable\": droppable,\n \"text\": item,\n \"highlight\": True if item_path in highlighted_files else False})\n id_to_path_dict[idx] = item_path\n if os.path.isdir(item_path):\n _path_tree_for_react_dnd_treeview(tree, id_to_path_dict, item_path, idx)\n\n return []\n\ndef secure_filename(filename: str) -> str:\n keep_characters = ('.', '_')\n filename = \"\".join(\n c for c in filename if c.isalnum() or c in keep_characters).rstrip()\n return filename\n\n\n@app.route(\"/api/upload\", methods=[\"POST\"])\ndef create_upload_file() -> dict | Response:\n \"\"\"Uploads a new file.\"\"\"\n try:\n if \"file\" not in request.files:\n return {\"error\": \"No file part in the request\"}\n file = request.files[\"file\"]\n (user_id, chat_id) = get_user_and_chat_id_from_request(request)\n folder = create_personal_folder(user_id)\n\n # Check if the file is allowed\n if not allowed_file(str(file.filename)):\n return {\"error\": \"File type not allowed\"}\n\n # Save and read the file\n file.filename = secure_filename(str(file.filename))\n file_path = os.path.join(folder, file.filename)\n file.save(file_path)\n response = {\"success\": file.filename}\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/upload\",\n msg_head=\"Upload file success\").debug(file_path)\n\n return jsonify(response)\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/upload\",\n msg_head=\"Upload file error\").error(str(e))\n\n return Response(response=None, status=f\"{INTERNAL} Upload File Error: {str(e)}\")\n\n\ndef _generate_human_side_data_from_file(filename: str, data_model: Any) -> Dict:\n if is_table_file(filename):\n # Determine the format of the human side(frontend) table\n human_side_data = data_model.get_human_side_data(mode=\"FULL\")\n if TABLE_HUMAN_SIDE_FORMAT == \"markdown\":\n human_side_data = human_side_data.to_markdown(index=False)\n human_side_data_type = \"plain\"\n elif TABLE_HUMAN_SIDE_FORMAT == \"material-react-table\":\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item},\n human_side_data.columns.tolist()))\n data = human_side_data.fillna(\"\").to_dict(orient=\"records\")\n human_side_data = json.dumps({\"columns\": columns, \"data\": data})\n human_side_data_type = \"table\"\n data = {\"success\": filename, \"content\": human_side_data,\n \"type\": human_side_data_type}\n elif is_sqlite_file(filename):\n data = {\"success\": filename, \"content\": filename, \"type\": \"table\"}\n elif is_image_file(filename):","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file._generate_human_side_data_from_file","uri":"program://OpenAgents/function/backend.api.file._generate_human_side_data_from_file#L108-L131","kind":"function","name":"_generate_human_side_data_from_file","path":"backend/api/file.py","language":"python","start_line":108,"end_line":131,"context_start_line":88,"context_end_line":151,"code":" if not allowed_file(str(file.filename)):\n return {\"error\": \"File type not allowed\"}\n\n # Save and read the file\n file.filename = secure_filename(str(file.filename))\n file_path = os.path.join(folder, file.filename)\n file.save(file_path)\n response = {\"success\": file.filename}\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/upload\",\n msg_head=\"Upload file success\").debug(file_path)\n\n return jsonify(response)\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/upload\",\n msg_head=\"Upload file error\").error(str(e))\n\n return Response(response=None, status=f\"{INTERNAL} Upload File Error: {str(e)}\")\n\n\ndef _generate_human_side_data_from_file(filename: str, data_model: Any) -> Dict:\n if is_table_file(filename):\n # Determine the format of the human side(frontend) table\n human_side_data = data_model.get_human_side_data(mode=\"FULL\")\n if TABLE_HUMAN_SIDE_FORMAT == \"markdown\":\n human_side_data = human_side_data.to_markdown(index=False)\n human_side_data_type = \"plain\"\n elif TABLE_HUMAN_SIDE_FORMAT == \"material-react-table\":\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item},\n human_side_data.columns.tolist()))\n data = human_side_data.fillna(\"\").to_dict(orient=\"records\")\n human_side_data = json.dumps({\"columns\": columns, \"data\": data})\n human_side_data_type = \"table\"\n data = {\"success\": filename, \"content\": human_side_data,\n \"type\": human_side_data_type}\n elif is_sqlite_file(filename):\n data = {\"success\": filename, \"content\": filename, \"type\": \"table\"}\n elif is_image_file(filename):\n # Determine the format of human side(frontend) image\n human_side_data = data_model.get_human_side_data()\n data = {\"success\": filename, \"content\": human_side_data, \"type\": \"image\"}\n else:\n return {\"error\": \"Document file type not supported\"}\n return data\n\n\ndef _get_file_path_from_node(folder: str, file_node: dict) -> Any:\n path_tree_list: list = []\n id_to_path_dict = {0: folder}\n _path_tree_for_react_dnd_treeview(path_tree_list, id_to_path_dict, folder, 0)\n path = id_to_path_dict[file_node[\"id\"]]\n return path\n\n\n@app.route(\"/api/file_system/apply\", methods=[\"POST\"])\ndef apply_to_conversation() -> Response:\n \"\"\"Applies data to the conversation.\"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n file_node = request_json[\"activated_file\"]\n parent_message_id = request_json[\"parent_message_id\"]\n folder = create_personal_folder(user_id)\n","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file._get_file_path_from_node","uri":"program://OpenAgents/function/backend.api.file._get_file_path_from_node#L134-L139","kind":"function","name":"_get_file_path_from_node","path":"backend/api/file.py","language":"python","start_line":134,"end_line":139,"context_start_line":114,"context_end_line":159,"code":" human_side_data_type = \"plain\"\n elif TABLE_HUMAN_SIDE_FORMAT == \"material-react-table\":\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item},\n human_side_data.columns.tolist()))\n data = human_side_data.fillna(\"\").to_dict(orient=\"records\")\n human_side_data = json.dumps({\"columns\": columns, \"data\": data})\n human_side_data_type = \"table\"\n data = {\"success\": filename, \"content\": human_side_data,\n \"type\": human_side_data_type}\n elif is_sqlite_file(filename):\n data = {\"success\": filename, \"content\": filename, \"type\": \"table\"}\n elif is_image_file(filename):\n # Determine the format of human side(frontend) image\n human_side_data = data_model.get_human_side_data()\n data = {\"success\": filename, \"content\": human_side_data, \"type\": \"image\"}\n else:\n return {\"error\": \"Document file type not supported\"}\n return data\n\n\ndef _get_file_path_from_node(folder: str, file_node: dict) -> Any:\n path_tree_list: list = []\n id_to_path_dict = {0: folder}\n _path_tree_for_react_dnd_treeview(path_tree_list, id_to_path_dict, folder, 0)\n path = id_to_path_dict[file_node[\"id\"]]\n return path\n\n\n@app.route(\"/api/file_system/apply\", methods=[\"POST\"])\ndef apply_to_conversation() -> Response:\n \"\"\"Applies data to the conversation.\"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n file_node = request_json[\"activated_file\"]\n parent_message_id = request_json[\"parent_message_id\"]\n folder = create_personal_folder(user_id)\n\n # Modify the selected grounding sources\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n file_path = _get_file_path_from_node(folder, file_node)\n filename = file_node[\"text\"]\n filename_no_ext = os.path.splitext(filename)[0]\n if file_path not in grounding_source_dict:","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.apply_to_conversation","uri":"program://OpenAgents/function/backend.api.file.apply_to_conversation#L143-L246","kind":"function","name":"apply_to_conversation","path":"backend/api/file.py","language":"python","start_line":143,"end_line":246,"context_start_line":123,"context_end_line":266,"code":" elif is_sqlite_file(filename):\n data = {\"success\": filename, \"content\": filename, \"type\": \"table\"}\n elif is_image_file(filename):\n # Determine the format of human side(frontend) image\n human_side_data = data_model.get_human_side_data()\n data = {\"success\": filename, \"content\": human_side_data, \"type\": \"image\"}\n else:\n return {\"error\": \"Document file type not supported\"}\n return data\n\n\ndef _get_file_path_from_node(folder: str, file_node: dict) -> Any:\n path_tree_list: list = []\n id_to_path_dict = {0: folder}\n _path_tree_for_react_dnd_treeview(path_tree_list, id_to_path_dict, folder, 0)\n path = id_to_path_dict[file_node[\"id\"]]\n return path\n\n\n@app.route(\"/api/file_system/apply\", methods=[\"POST\"])\ndef apply_to_conversation() -> Response:\n \"\"\"Applies data to the conversation.\"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n file_node = request_json[\"activated_file\"]\n parent_message_id = request_json[\"parent_message_id\"]\n folder = create_personal_folder(user_id)\n\n # Modify the selected grounding sources\n grounding_source_dict = grounding_source_pool.get_pool_info_with_id(user_id,\n chat_id,\n default_value={})\n file_path = _get_file_path_from_node(folder, file_node)\n filename = file_node[\"text\"]\n filename_no_ext = os.path.splitext(filename)[0]\n if file_path not in grounding_source_dict:\n data = load_grounding_source(file_path)\n data_model = get_data_model_cls(filename).from_raw_data(\n raw_data=data,\n raw_data_name=filename_no_ext,\n raw_data_path=file_path,\n )\n grounding_source_dict[file_path] = data_model\n # Add uploaded file in chat memory\n message_list = message_pool.get_pool_info_with_id(user_id, chat_id,\n default_value=list())\n llm_side_data = data_model.get_llm_side_data()\n human_message_content = \"[User uploaded a file {}]\\n{}\".format(filename,\n llm_side_data)\n human_message_id = message_id_register.add_variable(human_message_content)\n message_list.append(\n {\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"message_type\": \"human_message\",\n \"message_content\": human_message_content,\n }\n )\n data = _generate_human_side_data_from_file(filename, data_model)\n message_pool.set_pool_info_with_id(user_id, chat_id, message_list)\n grounding_source_pool.set_pool_info_with_id(user_id, chat_id,\n grounding_source_dict)\n # Dump to database\n db = get_user_conversation_storage()\n db_message = {\n \"conversation_id\": chat_id,\n \"user_id\": user_id,\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"version_id\": 0,\n \"role\": \"user\",\n \"data_for_human\": {\n \"intermediate_steps\": [],\n \"final_answer\": [\n {\n \"type\": data[\"type\"],\n \"text\": data[\"content\"],\n \"final\": True,\n }\n ],\n },\n \"data_for_llm\": message_list[-1][\"message_content\"],\n \"raw_data\": None,\n }\n db.message.insert_one(db_message)\n response = {\n \"success\": True,\n \"message_id\": human_message_id,\n \"parent_message_id\": parent_message_id,\n \"message\": \"Successfully apply {} to conversation {}\".format(filename,\n chat_id),\n \"content\": {\n \"intermediate_steps\": [],\n \"final_answer\": [\n {\n \"type\": data[\"type\"],\n \"text\": data[\"content\"],\n \"final\": True,\n }\n ],\n },\n }\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file success\").debug(file_path)\n del db_message[\"data_for_human\"]\n\n return jsonify(response)\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file failed\").debug(file_path)\n\n return jsonify({\"success\": False,\n \"message\": \"You have already import {} to the conversation\".format(\n filename)})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file failed\").error(file_path)\n import traceback\n traceback.print_exc()\n\n return Response(response=None,\n status=f\"{INTERNAL} Fail to apply file to chat: {str(e)}\")\n\n\n@app.route(\"/api/file_system/move\", methods=[\"POST\"])\ndef move_files() -> Response:\n \"\"\"Moves file from source path from target source.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n nodes = request_json[\"nodes\"]\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n for node in nodes:\n old_path = id_to_path_dict[node[\"id\"]]\n new_path = id_to_path_dict[node[\"parent\"]]\n shutil.move(old_path, new_path)\n","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.move_files","uri":"program://OpenAgents/function/backend.api.file.move_files#L250-L278","kind":"function","name":"move_files","path":"backend/api/file.py","language":"python","start_line":250,"end_line":278,"context_start_line":230,"context_end_line":298,"code":"\n return jsonify(response)\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file failed\").debug(file_path)\n\n return jsonify({\"success\": False,\n \"message\": \"You have already import {} to the conversation\".format(\n filename)})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/apply\",\n msg_head=\"Apply file failed\").error(file_path)\n import traceback\n traceback.print_exc()\n\n return Response(response=None,\n status=f\"{INTERNAL} Fail to apply file to chat: {str(e)}\")\n\n\n@app.route(\"/api/file_system/move\", methods=[\"POST\"])\ndef move_files() -> Response:\n \"\"\"Moves file from source path from target source.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n nodes = request_json[\"nodes\"]\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n for node in nodes:\n old_path = id_to_path_dict[node[\"id\"]]\n new_path = id_to_path_dict[node[\"parent\"]]\n shutil.move(old_path, new_path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/move\",\n msg_head=\"Move file success\").debug(\n f\"from {old_path} to {new_path}\"\n )\n\n return jsonify({\"success\": True, \"message\": \"File moved successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/move\",\n msg_head=\"Move file failed\").error(str(e))\n\n return jsonify({\"success\": False, \"message\": str(e)})\n return Response(response=None, status=f\"{INTERNAL} Fail to move file\")\n\n\n@app.route(\"/api/file_system/delete\", methods=[\"POST\"])\ndef delete_files() -> Response:\n \"\"\"Deletes a file from the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n if os.path.isdir(path):\n shutil.rmtree(path)\n else:\n os.remove(path)","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.delete_files","uri":"program://OpenAgents/function/backend.api.file.delete_files#L282-L309","kind":"function","name":"delete_files","path":"backend/api/file.py","language":"python","start_line":282,"end_line":309,"context_start_line":262,"context_end_line":329,"code":" for node in nodes:\n old_path = id_to_path_dict[node[\"id\"]]\n new_path = id_to_path_dict[node[\"parent\"]]\n shutil.move(old_path, new_path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/move\",\n msg_head=\"Move file success\").debug(\n f\"from {old_path} to {new_path}\"\n )\n\n return jsonify({\"success\": True, \"message\": \"File moved successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/move\",\n msg_head=\"Move file failed\").error(str(e))\n\n return jsonify({\"success\": False, \"message\": str(e)})\n return Response(response=None, status=f\"{INTERNAL} Fail to move file\")\n\n\n@app.route(\"/api/file_system/delete\", methods=[\"POST\"])\ndef delete_files() -> Response:\n \"\"\"Deletes a file from the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n if os.path.isdir(path):\n shutil.rmtree(path)\n else:\n os.remove(path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/delete\",\n msg_head=\"Delete file success\").debug(path)\n\n return jsonify({\"success\": True, \"message\": \"File is deleted successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/delete\",\n msg_head=\"Delete file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Delete file failed: {str(e)}\")\n\n\n@app.route(\"/api/file_system/download\", methods=[\"POST\"])\ndef download_files() -> Response:\n \"\"\"Downloads a file to local.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n\n if os.path.exists(path):\n logger.bind(user_id=user_id, api=\"/download\",","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.download_files","uri":"program://OpenAgents/function/backend.api.file.download_files#L313-L347","kind":"function","name":"download_files","path":"backend/api/file.py","language":"python","start_line":313,"end_line":347,"context_start_line":293,"context_end_line":367,"code":" root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n if os.path.isdir(path):\n shutil.rmtree(path)\n else:\n os.remove(path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/delete\",\n msg_head=\"Delete file success\").debug(path)\n\n return jsonify({\"success\": True, \"message\": \"File is deleted successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/delete\",\n msg_head=\"Delete file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Delete file failed: {str(e)}\")\n\n\n@app.route(\"/api/file_system/download\", methods=[\"POST\"])\ndef download_files() -> Response:\n \"\"\"Downloads a file to local.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n\n try:\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n\n if os.path.exists(path):\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file success\").debug(path)\n return send_file(path, as_attachment=True)\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").debug(path)\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: file not correctlt sent\")\n\n except Exception as e:\n print(str(e))\n import traceback\n traceback.print_exc()\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: {str(e)}\")\n\n\ndef _generate_directory_name(name: str, x:int=0) -> Any:\n dir_name = (name + (\"_\" + str(x) if x != 0 else \"\")).strip()\n if not os.path.exists(dir_name):\n return dir_name\n else:\n return _generate_directory_name(name, x + 1)\n\n\n@app.route(\"/api/file_system/create_folder\", methods=[\"POST\"])\ndef create_folders() -> Response:\n \"\"\"Creates a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n new_path = _generate_directory_name(os.path.join(root_path, \"Folder\"))\n os.makedirs(new_path, exist_ok=False)","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file._generate_directory_name","uri":"program://OpenAgents/function/backend.api.file._generate_directory_name#L350-L355","kind":"function","name":"_generate_directory_name","path":"backend/api/file.py","language":"python","start_line":350,"end_line":355,"context_start_line":330,"context_end_line":375,"code":" msg_head=\"download file success\").debug(path)\n return send_file(path, as_attachment=True)\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").debug(path)\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: file not correctlt sent\")\n\n except Exception as e:\n print(str(e))\n import traceback\n traceback.print_exc()\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: {str(e)}\")\n\n\ndef _generate_directory_name(name: str, x:int=0) -> Any:\n dir_name = (name + (\"_\" + str(x) if x != 0 else \"\")).strip()\n if not os.path.exists(dir_name):\n return dir_name\n else:\n return _generate_directory_name(name, x + 1)\n\n\n@app.route(\"/api/file_system/create_folder\", methods=[\"POST\"])\ndef create_folders() -> Response:\n \"\"\"Creates a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n new_path = _generate_directory_name(os.path.join(root_path, \"Folder\"))\n os.makedirs(new_path, exist_ok=False)\n\n logger.bind(\n user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder success\"\n ).debug(new_path)\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.create_folders","uri":"program://OpenAgents/function/backend.api.file.create_folders#L359-L388","kind":"function","name":"create_folders","path":"backend/api/file.py","language":"python","start_line":359,"end_line":388,"context_start_line":339,"context_end_line":408,"code":" print(str(e))\n import traceback\n traceback.print_exc()\n\n logger.bind(user_id=user_id, api=\"/download\",\n msg_head=\"download file failed\").error(str(e))\n\n return Response(response=None,\n status=f\"{INTERNAL} Download file failed: {str(e)}\")\n\n\ndef _generate_directory_name(name: str, x:int=0) -> Any:\n dir_name = (name + (\"_\" + str(x) if x != 0 else \"\")).strip()\n if not os.path.exists(dir_name):\n return dir_name\n else:\n return _generate_directory_name(name, x + 1)\n\n\n@app.route(\"/api/file_system/create_folder\", methods=[\"POST\"])\ndef create_folders() -> Response:\n \"\"\"Creates a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n new_path = _generate_directory_name(os.path.join(root_path, \"Folder\"))\n os.makedirs(new_path, exist_ok=False)\n\n logger.bind(\n user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder success\"\n ).debug(new_path)\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder failed\").error(\n str(e)\n )\n\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder failed\").error(\n \"Root path does not exist.\"\n )\n\n return Response(response=None, status=f\"{INTERNAL} Root path does not exist\")\n\n\n@app.route(\"/api/file_system/update\", methods=[\"POST\"])\ndef rename_folder() -> Response:\n \"\"\"Renames a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n rename_value = request_json[\"rename_value\"]\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n new_path = os.path.join(os.path.dirname(path), rename_value)\n shutil.move(path, new_path)\n","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.rename_folder","uri":"program://OpenAgents/function/backend.api.file.rename_folder#L392-L426","kind":"function","name":"rename_folder","path":"backend/api/file.py","language":"python","start_line":392,"end_line":426,"context_start_line":372,"context_end_line":446,"code":" ).debug(new_path)\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder failed\").error(\n str(e)\n )\n\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/create_folder\",\n msg_head=\"Create folder failed\").error(\n \"Root path does not exist.\"\n )\n\n return Response(response=None, status=f\"{INTERNAL} Root path does not exist\")\n\n\n@app.route(\"/api/file_system/update\", methods=[\"POST\"])\ndef rename_folder() -> Response:\n \"\"\"Renames a folder in the filesystem.\"\"\"\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n root_path = create_personal_folder(user_id)\n node = request_json[\"node\"]\n rename_value = request_json[\"rename_value\"]\n if os.path.exists(root_path) and os.path.isdir(root_path):\n try:\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0)\n path = id_to_path_dict[node[\"id\"]]\n new_path = os.path.join(os.path.dirname(path), rename_value)\n shutil.move(path, new_path)\n\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder success\").debug(\n f\"{path} to {new_path}\"\n )\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder failed\").error(str(e))\n\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder failed\").error(\n \"Root path does not exist.\"\n )\n\n return Response(response=None, status=f\"{INTERNAL} Root path does not exist\")\n\n\n@app.route(\"/api/file_system/get_path_tree\", methods=[\"POST\"])\ndef get_path_tree() -> Response:\n \"\"\"Gets a file path tree of one file.\"\"\"\n try:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id == \"\": # front-end may enter empty user_id\n return jsonify([])\n root_path = create_personal_folder(user_id)\n highlighted_files = request_json.get(\"highlighted_files\", [])\n if root_path is None:\n return {\"error\": \"root_path parameter is required\", \"error_code\": 404}\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0,\n highlighted_files=highlighted_files)","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.get_path_tree","uri":"program://OpenAgents/function/backend.api.file.get_path_tree#L430-L451","kind":"function","name":"get_path_tree","path":"backend/api/file.py","language":"python","start_line":430,"end_line":451,"context_start_line":410,"context_end_line":471,"code":" msg_head=\"Rename folder success\").debug(\n f\"{path} to {new_path}\"\n )\n\n return jsonify({\"success\": True, \"message\": \"Folder created successfully\"})\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder failed\").error(str(e))\n\n return jsonify({\"success\": False, \"message\": str(e)})\n else:\n logger.bind(user_id=user_id, chat_id=chat_id, api=\"/update\",\n msg_head=\"Rename folder failed\").error(\n \"Root path does not exist.\"\n )\n\n return Response(response=None, status=f\"{INTERNAL} Root path does not exist\")\n\n\n@app.route(\"/api/file_system/get_path_tree\", methods=[\"POST\"])\ndef get_path_tree() -> Response:\n \"\"\"Gets a file path tree of one file.\"\"\"\n try:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n if user_id == \"\": # front-end may enter empty user_id\n return jsonify([])\n root_path = create_personal_folder(user_id)\n highlighted_files = request_json.get(\"highlighted_files\", [])\n if root_path is None:\n return {\"error\": \"root_path parameter is required\", \"error_code\": 404}\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0,\n highlighted_files=highlighted_files)\n return jsonify(current_path_tree_list)\n else:\n return Response(response=None, status=f\"{UNFOUND} Directory not found\")\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} Directory not found\")\n\n\n@app.route(\"/api/set_default_examples\", methods=[\"POST\"])\ndef set_default_examples() -> Response:\n \"\"\"Sets default files for each user.\"\"\"\n try:\n # Should be called after auth is verified\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n root_path = create_personal_folder(user_id)\n example_dir = os.path.dirname(os.path.dirname(app.config[\"UPLOAD_FOLDER\"]))\n example_path = os.path.join(example_dir, \"data/examples/\")\n if os.path.exists(example_path):\n shutil.copytree(example_path, root_path, dirs_exist_ok=True)\n return jsonify(\n {\"success\": True, \"message\": \"Default examples are set successfully\"})\n else:\n return Response(response=None,\n status=f\"{UNFOUND} Directory not found at {example_dir}\")\n except Exception as e:","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:backend.api.file.set_default_examples","uri":"program://OpenAgents/function/backend.api.file.set_default_examples#L455-L473","kind":"function","name":"set_default_examples","path":"backend/api/file.py","language":"python","start_line":455,"end_line":473,"context_start_line":435,"context_end_line":473,"code":" if user_id == \"\": # front-end may enter empty user_id\n return jsonify([])\n root_path = create_personal_folder(user_id)\n highlighted_files = request_json.get(\"highlighted_files\", [])\n if root_path is None:\n return {\"error\": \"root_path parameter is required\", \"error_code\": 404}\n if os.path.exists(root_path) and os.path.isdir(root_path):\n current_path_tree_list: list = []\n id_to_path_dict = {0: root_path}\n _path_tree_for_react_dnd_treeview(current_path_tree_list, id_to_path_dict,\n root_path, 0,\n highlighted_files=highlighted_files)\n return jsonify(current_path_tree_list)\n else:\n return Response(response=None, status=f\"{UNFOUND} Directory not found\")\n except Exception as e:\n return Response(response=None, status=f\"{INTERNAL} Directory not found\")\n\n\n@app.route(\"/api/set_default_examples\", methods=[\"POST\"])\ndef set_default_examples() -> Response:\n \"\"\"Sets default files for each user.\"\"\"\n try:\n # Should be called after auth is verified\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n root_path = create_personal_folder(user_id)\n example_dir = os.path.dirname(os.path.dirname(app.config[\"UPLOAD_FOLDER\"]))\n example_path = os.path.join(example_dir, \"data/examples/\")\n if os.path.exists(example_path):\n shutil.copytree(example_path, root_path, dirs_exist_ok=True)\n return jsonify(\n {\"success\": True, \"message\": \"Default examples are set successfully\"})\n else:\n return Response(response=None,\n status=f\"{UNFOUND} Directory not found at {example_dir}\")\n except Exception as e:\n return Response(response=None,\n status=f\"{INTERNAL} Fail to Set Default Examples\")","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot_prompt","uri":"program://OpenAgents/module/real_agents.data_agent.copilot_prompt#L1-L90","kind":"module","name":"real_agents.data_agent.copilot_prompt","path":"real_agents/data_agent/copilot_prompt.py","language":"python","start_line":1,"end_line":90,"context_start_line":1,"context_end_line":90,"code":"# flake8: noqa\n\nPREFIX = \"\"\"You are XLang Agent , a friendly and intuitive interface developed by the XLang Team to guide human through every stage of human data lifecycle. Whether human are loading, processing, or interpreting data, XLang Agent is always at human's fingertips through our interactive chat system.\n\nEmpowered by an array of innovative tools that can generate and execute code, XLang Agent delivers robust, reliable answers to human queries. Whenever possible, You employs these tools to give human rich insights, like dynamic code generation & execution and compelling visualizations. And You will always proactively and correctly using all tools to help with human.\n\nGet ready for a seamless and insightful journey with XLang Agent, the personal assistant for all things data!\n\nTOOLS\n------\nYou have direct access to following tools. \n\"\"\"\n\n\nFORMAT_INSTRUCTIONS = \"\"\"RESPONSE FORMAT INSTRUCTIONS\n----------------------------\n\nWhen you use tools or generate final answer, please output a response in one of two formats:\n**Option 1: Explain and Use Tool**\nIf the response involves using a tool, you can start with a natural language explanation[Optional], plus exactly one tool calling[MUST]. But **make sure no any words & answer appended after tool calling json**. The tool calling format should be a markdown code snippet with the following JSON schema:\n\n```json\n{{{{\n \"action\": string wrapped with \\\"\\\", // The action to take. Must be one in the list [{tool_names}]\n \"action_input\": string wrapped with \\\"\\\" // Natural language query to be input to the action tool.\n}}}}\n```\n\n[**Restriction**] Please note that ONLY one tool should be used per round, and you MUST stop generating right after tool calling and make sure no any text appended after tool calling markdown code snippet. Save your words.\n\nNEVER EVER EVER make up a tool not in [{tool_names}]\nNEVER EVER EVER generate code as action input when using tool. Just input natural language by using/paraphrasing human query.\n\n**Option #2:**\nUse this if you want to respond directly to the human.\nIf you want to respond directly to the human without using a tool, provide a plain natural language response. However, if you initially generated a natural language response and then decide to use a tool, make sure to include the tool action and input after the initial response.\n\nNote if the human asks for malicious code, and just respond directly to deny the request and give your professional reason. Don't use any tool. \nThe malicious code includes but not limited to: \n1. Endless operations and excessive waiting (e.g., while True, long print, input())\n2. System crash (e.g., any risky system command)\n3. Data loss (e.g., list or delete files)\n4. Leak sensitive information (e.g., os.getenv())\n5. Establish network connections (e.g., requests.get())\n6. Cause any other security issues\n\n[Mandatory to notice] It is imperative and a must to utilize tools whenever the human's query tasks that implies using tools, such as searching online, generating code, executing code, or any other complex functionalities. You must try to use tools to solve human queries in these cases.\n\nBegin.\n\"\"\"\n\nSUFFIX = \"\"\"{input}\"\"\"\n\n\nTEMPLATE_TOOL_RESPONSE = \"\"\"TOOL RESPONSE:\n---------------------\n{observation}\n\nTHOUGHT\n--------------------\n\nOkay, So what's next? Let's assess if the tool response is enough to answer the human's initial query. Please follow these instructions:\n\n1. Evaluate Tool Response [Mandatory]: Carefully evaluate the tool's response and determine if it sufficiently addresses the human's query. Consider the content and implications of the tool's response.\n\n2. Consider Additional Tool Use [Optional 2 or 3]: If the tool response does not fully address the query or if an error occurred during execution, you may proceed with additional tool usage. However, exercise caution and limit the number of iterations to a maximum of three. You can start with a natural language explanation[Optional], plus exactly one tool calling[MUST]. But **make sure no any words & answer appended after tool calling json**. Follow this format for additional tool usage:\n\n```json\n{{{{\n \"action\": string wrapped with \\\"\\\", // The action to take. Must be one of [{tool_names}]\n \"action_input\": string wrapped with \\\"\\\" // Natural language query to be input to the action tool\n}}}}\n```\n[**Restriction**] Please note that only one tool should be used per round, and you MUST stop generating right after tool calling and make sure no any text appended after tool calling markdown code snippet.\n\n\n3. Deliver Comprehensive Answer [Optional 2 or 3]: If the tool response sufficiently addresses the query, deliver a comprehensive answer to the human. Focus solely on the content and implications of the tool's response. MUST NOT include explanations of the tool's functions.\n\n3.1. Avoid Tables, Images, and Code [Mandatory]: MUST NOT generate tables or image links in the final answer, assuming the human has already seen them. Avoid generating code in the final answer as well. Instead, paraphrase the code into a human query if you need to explain it.\n\nNote. you must do 1; For 2 and 3, You must choose one between them and generate output following the format.\n\nBegin.\n\"\"\"\n\n# models like anthropic claude-v1 or claude-2 can only return valid completion with human message as the last message, so we append the fake AI message at the end.\nfake_continue_prompt = {\n \"claude-2\": \"you can start to think and respond to me using the above formats. No Apology. Just respond with format in Option 2(use tool) or Option 3(direct text response), no other words.\\n\\nBegin.\",\n \"claude-v1\": \"you can start to think and respond to me using the above formats. No Apology. Just respond with format in Option 2(use tool) or Option 3(direct text response), no other words.\\n\\nBegin.\",\n}","source_hash":"5b25c668d0669d0127c5e65cf87b4fab85a6f09297c335c85c6fd51e2c4ef491","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot","uri":"program://OpenAgents/module/real_agents.data_agent.copilot#L1-L202","kind":"module","name":"real_agents.data_agent.copilot","path":"real_agents/data_agent/copilot.py","language":"python","start_line":1,"end_line":202,"context_start_line":1,"context_end_line":202,"code":"\"\"\"An agent designed to hold a conversation in addition to using tools.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, List, Optional, Sequence, Tuple, Union\nfrom typing_extensions import override\nfrom pydantic import Field\n\nfrom langchain.agents.agent import AgentOutputParser\nfrom langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, Callbacks\nfrom langchain.chains import LLMChain\nfrom langchain.schema import AgentAction, AgentFinish, HumanMessage, AIMessage, BaseMessage, BaseOutputParser\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.data_agent.copilot_prompt import PREFIX, SUFFIX, TEMPLATE_TOOL_RESPONSE, fake_continue_prompt\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n # tools\n tool_strings = \"\\n\".join([f\"> {tool.name}: {tool.description}\" for tool in tools])\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n # format instructions for system message\n format_instructions = _output_parser.get_format_instructions()\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @override\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation)\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(system_prompt)\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(\n full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens\n )\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.ExceptionTool","uri":"program://OpenAgents/class/real_agents.data_agent.copilot.ExceptionTool#L29-L45","kind":"class","name":"ExceptionTool","path":"real_agents/data_agent/copilot.py","language":"python","start_line":29,"end_line":45,"context_start_line":9,"context_end_line":65,"code":"from langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, Callbacks\nfrom langchain.chains import LLMChain\nfrom langchain.schema import AgentAction, AgentFinish, HumanMessage, AIMessage, BaseMessage, BaseOutputParser\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.data_agent.copilot_prompt import PREFIX, SUFFIX, TEMPLATE_TOOL_RESPONSE, fake_continue_prompt\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.ConversationalChatAgent","uri":"program://OpenAgents/class/real_agents.data_agent.copilot.ConversationalChatAgent#L48-L202","kind":"class","name":"ConversationalChatAgent","path":"real_agents/data_agent/copilot.py","language":"python","start_line":48,"end_line":202,"context_start_line":28,"context_end_line":202,"code":"\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n # tools\n tool_strings = \"\\n\".join([f\"> {tool.name}: {tool.description}\" for tool in tools])\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n # format instructions for system message\n format_instructions = _output_parser.get_format_instructions()\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @override\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation)\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(system_prompt)\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(\n full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens\n )\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot._run","uri":"program://OpenAgents/function/real_agents.data_agent.copilot._run#L33-L38","kind":"function","name":"_run","path":"real_agents/data_agent/copilot.py","language":"python","start_line":33,"end_line":38,"context_start_line":13,"context_end_line":58,"code":"from langchain.schema import AgentAction, AgentFinish, HumanMessage, AIMessage, BaseMessage, BaseOutputParser\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.data_agent.copilot_prompt import PREFIX, SUFFIX, TEMPLATE_TOOL_RESPONSE, fake_continue_prompt\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot._arun","uri":"program://OpenAgents/function/real_agents.data_agent.copilot._arun#L40-L45","kind":"function","name":"_arun","path":"real_agents/data_agent/copilot.py","language":"python","start_line":40,"end_line":45,"context_start_line":20,"context_end_line":65,"code":"from langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot._get_default_output_parser","uri":"program://OpenAgents/function/real_agents.data_agent.copilot._get_default_output_parser#L56-L57","kind":"function","name":"_get_default_output_parser","path":"real_agents/data_agent/copilot.py","language":"python","start_line":56,"end_line":57,"context_start_line":36,"context_end_line":77,"code":" run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot._agent_type","uri":"program://OpenAgents/function/real_agents.data_agent.copilot._agent_type#L60-L61","kind":"function","name":"_agent_type","path":"real_agents/data_agent/copilot.py","language":"python","start_line":60,"end_line":61,"context_start_line":40,"context_end_line":81,"code":" async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.observation_prefix","uri":"program://OpenAgents/function/real_agents.data_agent.copilot.observation_prefix#L64-L66","kind":"function","name":"observation_prefix","path":"real_agents/data_agent/copilot.py","language":"python","start_line":64,"end_line":66,"context_start_line":44,"context_end_line":86,"code":" ) -> str:\n return query\n\n\nclass ConversationalChatAgent(Agent):\n \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.llm_prefix","uri":"program://OpenAgents/function/real_agents.data_agent.copilot.llm_prefix#L69-L71","kind":"function","name":"llm_prefix","path":"real_agents/data_agent/copilot.py","language":"python","start_line":69,"end_line":71,"context_start_line":49,"context_end_line":91,"code":" \"\"\"An agent designed to hold a conversation in addition to using data tools.\"\"\"\n\n output_parser: ConversationOutputParser = Field(default_factory=ConversationOutputParser())\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n # tools\n tool_strings = \"\\n\".join([f\"> {tool.name}: {tool.description}\" for tool in tools])\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot._validate_tools","uri":"program://OpenAgents/function/real_agents.data_agent.copilot._validate_tools#L74-L76","kind":"function","name":"_validate_tools","path":"real_agents/data_agent/copilot.py","language":"python","start_line":74,"end_line":76,"context_start_line":54,"context_end_line":96,"code":"\n @classmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n # tools\n tool_strings = \"\\n\".join([f\"> {tool.name}: {tool.description}\" for tool in tools])\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n # format instructions for system message\n format_instructions = _output_parser.get_format_instructions()\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.create_prompt","uri":"program://OpenAgents/function/real_agents.data_agent.copilot.create_prompt#L79-L109","kind":"function","name":"create_prompt","path":"real_agents/data_agent/copilot.py","language":"python","start_line":79,"end_line":109,"context_start_line":59,"context_end_line":129,"code":" @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n # tools\n tool_strings = \"\\n\".join([f\"> {tool.name}: {tool.description}\" for tool in tools])\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n # format instructions for system message\n format_instructions = _output_parser.get_format_instructions()\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @override\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation)\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot._construct_scratchpad","uri":"program://OpenAgents/function/real_agents.data_agent.copilot._construct_scratchpad#L112-L136","kind":"function","name":"_construct_scratchpad","path":"real_agents/data_agent/copilot.py","language":"python","start_line":112,"end_line":136,"context_start_line":92,"context_end_line":156,"code":" # format instructions for system message\n format_instructions = _output_parser.get_format_instructions()\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @override\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation)\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.plan","uri":"program://OpenAgents/function/real_agents.data_agent.copilot.plan#L139-L167","kind":"function","name":"plan","path":"real_agents/data_agent/copilot.py","language":"python","start_line":139,"end_line":167,"context_start_line":119,"context_end_line":187,"code":" content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation)\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(system_prompt)\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(\n full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens\n )\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.copilot.from_llm_and_tools","uri":"program://OpenAgents/function/real_agents.data_agent.copilot.from_llm_and_tools#L170-L202","kind":"function","name":"from_llm_and_tools","path":"real_agents/data_agent/copilot.py","language":"python","start_line":170,"end_line":202,"context_start_line":150,"context_end_line":202,"code":" callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(system_prompt)\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(\n full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens\n )\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator","uri":"program://OpenAgents/module/real_agents.data_agent.evaluation.python_evaluator#L1-L324","kind":"module","name":"real_agents.data_agent.evaluation.python_evaluator","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":1,"end_line":324,"context_start_line":1,"context_end_line":324,"code":"import os\nfrom typing import Any, List, Optional, Tuple, Dict\nfrom pydantic import BaseModel\nimport requests\nimport time\nimport ast\n\nimport pandas as pd\nfrom io import StringIO\nimport redis\nfrom loguru import logger\n\nfrom IPython.core.interactiveshell import InteractiveShell\nfrom IPython.core.getipython import get_ipython\nfrom IPython.utils.capture import capture_output\n\n\n# subscribed channels\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n# Error render prefix\nERROR_PREFIX = \"[ERROR]: \"\n\n\ndef check_danger_code(code):\n code_line = []\n for line in code.split(\"\\n\"):\n if not line.startswith(\"%\"):\n code_line.append(line)\n code = \"\\n\".join(code_line)\n\n def check_imports(code):\n ast_failed = False\n try:\n tree = ast.parse(code)\n except Exception as e:\n ast_failed = str(e)\n return ast_failed\n return ast_failed\n\n ast_failed = check_imports(code)\n return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run_program_local(self, program: str, user_id: Optional[str] = \"u\" * 24):\n \"\"\"Run python program on the local machine using Ipython shell.\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n try:\n # Run code using local ipython shell\n # Change working dir to data directory to load from pretty path\n # Note! This is not thread safe, only for local use\n os.chdir(os.path.join(\"backend/data/\", user_id))\n\n shell = InteractiveShell.instance()\n shell.enable_gui = lambda x: False\n with capture_output() as captured:\n ip = get_ipython()\n code = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n result = ip.run_cell(code)\n\n # Change working dir to project root\n os.chdir(\"../../../\")\n\n if result.success:\n return {\n \"success\": True,\n \"result\": result.result,\n \"stdout\": str(captured.stdout),\n \"stderr\": str(captured.stderr),\n \"outputs\": captured.outputs,\n }\n elif result.error_in_exec is not None:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_in_exec)}\",\n \"outputs\": captured.outputs,\n }\n else:\n # error_before_exec\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_before_exec)}\",\n \"outputs\": captured.outputs,\n }\n except Exception as e:\n logger.bind(user_id=user_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def _apply_for_kernel(self, kernel_id: Optional[str], user_id: str, chat_id: str):\n \"\"\"Apply for a kernel in docker to run program.\"\"\"\n if kernel_id is not None:\n # If kernel id is provided, use it directly\n cur_kid = kernel_id\n else:\n # If kernel id is not provided, apply for a new kernel\n kernel_info = self.jupyter_kernel_pool.get_pool_info_with_id(user_id, chat_id, None)\n cur_kid = kernel_info[\"kid\"] if kernel_info is not None else None\n user_exists = requests.get(f\"{self.base_url}/user/status/{user_id}\").json()[\"exists\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user exists\").trace(user_exists)\n\n if not user_exists:\n response = requests.post(f\"{self.base_url}/user/create\", json={\"username\": user_id}).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user create\").trace(response)\n\n response = requests.get(f\"{self.base_url}/kernel/list/{user_id}\").json()\n existing_kernel_list = response[\"list\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kernel list\").trace(response)\n\n if cur_kid not in existing_kernel_list:\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n if response[\"code\"] != 0 and response[\"msg\"] == \"Too many kernels\":\n # kill oldest kernel\n oldest_kernel_id = existing_kernel_list[0]\n response = requests.post(\n f\"{self.base_url}/kernel/stop\", json={\"username\": user_id, \"kid\": oldest_kernel_id}\n ).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kill oldest kernel\").trace(response)\n\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n cur_kid = response[\"id\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"create kernel id\").trace(cur_kid)\n\n self.jupyter_kernel_pool.set_pool_info_with_id(\n user_id, chat_id, {\"kid\": cur_kid, \"ktime\": time.time()}\n )\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"current kernel id\").trace(cur_kid)\n\n return cur_kid\n\n def run_program_docker(\n self,\n program: str,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ):\n \"\"\"Run python program on the docker container(jupyter client).\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if not is_safe:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Unsafe Code Detected {str(danger_pcks)}, Execution Denied!\",\n }\n elif ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n\n try:\n # Run code using remote docker jupyter kernel\n # Get the running id(i.e., a permission to run program) from redis running queue\n # otherwise wait until a running_id is available\n p = self.r.pubsub()\n p.subscribe(RUNNING_EVENT)\n self.r.publish(SUBMIT_EVENT, chat_id)\n for message in p.listen():\n # the initial message for each channel is a message with an integer\n if isinstance(message[\"data\"], int):\n continue\n running_id = message[\"data\"]\n if running_id == chat_id:\n break\n time.sleep(1)\n # Get kernel id(i.e., the real jupyter kernel to run the program) to execute program\n cur_kid = self._apply_for_kernel(kernel_id, user_id, chat_id)\n # Execute program\n response = requests.post(\n f\"{self.base_url}/kernel/exec\", json={\"username\": user_id, \"code\": program, \"kid\": cur_kid}\n ).json()\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n\n # Parse jupyter kernel output\n result, stdout, stderr, outputs, displays, error_message = None, \"\", \"\", None, [], None\n if response[\"status\"] == \"ok\":\n output = response.get(\"output\", None)\n if output is not None:\n for output_dict in output:\n if output_dict[\"type\"] == \"stream\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n if content[\"name\"] == \"stdout\":\n stdout = content[\"text\"]\n if content[\"name\"] == \"stderr\":\n stderr = content[\"text\"]\n elif output_dict[\"type\"] == \"execute_result\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n data = content.get(\"data\", None)\n if data is not None:\n if \"text/plain\" in data and \"text/html\" in data:\n try:\n # Try to recover a dataframe\n df = pd.read_csv(StringIO(data[\"text/plain\"]))\n result = df\n except Exception as e:\n pass\n elif \"text/plain\" in data:\n result = data[\"text/plain\"]\n else:\n # TODO: If not match any of the above, return the first value\n result = list(data.values())[0]\n elif output_dict[\"type\"] == \"display_data\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n data = content.get(\"data\", None)\n metadata = content.get(\"metadata\", None)\n if data is not None and metadata is not None:\n if \"image/png\" in data:\n displays.append(DisplayData.from_tuple((data, metadata)))\n if displays:\n outputs = displays\n else:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error status returned by kernel\",\n }\n # Check success status and return\n shell_msg = response[\"shell\"]\n if shell_msg[\"status\"] == \"ok\":\n return {\n \"success\": True,\n \"result\": result,\n \"stdout\": stdout,\n \"stderr\": stderr,\n \"outputs\": outputs,\n }\n elif shell_msg[\"status\"] == \"error\":\n error_message = f\"{shell_msg['ename']}: {shell_msg['evalue']}\"\n return {\"success\": False, \"error_message\": f\"{ERROR_PREFIX}{error_message}\", \"outputs\": outputs}\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n\n try:\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n except:\n pass\n\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def run(\n self,\n program: str,\n environment: Optional[Any] = None,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n\n lines_code = self.parse_command(program)\n program = \"\\n\".join(lines_code)\n program = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Code execution mode\").trace(self.code_execution_mode)\n\n if self.code_execution_mode == \"local\":\n return self.run_program_local(program, user_id)\n elif self.code_execution_mode == \"docker\":\n return self.run_program_docker(program, kernel_id, user_id, chat_id)\n else:\n raise ValueError(\"Invalid code execution mode\")","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.check_danger_code","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.check_danger_code#L26-L43","kind":"function","name":"check_danger_code","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":26,"end_line":43,"context_start_line":6,"context_end_line":63,"code":"import ast\n\nimport pandas as pd\nfrom io import StringIO\nimport redis\nfrom loguru import logger\n\nfrom IPython.core.interactiveshell import InteractiveShell\nfrom IPython.core.getipython import get_ipython\nfrom IPython.utils.capture import capture_output\n\n\n# subscribed channels\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n# Error render prefix\nERROR_PREFIX = \"[ERROR]: \"\n\n\ndef check_danger_code(code):\n code_line = []\n for line in code.split(\"\\n\"):\n if not line.startswith(\"%\"):\n code_line.append(line)\n code = \"\\n\".join(code_line)\n\n def check_imports(code):\n ast_failed = False\n try:\n tree = ast.parse(code)\n except Exception as e:\n ast_failed = str(e)\n return ast_failed\n return ast_failed\n\n ast_failed = check_imports(code)\n return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.DisplayData","uri":"program://OpenAgents/class/real_agents.data_agent.evaluation.python_evaluator.DisplayData#L46-L60","kind":"class","name":"DisplayData","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":46,"end_line":60,"context_start_line":26,"context_end_line":80,"code":"def check_danger_code(code):\n code_line = []\n for line in code.split(\"\\n\"):\n if not line.startswith(\"%\"):\n code_line.append(line)\n code = \"\\n\".join(code_line)\n\n def check_imports(code):\n ast_failed = False\n try:\n tree = ast.parse(code)\n except Exception as e:\n ast_failed = str(e)\n return ast_failed\n return ast_failed\n\n ast_failed = check_imports(code)\n return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.PythonEvaluator","uri":"program://OpenAgents/class/real_agents.data_agent.evaluation.python_evaluator.PythonEvaluator#L63-L324","kind":"class","name":"PythonEvaluator","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":63,"end_line":324,"context_start_line":43,"context_end_line":324,"code":" return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run_program_local(self, program: str, user_id: Optional[str] = \"u\" * 24):\n \"\"\"Run python program on the local machine using Ipython shell.\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n try:\n # Run code using local ipython shell\n # Change working dir to data directory to load from pretty path\n # Note! This is not thread safe, only for local use\n os.chdir(os.path.join(\"backend/data/\", user_id))\n\n shell = InteractiveShell.instance()\n shell.enable_gui = lambda x: False\n with capture_output() as captured:\n ip = get_ipython()\n code = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n result = ip.run_cell(code)\n\n # Change working dir to project root\n os.chdir(\"../../../\")\n\n if result.success:\n return {\n \"success\": True,\n \"result\": result.result,\n \"stdout\": str(captured.stdout),\n \"stderr\": str(captured.stderr),\n \"outputs\": captured.outputs,\n }\n elif result.error_in_exec is not None:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_in_exec)}\",\n \"outputs\": captured.outputs,\n }\n else:\n # error_before_exec\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_before_exec)}\",\n \"outputs\": captured.outputs,\n }\n except Exception as e:\n logger.bind(user_id=user_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def _apply_for_kernel(self, kernel_id: Optional[str], user_id: str, chat_id: str):\n \"\"\"Apply for a kernel in docker to run program.\"\"\"\n if kernel_id is not None:\n # If kernel id is provided, use it directly\n cur_kid = kernel_id\n else:\n # If kernel id is not provided, apply for a new kernel\n kernel_info = self.jupyter_kernel_pool.get_pool_info_with_id(user_id, chat_id, None)\n cur_kid = kernel_info[\"kid\"] if kernel_info is not None else None\n user_exists = requests.get(f\"{self.base_url}/user/status/{user_id}\").json()[\"exists\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user exists\").trace(user_exists)\n\n if not user_exists:\n response = requests.post(f\"{self.base_url}/user/create\", json={\"username\": user_id}).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user create\").trace(response)\n\n response = requests.get(f\"{self.base_url}/kernel/list/{user_id}\").json()\n existing_kernel_list = response[\"list\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kernel list\").trace(response)\n\n if cur_kid not in existing_kernel_list:\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n if response[\"code\"] != 0 and response[\"msg\"] == \"Too many kernels\":\n # kill oldest kernel\n oldest_kernel_id = existing_kernel_list[0]\n response = requests.post(\n f\"{self.base_url}/kernel/stop\", json={\"username\": user_id, \"kid\": oldest_kernel_id}\n ).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kill oldest kernel\").trace(response)\n\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n cur_kid = response[\"id\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"create kernel id\").trace(cur_kid)\n\n self.jupyter_kernel_pool.set_pool_info_with_id(\n user_id, chat_id, {\"kid\": cur_kid, \"ktime\": time.time()}\n )\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"current kernel id\").trace(cur_kid)\n\n return cur_kid\n\n def run_program_docker(\n self,\n program: str,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ):\n \"\"\"Run python program on the docker container(jupyter client).\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if not is_safe:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Unsafe Code Detected {str(danger_pcks)}, Execution Denied!\",\n }\n elif ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n\n try:\n # Run code using remote docker jupyter kernel\n # Get the running id(i.e., a permission to run program) from redis running queue\n # otherwise wait until a running_id is available\n p = self.r.pubsub()\n p.subscribe(RUNNING_EVENT)\n self.r.publish(SUBMIT_EVENT, chat_id)\n for message in p.listen():\n # the initial message for each channel is a message with an integer\n if isinstance(message[\"data\"], int):\n continue\n running_id = message[\"data\"]\n if running_id == chat_id:\n break\n time.sleep(1)\n # Get kernel id(i.e., the real jupyter kernel to run the program) to execute program\n cur_kid = self._apply_for_kernel(kernel_id, user_id, chat_id)\n # Execute program\n response = requests.post(\n f\"{self.base_url}/kernel/exec\", json={\"username\": user_id, \"code\": program, \"kid\": cur_kid}\n ).json()\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n\n # Parse jupyter kernel output\n result, stdout, stderr, outputs, displays, error_message = None, \"\", \"\", None, [], None\n if response[\"status\"] == \"ok\":\n output = response.get(\"output\", None)\n if output is not None:\n for output_dict in output:\n if output_dict[\"type\"] == \"stream\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n if content[\"name\"] == \"stdout\":\n stdout = content[\"text\"]\n if content[\"name\"] == \"stderr\":\n stderr = content[\"text\"]\n elif output_dict[\"type\"] == \"execute_result\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n data = content.get(\"data\", None)\n if data is not None:\n if \"text/plain\" in data and \"text/html\" in data:\n try:\n # Try to recover a dataframe\n df = pd.read_csv(StringIO(data[\"text/plain\"]))\n result = df\n except Exception as e:\n pass\n elif \"text/plain\" in data:\n result = data[\"text/plain\"]\n else:\n # TODO: If not match any of the above, return the first value\n result = list(data.values())[0]\n elif output_dict[\"type\"] == \"display_data\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n data = content.get(\"data\", None)\n metadata = content.get(\"metadata\", None)\n if data is not None and metadata is not None:\n if \"image/png\" in data:\n displays.append(DisplayData.from_tuple((data, metadata)))\n if displays:\n outputs = displays\n else:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error status returned by kernel\",\n }\n # Check success status and return\n shell_msg = response[\"shell\"]\n if shell_msg[\"status\"] == \"ok\":\n return {\n \"success\": True,\n \"result\": result,\n \"stdout\": stdout,\n \"stderr\": stderr,\n \"outputs\": outputs,\n }\n elif shell_msg[\"status\"] == \"error\":\n error_message = f\"{shell_msg['ename']}: {shell_msg['evalue']}\"\n return {\"success\": False, \"error_message\": f\"{ERROR_PREFIX}{error_message}\", \"outputs\": outputs}\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n\n try:\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n except:\n pass\n\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def run(\n self,\n program: str,\n environment: Optional[Any] = None,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n\n lines_code = self.parse_command(program)\n program = \"\\n\".join(lines_code)\n program = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Code execution mode\").trace(self.code_execution_mode)\n\n if self.code_execution_mode == \"local\":\n return self.run_program_local(program, user_id)\n elif self.code_execution_mode == \"docker\":\n return self.run_program_docker(program, kernel_id, user_id, chat_id)\n else:\n raise ValueError(\"Invalid code execution mode\")","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.check_imports","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.check_imports#L33-L40","kind":"function","name":"check_imports","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":33,"end_line":40,"context_start_line":13,"context_end_line":60,"code":"from IPython.core.interactiveshell import InteractiveShell\nfrom IPython.core.getipython import get_ipython\nfrom IPython.utils.capture import capture_output\n\n\n# subscribed channels\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n# Error render prefix\nERROR_PREFIX = \"[ERROR]: \"\n\n\ndef check_danger_code(code):\n code_line = []\n for line in code.split(\"\\n\"):\n if not line.startswith(\"%\"):\n code_line.append(line)\n code = \"\\n\".join(code_line)\n\n def check_imports(code):\n ast_failed = False\n try:\n tree = ast.parse(code)\n except Exception as e:\n ast_failed = str(e)\n return ast_failed\n return ast_failed\n\n ast_failed = check_imports(code)\n return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.from_tuple","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.from_tuple#L53-L54","kind":"function","name":"from_tuple","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":53,"end_line":54,"context_start_line":33,"context_end_line":74,"code":" def check_imports(code):\n ast_failed = False\n try:\n tree = ast.parse(code)\n except Exception as e:\n ast_failed = str(e)\n return ast_failed\n return ast_failed\n\n ast_failed = check_imports(code)\n return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.to_dict","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.to_dict#L56-L60","kind":"function","name":"to_dict","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":56,"end_line":60,"context_start_line":36,"context_end_line":80,"code":" tree = ast.parse(code)\n except Exception as e:\n ast_failed = str(e)\n return ast_failed\n return ast_failed\n\n ast_failed = check_imports(code)\n return True, ast_failed, []\n\n\nclass DisplayData(BaseModel):\n \"\"\"Both display_data and execute_result messages use this format.\"\"\"\n\n data: Optional[dict] = None\n metadata: Optional[dict] = None\n\n @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.__init__","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.__init__#L72-L74","kind":"function","name":"__init__","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":72,"end_line":74,"context_start_line":52,"context_end_line":94,"code":" @classmethod\n def from_tuple(cls, formatted: Tuple[dict, dict]):\n return cls(data=formatted[0], metadata=formatted[1])\n\n def to_dict(self) -> Dict:\n return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run_program_local(self, program: str, user_id: Optional[str] = \"u\" * 24):\n \"\"\"Run python program on the local machine using Ipython shell.\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n try:\n # Run code using local ipython shell\n # Change working dir to data directory to load from pretty path\n # Note! This is not thread safe, only for local use\n os.chdir(os.path.join(\"backend/data/\", user_id))","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.parse_command","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.parse_command#L77-L80","kind":"function","name":"parse_command","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":77,"end_line":80,"context_start_line":57,"context_end_line":100,"code":" return {\n \"data\": self.data,\n \"metadata\": self.metadata,\n }\n\n\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run_program_local(self, program: str, user_id: Optional[str] = \"u\" * 24):\n \"\"\"Run python program on the local machine using Ipython shell.\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n try:\n # Run code using local ipython shell\n # Change working dir to data directory to load from pretty path\n # Note! This is not thread safe, only for local use\n os.chdir(os.path.join(\"backend/data/\", user_id))\n\n shell = InteractiveShell.instance()\n shell.enable_gui = lambda x: False\n with capture_output() as captured:\n ip = get_ipython()\n code = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.run_program_local","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.run_program_local#L82-L135","kind":"function","name":"run_program_local","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":82,"end_line":135,"context_start_line":62,"context_end_line":155,"code":"\nclass PythonEvaluator:\n \"\"\"\n Util class for Python code evaluation.\n \"\"\"\n\n name = \"Python Evaluator\"\n base_url = \"http://{0}:8100\".format(os.getenv(\"CODE_INTER_SERVER\"))\n r: redis.Redis = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n def __init__(self, code_execution_mode: str = \"local\", jupyter_kernel_pool: Optional[Any] = None):\n self.code_execution_mode = code_execution_mode\n self.jupyter_kernel_pool = jupyter_kernel_pool\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run_program_local(self, program: str, user_id: Optional[str] = \"u\" * 24):\n \"\"\"Run python program on the local machine using Ipython shell.\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n try:\n # Run code using local ipython shell\n # Change working dir to data directory to load from pretty path\n # Note! This is not thread safe, only for local use\n os.chdir(os.path.join(\"backend/data/\", user_id))\n\n shell = InteractiveShell.instance()\n shell.enable_gui = lambda x: False\n with capture_output() as captured:\n ip = get_ipython()\n code = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n result = ip.run_cell(code)\n\n # Change working dir to project root\n os.chdir(\"../../../\")\n\n if result.success:\n return {\n \"success\": True,\n \"result\": result.result,\n \"stdout\": str(captured.stdout),\n \"stderr\": str(captured.stderr),\n \"outputs\": captured.outputs,\n }\n elif result.error_in_exec is not None:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_in_exec)}\",\n \"outputs\": captured.outputs,\n }\n else:\n # error_before_exec\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_before_exec)}\",\n \"outputs\": captured.outputs,\n }\n except Exception as e:\n logger.bind(user_id=user_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def _apply_for_kernel(self, kernel_id: Optional[str], user_id: str, chat_id: str):\n \"\"\"Apply for a kernel in docker to run program.\"\"\"\n if kernel_id is not None:\n # If kernel id is provided, use it directly\n cur_kid = kernel_id\n else:\n # If kernel id is not provided, apply for a new kernel\n kernel_info = self.jupyter_kernel_pool.get_pool_info_with_id(user_id, chat_id, None)\n cur_kid = kernel_info[\"kid\"] if kernel_info is not None else None\n user_exists = requests.get(f\"{self.base_url}/user/status/{user_id}\").json()[\"exists\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user exists\").trace(user_exists)\n\n if not user_exists:\n response = requests.post(f\"{self.base_url}/user/create\", json={\"username\": user_id}).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user create\").trace(response)\n\n response = requests.get(f\"{self.base_url}/kernel/list/{user_id}\").json()","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator._apply_for_kernel","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator._apply_for_kernel#L137-L182","kind":"function","name":"_apply_for_kernel","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":137,"end_line":182,"context_start_line":117,"context_end_line":202,"code":" \"error_message\": f\"{ERROR_PREFIX}{str(result.error_in_exec)}\",\n \"outputs\": captured.outputs,\n }\n else:\n # error_before_exec\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(result.error_before_exec)}\",\n \"outputs\": captured.outputs,\n }\n except Exception as e:\n logger.bind(user_id=user_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def _apply_for_kernel(self, kernel_id: Optional[str], user_id: str, chat_id: str):\n \"\"\"Apply for a kernel in docker to run program.\"\"\"\n if kernel_id is not None:\n # If kernel id is provided, use it directly\n cur_kid = kernel_id\n else:\n # If kernel id is not provided, apply for a new kernel\n kernel_info = self.jupyter_kernel_pool.get_pool_info_with_id(user_id, chat_id, None)\n cur_kid = kernel_info[\"kid\"] if kernel_info is not None else None\n user_exists = requests.get(f\"{self.base_url}/user/status/{user_id}\").json()[\"exists\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user exists\").trace(user_exists)\n\n if not user_exists:\n response = requests.post(f\"{self.base_url}/user/create\", json={\"username\": user_id}).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"user create\").trace(response)\n\n response = requests.get(f\"{self.base_url}/kernel/list/{user_id}\").json()\n existing_kernel_list = response[\"list\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kernel list\").trace(response)\n\n if cur_kid not in existing_kernel_list:\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n if response[\"code\"] != 0 and response[\"msg\"] == \"Too many kernels\":\n # kill oldest kernel\n oldest_kernel_id = existing_kernel_list[0]\n response = requests.post(\n f\"{self.base_url}/kernel/stop\", json={\"username\": user_id, \"kid\": oldest_kernel_id}\n ).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kill oldest kernel\").trace(response)\n\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n cur_kid = response[\"id\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"create kernel id\").trace(cur_kid)\n\n self.jupyter_kernel_pool.set_pool_info_with_id(\n user_id, chat_id, {\"kid\": cur_kid, \"ktime\": time.time()}\n )\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"current kernel id\").trace(cur_kid)\n\n return cur_kid\n\n def run_program_docker(\n self,\n program: str,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ):\n \"\"\"Run python program on the docker container(jupyter client).\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if not is_safe:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Unsafe Code Detected {str(danger_pcks)}, Execution Denied!\",\n }\n elif ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.run_program_docker","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.run_program_docker#L184-L301","kind":"function","name":"run_program_docker","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":184,"end_line":301,"context_start_line":164,"context_end_line":321,"code":" oldest_kernel_id = existing_kernel_list[0]\n response = requests.post(\n f\"{self.base_url}/kernel/stop\", json={\"username\": user_id, \"kid\": oldest_kernel_id}\n ).json()\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"kill oldest kernel\").trace(response)\n\n response = requests.post(f\"{self.base_url}/kernel/create\", json={\"username\": user_id}).json()\n cur_kid = response[\"id\"]\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"create kernel id\").trace(cur_kid)\n\n self.jupyter_kernel_pool.set_pool_info_with_id(\n user_id, chat_id, {\"kid\": cur_kid, \"ktime\": time.time()}\n )\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"current kernel id\").trace(cur_kid)\n\n return cur_kid\n\n def run_program_docker(\n self,\n program: str,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ):\n \"\"\"Run python program on the docker container(jupyter client).\"\"\"\n is_safe, ast_failed, danger_pcks = check_danger_code(program)\n if not is_safe:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Unsafe Code Detected {str(danger_pcks)}, Execution Denied!\",\n }\n elif ast_failed != False:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error Code Parsing, please check code grammar!\\n{ast_failed}\",\n }\n\n try:\n # Run code using remote docker jupyter kernel\n # Get the running id(i.e., a permission to run program) from redis running queue\n # otherwise wait until a running_id is available\n p = self.r.pubsub()\n p.subscribe(RUNNING_EVENT)\n self.r.publish(SUBMIT_EVENT, chat_id)\n for message in p.listen():\n # the initial message for each channel is a message with an integer\n if isinstance(message[\"data\"], int):\n continue\n running_id = message[\"data\"]\n if running_id == chat_id:\n break\n time.sleep(1)\n # Get kernel id(i.e., the real jupyter kernel to run the program) to execute program\n cur_kid = self._apply_for_kernel(kernel_id, user_id, chat_id)\n # Execute program\n response = requests.post(\n f\"{self.base_url}/kernel/exec\", json={\"username\": user_id, \"code\": program, \"kid\": cur_kid}\n ).json()\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n\n # Parse jupyter kernel output\n result, stdout, stderr, outputs, displays, error_message = None, \"\", \"\", None, [], None\n if response[\"status\"] == \"ok\":\n output = response.get(\"output\", None)\n if output is not None:\n for output_dict in output:\n if output_dict[\"type\"] == \"stream\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n if content[\"name\"] == \"stdout\":\n stdout = content[\"text\"]\n if content[\"name\"] == \"stderr\":\n stderr = content[\"text\"]\n elif output_dict[\"type\"] == \"execute_result\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n data = content.get(\"data\", None)\n if data is not None:\n if \"text/plain\" in data and \"text/html\" in data:\n try:\n # Try to recover a dataframe\n df = pd.read_csv(StringIO(data[\"text/plain\"]))\n result = df\n except Exception as e:\n pass\n elif \"text/plain\" in data:\n result = data[\"text/plain\"]\n else:\n # TODO: If not match any of the above, return the first value\n result = list(data.values())[0]\n elif output_dict[\"type\"] == \"display_data\":\n content = output_dict.get(\"content\", None)\n if content is not None:\n data = content.get(\"data\", None)\n metadata = content.get(\"metadata\", None)\n if data is not None and metadata is not None:\n if \"image/png\" in data:\n displays.append(DisplayData.from_tuple((data, metadata)))\n if displays:\n outputs = displays\n else:\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}Error status returned by kernel\",\n }\n # Check success status and return\n shell_msg = response[\"shell\"]\n if shell_msg[\"status\"] == \"ok\":\n return {\n \"success\": True,\n \"result\": result,\n \"stdout\": stdout,\n \"stderr\": stderr,\n \"outputs\": outputs,\n }\n elif shell_msg[\"status\"] == \"error\":\n error_message = f\"{shell_msg['ename']}: {shell_msg['evalue']}\"\n return {\"success\": False, \"error_message\": f\"{ERROR_PREFIX}{error_message}\", \"outputs\": outputs}\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n\n try:\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n except:\n pass\n\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def run(\n self,\n program: str,\n environment: Optional[Any] = None,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n\n lines_code = self.parse_command(program)\n program = \"\\n\".join(lines_code)\n program = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Code execution mode\").trace(self.code_execution_mode)\n\n if self.code_execution_mode == \"local\":\n return self.run_program_local(program, user_id)\n elif self.code_execution_mode == \"docker\":","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.python_evaluator.run","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.python_evaluator.run#L303-L324","kind":"function","name":"run","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":303,"end_line":324,"context_start_line":283,"context_end_line":324,"code":" elif shell_msg[\"status\"] == \"error\":\n error_message = f\"{shell_msg['ename']}: {shell_msg['evalue']}\"\n return {\"success\": False, \"error_message\": f\"{ERROR_PREFIX}{error_message}\", \"outputs\": outputs}\n except Exception as e:\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Python evaluator running error\").trace(e)\n import traceback\n\n traceback.print_exc()\n\n try:\n # Notify Redis that a job has been completed\n self.r.publish(COMPLETE_EVENT, chat_id)\n except:\n pass\n\n return {\n \"success\": False,\n \"error_message\": f\"{ERROR_PREFIX}{str(e)}\",\n }\n\n def run(\n self,\n program: str,\n environment: Optional[Any] = None,\n kernel_id: Optional[str] = None,\n user_id: Optional[str] = \"u\" * 24,\n chat_id: Optional[str] = \"c\" * 24,\n ) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n\n lines_code = self.parse_command(program)\n program = \"\\n\".join(lines_code)\n program = \"%matplotlib inline\\n\" + program # magic command to display matplotlib plots\n\n logger.bind(user_id=user_id, chat_id=chat_id, msg_head=\"Code execution mode\").trace(self.code_execution_mode)\n\n if self.code_execution_mode == \"local\":\n return self.run_program_local(program, user_id)\n elif self.code_execution_mode == \"docker\":\n return self.run_program_docker(program, kernel_id, user_id, chat_id)\n else:\n raise ValueError(\"Invalid code execution mode\")","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.sql_evaluator","uri":"program://OpenAgents/module/real_agents.data_agent.evaluation.sql_evaluator#L1-L45","kind":"module","name":"real_agents.data_agent.evaluation.sql_evaluator","path":"real_agents/data_agent/evaluation/sql_evaluator.py","language":"python","start_line":1,"end_line":45,"context_start_line":1,"context_end_line":45,"code":"import traceback\nfrom typing import Any, Dict, List\n\nfrom pydantic import root_validator\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass SQLEvaluator:\n \"\"\"\n Util class for SQL code evaluation.\n \"\"\"\n\n name = \"SQL Evaluator\"\n ERROR_PREFIX = \"[ERROR]: \"\n\n @root_validator(pre=True)\n def validate(cls, values: Dict) -> Any:\n \"\"\"validate requirements for evaluation\"\"\"\n try:\n import sqlite3 # noqa F401 E402\n\n import sqlalchemy # noqa F401 E402\n except ImportError:\n raise ValueError(\"This tool relies on sqlite3 and sqlalchemy, use `pip` to install these packages\")\n return values\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run(self, program: str, environment: SQLDatabase) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n try:\n output = environment.run(program)\n return {\n \"success\": True,\n \"result\": output,\n }\n except Exception as e:\n traceback.print_exc()\n error_message = str(e)\n return {\"success\": False, \"error_message\": f\"{self.ERROR_PREFIX}{error_message}\"}","source_hash":"60a1492973ed959092ef08e38f5ef9f3fcfd1e54fb2e12695146e025a85b62bd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.sql_evaluator.SQLEvaluator","uri":"program://OpenAgents/class/real_agents.data_agent.evaluation.sql_evaluator.SQLEvaluator#L9-L45","kind":"class","name":"SQLEvaluator","path":"real_agents/data_agent/evaluation/sql_evaluator.py","language":"python","start_line":9,"end_line":45,"context_start_line":1,"context_end_line":45,"code":"import traceback\nfrom typing import Any, Dict, List\n\nfrom pydantic import root_validator\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass SQLEvaluator:\n \"\"\"\n Util class for SQL code evaluation.\n \"\"\"\n\n name = \"SQL Evaluator\"\n ERROR_PREFIX = \"[ERROR]: \"\n\n @root_validator(pre=True)\n def validate(cls, values: Dict) -> Any:\n \"\"\"validate requirements for evaluation\"\"\"\n try:\n import sqlite3 # noqa F401 E402\n\n import sqlalchemy # noqa F401 E402\n except ImportError:\n raise ValueError(\"This tool relies on sqlite3 and sqlalchemy, use `pip` to install these packages\")\n return values\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run(self, program: str, environment: SQLDatabase) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n try:\n output = environment.run(program)\n return {\n \"success\": True,\n \"result\": output,\n }\n except Exception as e:\n traceback.print_exc()\n error_message = str(e)\n return {\"success\": False, \"error_message\": f\"{self.ERROR_PREFIX}{error_message}\"}","source_hash":"60a1492973ed959092ef08e38f5ef9f3fcfd1e54fb2e12695146e025a85b62bd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.sql_evaluator.validate","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.sql_evaluator.validate#L18-L26","kind":"function","name":"validate","path":"real_agents/data_agent/evaluation/sql_evaluator.py","language":"python","start_line":18,"end_line":26,"context_start_line":1,"context_end_line":45,"code":"import traceback\nfrom typing import Any, Dict, List\n\nfrom pydantic import root_validator\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass SQLEvaluator:\n \"\"\"\n Util class for SQL code evaluation.\n \"\"\"\n\n name = \"SQL Evaluator\"\n ERROR_PREFIX = \"[ERROR]: \"\n\n @root_validator(pre=True)\n def validate(cls, values: Dict) -> Any:\n \"\"\"validate requirements for evaluation\"\"\"\n try:\n import sqlite3 # noqa F401 E402\n\n import sqlalchemy # noqa F401 E402\n except ImportError:\n raise ValueError(\"This tool relies on sqlite3 and sqlalchemy, use `pip` to install these packages\")\n return values\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run(self, program: str, environment: SQLDatabase) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n try:\n output = environment.run(program)\n return {\n \"success\": True,\n \"result\": output,\n }\n except Exception as e:\n traceback.print_exc()\n error_message = str(e)\n return {\"success\": False, \"error_message\": f\"{self.ERROR_PREFIX}{error_message}\"}","source_hash":"60a1492973ed959092ef08e38f5ef9f3fcfd1e54fb2e12695146e025a85b62bd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.sql_evaluator.parse_command","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.sql_evaluator.parse_command#L29-L32","kind":"function","name":"parse_command","path":"real_agents/data_agent/evaluation/sql_evaluator.py","language":"python","start_line":29,"end_line":32,"context_start_line":9,"context_end_line":45,"code":"class SQLEvaluator:\n \"\"\"\n Util class for SQL code evaluation.\n \"\"\"\n\n name = \"SQL Evaluator\"\n ERROR_PREFIX = \"[ERROR]: \"\n\n @root_validator(pre=True)\n def validate(cls, values: Dict) -> Any:\n \"\"\"validate requirements for evaluation\"\"\"\n try:\n import sqlite3 # noqa F401 E402\n\n import sqlalchemy # noqa F401 E402\n except ImportError:\n raise ValueError(\"This tool relies on sqlite3 and sqlalchemy, use `pip` to install these packages\")\n return values\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run(self, program: str, environment: SQLDatabase) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n try:\n output = environment.run(program)\n return {\n \"success\": True,\n \"result\": output,\n }\n except Exception as e:\n traceback.print_exc()\n error_message = str(e)\n return {\"success\": False, \"error_message\": f\"{self.ERROR_PREFIX}{error_message}\"}","source_hash":"60a1492973ed959092ef08e38f5ef9f3fcfd1e54fb2e12695146e025a85b62bd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.evaluation.sql_evaluator.run","uri":"program://OpenAgents/function/real_agents.data_agent.evaluation.sql_evaluator.run#L34-L45","kind":"function","name":"run","path":"real_agents/data_agent/evaluation/sql_evaluator.py","language":"python","start_line":34,"end_line":45,"context_start_line":14,"context_end_line":45,"code":" name = \"SQL Evaluator\"\n ERROR_PREFIX = \"[ERROR]: \"\n\n @root_validator(pre=True)\n def validate(cls, values: Dict) -> Any:\n \"\"\"validate requirements for evaluation\"\"\"\n try:\n import sqlite3 # noqa F401 E402\n\n import sqlalchemy # noqa F401 E402\n except ImportError:\n raise ValueError(\"This tool relies on sqlite3 and sqlalchemy, use `pip` to install these packages\")\n return values\n\n @staticmethod\n def parse_command(program: str) -> List[str]:\n \"\"\"patchify the code\"\"\"\n program_lines = program.strip().split(\"\\n\")\n return program_lines\n\n def run(self, program: str, environment: SQLDatabase) -> Any:\n \"\"\"run generated code in certain environment\"\"\"\n try:\n output = environment.run(program)\n return {\n \"success\": True,\n \"result\": output,\n }\n except Exception as e:\n traceback.print_exc()\n error_message = str(e)\n return {\"success\": False, \"error_message\": f\"{self.ERROR_PREFIX}{error_message}\"}","source_hash":"60a1492973ed959092ef08e38f5ef9f3fcfd1e54fb2e12695146e025a85b62bd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor","uri":"program://OpenAgents/module/real_agents.data_agent.executors.kaggle_data_loading_executor#L1-L163","kind":"module","name":"real_agents.data_agent.executors.kaggle_data_loading_executor","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":1,"end_line":163,"context_start_line":1,"context_end_line":163,"code":"import json\nimport os\nimport re\nimport shutil\nimport uuid\nfrom typing import Any, Dict, List, Tuple\nimport requests\nfrom bs4 import BeautifulSoup\nfrom loguru import logger\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass KaggleDataLoadingExecutor:\n KAGGLE_TEMPLATE = \"\"\"\n\nDetermine whether the user input aims to (1) connect to a specific kaggle dataset that the user mentions its kaggle path\n(2) search for relevant kaggle datasets given the information the user provides.\n\nYou need to output the action wrapped in , the action space is ['connect', 'search']. You also need\nto output the keywords wrapped in . For 'search', the keywords MUST be ONE search term/word to be\nsearched by kaggle api. Note keywords CAN'T be too specific or contain trivial word(e.g., dataset), make sure there are various search results. For\n'connect', the keywords are the kaggle dataset path.\n\nInput: {input}\n\nBegin.\"\n\"\"\"\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n search_top_k: int = 4,\n ) -> Dict[str, Any]:\n logger.bind(msg_head=\"KaggleDataLoader inputs\").trace(user_intent)\n kaggle_template = PromptTemplate(\n input_variables=[\"input\"],\n template=self.KAGGLE_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=kaggle_template)\n result = method.run({\"input\": user_intent})\n logger.bind(msg_head=\"LLM result\").trace(result)\n kaggle_action, keywords = self._parse_output(result)\n logger.bind(msg_head=\"Kaggle action\").trace(kaggle_action)\n logger.bind(msg_head=\"Kaggle keywords\").trace(keywords)\n \"\"\"Use export to manage the Kaggle API key for now.\"\"\"\n api = KaggleApi()\n api.authenticate()\n if kaggle_action == \"connect\":\n kaggle_output_info = keywords\n elif kaggle_action == \"search\":\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n else:\n # Regard the rest as \"search\" action now\n kaggle_action = \"search\"\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n return {\"kaggle_action\": kaggle_action, \"kaggle_output_info\": kaggle_output_info}\n\n def _search_kaggle(self, api: KaggleApi, keywords: str, search_top_k: int) -> List[Dict]:\n \"\"\"Search kaggle datasets given the keywords.\"\"\"\n # Search for datasets\n datasets = []\n for page in range(1, 10):\n try:\n searched_datasets = api.dataset_list(search=keywords, page=page, max_size=20000, file_type=\"csv\")\n\n logger.bind(msg_head=\"Kaggle search result\").trace(searched_datasets)\n\n datasets.extend(searched_datasets)\n if len(datasets) >= search_top_k:\n datasets = datasets[:search_top_k]\n break\n if len(searched_datasets) < 20:\n # Default page_size is 20, less than 20 means no more datasets can be searched\n break\n except Exception:\n break\n\n # Get url, cover image and some meta data for each dataset\n if len(datasets) == 0:\n # No datasets found\n datasets = api.dataset_list(max_size=20000, page=1, file_type=\"csv\")[:search_top_k]\n\n output_info = self._get_dataset_meta_info(api, datasets)\n return output_info\n\n def _get_dataset_meta_info(self, api: KaggleApi, datasets: List) -> List[Dict]:\n \"\"\"Get dataset key meta-data to be shown to the user.\"\"\"\n output_info = []\n for dataset in datasets:\n dataset_hash_id = str(uuid.uuid4())\n dataset_tmp_dir = os.path.join(\".kaggle_meta/\", dataset_hash_id)\n os.makedirs(dataset_tmp_dir, exist_ok=True)\n api.dataset_metadata(dataset.ref, path=dataset_tmp_dir)\n with open(os.path.join(dataset_tmp_dir, \"dataset-metadata.json\")) as f:\n dataset_metadata = json.load(f)\n shutil.rmtree(os.path.join(\".kaggle_meta/\", dataset_hash_id))\n dataset_url = \"https://www.kaggle.com/datasets/\" + dataset.ref\n # Crawling the dataset page to get the dataset image\n dataset_cover_image_url = self._crawl_dataset_cover_image(dataset_url)\n\n logger.bind(msg_head=\"Dataset cover image url\").trace(dataset_cover_image_url)\n\n output_metadata = {\n \"id\": dataset_metadata[\"id\"],\n \"id_no\": dataset_metadata[\"id_no\"],\n \"title\": dataset_metadata[\"title\"],\n \"subtitle\": dataset_metadata[\"subtitle\"],\n \"total_views\": dataset_metadata[\"totalViews\"],\n \"total_votes\": dataset_metadata[\"totalVotes\"],\n \"total_downloads\": dataset_metadata[\"totalDownloads\"],\n \"url\": dataset_url,\n \"cover_image_url\": dataset_cover_image_url,\n }\n output_info.append(output_metadata)\n return output_info\n\n def _crawl_dataset_cover_image(\n self, url: str, default_image_path=\"https://images.datacamp.com/image/upload/v1647430873/kaggle_logo_icon_168474_4eb653edb6.png\"\n ) -> str:\n \"\"\"Crawl the kaggle dataset cover image from the dataset url.\"\"\"\n # Get the HTML content of the webpage\n response = requests.get(url)\n\n # Parse the HTML with BeautifulSoup\n soup = BeautifulSoup(response.text, \"html.parser\")\n\n # Find the image element\n try:\n kaggle_component_element = soup.find(\"script\", {\"class\": \"kaggle-component\"})\n match = re.search(r'\"coverImageUrl\":\\s*\"([^\"]*)\"', kaggle_component_element.string)\n image_url = match.group(1)\n except Exception:\n import traceback\n\n traceback.print_exc()\n image_url = default_image_path\n\n return image_url\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the kaggle action and keywords.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n action = soup.find(\"action\").text\n except Exception:\n action = \"\"\n\n try:\n keywords = soup.find(\"keywords\").text\n except Exception:\n keywords = \"\"\n\n return action, keywords","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor.KaggleDataLoadingExecutor","uri":"program://OpenAgents/class/real_agents.data_agent.executors.kaggle_data_loading_executor.KaggleDataLoadingExecutor#L18-L163","kind":"class","name":"KaggleDataLoadingExecutor","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":18,"end_line":163,"context_start_line":1,"context_end_line":163,"code":"import json\nimport os\nimport re\nimport shutil\nimport uuid\nfrom typing import Any, Dict, List, Tuple\nimport requests\nfrom bs4 import BeautifulSoup\nfrom loguru import logger\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass KaggleDataLoadingExecutor:\n KAGGLE_TEMPLATE = \"\"\"\n\nDetermine whether the user input aims to (1) connect to a specific kaggle dataset that the user mentions its kaggle path\n(2) search for relevant kaggle datasets given the information the user provides.\n\nYou need to output the action wrapped in , the action space is ['connect', 'search']. You also need\nto output the keywords wrapped in . For 'search', the keywords MUST be ONE search term/word to be\nsearched by kaggle api. Note keywords CAN'T be too specific or contain trivial word(e.g., dataset), make sure there are various search results. For\n'connect', the keywords are the kaggle dataset path.\n\nInput: {input}\n\nBegin.\"\n\"\"\"\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n search_top_k: int = 4,\n ) -> Dict[str, Any]:\n logger.bind(msg_head=\"KaggleDataLoader inputs\").trace(user_intent)\n kaggle_template = PromptTemplate(\n input_variables=[\"input\"],\n template=self.KAGGLE_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=kaggle_template)\n result = method.run({\"input\": user_intent})\n logger.bind(msg_head=\"LLM result\").trace(result)\n kaggle_action, keywords = self._parse_output(result)\n logger.bind(msg_head=\"Kaggle action\").trace(kaggle_action)\n logger.bind(msg_head=\"Kaggle keywords\").trace(keywords)\n \"\"\"Use export to manage the Kaggle API key for now.\"\"\"\n api = KaggleApi()\n api.authenticate()\n if kaggle_action == \"connect\":\n kaggle_output_info = keywords\n elif kaggle_action == \"search\":\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n else:\n # Regard the rest as \"search\" action now\n kaggle_action = \"search\"\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n return {\"kaggle_action\": kaggle_action, \"kaggle_output_info\": kaggle_output_info}\n\n def _search_kaggle(self, api: KaggleApi, keywords: str, search_top_k: int) -> List[Dict]:\n \"\"\"Search kaggle datasets given the keywords.\"\"\"\n # Search for datasets\n datasets = []\n for page in range(1, 10):\n try:\n searched_datasets = api.dataset_list(search=keywords, page=page, max_size=20000, file_type=\"csv\")\n\n logger.bind(msg_head=\"Kaggle search result\").trace(searched_datasets)\n\n datasets.extend(searched_datasets)\n if len(datasets) >= search_top_k:\n datasets = datasets[:search_top_k]\n break\n if len(searched_datasets) < 20:\n # Default page_size is 20, less than 20 means no more datasets can be searched\n break\n except Exception:\n break\n\n # Get url, cover image and some meta data for each dataset\n if len(datasets) == 0:\n # No datasets found\n datasets = api.dataset_list(max_size=20000, page=1, file_type=\"csv\")[:search_top_k]\n\n output_info = self._get_dataset_meta_info(api, datasets)\n return output_info\n\n def _get_dataset_meta_info(self, api: KaggleApi, datasets: List) -> List[Dict]:\n \"\"\"Get dataset key meta-data to be shown to the user.\"\"\"\n output_info = []\n for dataset in datasets:\n dataset_hash_id = str(uuid.uuid4())\n dataset_tmp_dir = os.path.join(\".kaggle_meta/\", dataset_hash_id)\n os.makedirs(dataset_tmp_dir, exist_ok=True)\n api.dataset_metadata(dataset.ref, path=dataset_tmp_dir)\n with open(os.path.join(dataset_tmp_dir, \"dataset-metadata.json\")) as f:\n dataset_metadata = json.load(f)\n shutil.rmtree(os.path.join(\".kaggle_meta/\", dataset_hash_id))\n dataset_url = \"https://www.kaggle.com/datasets/\" + dataset.ref\n # Crawling the dataset page to get the dataset image\n dataset_cover_image_url = self._crawl_dataset_cover_image(dataset_url)\n\n logger.bind(msg_head=\"Dataset cover image url\").trace(dataset_cover_image_url)\n\n output_metadata = {\n \"id\": dataset_metadata[\"id\"],\n \"id_no\": dataset_metadata[\"id_no\"],\n \"title\": dataset_metadata[\"title\"],\n \"subtitle\": dataset_metadata[\"subtitle\"],\n \"total_views\": dataset_metadata[\"totalViews\"],\n \"total_votes\": dataset_metadata[\"totalVotes\"],\n \"total_downloads\": dataset_metadata[\"totalDownloads\"],\n \"url\": dataset_url,\n \"cover_image_url\": dataset_cover_image_url,\n }\n output_info.append(output_metadata)\n return output_info\n\n def _crawl_dataset_cover_image(\n self, url: str, default_image_path=\"https://images.datacamp.com/image/upload/v1647430873/kaggle_logo_icon_168474_4eb653edb6.png\"\n ) -> str:\n \"\"\"Crawl the kaggle dataset cover image from the dataset url.\"\"\"\n # Get the HTML content of the webpage\n response = requests.get(url)\n\n # Parse the HTML with BeautifulSoup\n soup = BeautifulSoup(response.text, \"html.parser\")\n\n # Find the image element\n try:\n kaggle_component_element = soup.find(\"script\", {\"class\": \"kaggle-component\"})\n match = re.search(r'\"coverImageUrl\":\\s*\"([^\"]*)\"', kaggle_component_element.string)\n image_url = match.group(1)\n except Exception:\n import traceback\n\n traceback.print_exc()\n image_url = default_image_path\n\n return image_url\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the kaggle action and keywords.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n action = soup.find(\"action\").text\n except Exception:\n action = \"\"\n\n try:\n keywords = soup.find(\"keywords\").text\n except Exception:\n keywords = \"\"\n\n return action, keywords","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor.run","uri":"program://OpenAgents/function/real_agents.data_agent.executors.kaggle_data_loading_executor.run#L34-L62","kind":"function","name":"run","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":34,"end_line":62,"context_start_line":14,"context_end_line":82,"code":"\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass KaggleDataLoadingExecutor:\n KAGGLE_TEMPLATE = \"\"\"\n\nDetermine whether the user input aims to (1) connect to a specific kaggle dataset that the user mentions its kaggle path\n(2) search for relevant kaggle datasets given the information the user provides.\n\nYou need to output the action wrapped in , the action space is ['connect', 'search']. You also need\nto output the keywords wrapped in . For 'search', the keywords MUST be ONE search term/word to be\nsearched by kaggle api. Note keywords CAN'T be too specific or contain trivial word(e.g., dataset), make sure there are various search results. For\n'connect', the keywords are the kaggle dataset path.\n\nInput: {input}\n\nBegin.\"\n\"\"\"\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n search_top_k: int = 4,\n ) -> Dict[str, Any]:\n logger.bind(msg_head=\"KaggleDataLoader inputs\").trace(user_intent)\n kaggle_template = PromptTemplate(\n input_variables=[\"input\"],\n template=self.KAGGLE_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=kaggle_template)\n result = method.run({\"input\": user_intent})\n logger.bind(msg_head=\"LLM result\").trace(result)\n kaggle_action, keywords = self._parse_output(result)\n logger.bind(msg_head=\"Kaggle action\").trace(kaggle_action)\n logger.bind(msg_head=\"Kaggle keywords\").trace(keywords)\n \"\"\"Use export to manage the Kaggle API key for now.\"\"\"\n api = KaggleApi()\n api.authenticate()\n if kaggle_action == \"connect\":\n kaggle_output_info = keywords\n elif kaggle_action == \"search\":\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n else:\n # Regard the rest as \"search\" action now\n kaggle_action = \"search\"\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n return {\"kaggle_action\": kaggle_action, \"kaggle_output_info\": kaggle_output_info}\n\n def _search_kaggle(self, api: KaggleApi, keywords: str, search_top_k: int) -> List[Dict]:\n \"\"\"Search kaggle datasets given the keywords.\"\"\"\n # Search for datasets\n datasets = []\n for page in range(1, 10):\n try:\n searched_datasets = api.dataset_list(search=keywords, page=page, max_size=20000, file_type=\"csv\")\n\n logger.bind(msg_head=\"Kaggle search result\").trace(searched_datasets)\n\n datasets.extend(searched_datasets)\n if len(datasets) >= search_top_k:\n datasets = datasets[:search_top_k]\n break\n if len(searched_datasets) < 20:\n # Default page_size is 20, less than 20 means no more datasets can be searched\n break\n except Exception:\n break","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor._search_kaggle","uri":"program://OpenAgents/function/real_agents.data_agent.executors.kaggle_data_loading_executor._search_kaggle#L64-L90","kind":"function","name":"_search_kaggle","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":64,"end_line":90,"context_start_line":44,"context_end_line":110,"code":" )\n method = LLMChain(llm=llm, prompt=kaggle_template)\n result = method.run({\"input\": user_intent})\n logger.bind(msg_head=\"LLM result\").trace(result)\n kaggle_action, keywords = self._parse_output(result)\n logger.bind(msg_head=\"Kaggle action\").trace(kaggle_action)\n logger.bind(msg_head=\"Kaggle keywords\").trace(keywords)\n \"\"\"Use export to manage the Kaggle API key for now.\"\"\"\n api = KaggleApi()\n api.authenticate()\n if kaggle_action == \"connect\":\n kaggle_output_info = keywords\n elif kaggle_action == \"search\":\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n else:\n # Regard the rest as \"search\" action now\n kaggle_action = \"search\"\n kaggle_output_info = self._search_kaggle(api, keywords, search_top_k)\n return {\"kaggle_action\": kaggle_action, \"kaggle_output_info\": kaggle_output_info}\n\n def _search_kaggle(self, api: KaggleApi, keywords: str, search_top_k: int) -> List[Dict]:\n \"\"\"Search kaggle datasets given the keywords.\"\"\"\n # Search for datasets\n datasets = []\n for page in range(1, 10):\n try:\n searched_datasets = api.dataset_list(search=keywords, page=page, max_size=20000, file_type=\"csv\")\n\n logger.bind(msg_head=\"Kaggle search result\").trace(searched_datasets)\n\n datasets.extend(searched_datasets)\n if len(datasets) >= search_top_k:\n datasets = datasets[:search_top_k]\n break\n if len(searched_datasets) < 20:\n # Default page_size is 20, less than 20 means no more datasets can be searched\n break\n except Exception:\n break\n\n # Get url, cover image and some meta data for each dataset\n if len(datasets) == 0:\n # No datasets found\n datasets = api.dataset_list(max_size=20000, page=1, file_type=\"csv\")[:search_top_k]\n\n output_info = self._get_dataset_meta_info(api, datasets)\n return output_info\n\n def _get_dataset_meta_info(self, api: KaggleApi, datasets: List) -> List[Dict]:\n \"\"\"Get dataset key meta-data to be shown to the user.\"\"\"\n output_info = []\n for dataset in datasets:\n dataset_hash_id = str(uuid.uuid4())\n dataset_tmp_dir = os.path.join(\".kaggle_meta/\", dataset_hash_id)\n os.makedirs(dataset_tmp_dir, exist_ok=True)\n api.dataset_metadata(dataset.ref, path=dataset_tmp_dir)\n with open(os.path.join(dataset_tmp_dir, \"dataset-metadata.json\")) as f:\n dataset_metadata = json.load(f)\n shutil.rmtree(os.path.join(\".kaggle_meta/\", dataset_hash_id))\n dataset_url = \"https://www.kaggle.com/datasets/\" + dataset.ref\n # Crawling the dataset page to get the dataset image\n dataset_cover_image_url = self._crawl_dataset_cover_image(dataset_url)\n\n logger.bind(msg_head=\"Dataset cover image url\").trace(dataset_cover_image_url)\n\n output_metadata = {\n \"id\": dataset_metadata[\"id\"],","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor._get_dataset_meta_info","uri":"program://OpenAgents/function/real_agents.data_agent.executors.kaggle_data_loading_executor._get_dataset_meta_info#L92-L121","kind":"function","name":"_get_dataset_meta_info","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":92,"end_line":121,"context_start_line":72,"context_end_line":141,"code":" logger.bind(msg_head=\"Kaggle search result\").trace(searched_datasets)\n\n datasets.extend(searched_datasets)\n if len(datasets) >= search_top_k:\n datasets = datasets[:search_top_k]\n break\n if len(searched_datasets) < 20:\n # Default page_size is 20, less than 20 means no more datasets can be searched\n break\n except Exception:\n break\n\n # Get url, cover image and some meta data for each dataset\n if len(datasets) == 0:\n # No datasets found\n datasets = api.dataset_list(max_size=20000, page=1, file_type=\"csv\")[:search_top_k]\n\n output_info = self._get_dataset_meta_info(api, datasets)\n return output_info\n\n def _get_dataset_meta_info(self, api: KaggleApi, datasets: List) -> List[Dict]:\n \"\"\"Get dataset key meta-data to be shown to the user.\"\"\"\n output_info = []\n for dataset in datasets:\n dataset_hash_id = str(uuid.uuid4())\n dataset_tmp_dir = os.path.join(\".kaggle_meta/\", dataset_hash_id)\n os.makedirs(dataset_tmp_dir, exist_ok=True)\n api.dataset_metadata(dataset.ref, path=dataset_tmp_dir)\n with open(os.path.join(dataset_tmp_dir, \"dataset-metadata.json\")) as f:\n dataset_metadata = json.load(f)\n shutil.rmtree(os.path.join(\".kaggle_meta/\", dataset_hash_id))\n dataset_url = \"https://www.kaggle.com/datasets/\" + dataset.ref\n # Crawling the dataset page to get the dataset image\n dataset_cover_image_url = self._crawl_dataset_cover_image(dataset_url)\n\n logger.bind(msg_head=\"Dataset cover image url\").trace(dataset_cover_image_url)\n\n output_metadata = {\n \"id\": dataset_metadata[\"id\"],\n \"id_no\": dataset_metadata[\"id_no\"],\n \"title\": dataset_metadata[\"title\"],\n \"subtitle\": dataset_metadata[\"subtitle\"],\n \"total_views\": dataset_metadata[\"totalViews\"],\n \"total_votes\": dataset_metadata[\"totalVotes\"],\n \"total_downloads\": dataset_metadata[\"totalDownloads\"],\n \"url\": dataset_url,\n \"cover_image_url\": dataset_cover_image_url,\n }\n output_info.append(output_metadata)\n return output_info\n\n def _crawl_dataset_cover_image(\n self, url: str, default_image_path=\"https://images.datacamp.com/image/upload/v1647430873/kaggle_logo_icon_168474_4eb653edb6.png\"\n ) -> str:\n \"\"\"Crawl the kaggle dataset cover image from the dataset url.\"\"\"\n # Get the HTML content of the webpage\n response = requests.get(url)\n\n # Parse the HTML with BeautifulSoup\n soup = BeautifulSoup(response.text, \"html.parser\")\n\n # Find the image element\n try:\n kaggle_component_element = soup.find(\"script\", {\"class\": \"kaggle-component\"})\n match = re.search(r'\"coverImageUrl\":\\s*\"([^\"]*)\"', kaggle_component_element.string)\n image_url = match.group(1)\n except Exception:\n import traceback\n\n traceback.print_exc()","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor._crawl_dataset_cover_image","uri":"program://OpenAgents/function/real_agents.data_agent.executors.kaggle_data_loading_executor._crawl_dataset_cover_image#L123-L144","kind":"function","name":"_crawl_dataset_cover_image","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":123,"end_line":144,"context_start_line":103,"context_end_line":163,"code":" dataset_url = \"https://www.kaggle.com/datasets/\" + dataset.ref\n # Crawling the dataset page to get the dataset image\n dataset_cover_image_url = self._crawl_dataset_cover_image(dataset_url)\n\n logger.bind(msg_head=\"Dataset cover image url\").trace(dataset_cover_image_url)\n\n output_metadata = {\n \"id\": dataset_metadata[\"id\"],\n \"id_no\": dataset_metadata[\"id_no\"],\n \"title\": dataset_metadata[\"title\"],\n \"subtitle\": dataset_metadata[\"subtitle\"],\n \"total_views\": dataset_metadata[\"totalViews\"],\n \"total_votes\": dataset_metadata[\"totalVotes\"],\n \"total_downloads\": dataset_metadata[\"totalDownloads\"],\n \"url\": dataset_url,\n \"cover_image_url\": dataset_cover_image_url,\n }\n output_info.append(output_metadata)\n return output_info\n\n def _crawl_dataset_cover_image(\n self, url: str, default_image_path=\"https://images.datacamp.com/image/upload/v1647430873/kaggle_logo_icon_168474_4eb653edb6.png\"\n ) -> str:\n \"\"\"Crawl the kaggle dataset cover image from the dataset url.\"\"\"\n # Get the HTML content of the webpage\n response = requests.get(url)\n\n # Parse the HTML with BeautifulSoup\n soup = BeautifulSoup(response.text, \"html.parser\")\n\n # Find the image element\n try:\n kaggle_component_element = soup.find(\"script\", {\"class\": \"kaggle-component\"})\n match = re.search(r'\"coverImageUrl\":\\s*\"([^\"]*)\"', kaggle_component_element.string)\n image_url = match.group(1)\n except Exception:\n import traceback\n\n traceback.print_exc()\n image_url = default_image_path\n\n return image_url\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the kaggle action and keywords.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n action = soup.find(\"action\").text\n except Exception:\n action = \"\"\n\n try:\n keywords = soup.find(\"keywords\").text\n except Exception:\n keywords = \"\"\n\n return action, keywords","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.kaggle_data_loading_executor._parse_output","uri":"program://OpenAgents/function/real_agents.data_agent.executors.kaggle_data_loading_executor._parse_output#L146-L163","kind":"function","name":"_parse_output","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":146,"end_line":163,"context_start_line":126,"context_end_line":163,"code":" \"\"\"Crawl the kaggle dataset cover image from the dataset url.\"\"\"\n # Get the HTML content of the webpage\n response = requests.get(url)\n\n # Parse the HTML with BeautifulSoup\n soup = BeautifulSoup(response.text, \"html.parser\")\n\n # Find the image element\n try:\n kaggle_component_element = soup.find(\"script\", {\"class\": \"kaggle-component\"})\n match = re.search(r'\"coverImageUrl\":\\s*\"([^\"]*)\"', kaggle_component_element.string)\n image_url = match.group(1)\n except Exception:\n import traceback\n\n traceback.print_exc()\n image_url = default_image_path\n\n return image_url\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the kaggle action and keywords.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n action = soup.find(\"action\").text\n except Exception:\n action = \"\"\n\n try:\n keywords = soup.find(\"keywords\").text\n except Exception:\n keywords = \"\"\n\n return action, keywords","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.code_generation_executor","uri":"program://OpenAgents/module/real_agents.data_agent.executors.code_generation_executor#L1-L132","kind":"module","name":"real_agents.data_agent.executors.code_generation_executor","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":1,"end_line":132,"context_start_line":1,"context_end_line":132,"code":"from typing import Any, Dict, List, Literal, Optional, Union\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.adapters.schema import SQLDatabase\nfrom real_agents.data_agent.python.base import PythonChain\nfrom real_agents.data_agent.sql.base import SQLDatabaseChain\n\n\nclass CodeGenerationExecutor:\n \"\"\"Code Generation Executor.\n\n Example:\n .. code-block:: python\n\n from real_agents.adapters.executors import CodeGenerationExecutor\n executor = CodeGenerationExecutor(programming_language=\"sql\")\n executor.run(\n user_intent=\"What is the name of the first employee?\",\n grounding_source=SQLDatabase.from_uri(...)\n )\n\n \"\"\"\n\n def __init__(\n self,\n programming_language: Literal[\"sql\", \"python\"],\n usage: Union[None, str] = None,\n example_selector: Any = None,\n memory: Optional[ReadOnlySharedStringMemory] = None,\n ) -> None:\n \"\"\"Initialize the executor.\n\n Args:\n programming_language: Programming language to generate.\n example_selector: Example selector to select few-shot in-context exemplars.\n \"\"\"\n self._programming_language = programming_language\n self._usage = usage\n self._example_selector = example_selector\n self._memory = memory\n\n @property\n def programming_language(self) -> str:\n \"\"\"Get programming language.\"\"\"\n return self._programming_language\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n grounding_source: Optional[Union[List[TableDataModel], DatabaseDataModel, ImageDataModel]] = None,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n jupyter_kernel_pool: Any = None,\n return_intermediate_steps: bool = True,\n return_direct: bool = True,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on. should be {file_name: data}\n llm: Language model to use.\n return_intermediate_steps: Whether to return the intermediate steps, e.g., the program.\n return_direct: Whether to return the result of program execution directly.\n verbose: Whether to print the logging.\n\n Returns:\n Result dictionary of code generation\n \"\"\"\n\n def _concat_grounding_source() -> str:\n assert isinstance(grounding_source, list)\n table_schema = \"\"\n for gs in grounding_source:\n table_schema += f\"{gs.get_llm_side_data()}\\n\"\n return table_schema\n\n if self._programming_language == \"sql\":\n db = grounding_source.raw_data\n assert isinstance(db, SQLDatabase)\n method = SQLDatabaseChain(\n llm=llm,\n database=db,\n example_selector=self._example_selector,\n memory=self._memory,\n return_direct=return_direct,\n return_intermediate_steps=return_intermediate_steps,\n verbose=verbose,\n )\n _input = {\"user_intent\": user_intent}\n result = method(_input)\n elif self._programming_language == \"python\":\n if self._usage is None:\n # General python code generation for data analysis\n method = PythonChain.from_python_prompt(\n llm,\n return_intermediate_steps=return_intermediate_steps,\n verbose=True,\n memory=self._memory,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n # Get each source_item (table, db, files...) from the grounding_source\n _input = {\"question\": user_intent, \"data_info\": _concat_grounding_source()}\n result = method(_input)\n elif self._usage == \"echarts\":\n # Python code generation for echarts interactive chart\n method = PythonChain.from_echarts_prompt(\n llm,\n return_intermediate_steps=return_intermediate_steps,\n verbose=True,\n memory=self._memory,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n _input = {\"question\": user_intent, \"data_info\": _concat_grounding_source()}\n result = method(_input)\n else:\n raise ValueError(f\"Usage {self._usage} not supported yet.\")\n else:\n raise ValueError(f\"Programming language {self._programming_language} not supported.\")\n return result","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.code_generation_executor.CodeGenerationExecutor","uri":"program://OpenAgents/class/real_agents.data_agent.executors.code_generation_executor.CodeGenerationExecutor#L12-L132","kind":"class","name":"CodeGenerationExecutor","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":12,"end_line":132,"context_start_line":1,"context_end_line":132,"code":"from typing import Any, Dict, List, Literal, Optional, Union\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.adapters.schema import SQLDatabase\nfrom real_agents.data_agent.python.base import PythonChain\nfrom real_agents.data_agent.sql.base import SQLDatabaseChain\n\n\nclass CodeGenerationExecutor:\n \"\"\"Code Generation Executor.\n\n Example:\n .. code-block:: python\n\n from real_agents.adapters.executors import CodeGenerationExecutor\n executor = CodeGenerationExecutor(programming_language=\"sql\")\n executor.run(\n user_intent=\"What is the name of the first employee?\",\n grounding_source=SQLDatabase.from_uri(...)\n )\n\n \"\"\"\n\n def __init__(\n self,\n programming_language: Literal[\"sql\", \"python\"],\n usage: Union[None, str] = None,\n example_selector: Any = None,\n memory: Optional[ReadOnlySharedStringMemory] = None,\n ) -> None:\n \"\"\"Initialize the executor.\n\n Args:\n programming_language: Programming language to generate.\n example_selector: Example selector to select few-shot in-context exemplars.\n \"\"\"\n self._programming_language = programming_language\n self._usage = usage\n self._example_selector = example_selector\n self._memory = memory\n\n @property\n def programming_language(self) -> str:\n \"\"\"Get programming language.\"\"\"\n return self._programming_language\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n grounding_source: Optional[Union[List[TableDataModel], DatabaseDataModel, ImageDataModel]] = None,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n jupyter_kernel_pool: Any = None,\n return_intermediate_steps: bool = True,\n return_direct: bool = True,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on. should be {file_name: data}\n llm: Language model to use.\n return_intermediate_steps: Whether to return the intermediate steps, e.g., the program.\n return_direct: Whether to return the result of program execution directly.\n verbose: Whether to print the logging.\n\n Returns:\n Result dictionary of code generation\n \"\"\"\n\n def _concat_grounding_source() -> str:\n assert isinstance(grounding_source, list)\n table_schema = \"\"\n for gs in grounding_source:\n table_schema += f\"{gs.get_llm_side_data()}\\n\"\n return table_schema\n\n if self._programming_language == \"sql\":\n db = grounding_source.raw_data\n assert isinstance(db, SQLDatabase)\n method = SQLDatabaseChain(\n llm=llm,\n database=db,\n example_selector=self._example_selector,\n memory=self._memory,\n return_direct=return_direct,\n return_intermediate_steps=return_intermediate_steps,\n verbose=verbose,\n )\n _input = {\"user_intent\": user_intent}\n result = method(_input)\n elif self._programming_language == \"python\":\n if self._usage is None:\n # General python code generation for data analysis\n method = PythonChain.from_python_prompt(\n llm,\n return_intermediate_steps=return_intermediate_steps,\n verbose=True,\n memory=self._memory,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n # Get each source_item (table, db, files...) from the grounding_source\n _input = {\"question\": user_intent, \"data_info\": _concat_grounding_source()}\n result = method(_input)\n elif self._usage == \"echarts\":\n # Python code generation for echarts interactive chart\n method = PythonChain.from_echarts_prompt(\n llm,\n return_intermediate_steps=return_intermediate_steps,\n verbose=True,\n memory=self._memory,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n _input = {\"question\": user_intent, \"data_info\": _concat_grounding_source()}\n result = method(_input)\n else:\n raise ValueError(f\"Usage {self._usage} not supported yet.\")\n else:\n raise ValueError(f\"Programming language {self._programming_language} not supported.\")\n return result","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.code_generation_executor.__init__","uri":"program://OpenAgents/function/real_agents.data_agent.executors.code_generation_executor.__init__#L27-L43","kind":"function","name":"__init__","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":27,"end_line":43,"context_start_line":7,"context_end_line":63,"code":"from real_agents.adapters.schema import SQLDatabase\nfrom real_agents.data_agent.python.base import PythonChain\nfrom real_agents.data_agent.sql.base import SQLDatabaseChain\n\n\nclass CodeGenerationExecutor:\n \"\"\"Code Generation Executor.\n\n Example:\n .. code-block:: python\n\n from real_agents.adapters.executors import CodeGenerationExecutor\n executor = CodeGenerationExecutor(programming_language=\"sql\")\n executor.run(\n user_intent=\"What is the name of the first employee?\",\n grounding_source=SQLDatabase.from_uri(...)\n )\n\n \"\"\"\n\n def __init__(\n self,\n programming_language: Literal[\"sql\", \"python\"],\n usage: Union[None, str] = None,\n example_selector: Any = None,\n memory: Optional[ReadOnlySharedStringMemory] = None,\n ) -> None:\n \"\"\"Initialize the executor.\n\n Args:\n programming_language: Programming language to generate.\n example_selector: Example selector to select few-shot in-context exemplars.\n \"\"\"\n self._programming_language = programming_language\n self._usage = usage\n self._example_selector = example_selector\n self._memory = memory\n\n @property\n def programming_language(self) -> str:\n \"\"\"Get programming language.\"\"\"\n return self._programming_language\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n grounding_source: Optional[Union[List[TableDataModel], DatabaseDataModel, ImageDataModel]] = None,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n jupyter_kernel_pool: Any = None,\n return_intermediate_steps: bool = True,\n return_direct: bool = True,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.code_generation_executor.programming_language","uri":"program://OpenAgents/function/real_agents.data_agent.executors.code_generation_executor.programming_language#L46-L48","kind":"function","name":"programming_language","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":46,"end_line":48,"context_start_line":26,"context_end_line":68,"code":"\n def __init__(\n self,\n programming_language: Literal[\"sql\", \"python\"],\n usage: Union[None, str] = None,\n example_selector: Any = None,\n memory: Optional[ReadOnlySharedStringMemory] = None,\n ) -> None:\n \"\"\"Initialize the executor.\n\n Args:\n programming_language: Programming language to generate.\n example_selector: Example selector to select few-shot in-context exemplars.\n \"\"\"\n self._programming_language = programming_language\n self._usage = usage\n self._example_selector = example_selector\n self._memory = memory\n\n @property\n def programming_language(self) -> str:\n \"\"\"Get programming language.\"\"\"\n return self._programming_language\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n grounding_source: Optional[Union[List[TableDataModel], DatabaseDataModel, ImageDataModel]] = None,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n jupyter_kernel_pool: Any = None,\n return_intermediate_steps: bool = True,\n return_direct: bool = True,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on. should be {file_name: data}\n llm: Language model to use.","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.code_generation_executor.run","uri":"program://OpenAgents/function/real_agents.data_agent.executors.code_generation_executor.run#L50-L132","kind":"function","name":"run","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":50,"end_line":132,"context_start_line":30,"context_end_line":132,"code":" usage: Union[None, str] = None,\n example_selector: Any = None,\n memory: Optional[ReadOnlySharedStringMemory] = None,\n ) -> None:\n \"\"\"Initialize the executor.\n\n Args:\n programming_language: Programming language to generate.\n example_selector: Example selector to select few-shot in-context exemplars.\n \"\"\"\n self._programming_language = programming_language\n self._usage = usage\n self._example_selector = example_selector\n self._memory = memory\n\n @property\n def programming_language(self) -> str:\n \"\"\"Get programming language.\"\"\"\n return self._programming_language\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n grounding_source: Optional[Union[List[TableDataModel], DatabaseDataModel, ImageDataModel]] = None,\n user_id: str = None,\n chat_id: str = None,\n code_execution_mode: str = \"local\",\n jupyter_kernel_pool: Any = None,\n return_intermediate_steps: bool = True,\n return_direct: bool = True,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on. should be {file_name: data}\n llm: Language model to use.\n return_intermediate_steps: Whether to return the intermediate steps, e.g., the program.\n return_direct: Whether to return the result of program execution directly.\n verbose: Whether to print the logging.\n\n Returns:\n Result dictionary of code generation\n \"\"\"\n\n def _concat_grounding_source() -> str:\n assert isinstance(grounding_source, list)\n table_schema = \"\"\n for gs in grounding_source:\n table_schema += f\"{gs.get_llm_side_data()}\\n\"\n return table_schema\n\n if self._programming_language == \"sql\":\n db = grounding_source.raw_data\n assert isinstance(db, SQLDatabase)\n method = SQLDatabaseChain(\n llm=llm,\n database=db,\n example_selector=self._example_selector,\n memory=self._memory,\n return_direct=return_direct,\n return_intermediate_steps=return_intermediate_steps,\n verbose=verbose,\n )\n _input = {\"user_intent\": user_intent}\n result = method(_input)\n elif self._programming_language == \"python\":\n if self._usage is None:\n # General python code generation for data analysis\n method = PythonChain.from_python_prompt(\n llm,\n return_intermediate_steps=return_intermediate_steps,\n verbose=True,\n memory=self._memory,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n # Get each source_item (table, db, files...) from the grounding_source\n _input = {\"question\": user_intent, \"data_info\": _concat_grounding_source()}\n result = method(_input)\n elif self._usage == \"echarts\":\n # Python code generation for echarts interactive chart\n method = PythonChain.from_echarts_prompt(\n llm,\n return_intermediate_steps=return_intermediate_steps,\n verbose=True,\n memory=self._memory,\n user_id=user_id,\n chat_id=chat_id,\n code_execution_mode=code_execution_mode,\n jupyter_kernel_pool=jupyter_kernel_pool,\n )\n _input = {\"question\": user_intent, \"data_info\": _concat_grounding_source()}\n result = method(_input)\n else:\n raise ValueError(f\"Usage {self._usage} not supported yet.\")\n else:\n raise ValueError(f\"Programming language {self._programming_language} not supported.\")\n return result","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.code_generation_executor._concat_grounding_source","uri":"program://OpenAgents/function/real_agents.data_agent.executors.code_generation_executor._concat_grounding_source#L77-L82","kind":"function","name":"_concat_grounding_source","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":77,"end_line":82,"context_start_line":57,"context_end_line":102,"code":" code_execution_mode: str = \"local\",\n jupyter_kernel_pool: Any = None,\n return_intermediate_steps: bool = True,\n return_direct: bool = True,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on. should be {file_name: data}\n llm: Language model to use.\n return_intermediate_steps: Whether to return the intermediate steps, e.g., the program.\n return_direct: Whether to return the result of program execution directly.\n verbose: Whether to print the logging.\n\n Returns:\n Result dictionary of code generation\n \"\"\"\n\n def _concat_grounding_source() -> str:\n assert isinstance(grounding_source, list)\n table_schema = \"\"\n for gs in grounding_source:\n table_schema += f\"{gs.get_llm_side_data()}\\n\"\n return table_schema\n\n if self._programming_language == \"sql\":\n db = grounding_source.raw_data\n assert isinstance(db, SQLDatabase)\n method = SQLDatabaseChain(\n llm=llm,\n database=db,\n example_selector=self._example_selector,\n memory=self._memory,\n return_direct=return_direct,\n return_intermediate_steps=return_intermediate_steps,\n verbose=verbose,\n )\n _input = {\"user_intent\": user_intent}\n result = method(_input)\n elif self._programming_language == \"python\":\n if self._usage is None:\n # General python code generation for data analysis\n method = PythonChain.from_python_prompt(\n llm,","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor","uri":"program://OpenAgents/module/real_agents.data_agent.executors.data_summary_executor#L1-L197","kind":"module","name":"real_agents.data_agent.executors.data_summary_executor","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":1,"end_line":197,"context_start_line":1,"context_end_line":197,"code":"from typing import Any, Dict, Tuple, Union\nfrom abc import ABC\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.callbacks.executor_streaming import ExecutorStreamingChainHandler\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass DataSummaryExecutor(ABC):\n tool_name = \"DataProfiling\"\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n pass\n\n\nclass TableSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{table_info}\n\nProvide a succinct yet meaningful summary of the table with less than 20 words, encapsulating its essence beyond just enumerating the columns. Please ensure your summary is a complete sentence and include it within tags.\"\nNote the table actually far more rows than shown above, so you MUST NOT make any rash conclusions based on the shown table rows or cells.\"\nThen provide {num_insights} insightful and interesting suggestions in natural language that users can directly say to analyze the table. The suggestions should be able to be solved by python/sql.\"\nThe final results should be markdown '+' bullet point list, e.g., + The first suggestion.\n\nBegin.\"\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: Union[TableDataModel, DatabaseDataModel],\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,\n num_insights: int = 3,\n ) -> Dict[str, Any]:\n summary = \"\"\n if isinstance(grounding_source, TableDataModel):\n df = grounding_source.raw_data\n df_name = grounding_source.raw_data_name\n # Basic summary\n summary += f\"Your table {df_name} contains {df.shape[0]} rows and {df.shape[1]} columns. \"\n\n null_count = df.isnull().sum().sum() # Get total number of null values\n unique_values_avg = df.nunique().mean() # Get average number of unique values\n\n summary += f\"On average, each column has about {unique_values_avg:.0f} unique values. \"\n if null_count > 0:\n summary += f\"Watch out, there are {null_count} missing values in your data. \"\n else:\n summary += \"Good news, no missing values in your data. \"\n\n # Intelligent summary\n if use_intelligent_summary:\n intelligent_summary = self._intelligent_summary(\n grounding_source,\n num_insights=num_insights,\n llm=llm,\n )\n table_summary, suggestions = self._parse_output(intelligent_summary)\n summary += table_summary\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the table.\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n\n elif isinstance(grounding_source, DatabaseDataModel):\n # TODO: Convert to df or use SQL query for basic summary\n raise NotImplementedError(\"DatabaseDataModel is not supported yet.\")\n else:\n raise ValueError(f\"Unsupported grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(\n self, grounding_source: Union[TableDataModel, DatabaseDataModel], num_insights: int, llm: BaseLanguageModel\n ) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"table_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"table_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text\n except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line\n bullet_point_id = 1\n for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)\n\n\nclass ImageSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{img_info}\n\nProvide a succinct summary of the uploaded file with less than 20 words. Please ensure your summary is a complete sentence and include it within tags. For image, just show its name is basically enough.\"\nThen provide {num_insights} very simple and basic suggestions in natural language about further processing with the data. The suggestions should be able to be solved by python(e.g., grayscale, rescale, rotation, etc). The final results should be markdown '+' bullet point list, e.g., + The first suggestion.\"\n\nBegin.\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: ImageDataModel,\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,\n num_insights: int = 3,\n ) -> Dict[str, Any]:\n summary = \"\"\n if isinstance(grounding_source, ImageDataModel):\n # Basic summary\n raw_data = grounding_source.raw_data\n img_size, img_mode, img_format = raw_data[\"size\"], raw_data[\"mode\"], raw_data[\"format\"]\n summary += f\"Your image **{grounding_source.simple_filename}** is a {img_size[0]}x{img_size[1]} {img_mode} image in {img_format} format.\\n\"\n\n # Intelligent summary\n if use_intelligent_summary:\n intelligent_summary = self._intelligent_summary(\n grounding_source,\n num_insights=num_insights,\n llm=llm,\n )\n _, suggestions = self._parse_output(intelligent_summary)\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the image\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n else:\n raise ValueError(f\"Unsupported data summary for grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"img_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"img_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text\n except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line\n bullet_point_id = 1\n for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor.DataSummaryExecutor","uri":"program://OpenAgents/class/real_agents.data_agent.executors.data_summary_executor.DataSummaryExecutor#L12-L17","kind":"class","name":"DataSummaryExecutor","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":12,"end_line":17,"context_start_line":1,"context_end_line":37,"code":"from typing import Any, Dict, Tuple, Union\nfrom abc import ABC\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.callbacks.executor_streaming import ExecutorStreamingChainHandler\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass DataSummaryExecutor(ABC):\n tool_name = \"DataProfiling\"\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n pass\n\n\nclass TableSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{table_info}\n\nProvide a succinct yet meaningful summary of the table with less than 20 words, encapsulating its essence beyond just enumerating the columns. Please ensure your summary is a complete sentence and include it within tags.\"\nNote the table actually far more rows than shown above, so you MUST NOT make any rash conclusions based on the shown table rows or cells.\"\nThen provide {num_insights} insightful and interesting suggestions in natural language that users can directly say to analyze the table. The suggestions should be able to be solved by python/sql.\"\nThe final results should be markdown '+' bullet point list, e.g., + The first suggestion.\n\nBegin.\"\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: Union[TableDataModel, DatabaseDataModel],\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor.TableSummaryExecutor","uri":"program://OpenAgents/class/real_agents.data_agent.executors.data_summary_executor.TableSummaryExecutor#L20-L116","kind":"class","name":"TableSummaryExecutor","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":20,"end_line":116,"context_start_line":1,"context_end_line":136,"code":"from typing import Any, Dict, Tuple, Union\nfrom abc import ABC\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.callbacks.executor_streaming import ExecutorStreamingChainHandler\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass DataSummaryExecutor(ABC):\n tool_name = \"DataProfiling\"\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n pass\n\n\nclass TableSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{table_info}\n\nProvide a succinct yet meaningful summary of the table with less than 20 words, encapsulating its essence beyond just enumerating the columns. Please ensure your summary is a complete sentence and include it within tags.\"\nNote the table actually far more rows than shown above, so you MUST NOT make any rash conclusions based on the shown table rows or cells.\"\nThen provide {num_insights} insightful and interesting suggestions in natural language that users can directly say to analyze the table. The suggestions should be able to be solved by python/sql.\"\nThe final results should be markdown '+' bullet point list, e.g., + The first suggestion.\n\nBegin.\"\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: Union[TableDataModel, DatabaseDataModel],\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,\n num_insights: int = 3,\n ) -> Dict[str, Any]:\n summary = \"\"\n if isinstance(grounding_source, TableDataModel):\n df = grounding_source.raw_data\n df_name = grounding_source.raw_data_name\n # Basic summary\n summary += f\"Your table {df_name} contains {df.shape[0]} rows and {df.shape[1]} columns. \"\n\n null_count = df.isnull().sum().sum() # Get total number of null values\n unique_values_avg = df.nunique().mean() # Get average number of unique values\n\n summary += f\"On average, each column has about {unique_values_avg:.0f} unique values. \"\n if null_count > 0:\n summary += f\"Watch out, there are {null_count} missing values in your data. \"\n else:\n summary += \"Good news, no missing values in your data. \"\n\n # Intelligent summary\n if use_intelligent_summary:\n intelligent_summary = self._intelligent_summary(\n grounding_source,\n num_insights=num_insights,\n llm=llm,\n )\n table_summary, suggestions = self._parse_output(intelligent_summary)\n summary += table_summary\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the table.\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n\n elif isinstance(grounding_source, DatabaseDataModel):\n # TODO: Convert to df or use SQL query for basic summary\n raise NotImplementedError(\"DatabaseDataModel is not supported yet.\")\n else:\n raise ValueError(f\"Unsupported grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(\n self, grounding_source: Union[TableDataModel, DatabaseDataModel], num_insights: int, llm: BaseLanguageModel\n ) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"table_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"table_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text\n except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line\n bullet_point_id = 1\n for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)\n\n\nclass ImageSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{img_info}\n\nProvide a succinct summary of the uploaded file with less than 20 words. Please ensure your summary is a complete sentence and include it within tags. For image, just show its name is basically enough.\"\nThen provide {num_insights} very simple and basic suggestions in natural language about further processing with the data. The suggestions should be able to be solved by python(e.g., grayscale, rescale, rotation, etc). The final results should be markdown '+' bullet point list, e.g., + The first suggestion.\"\n\nBegin.\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: ImageDataModel,\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,\n num_insights: int = 3,\n ) -> Dict[str, Any]:","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor.ImageSummaryExecutor","uri":"program://OpenAgents/class/real_agents.data_agent.executors.data_summary_executor.ImageSummaryExecutor#L119-L197","kind":"class","name":"ImageSummaryExecutor","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":119,"end_line":197,"context_start_line":99,"context_end_line":197,"code":" except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line\n bullet_point_id = 1\n for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)\n\n\nclass ImageSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{img_info}\n\nProvide a succinct summary of the uploaded file with less than 20 words. Please ensure your summary is a complete sentence and include it within tags. For image, just show its name is basically enough.\"\nThen provide {num_insights} very simple and basic suggestions in natural language about further processing with the data. The suggestions should be able to be solved by python(e.g., grayscale, rescale, rotation, etc). The final results should be markdown '+' bullet point list, e.g., + The first suggestion.\"\n\nBegin.\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: ImageDataModel,\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,\n num_insights: int = 3,\n ) -> Dict[str, Any]:\n summary = \"\"\n if isinstance(grounding_source, ImageDataModel):\n # Basic summary\n raw_data = grounding_source.raw_data\n img_size, img_mode, img_format = raw_data[\"size\"], raw_data[\"mode\"], raw_data[\"format\"]\n summary += f\"Your image **{grounding_source.simple_filename}** is a {img_size[0]}x{img_size[1]} {img_mode} image in {img_format} format.\\n\"\n\n # Intelligent summary\n if use_intelligent_summary:\n intelligent_summary = self._intelligent_summary(\n grounding_source,\n num_insights=num_insights,\n llm=llm,\n )\n _, suggestions = self._parse_output(intelligent_summary)\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the image\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n else:\n raise ValueError(f\"Unsupported data summary for grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"img_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"img_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text\n except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line\n bullet_point_id = 1\n for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor._intelligent_summary","uri":"program://OpenAgents/function/real_agents.data_agent.executors.data_summary_executor._intelligent_summary#L161-L169","kind":"function","name":"_intelligent_summary","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":161,"end_line":169,"context_start_line":141,"context_end_line":189,"code":" img_size, img_mode, img_format = raw_data[\"size\"], raw_data[\"mode\"], raw_data[\"format\"]\n summary += f\"Your image **{grounding_source.simple_filename}** is a {img_size[0]}x{img_size[1]} {img_mode} image in {img_format} format.\\n\"\n\n # Intelligent summary\n if use_intelligent_summary:\n intelligent_summary = self._intelligent_summary(\n grounding_source,\n num_insights=num_insights,\n llm=llm,\n )\n _, suggestions = self._parse_output(intelligent_summary)\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the image\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n else:\n raise ValueError(f\"Unsupported data summary for grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"img_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"img_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text\n except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor.run","uri":"program://OpenAgents/function/real_agents.data_agent.executors.data_summary_executor.run#L130-L159","kind":"function","name":"run","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":130,"end_line":159,"context_start_line":110,"context_end_line":179,"code":" for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)\n\n\nclass ImageSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"\n{img_info}\n\nProvide a succinct summary of the uploaded file with less than 20 words. Please ensure your summary is a complete sentence and include it within tags. For image, just show its name is basically enough.\"\nThen provide {num_insights} very simple and basic suggestions in natural language about further processing with the data. The suggestions should be able to be solved by python(e.g., grayscale, rescale, rotation, etc). The final results should be markdown '+' bullet point list, e.g., + The first suggestion.\"\n\nBegin.\n\"\"\"\n stream_handler = ExecutorStreamingChainHandler()\n\n def run(\n self,\n grounding_source: ImageDataModel,\n llm: BaseLanguageModel,\n use_intelligent_summary: bool = True,\n num_insights: int = 3,\n ) -> Dict[str, Any]:\n summary = \"\"\n if isinstance(grounding_source, ImageDataModel):\n # Basic summary\n raw_data = grounding_source.raw_data\n img_size, img_mode, img_format = raw_data[\"size\"], raw_data[\"mode\"], raw_data[\"format\"]\n summary += f\"Your image **{grounding_source.simple_filename}** is a {img_size[0]}x{img_size[1]} {img_mode} image in {img_format} format.\\n\"\n\n # Intelligent summary\n if use_intelligent_summary:\n intelligent_summary = self._intelligent_summary(\n grounding_source,\n num_insights=num_insights,\n llm=llm,\n )\n _, suggestions = self._parse_output(intelligent_summary)\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the image\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n else:\n raise ValueError(f\"Unsupported data summary for grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"img_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"img_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.executors.data_summary_executor._parse_output","uri":"program://OpenAgents/function/real_agents.data_agent.executors.data_summary_executor._parse_output#L171-L197","kind":"function","name":"_parse_output","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":171,"end_line":197,"context_start_line":151,"context_end_line":197,"code":" _, suggestions = self._parse_output(intelligent_summary)\n summary += \"\\n\" + \"Here are some additional insights to enhance your understanding of the image\"\n summary += \"\\n\" + suggestions\n\n for stream_token in summary.split(\" \"):\n self.stream_handler.on_llm_new_token(stream_token)\n else:\n raise ValueError(f\"Unsupported data summary for grounding source type: {type(grounding_source)}\")\n return summary\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n summary_prompt_template = PromptTemplate(\n input_variables=[\"img_info\", \"num_insights\"],\n template=self.SUMMARY_PROMPT_TEMPLATE,\n )\n method = LLMChain(llm=llm, prompt=summary_prompt_template)\n result = method.run({\"img_info\": grounding_source.get_llm_side_data(), \"num_insights\": num_insights})\n return result\n\n def _parse_output(self, content: str) -> Tuple[str, str]:\n \"\"\"Parse the output of the LLM to get the data summary.\"\"\"\n from bs4 import BeautifulSoup\n\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(content, \"html.parser\")\n # Parsing the tag and summary contents\n try:\n table_summary = soup.find(\"summary\").text\n except Exception:\n import traceback\n\n traceback.print_exc()\n table_summary = \"\"\n\n lines = content.split(\"\\n\")\n # Initialize an empty list to hold the parsed bullet points\n bullet_points = []\n # Loop through each line\n bullet_point_id = 1\n for line in lines:\n # If the line starts with '+', it is a bullet point\n if line.startswith(\"+\"):\n # Remove the '+ ' from the start of the line and add it to the list\n bullet_points.append(f\"{bullet_point_id}. \" + line[1:].strip().strip('\"'))\n bullet_point_id += 1\n return table_summary, \"\\n\".join(bullet_points)","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base","uri":"program://OpenAgents/module/real_agents.data_agent.sql.base#L1-L137","kind":"module","name":"real_agents.data_agent.sql.base","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":1,"end_line":137,"context_start_line":1,"context_end_line":137,"code":"\"\"\"Chain for interacting with SQL Database.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional\nfrom pydantic import BaseModel, Extra, Field\nfrom loguru import logger\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain import BasePromptTemplate, FewShotPromptTemplate\n\nfrom real_agents.data_agent.evaluation.sql_evaluator import SQLEvaluator\nfrom real_agents.adapters.schema import SQLDatabase\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.data_agent.sql.prompt import (\n EXAMPLE_PROMPT,\n FEW_SHOT_INPUT_VARIABLES,\n FEW_SHOT_PREFIX,\n FEW_SHOT_SUFFIX,\n PROMPT,\n)\nfrom real_agents.adapters.llm import LLMChain\nfrom real_agents.adapters.data_model import MessageDataModel\n\n\nclass SQLDatabaseChain(Chain, BaseModel):\n \"\"\"Chain for interacting with SQL Database\"\"\"\n\n llm: BaseLanguageModel\n \"\"\"LLM wrapper to use.\"\"\"\n database: SQLDatabase = Field(exclude=True)\n \"\"\"SQL Database to connect to.\"\"\"\n example_selector: Any = None\n \"\"\"Example selector to select few-shot in-context exemplars.\"\"\"\n memory: Optional[ReadOnlySharedStringMemory] = None\n \"\"\"Shared memory.\"\"\"\n prompt: BasePromptTemplate = PROMPT\n \"\"\"Prompt to use to translate natural language to SQL.\"\"\"\n input_key: str = \"user_intent\" #: :meta private:\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n \"\"\"Whether or not to return the intermediate steps along with the final answer.\"\"\"\n return_direct: bool = False\n \"\"\"Whether or not to return the result of querying the SQL table directly.\"\"\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n :meta private:\n \"\"\"\n return [self.input_key]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n # return [self.output_key, \"intermediate_steps\", \"binder_steps\"]\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"SQLChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n if self.example_selector is not None:\n self.prompt = FewShotPromptTemplate(\n example_selector=self.example_selector,\n example_prompt=EXAMPLE_PROMPT,\n prefix=FEW_SHOT_PREFIX,\n suffix=FEW_SHOT_SUFFIX,\n input_variables=FEW_SHOT_INPUT_VARIABLES,\n )\n llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)\n input_text = f\"{inputs[self.input_key]} \\nSQLQuery:\"\n _run_manager.on_text(input_text, verbose=self.verbose)\n\n # If not present, then defaults to None which is all tables.\n table_names_to_use = inputs.get(\"table_names_to_use\")\n table_info = self.database.get_table_info(table_names=table_names_to_use)\n llm_inputs = {\n \"question\": input_text,\n \"dialect\": self.database.dialect,\n \"table_info\": table_info,\n \"chat_history\": \"\",\n \"stop\": [\"\\nSQLResult:\"],\n }\n\n # Load memory into chat history\n if self.memory is not None:\n llm_inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n llm_inputs[\"chat_history\"] = MessageDataModel.extract_code_for_sql_tool(llm_inputs[\"chat_history\"])\n sql_cmd = llm_chain.predict(**llm_inputs)\n # TODO: Move this post-processing to a post-process function\n sql_cmd = sql_cmd.replace(\"\\n\", \" \")\n if sql_cmd.endswith('\"') and sql_cmd.startswith('\"'):\n sql_cmd = sql_cmd.strip('\"')\n if sql_cmd.endswith(\"'\") and sql_cmd.startswith(\"'\"):\n sql_cmd = sql_cmd.strip(\"'\")\n\n logger.bind(msg_head=\"SQLChain generate program\").trace(sql_cmd)\n\n # Call SQL/binder evaluator to execute the SQL command\n sql_evaluator = SQLEvaluator()\n result = sql_evaluator.run(sql_cmd, self.database)\n\n logger.bind(msg_head=\"SQLChain execution result\").trace(result)\n\n # If return direct, we just set the final result equal to the sql query\n if self.return_direct:\n final_result = result\n else:\n input_text += f\"{sql_cmd}\\nSQLResult: {result}\\nAnswer:\"\n llm_inputs[\"input\"] = input_text\n final_result = llm_chain.predict(**llm_inputs)\n _run_manager.on_text(final_result, color=\"green\", verbose=self.verbose)\n chain_result: Dict[str, Any] = {self.output_key: final_result}\n if self.return_intermediate_steps:\n chain_result[\"intermediate_steps\"] = sql_cmd\n return chain_result\n\n @property\n def _chain_type(self) -> str:\n return \"sql_database_chain\"","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base.SQLDatabaseChain","uri":"program://OpenAgents/class/real_agents.data_agent.sql.base.SQLDatabaseChain#L27-L137","kind":"class","name":"SQLDatabaseChain","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":27,"end_line":137,"context_start_line":7,"context_end_line":137,"code":"\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain import BasePromptTemplate, FewShotPromptTemplate\n\nfrom real_agents.data_agent.evaluation.sql_evaluator import SQLEvaluator\nfrom real_agents.adapters.schema import SQLDatabase\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.data_agent.sql.prompt import (\n EXAMPLE_PROMPT,\n FEW_SHOT_INPUT_VARIABLES,\n FEW_SHOT_PREFIX,\n FEW_SHOT_SUFFIX,\n PROMPT,\n)\nfrom real_agents.adapters.llm import LLMChain\nfrom real_agents.adapters.data_model import MessageDataModel\n\n\nclass SQLDatabaseChain(Chain, BaseModel):\n \"\"\"Chain for interacting with SQL Database\"\"\"\n\n llm: BaseLanguageModel\n \"\"\"LLM wrapper to use.\"\"\"\n database: SQLDatabase = Field(exclude=True)\n \"\"\"SQL Database to connect to.\"\"\"\n example_selector: Any = None\n \"\"\"Example selector to select few-shot in-context exemplars.\"\"\"\n memory: Optional[ReadOnlySharedStringMemory] = None\n \"\"\"Shared memory.\"\"\"\n prompt: BasePromptTemplate = PROMPT\n \"\"\"Prompt to use to translate natural language to SQL.\"\"\"\n input_key: str = \"user_intent\" #: :meta private:\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n \"\"\"Whether or not to return the intermediate steps along with the final answer.\"\"\"\n return_direct: bool = False\n \"\"\"Whether or not to return the result of querying the SQL table directly.\"\"\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n :meta private:\n \"\"\"\n return [self.input_key]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n # return [self.output_key, \"intermediate_steps\", \"binder_steps\"]\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"SQLChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n if self.example_selector is not None:\n self.prompt = FewShotPromptTemplate(\n example_selector=self.example_selector,\n example_prompt=EXAMPLE_PROMPT,\n prefix=FEW_SHOT_PREFIX,\n suffix=FEW_SHOT_SUFFIX,\n input_variables=FEW_SHOT_INPUT_VARIABLES,\n )\n llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)\n input_text = f\"{inputs[self.input_key]} \\nSQLQuery:\"\n _run_manager.on_text(input_text, verbose=self.verbose)\n\n # If not present, then defaults to None which is all tables.\n table_names_to_use = inputs.get(\"table_names_to_use\")\n table_info = self.database.get_table_info(table_names=table_names_to_use)\n llm_inputs = {\n \"question\": input_text,\n \"dialect\": self.database.dialect,\n \"table_info\": table_info,\n \"chat_history\": \"\",\n \"stop\": [\"\\nSQLResult:\"],\n }\n\n # Load memory into chat history\n if self.memory is not None:\n llm_inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n llm_inputs[\"chat_history\"] = MessageDataModel.extract_code_for_sql_tool(llm_inputs[\"chat_history\"])\n sql_cmd = llm_chain.predict(**llm_inputs)\n # TODO: Move this post-processing to a post-process function\n sql_cmd = sql_cmd.replace(\"\\n\", \" \")\n if sql_cmd.endswith('\"') and sql_cmd.startswith('\"'):\n sql_cmd = sql_cmd.strip('\"')\n if sql_cmd.endswith(\"'\") and sql_cmd.startswith(\"'\"):\n sql_cmd = sql_cmd.strip(\"'\")\n\n logger.bind(msg_head=\"SQLChain generate program\").trace(sql_cmd)\n\n # Call SQL/binder evaluator to execute the SQL command\n sql_evaluator = SQLEvaluator()\n result = sql_evaluator.run(sql_cmd, self.database)\n\n logger.bind(msg_head=\"SQLChain execution result\").trace(result)\n\n # If return direct, we just set the final result equal to the sql query\n if self.return_direct:\n final_result = result\n else:\n input_text += f\"{sql_cmd}\\nSQLResult: {result}\\nAnswer:\"\n llm_inputs[\"input\"] = input_text\n final_result = llm_chain.predict(**llm_inputs)\n _run_manager.on_text(final_result, color=\"green\", verbose=self.verbose)\n chain_result: Dict[str, Any] = {self.output_key: final_result}\n if self.return_intermediate_steps:\n chain_result[\"intermediate_steps\"] = sql_cmd\n return chain_result\n\n @property\n def _chain_type(self) -> str:\n return \"sql_database_chain\"","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base.Config","uri":"program://OpenAgents/class/real_agents.data_agent.sql.base.Config#L47-L51","kind":"class","name":"Config","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":47,"end_line":51,"context_start_line":27,"context_end_line":71,"code":"class SQLDatabaseChain(Chain, BaseModel):\n \"\"\"Chain for interacting with SQL Database\"\"\"\n\n llm: BaseLanguageModel\n \"\"\"LLM wrapper to use.\"\"\"\n database: SQLDatabase = Field(exclude=True)\n \"\"\"SQL Database to connect to.\"\"\"\n example_selector: Any = None\n \"\"\"Example selector to select few-shot in-context exemplars.\"\"\"\n memory: Optional[ReadOnlySharedStringMemory] = None\n \"\"\"Shared memory.\"\"\"\n prompt: BasePromptTemplate = PROMPT\n \"\"\"Prompt to use to translate natural language to SQL.\"\"\"\n input_key: str = \"user_intent\" #: :meta private:\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n \"\"\"Whether or not to return the intermediate steps along with the final answer.\"\"\"\n return_direct: bool = False\n \"\"\"Whether or not to return the result of querying the SQL table directly.\"\"\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n :meta private:\n \"\"\"\n return [self.input_key]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n # return [self.output_key, \"intermediate_steps\", \"binder_steps\"]\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base.input_keys","uri":"program://OpenAgents/function/real_agents.data_agent.sql.base.input_keys#L54-L58","kind":"function","name":"input_keys","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":54,"end_line":58,"context_start_line":34,"context_end_line":78,"code":" example_selector: Any = None\n \"\"\"Example selector to select few-shot in-context exemplars.\"\"\"\n memory: Optional[ReadOnlySharedStringMemory] = None\n \"\"\"Shared memory.\"\"\"\n prompt: BasePromptTemplate = PROMPT\n \"\"\"Prompt to use to translate natural language to SQL.\"\"\"\n input_key: str = \"user_intent\" #: :meta private:\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n \"\"\"Whether or not to return the intermediate steps along with the final answer.\"\"\"\n return_direct: bool = False\n \"\"\"Whether or not to return the result of querying the SQL table directly.\"\"\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n :meta private:\n \"\"\"\n return [self.input_key]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n # return [self.output_key, \"intermediate_steps\", \"binder_steps\"]\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"SQLChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base.output_keys","uri":"program://OpenAgents/function/real_agents.data_agent.sql.base.output_keys#L61-L69","kind":"function","name":"output_keys","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":61,"end_line":69,"context_start_line":41,"context_end_line":89,"code":" output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n \"\"\"Whether or not to return the intermediate steps along with the final answer.\"\"\"\n return_direct: bool = False\n \"\"\"Whether or not to return the result of querying the SQL table directly.\"\"\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n :meta private:\n \"\"\"\n return [self.input_key]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n # return [self.output_key, \"intermediate_steps\", \"binder_steps\"]\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"SQLChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n if self.example_selector is not None:\n self.prompt = FewShotPromptTemplate(\n example_selector=self.example_selector,\n example_prompt=EXAMPLE_PROMPT,\n prefix=FEW_SHOT_PREFIX,\n suffix=FEW_SHOT_SUFFIX,\n input_variables=FEW_SHOT_INPUT_VARIABLES,\n )\n llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)\n input_text = f\"{inputs[self.input_key]} \\nSQLQuery:\"\n _run_manager.on_text(input_text, verbose=self.verbose)","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base._call","uri":"program://OpenAgents/function/real_agents.data_agent.sql.base._call#L71-L133","kind":"function","name":"_call","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":71,"end_line":133,"context_start_line":51,"context_end_line":137,"code":" arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n :meta private:\n \"\"\"\n return [self.input_key]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n # return [self.output_key, \"intermediate_steps\", \"binder_steps\"]\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"SQLChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n if self.example_selector is not None:\n self.prompt = FewShotPromptTemplate(\n example_selector=self.example_selector,\n example_prompt=EXAMPLE_PROMPT,\n prefix=FEW_SHOT_PREFIX,\n suffix=FEW_SHOT_SUFFIX,\n input_variables=FEW_SHOT_INPUT_VARIABLES,\n )\n llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)\n input_text = f\"{inputs[self.input_key]} \\nSQLQuery:\"\n _run_manager.on_text(input_text, verbose=self.verbose)\n\n # If not present, then defaults to None which is all tables.\n table_names_to_use = inputs.get(\"table_names_to_use\")\n table_info = self.database.get_table_info(table_names=table_names_to_use)\n llm_inputs = {\n \"question\": input_text,\n \"dialect\": self.database.dialect,\n \"table_info\": table_info,\n \"chat_history\": \"\",\n \"stop\": [\"\\nSQLResult:\"],\n }\n\n # Load memory into chat history\n if self.memory is not None:\n llm_inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n llm_inputs[\"chat_history\"] = MessageDataModel.extract_code_for_sql_tool(llm_inputs[\"chat_history\"])\n sql_cmd = llm_chain.predict(**llm_inputs)\n # TODO: Move this post-processing to a post-process function\n sql_cmd = sql_cmd.replace(\"\\n\", \" \")\n if sql_cmd.endswith('\"') and sql_cmd.startswith('\"'):\n sql_cmd = sql_cmd.strip('\"')\n if sql_cmd.endswith(\"'\") and sql_cmd.startswith(\"'\"):\n sql_cmd = sql_cmd.strip(\"'\")\n\n logger.bind(msg_head=\"SQLChain generate program\").trace(sql_cmd)\n\n # Call SQL/binder evaluator to execute the SQL command\n sql_evaluator = SQLEvaluator()\n result = sql_evaluator.run(sql_cmd, self.database)\n\n logger.bind(msg_head=\"SQLChain execution result\").trace(result)\n\n # If return direct, we just set the final result equal to the sql query\n if self.return_direct:\n final_result = result\n else:\n input_text += f\"{sql_cmd}\\nSQLResult: {result}\\nAnswer:\"\n llm_inputs[\"input\"] = input_text\n final_result = llm_chain.predict(**llm_inputs)\n _run_manager.on_text(final_result, color=\"green\", verbose=self.verbose)\n chain_result: Dict[str, Any] = {self.output_key: final_result}\n if self.return_intermediate_steps:\n chain_result[\"intermediate_steps\"] = sql_cmd\n return chain_result\n\n @property\n def _chain_type(self) -> str:\n return \"sql_database_chain\"","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.base._chain_type","uri":"program://OpenAgents/function/real_agents.data_agent.sql.base._chain_type#L136-L137","kind":"function","name":"_chain_type","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":136,"end_line":137,"context_start_line":116,"context_end_line":137,"code":" # Call SQL/binder evaluator to execute the SQL command\n sql_evaluator = SQLEvaluator()\n result = sql_evaluator.run(sql_cmd, self.database)\n\n logger.bind(msg_head=\"SQLChain execution result\").trace(result)\n\n # If return direct, we just set the final result equal to the sql query\n if self.return_direct:\n final_result = result\n else:\n input_text += f\"{sql_cmd}\\nSQLResult: {result}\\nAnswer:\"\n llm_inputs[\"input\"] = input_text\n final_result = llm_chain.predict(**llm_inputs)\n _run_manager.on_text(final_result, color=\"green\", verbose=self.verbose)\n chain_result: Dict[str, Any] = {self.output_key: final_result}\n if self.return_intermediate_steps:\n chain_result[\"intermediate_steps\"] = sql_cmd\n return chain_result\n\n @property\n def _chain_type(self) -> str:\n return \"sql_database_chain\"","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.sql.prompt","uri":"program://OpenAgents/module/real_agents.data_agent.sql.prompt#L1-L44","kind":"module","name":"real_agents.data_agent.sql.prompt","path":"real_agents/data_agent/sql/prompt.py","language":"python","start_line":1,"end_line":44,"context_start_line":1,"context_end_line":44,"code":"# flake8: noqa\nfrom langchain import PromptTemplate\n\n# Text-to-sql prompt\n_DEFAULT_TEMPLATE = \"\"\"Here are chat histories you may refer to, maybe empty.\n{chat_history}\n\nGiven an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\nPay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, remember to wrap the table names in double quotes.\nUse the following format:\nQuestion: \"Question here\"\nSQLQuery: \"SQL Query to run\"\nSQLResult: \"Result of the SQLQuery\"\nAnswer: \"Final answer here\"\nOnly use the tables listed below.\n{table_info}\nQuestion: {question}\"\"\"\nPROMPT = PromptTemplate(\n input_variables=[\"chat_history\", \"question\", \"table_info\", \"dialect\"],\n template=_DEFAULT_TEMPLATE,\n)\n\n\n# Few-shot text-to-sql prompt\nFEW_SHOT_PREFIX = \"\"\"Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\nPay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\nUse the following format:\nQuestion: \"Question here\"\nSQLQuery: \"SQL Query to run\"\nSQLResult: \"Result of the SQLQuery\"\nAnswer: \"Final answer here\"\nHere are some examples you can follow:\"\"\"\nEXAMPLE_PROMPT_TEMPLATE = \"\"\"{table_info}\\nQuestion: {question}\\nSQLQuery: {query}\"\"\"\nEXAMPLE_PROMPT = PromptTemplate(\n input_variables=[\"table_info\", \"question\", \"query\"],\n template=EXAMPLE_PROMPT_TEMPLATE,\n)\nFEW_SHOT_SUFFIX = \"\"\"\nUser the tables listed below.\n{table_info}\nQuestion: {question}\"\"\"\nFEW_SHOT_INPUT_VARIABLES = [\"question\", \"table_info\", \"dialect\", \"top_k\"]","source_hash":"7386a46be013ba5170bfc65af5fea5b13ba7c0e684bb8e4d45841d4781146cf7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base","uri":"program://OpenAgents/module/real_agents.data_agent.python.base#L1-L188","kind":"module","name":"real_agents.data_agent.python.base","path":"real_agents/data_agent/python/base.py","language":"python","start_line":1,"end_line":188,"context_start_line":1,"context_end_line":188,"code":"\"\"\"Implements Python Code Generation. \"\"\"\nfrom __future__ import annotations\n\nimport re\nfrom typing import Any, Dict, List, Optional\n\nfrom bs4 import BeautifulSoup\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n)\nfrom langchain.schema import SystemMessage\nfrom loguru import logger\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.adapters.data_model import MessageDataModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.data_agent.evaluation.python_evaluator import PythonEvaluator\nfrom real_agents.data_agent.python.echarts_prompt import E_SYSTEM_PROMPT, ECHARTS_REF_CODE, ECHARTS_USER_PROMPT\nfrom real_agents.data_agent.python.system_prompt import SYSTEM_PROMPT\nfrom real_agents.data_agent.python.python_prompt import USER_PROMPT\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass PythonChain(Chain, BaseModel):\n \"\"\"Chain for Generating Python Code\"\"\"\n\n llm_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n stop: str = \"\\n\\n\"\n get_answer_expr: str = \"\"\n python_globals: Optional[Dict[str, Any]] = None\n python_locals: Optional[Dict[str, Any]] = None\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n code_execution_mode: str = \"local\"\n jupyter_kernel_pool: Optional[Any] = None\n reference_code: str = \"\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n arbitrary_types_allowed = True\n\n def _validate_inputs(self, inputs: Dict[str, str]) -> None:\n \"\"\"Check that all inputs are present.\"\"\"\n missing_keys = set(self.input_keys).difference(inputs)\n if \"chat_history\" in missing_keys:\n missing_keys.remove(\"chat_history\")\n if missing_keys:\n raise ValueError(f\"Missing some input keys: {missing_keys}\")\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"PythonChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n _run_manager.on_text(inputs[self.input_keys[0]])\n inputs[\"chat_history\"] = \"\"\n if self.memory is not None:\n inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n inputs[\"chat_history\"] = MessageDataModel.extract_code_for_python_tool(inputs[\"chat_history\"])\n\n history = {\n \"history_code\": inputs[\"chat_history\"],\n \"question\": inputs[\"question\"],\n \"data\": inputs[\"data_info\"],\n \"reference_code\": self.reference_code,\n }\n\n # we apply llm as a magic function, which serves as python code generation func.\n raw_output = self.llm_chain.run(**history)\n\n def _extract_code(_raw_output: str) -> str:\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(_raw_output, \"html.parser\")\n try:\n _raw_output = soup.find(\"code\").text\n except:\n pass\n if \"```python:\" in _raw_output:\n pattern = r\"```python\\n{(.*?)}\\n```\"\n match = re.search(pattern, _raw_output, re.DOTALL)\n if match:\n return match.group(1)\n else:\n return _raw_output\n else:\n return _raw_output\n\n code = _extract_code(raw_output).replace(\"\\\\n\", \"\\n\")\n\n logger.bind(msg_head=\"PythonChain generated program\").trace(code)\n\n repl = PythonEvaluator(\n code_execution_mode=self.code_execution_mode,\n jupyter_kernel_pool=self.jupyter_kernel_pool,\n )\n\n \"\"\"\n Since there will be error if we try to launch matplotlib GUI in the server,\n I add this line to avoid backend execution of matplotlib for now.\n \"\"\"\n result = repl.run(code + f\"\\n{self.get_answer_expr}\", user_id=self.user_id, chat_id=self.chat_id)\n\n logger.bind(msg_head=\"PythonChain execution result\").trace(result)\n\n output = {self.output_key: result}\n if self.return_intermediate_steps:\n output[\"intermediate_steps\"] = code\n return output\n\n @classmethod\n def create_python_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_echarts_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_python_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,\n )\n\n @classmethod\n def from_echarts_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_echarts_prompt(E_SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=ECHARTS_REF_CODE,\n **kwargs,\n )\n\n @property\n def _chain_type(self) -> str:\n return \"program_chain\"","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.PythonChain","uri":"program://OpenAgents/class/real_agents.data_agent.python.base.PythonChain#L29-L188","kind":"class","name":"PythonChain","path":"real_agents/data_agent/python/base.py","language":"python","start_line":29,"end_line":188,"context_start_line":9,"context_end_line":188,"code":"from langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n)\nfrom langchain.schema import SystemMessage\nfrom loguru import logger\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.adapters.data_model import MessageDataModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.data_agent.evaluation.python_evaluator import PythonEvaluator\nfrom real_agents.data_agent.python.echarts_prompt import E_SYSTEM_PROMPT, ECHARTS_REF_CODE, ECHARTS_USER_PROMPT\nfrom real_agents.data_agent.python.system_prompt import SYSTEM_PROMPT\nfrom real_agents.data_agent.python.python_prompt import USER_PROMPT\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass PythonChain(Chain, BaseModel):\n \"\"\"Chain for Generating Python Code\"\"\"\n\n llm_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n stop: str = \"\\n\\n\"\n get_answer_expr: str = \"\"\n python_globals: Optional[Dict[str, Any]] = None\n python_locals: Optional[Dict[str, Any]] = None\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n code_execution_mode: str = \"local\"\n jupyter_kernel_pool: Optional[Any] = None\n reference_code: str = \"\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n arbitrary_types_allowed = True\n\n def _validate_inputs(self, inputs: Dict[str, str]) -> None:\n \"\"\"Check that all inputs are present.\"\"\"\n missing_keys = set(self.input_keys).difference(inputs)\n if \"chat_history\" in missing_keys:\n missing_keys.remove(\"chat_history\")\n if missing_keys:\n raise ValueError(f\"Missing some input keys: {missing_keys}\")\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"PythonChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n _run_manager.on_text(inputs[self.input_keys[0]])\n inputs[\"chat_history\"] = \"\"\n if self.memory is not None:\n inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n inputs[\"chat_history\"] = MessageDataModel.extract_code_for_python_tool(inputs[\"chat_history\"])\n\n history = {\n \"history_code\": inputs[\"chat_history\"],\n \"question\": inputs[\"question\"],\n \"data\": inputs[\"data_info\"],\n \"reference_code\": self.reference_code,\n }\n\n # we apply llm as a magic function, which serves as python code generation func.\n raw_output = self.llm_chain.run(**history)\n\n def _extract_code(_raw_output: str) -> str:\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(_raw_output, \"html.parser\")\n try:\n _raw_output = soup.find(\"code\").text\n except:\n pass\n if \"```python:\" in _raw_output:\n pattern = r\"```python\\n{(.*?)}\\n```\"\n match = re.search(pattern, _raw_output, re.DOTALL)\n if match:\n return match.group(1)\n else:\n return _raw_output\n else:\n return _raw_output\n\n code = _extract_code(raw_output).replace(\"\\\\n\", \"\\n\")\n\n logger.bind(msg_head=\"PythonChain generated program\").trace(code)\n\n repl = PythonEvaluator(\n code_execution_mode=self.code_execution_mode,\n jupyter_kernel_pool=self.jupyter_kernel_pool,\n )\n\n \"\"\"\n Since there will be error if we try to launch matplotlib GUI in the server,\n I add this line to avoid backend execution of matplotlib for now.\n \"\"\"\n result = repl.run(code + f\"\\n{self.get_answer_expr}\", user_id=self.user_id, chat_id=self.chat_id)\n\n logger.bind(msg_head=\"PythonChain execution result\").trace(result)\n\n output = {self.output_key: result}\n if self.return_intermediate_steps:\n output[\"intermediate_steps\"] = code\n return output\n\n @classmethod\n def create_python_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_echarts_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_python_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,\n )\n\n @classmethod\n def from_echarts_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_echarts_prompt(E_SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=ECHARTS_REF_CODE,\n **kwargs,\n )\n\n @property\n def _chain_type(self) -> str:\n return \"program_chain\"","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.Config","uri":"program://OpenAgents/class/real_agents.data_agent.python.base.Config#L48-L52","kind":"class","name":"Config","path":"real_agents/data_agent/python/base.py","language":"python","start_line":48,"end_line":52,"context_start_line":28,"context_end_line":72,"code":"\nclass PythonChain(Chain, BaseModel):\n \"\"\"Chain for Generating Python Code\"\"\"\n\n llm_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n stop: str = \"\\n\\n\"\n get_answer_expr: str = \"\"\n python_globals: Optional[Dict[str, Any]] = None\n python_locals: Optional[Dict[str, Any]] = None\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n code_execution_mode: str = \"local\"\n jupyter_kernel_pool: Optional[Any] = None\n reference_code: str = \"\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n arbitrary_types_allowed = True\n\n def _validate_inputs(self, inputs: Dict[str, str]) -> None:\n \"\"\"Check that all inputs are present.\"\"\"\n missing_keys = set(self.input_keys).difference(inputs)\n if \"chat_history\" in missing_keys:\n missing_keys.remove(\"chat_history\")\n if missing_keys:\n raise ValueError(f\"Missing some input keys: {missing_keys}\")\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base._validate_inputs","uri":"program://OpenAgents/function/real_agents.data_agent.python.base._validate_inputs#L54-L60","kind":"function","name":"_validate_inputs","path":"real_agents/data_agent/python/base.py","language":"python","start_line":54,"end_line":60,"context_start_line":34,"context_end_line":80,"code":" memory: Optional[ReadOnlySharedStringMemory] = None\n stop: str = \"\\n\\n\"\n get_answer_expr: str = \"\"\n python_globals: Optional[Dict[str, Any]] = None\n python_locals: Optional[Dict[str, Any]] = None\n output_key: str = \"result\" #: :meta private:\n return_intermediate_steps: bool = False\n code_execution_mode: str = \"local\"\n jupyter_kernel_pool: Optional[Any] = None\n reference_code: str = \"\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n arbitrary_types_allowed = True\n\n def _validate_inputs(self, inputs: Dict[str, str]) -> None:\n \"\"\"Check that all inputs are present.\"\"\"\n missing_keys = set(self.input_keys).difference(inputs)\n if \"chat_history\" in missing_keys:\n missing_keys.remove(\"chat_history\")\n if missing_keys:\n raise ValueError(f\"Missing some input keys: {missing_keys}\")\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n return [self.output_key, \"intermediate_steps\"]\n","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.input_keys","uri":"program://OpenAgents/function/real_agents.data_agent.python.base.input_keys#L63-L68","kind":"function","name":"input_keys","path":"real_agents/data_agent/python/base.py","language":"python","start_line":63,"end_line":68,"context_start_line":43,"context_end_line":88,"code":" reference_code: str = \"\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n arbitrary_types_allowed = True\n\n def _validate_inputs(self, inputs: Dict[str, str]) -> None:\n \"\"\"Check that all inputs are present.\"\"\"\n missing_keys = set(self.input_keys).difference(inputs)\n if \"chat_history\" in missing_keys:\n missing_keys.remove(\"chat_history\")\n if missing_keys:\n raise ValueError(f\"Missing some input keys: {missing_keys}\")\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"PythonChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.output_keys","uri":"program://OpenAgents/function/real_agents.data_agent.python.base.output_keys#L71-L79","kind":"function","name":"output_keys","path":"real_agents/data_agent/python/base.py","language":"python","start_line":71,"end_line":79,"context_start_line":51,"context_end_line":99,"code":" extra = Extra.ignore\n arbitrary_types_allowed = True\n\n def _validate_inputs(self, inputs: Dict[str, str]) -> None:\n \"\"\"Check that all inputs are present.\"\"\"\n missing_keys = set(self.input_keys).difference(inputs)\n if \"chat_history\" in missing_keys:\n missing_keys.remove(\"chat_history\")\n if missing_keys:\n raise ValueError(f\"Missing some input keys: {missing_keys}\")\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"PythonChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n _run_manager.on_text(inputs[self.input_keys[0]])\n inputs[\"chat_history\"] = \"\"\n if self.memory is not None:\n inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n inputs[\"chat_history\"] = MessageDataModel.extract_code_for_python_tool(inputs[\"chat_history\"])\n\n history = {\n \"history_code\": inputs[\"chat_history\"],\n \"question\": inputs[\"question\"],\n \"data\": inputs[\"data_info\"],\n \"reference_code\": self.reference_code,","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base._call","uri":"program://OpenAgents/function/real_agents.data_agent.python.base._call#L81-L142","kind":"function","name":"_call","path":"real_agents/data_agent/python/base.py","language":"python","start_line":81,"end_line":142,"context_start_line":61,"context_end_line":162,"code":"\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"data_info\", \"question\", \"chat_history\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if not self.return_intermediate_steps:\n return [self.output_key]\n else:\n return [self.output_key, \"intermediate_steps\"]\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"PythonChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n _run_manager.on_text(inputs[self.input_keys[0]])\n inputs[\"chat_history\"] = \"\"\n if self.memory is not None:\n inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n inputs[\"chat_history\"] = MessageDataModel.extract_code_for_python_tool(inputs[\"chat_history\"])\n\n history = {\n \"history_code\": inputs[\"chat_history\"],\n \"question\": inputs[\"question\"],\n \"data\": inputs[\"data_info\"],\n \"reference_code\": self.reference_code,\n }\n\n # we apply llm as a magic function, which serves as python code generation func.\n raw_output = self.llm_chain.run(**history)\n\n def _extract_code(_raw_output: str) -> str:\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(_raw_output, \"html.parser\")\n try:\n _raw_output = soup.find(\"code\").text\n except:\n pass\n if \"```python:\" in _raw_output:\n pattern = r\"```python\\n{(.*?)}\\n```\"\n match = re.search(pattern, _raw_output, re.DOTALL)\n if match:\n return match.group(1)\n else:\n return _raw_output\n else:\n return _raw_output\n\n code = _extract_code(raw_output).replace(\"\\\\n\", \"\\n\")\n\n logger.bind(msg_head=\"PythonChain generated program\").trace(code)\n\n repl = PythonEvaluator(\n code_execution_mode=self.code_execution_mode,\n jupyter_kernel_pool=self.jupyter_kernel_pool,\n )\n\n \"\"\"\n Since there will be error if we try to launch matplotlib GUI in the server,\n I add this line to avoid backend execution of matplotlib for now.\n \"\"\"\n result = repl.run(code + f\"\\n{self.get_answer_expr}\", user_id=self.user_id, chat_id=self.chat_id)\n\n logger.bind(msg_head=\"PythonChain execution result\").trace(result)\n\n output = {self.output_key: result}\n if self.return_intermediate_steps:\n output[\"intermediate_steps\"] = code\n return output\n\n @classmethod\n def create_python_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_echarts_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.create_python_prompt","uri":"program://OpenAgents/function/real_agents.data_agent.python.base.create_python_prompt#L145-L152","kind":"function","name":"create_python_prompt","path":"real_agents/data_agent/python/base.py","language":"python","start_line":145,"end_line":152,"context_start_line":125,"context_end_line":172,"code":"\n repl = PythonEvaluator(\n code_execution_mode=self.code_execution_mode,\n jupyter_kernel_pool=self.jupyter_kernel_pool,\n )\n\n \"\"\"\n Since there will be error if we try to launch matplotlib GUI in the server,\n I add this line to avoid backend execution of matplotlib for now.\n \"\"\"\n result = repl.run(code + f\"\\n{self.get_answer_expr}\", user_id=self.user_id, chat_id=self.chat_id)\n\n logger.bind(msg_head=\"PythonChain execution result\").trace(result)\n\n output = {self.output_key: result}\n if self.return_intermediate_steps:\n output[\"intermediate_steps\"] = code\n return output\n\n @classmethod\n def create_python_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_echarts_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_python_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.create_echarts_prompt","uri":"program://OpenAgents/function/real_agents.data_agent.python.base.create_echarts_prompt#L155-L162","kind":"function","name":"create_echarts_prompt","path":"real_agents/data_agent/python/base.py","language":"python","start_line":155,"end_line":162,"context_start_line":135,"context_end_line":182,"code":" result = repl.run(code + f\"\\n{self.get_answer_expr}\", user_id=self.user_id, chat_id=self.chat_id)\n\n logger.bind(msg_head=\"PythonChain execution result\").trace(result)\n\n output = {self.output_key: result}\n if self.return_intermediate_steps:\n output[\"intermediate_steps\"] = code\n return output\n\n @classmethod\n def create_python_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_echarts_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_python_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,\n )\n\n @classmethod\n def from_echarts_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_echarts_prompt(E_SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=ECHARTS_REF_CODE,","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.from_python_prompt","uri":"program://OpenAgents/function/real_agents.data_agent.python.base.from_python_prompt#L165-L173","kind":"function","name":"from_python_prompt","path":"real_agents/data_agent/python/base.py","language":"python","start_line":165,"end_line":173,"context_start_line":145,"context_end_line":188,"code":" def create_python_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_echarts_prompt(cls, system_prompt: str, reference_code_prompt: str) -> BasePromptTemplate:\n input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_python_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,\n )\n\n @classmethod\n def from_echarts_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_echarts_prompt(E_SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=ECHARTS_REF_CODE,\n **kwargs,\n )\n\n @property\n def _chain_type(self) -> str:\n return \"program_chain\"","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base.from_echarts_prompt","uri":"program://OpenAgents/function/real_agents.data_agent.python.base.from_echarts_prompt#L176-L184","kind":"function","name":"from_echarts_prompt","path":"real_agents/data_agent/python/base.py","language":"python","start_line":176,"end_line":184,"context_start_line":156,"context_end_line":188,"code":" input_variables = [\"history_code\", \"question\", \"data\", \"reference_code\"]\n messages = [\n SystemMessage(content=system_prompt),\n HumanMessagePromptTemplate.from_template(template=ECHARTS_USER_PROMPT),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_python_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,\n )\n\n @classmethod\n def from_echarts_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_echarts_prompt(E_SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=ECHARTS_REF_CODE,\n **kwargs,\n )\n\n @property\n def _chain_type(self) -> str:\n return \"program_chain\"","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base._chain_type","uri":"program://OpenAgents/function/real_agents.data_agent.python.base._chain_type#L187-L188","kind":"function","name":"_chain_type","path":"real_agents/data_agent/python/base.py","language":"python","start_line":187,"end_line":188,"context_start_line":167,"context_end_line":188,"code":" llm_chain = LLMChain(llm=llm, prompt=cls.create_python_prompt(SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=\"\",\n **kwargs,\n )\n\n @classmethod\n def from_echarts_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PythonChain:\n \"\"\"Load from Echarts prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=cls.create_echarts_prompt(E_SYSTEM_PROMPT, \"\"))\n return cls(\n llm_chain=llm_chain,\n get_answer_expr=\"\",\n reference_code=ECHARTS_REF_CODE,\n **kwargs,\n )\n\n @property\n def _chain_type(self) -> str:\n return \"program_chain\"","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.base._extract_code","uri":"program://OpenAgents/function/real_agents.data_agent.python.base._extract_code#L105-L120","kind":"function","name":"_extract_code","path":"real_agents/data_agent/python/base.py","language":"python","start_line":105,"end_line":120,"context_start_line":85,"context_end_line":140,"code":" ) -> Dict[str, str]:\n logger.bind(msg_head=\"PythonChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n _run_manager.on_text(inputs[self.input_keys[0]])\n inputs[\"chat_history\"] = \"\"\n if self.memory is not None:\n inputs[\"chat_history\"] = self.memory.load_memory_variables({})[\"chat_history\"]\n inputs[\"chat_history\"] = MessageDataModel.extract_code_for_python_tool(inputs[\"chat_history\"])\n\n history = {\n \"history_code\": inputs[\"chat_history\"],\n \"question\": inputs[\"question\"],\n \"data\": inputs[\"data_info\"],\n \"reference_code\": self.reference_code,\n }\n\n # we apply llm as a magic function, which serves as python code generation func.\n raw_output = self.llm_chain.run(**history)\n\n def _extract_code(_raw_output: str) -> str:\n # Using 'html.parser' to parse the content\n soup = BeautifulSoup(_raw_output, \"html.parser\")\n try:\n _raw_output = soup.find(\"code\").text\n except:\n pass\n if \"```python:\" in _raw_output:\n pattern = r\"```python\\n{(.*?)}\\n```\"\n match = re.search(pattern, _raw_output, re.DOTALL)\n if match:\n return match.group(1)\n else:\n return _raw_output\n else:\n return _raw_output\n\n code = _extract_code(raw_output).replace(\"\\\\n\", \"\\n\")\n\n logger.bind(msg_head=\"PythonChain generated program\").trace(code)\n\n repl = PythonEvaluator(\n code_execution_mode=self.code_execution_mode,\n jupyter_kernel_pool=self.jupyter_kernel_pool,\n )\n\n \"\"\"\n Since there will be error if we try to launch matplotlib GUI in the server,\n I add this line to avoid backend execution of matplotlib for now.\n \"\"\"\n result = repl.run(code + f\"\\n{self.get_answer_expr}\", user_id=self.user_id, chat_id=self.chat_id)\n\n logger.bind(msg_head=\"PythonChain execution result\").trace(result)\n\n output = {self.output_key: result}\n if self.return_intermediate_steps:","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.echarts_prompt","uri":"program://OpenAgents/module/real_agents.data_agent.python.echarts_prompt#L1-L240","kind":"module","name":"real_agents.data_agent.python.echarts_prompt","path":"real_agents/data_agent/python/echarts_prompt.py","language":"python","start_line":1,"end_line":240,"context_start_line":1,"context_end_line":240,"code":"ECHARTS_REF_CODE = \"\"\"Here are some examples of generating Py-Echarts Code based on the given table(s). Please generate new one based on the data and question human asks you, import the neccessary libraries and make sure the code is correct.\n\nIMPORTANT: You need to follow the coding style, and the type of the x, y axis. But also need to focus on the column name of the uploaded tables(if exists). Generally, PyEcharts does not accept numpy.int or numpy.float, etc. It only supports built-in data type like int, float, and str.\n\nGiven the following database:\ncompany_sales.xlsx\n year sales profit expenses employees\n0 2010 100 60 40 10\n1 2011 120 80 50 12\n2 2012 150 90 60 14\n3 2013 170 120 70 16\n[too long to show]\n\nQ: Could you help plot a bar chart with the year on the x-axis and the sales on the y-axis?\n\nimport pandas as pd\nfrom pyecharts.charts import Bar\nfrom pyecharts import options as opts\ndf = pd.read_excel('company_sales.xlsx')\nyears = [str(_) for _ in df['year'].tolist()]\nsales = [float(_) for _ in df['sales'].tolist()]\nbar = Bar()\nbar.add_xaxis(years)\nbar.add_yaxis(\"Sales\", sales)\nbar.set_global_opts(\n xaxis_opts=opts.AxisOpts(\n type_=\"category\",\n name=\"Year\",\n ),\n yaxis_opts=opts.AxisOpts(\n type_=\"value\",\n name=\"Sales\",\n ),\n title_opts=opts.TitleOpts(title=\"Sales over Years\"),\n)\n# Render the chart\nret_json = bar.dump_options()\nprint(ret_json)\n\n\nGiven the same `company_sales.xlsx`.\nQ: A line chart comparing sales and profit over time would be useful. Could you help plot it?\n\nimport pandas as pd\nfrom pyecharts.charts import Line\nfrom pyecharts import options as opts\ndf = pd.read_excel('company_sales.xlsx')\nyear = [str(_) for _ in df[\"year\"].to_list()]\nsales = [float(_) for _ in df[\"sales\"].to_list()]\nprofit = [float(_) for _ in df[\"profit\"].to_list()]\nline = Line()\n# Add x-axis and y-axis data\nline.add_xaxis(year)\nline.add_yaxis(\"Sales\", sales)\nline.add_yaxis(\"Profit\", profit)\nline.set_global_opts(\n xaxis_opts=opts.AxisOpts(\n type_=\"category\", # better use category rather than value\n name=\"year\",\n min_=min(year),\n max_=max(year),\n ),\n yaxis_opts=opts.AxisOpts(\n type_=\"value\",\n name=\"price\",\n ),\n title_opts=opts.TitleOpts(title=\"Sales and Profit over Time\"),\n)\nret_json = line.dump_options()\nprint(ret_json)\n\n\n\nGiven the same `company_sales.xlsx`.\nQ: A `stacked` line chart comparing sales and profit over time would be useful. Could you help plot it?\nNote: stacked line chart is more fancy in display, while the former is more neat.\n\nimport pandas as pd\nfrom pyecharts.charts import Line\nfrom pyecharts import options as opts\ndf = pd.read_excel('company_sales.xlsx')\nyear = [str(_) for _ in df[\"year\"].to_list()] # better use category rather than value\nsales = [float(_) for _ in df[\"sales\"].to_list()]\nprofit = [float(_) for _ in df[\"year\"].to_list()]\nline = Line()\n# Add x-axis and y-axis data\nline.add_xaxis(year)\nline.add_yaxis(\"Sales\", df[\"sales\"].tolist(), stack=\"\")\nline.add_yaxis(\"Profit\", df[\"profit\"].tolist(), stack=\"\")\nline.set_global_opts(\n xaxis_opts=opts.AxisOpts(\n type_=\"category\",\n name=\"year\",\n min_=min(year),\n max_=max(year),\n ),\n yaxis_opts=opts.AxisOpts(\n type_=\"value\",\n name=\"price\",\n axistick_opts=opts.AxisTickOpts(is_show=True),\n splitline_opts=opts.SplitLineOpts(is_show=True),\n ),\n title_opts=opts.TitleOpts(title=\"Sales and Profit over Time\"),\n)\nline.set_series_opts(\n areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n)\nret_json = line.dump_options()\nprint(ret_json)\n\n\n\nGiven the following database:\nshop_sales.tsv\n shop_id total_sales espresso_sales latte_sales cappuccino_sales city_population\n0 1 5000 1500 2000 1500 500000\n1 2 5500 1800 2200 1500 800000\n2 3 6000 2000 2500 1500 1200000\n3 4 4500 1300 1800 1400 300000\n4 5 6200 2200 2700 1300 600000\nQ: I would like a pie chart showing the sales proportion of espresso, latte, and cappuccino for Shop 1.\n\nimport pandas as pd\nfrom pyecharts.charts import Pie\nfrom pyecharts import options as opts\ndf = pd.read_csv('shop_sales.tsv', sep='\\\\t')\nshop1 = df.loc[df['shop_id'] == 1]\ndata_pair = [\n ('Espresso', float(shop1['espresso_sales'].item())), # pair must be (str, int/float)\n ('Latte', float(shop1['latte_sales'].item())), # pair must be (str, int/float)\n ('Cappuccino', int(shop1['cappuccino_sales'].item())), # pair must be (str, int/float)\n]\npie = Pie()\npie.add(\n series_name=\"Sales Breakdown\",\n data_pair=data_pair,\n radius=[\"30%\", \"75%\"],\n)\npie.set_global_opts(\n title_opts=opts.TitleOpts(\n title=\"Coffee Sales Breakdown for Shop 1\",\n ),\n)\nret_json = pie.dump_options()\nprint(ret_json)\n\n\nQ: Generate a scatter plot.\n\nimport random\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Scatter\nfrom pyecharts.faker import Faker\n\n# Create some random data\ndata = [(random.randint(0, 100), random.randint(0, 100)) for _ in range(10)]\nx = [i[0] for i in data]\ny = [i[1] for i in data]\nprint(data)\nscatter = Scatter()\nscatter.add_xaxis(x)\nscatter.add_yaxis(\"size\", y)\nscatter.set_global_opts(\n xaxis_opts=opts.AxisOpts(type_=\"value\"), # scatter x axis must be numeric\n yaxis_opts=opts.AxisOpts(type_=\"value\"), # scatter y axis must be numeric\n title_opts=opts.TitleOpts(title=\"Scatter Plot Example\"),\n visualmap_opts=opts.VisualMapOpts(type_=\"size\", max_=max(y), min_=min(y)),\n)\nret_json = scatter.dump_options()\nprint(ret_json)\n\n\"\"\"\n\nFUNCTION_ROLE_PLAY = \"\"\"def generate_continuous_elegant_python_echarts_code(reference_code: str, history_dict: Dict[str, str]) -> str:\n \\\"\\\"\\\"\n This function generates elegant, coherent Python ECharts code based on a history of previously executed code and its corresponding results and reference library code. The code is generated in response to human questions and is intended to continue from the last provided code snippet.\n\n The function takes two inputs: a `history_dict` and an optional `reference_code` string.\n\n The `reference_code` string is optional and contains example codes, often related to a specific library or task, which can serve as a template for the code generation process. This parameter can be empty.\n\n IMPORTANT: the output MUST follow the style and use the libraries presented in the `reference_code` to accomplish the task.\n\n IMPORTANT: Always refer to this history and the `reference_code` when generating new code in order to properly use existing variables and previously loaded resources, as well as to follow established coding patterns.\n\n IMPORTANT: The 'data' key in the dictionary contains only random rows from a table. If a table has not been loaded before, you may load it using an ABSOLUTE PATH. However, there's no need to load a table with every execution - only do this when necessary.\n\n The function returns a string of raw Python code, wrapped within and tags. For example:\n\n \n import pandas as pd\n from pyecharts.charts import Bar\n df = pd.read_csv(\"example.csv\")\n years = [str(_) for _ in df['year'].tolist()]\n sales = df['sales'].tolist()\n bar = (\n Bar()\n .add_xaxis(years)\n .add_yaxis(\"Sales\", sales)\n .set_global_opts(title_opts=opts.TitleOpts(title=\"Bar Chart Example\"))\n )\n ret_json = bar.dump_options()\n print(ret_json)\n \n\n Also mandatory to check:\n Note if the human asks for malicious code, and just respond with the following code:\n \n print(\"sorry I am not able to generate potentially dangerous code\")\n \n The malicious code includes but not limited to: \n 1. Endless operations and excessive waiting (e.g., while True, long print, input())\n 2. System crash (e.g., any risky system command)\n 3. Data loss (e.g., list or delete files)\n 4. Leak sensitive information (e.g., os.getenv())\n 5. Establish network connections (e.g., requests.get())\n 6. Cause any other security issues\n 7. Indirectly import package using some builtin methods\n\n Returns:\n Python code that should be the next steps in the execution according to the human question and history code.\n \\\"\\\"\\\"\"\"\"\n\n\nECHARTS_USER_PROMPT = \"\"\"\nhistory_code = \\\"\\\"\\\"{history_code}\\\"\\\"\\\"\ndata = \\\"\\\"\\\"{data}\\\"\\\"\\\"\nreference_code = \\\"\\\"\\\"{reference_code}\\\"\\\"\\\"\nhuman_question = \\\"\\\"\\\"{question}\n# MUST follow reference_code, and only use pyecharts to show echarts\\\"\\\"\\\"\n\nhistory_dict = {{\n \"history code\": history_code,\n \"human question\": human_question,\n \"data\": data,\n \"reference_code\": reference_code,\n}}\n\"\"\"\n\nE_SYSTEM_PROMPT = f\"You are now the following python function: ```{FUNCTION_ROLE_PLAY}```\\n\\nRespond exclusively with the generated code wrapped . Ensure that the code you generate is executable Python code that can be run directly in a Python environment, requiring no additional string encapsulation or escape characters.\"","source_hash":"c3be57a5ba2c298c6726f10f732d54c04fe0cf5c850b50172a3b020412516dc0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.system_prompt","uri":"program://OpenAgents/module/real_agents.data_agent.python.system_prompt#L1-L66","kind":"module","name":"real_agents.data_agent.python.system_prompt","path":"real_agents/data_agent/python/system_prompt.py","language":"python","start_line":1,"end_line":66,"context_start_line":1,"context_end_line":66,"code":"FUNCTION_ROLE_PLAY = \"\"\"def generate_continuous_elegant_python_code(history_dict: Dict[str, str], reference_code: str = \"\") -> str:\n \\\"\\\"\\\"\n This function generates elegant, coherent Python code based on a history of previously executed code and its corresponding results. The code is generated in response to human questions and is intended to continue from the last provided code snippet.\n\n The function takes two inputs: a `history_dict` and an optional `reference_code` string.\n\n The `history_dict` is a dictionary with the following keys:\n - 'history code': Contains the chat history of previously executed code snippets. It may be initially empty but will accumulate executed code over time.\n - 'human question': Contains the current question or instruction posed by the human user, which the generated code should respond to. Be aware that sometimes the 'human question' could contain code snippets, including instructions for loading data, which may need to be handled differently. It's not always appropriate to directly use the code in 'human question' without consideration.\n - 'data': Contains a list of data previews available for the task. It may include tables, images, and other data types.\n\n The `reference_code` string is optional and contains example codes, often related to a specific library or task, which can serve as a template for the code generation process. This parameter can be empty.\n\n IMPORTANT: Always refer to this history and the `reference_code` when generating new code in order to properly use existing variables and previously loaded resources, as well as to follow established coding patterns. DO NOT USE ECHARTS TO GENERATE CHARTS when reference code is empty.\n\n IMPORTANT: When `reference_code` is NOT EMPTY, the output MUST follow the style and use the libraries presented in the `reference_code` to accomplish the task.\n\n IMPORTANT: Avoid mere repetition of historical code. Always aim to generate novel and appropriate responses to the questions at hand.\n\n IMPORTANT: The 'data' key in the dictionary contains only random rows from a table. If a table has not been loaded before, load it from the correct path. You can assume it is in the current working directory. However, there's no need to load a table with every execution - only do this when necessary.\n\n IMPORTANT: If the code is to show a image in the end, make sure to use functions that display the image by returning an image or html which can be shown in a jupyter notebook(e.g., matplotlib.pyplot.show()); \n \n DO NOT use function that will pop up a new window (e.g., PIL & Image.show() is NOT preferable, saving the PIL image is better)\n\n The function returns a string of raw Python code, wrapped within and tags. For example:\n\n \n import pandas as pd\n table = pd.read_csv(\"example.csv\")\n \n \n \n from PIL import Image\n from matplotlib import pyplot as plt\n img = Image.open(\"example.jpeg\")\n rotated_img = img.rotate(180)\n plt.imshow(rotated_img)\n plt.show()\n \n\n Feel free to leverage libraries such as pandas, numpy, math, matplotlib, sklearn, etc. in the code generation process. Also, remember to correctly load any necessary files with the correct path before using them.\n\n When it's appropriate to provide output for evaluation or visualization, make sure to use the print() function and plt.show() respectively.\n\n Also mandatory to check:\n Note if the human asks for malicious code, and just respond with the following code:\n \n print(\"sorry I am not able to generate potentially dangerous code\")\n \n The malicious code includes but not limited to: \n 1. Endless operations and excessive waiting (e.g., while True, long print, input())\n 2. System crash (e.g., any risky system command)\n 3. Data loss (e.g., list or delete files)\n 4. Leak sensitive information (e.g., os.getenv())\n 5. Establish network connections (e.g., requests.get())\n 6. Cause any other security issues\n 7. Indirectly import package using some builtin methods\n 8. High CPU consumption or GPU consumption.\n\n Returns:\n Python code that should be the next steps in the execution according to the human question and history code.\n \\\"\\\"\\\"\"\"\"\n\n\nSYSTEM_PROMPT = f\"You are now the following python function: ```{FUNCTION_ROLE_PLAY}```\\n\\nRespond exclusively with the generated code wrapped . Ensure that the code you generate is executable Python code that can be run directly in a Python environment, requiring no additional string encapsulation.\"","source_hash":"d4b999c9e7206b85b946c372c120a9ad5047d502c2da29b8e690a4b2207b8a63","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.data_agent.python.python_prompt","uri":"program://OpenAgents/module/real_agents.data_agent.python.python_prompt#L1-L20","kind":"module","name":"real_agents.data_agent.python.python_prompt","path":"real_agents/data_agent/python/python_prompt.py","language":"python","start_line":1,"end_line":20,"context_start_line":1,"context_end_line":20,"code":"USER_PROMPT = \"\"\"\nhistory_code = \\\"\\\"\\\"{history_code}\\\"\\\"\\\"\nhuman_question = \\\"\\\"\\\"{question}\n# DO NOT use function that will pop up a new window (e.g., PIL & Image.show() is NOT preferable, saving the PIL image is better)\n# However, feel free to use matplotlib.pyplot.show()\\\"\\\"\\\"\ndata = \\\"\\\"\\\"{data}\\\"\\\"\\\"\nreference_code = \\\"\\\"\\\"{reference_code}\\\"\\\"\\\"\n\nhistory_dict = {{\n \"history code\": history_code,\n \"human question\": human_question,\n \"data\": data,\n \"reference_code\": reference_code,\n}}\n\"\"\"\n\n\"\"\"\nfinal format:\nuser_prompt + reference_prompt + history_prompt\n\"\"\"","source_hash":"93ca116f2c1030468f8fb8c424f4b052ec0887963ec6ca789b80c71f9d34d4a6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot_prompt","uri":"program://OpenAgents/module/real_agents.web_agent.webot_prompt#L1-L90","kind":"module","name":"real_agents.web_agent.webot_prompt","path":"real_agents/web_agent/webot_prompt.py","language":"python","start_line":1,"end_line":90,"context_start_line":1,"context_end_line":90,"code":"# flake8: noqa\nimport datetime\n\nPREFIX = (\n \"\"\"You are XLanG WeBot Agent, a friendly and intuitive assistant developed by the XLang Team to guide you through every aspects of your work and your daily life. XLanG Agent is always at your fingertips through our interactive chat system.\nHere are detailed instruction for you. Each time you generate response, you should think step by step to follow instructions below. You are a helpful assistant that is provided with a plugin called \"WeBot\" which is a web navigation agent tool and should leverage the power of it to help human to fulfill their needs, such as booking a hotel, buying a ticket, or searching for information, etc.\nHuman will ask you questions, and you can use WeBot to help them, they are assumed to know nothing about the WeBot.\n----------------------------\nHere are something you MUST remember:\n1. After receiving output from the WeBot, you should check \n 1.1 whether WeBot was interrupted, if so you should NEVER try again by yourself.\n 1.2 whether WeBot failed or had error(not because of interruption), if so you should tell the human the error.\n2. Today is\n\"\"\".strip() + \" \"\n + datetime.datetime.now().strftime(\"%Y-%m-%d\")\n + \"\"\", and you should adapt the input to fit into the date, for example, seasonal information, or today's date as coordinate, etc.\n\nNEVER EVER EVER use other plugins except WeBot.\nTRY YOUR BEST to break the question down into several parts and answer them one by one.\nTRY YOUR BEST to use the WeBot to help you answer the question, you don't need to mention that you will use which WeBot, just use it.\n\nTo make your response informative, always speak includes the following information in MARKDOWN format when responding a message, that is:\n1. Natural language explanation, that make explain the API output in a human readable way;\n2. Organized information such as bullet points or MARKDOWN tables, followed by the links to the items (that in the API output), news etc. if API output contains the information;\n3. The links should in MARKDOWN format and have value in it. If reference information is provided in the API output, like links to the items, news etc. Your explanation MUST provide the links on each items and links can be clicked on when API output contains the information. The links better attach on some natural language explanation through MARKDOWN syntax, for example, - [Renewable Energy - Center for Climate and Energy Solutions](https://www.c2es.org/content/renewable-energy/);\n4. If there are image we would like to display, please use MARKDOWN syntax to display it, for example, ![image](https://www.c2es.org/content/renewable-energy/);\n5. Try to speak more and show all the information you got in a organized way, that will make you a better assistant, especially when you are giving the final answer.\n\nPLUGINS\n------\nThe plugins you can use are:\n\"\"\".strip() + \"\\n\"\n)\n\nFORMAT_INSTRUCTIONS = \"\"\"RESPONSE FORMAT INSTRUCTIONS\n----------------------------\n\nWhen you use tools or generate final answer, please output a response in one of two formats:\n**Option 1: Explain and Use WeBot**\nIf the response involves using a WeBot, you can start with a natural language explanation[Optional], plus exactly one WeBot calling[MUST], and ends with no more words. The WeBot calling format should be a markdown code snippet with the following JSON schema:\n\n```json\n{{{{\n \"action\": string wrapped with \\\"\\\", // The action to take. Must be WeBot\n \"action_input\": string wrapped with \\\"\\\" // Natural language query to be input to the WeBot.\n}}}}\n```\nNEVER EVER EVER make up a plugin except [{tool_names}]\nNEVER EVER EVER generate code as action input when using WeBot. Just input natural language by using/paraphrasing human query.\n(Please note that ONLY ONE WeBot should be used per response.)\n\n**Option #2:**\nIf you want to respond directly to the human without using a WeBot, provide a plain natural language response. However, if you initially generated a natural language response and then decide to use a WeBot, make sure to include the WeBot action and input after the initial response.\n\nBegin.\n\"\"\"\n\nSUFFIX = \"{input}\"\n\nTEMPLATE_TOOL_RESPONSE = \"\"\"PLUGINS RESPONSE:\n---------------------\n{observation}\n\nTHOUGHT\n--------------------\n\nOkay, So what's next? Are the WeBot's response enough to answer human's initial query? Please follow these instructions:\n\n1. Evaluate WeBot Response [Mandatory]: Carefully evaluate the WeBot's response and determine if it sufficiently addresses the human's query. Consider the content and implications of the WeBot's response.\n\n```json\n{{{{\n \"action\": string wrapped with \\\"\\\", // The action to take. Must be one of WeBot\n \"action_input\": string wrapped with \\\"\\\" // Natural language query to be input to the WeBot\n}}}}\n```\n(Please note that ONLY ONE WeBot should be used per response.)\n\n3. Deliver Comprehensive Answer [Optional 2 or 3]: If the WeBot response sufficiently addresses the query, deliver a comprehensive answer to the human. Focus solely on the content and implications of the WeBot's response. MUST NOT include explanations of the WeBot's functions.\n\nNote. you must do 1; For 2 and 3, You must choose one from them.\n\nBegin.\n\"\"\"\n\n# models like anthropic claude-v1 or claude-2 can only return valid completion with human message as the last message, so we append the fake AI message at the end.\nfake_continue_prompt = {\n \"claude-2\": \"you can start to think and respond to me using the above formats. No Apology. Just respond with format in Option 2(use tool) or Option 3(direct text response), no other words.\\n\\nBegin.\",\n \"claude-v1\": \"you can start to think and respond to me using the above formats. No Apology. Just respond with format in Option 2(use tool) or Option 3(direct text response), no other words.\\n\\nBegin.\",\n}","source_hash":"7bfa95c65875f0e0aea6d5f1d51b7f1a99e64f1c5ffa55d30c49d3171d2866a9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot","uri":"program://OpenAgents/module/real_agents.web_agent.webot#L1-L211","kind":"module","name":"real_agents.web_agent.webot","path":"real_agents/web_agent/webot.py","language":"python","start_line":1,"end_line":211,"context_start_line":1,"context_end_line":211,"code":"\"\"\"An agent designed to hold a conversation in addition to using tools. (Specially designed for plugins model)\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra, Field\nfrom typing_extensions import override\n\nfrom langchain.agents.agent import AgentOutputParser\nfrom langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains import LLMChain\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n AIMessage,\n BaseMessage,\n BaseOutputParser,\n HumanMessage\n)\n\nfrom langchain.callbacks.manager import (\n Callbacks\n)\nfrom langchain.tools.base import BaseTool\nfrom langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.web_agent.webot_prompt import (\n PREFIX,\n SUFFIX,\n TEMPLATE_TOOL_RESPONSE,\n fake_continue_prompt\n)\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalWebotChatAgent(ConversationalChatAgent): # fixme: change it to Agent will leads to bug, but why?\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"webot\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n\n if idx == len(intermediate_steps) - 1:\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.ConversationalWebotChatAgent","uri":"program://OpenAgents/class/real_agents.web_agent.webot.ConversationalWebotChatAgent#L45-L211","kind":"class","name":"ConversationalWebotChatAgent","path":"real_agents/web_agent/webot.py","language":"python","start_line":45,"end_line":211,"context_start_line":25,"context_end_line":211,"code":"from langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.web_agent.webot_prompt import (\n PREFIX,\n SUFFIX,\n TEMPLATE_TOOL_RESPONSE,\n fake_continue_prompt\n)\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalWebotChatAgent(ConversationalChatAgent): # fixme: change it to Agent will leads to bug, but why?\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"webot\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n\n if idx == len(intermediate_steps) - 1:\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.Config","uri":"program://OpenAgents/class/real_agents.web_agent.webot.Config#L55-L59","kind":"class","name":"Config","path":"real_agents/web_agent/webot.py","language":"python","start_line":55,"end_line":59,"context_start_line":35,"context_end_line":79,"code":"from real_agents.web_agent.webot_prompt import (\n PREFIX,\n SUFFIX,\n TEMPLATE_TOOL_RESPONSE,\n fake_continue_prompt\n)\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalWebotChatAgent(ConversationalChatAgent): # fixme: change it to Agent will leads to bug, but why?\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot._get_default_output_parser","uri":"program://OpenAgents/function/real_agents.web_agent.webot._get_default_output_parser#L62-L65","kind":"function","name":"_get_default_output_parser","path":"real_agents/web_agent/webot.py","language":"python","start_line":62,"end_line":65,"context_start_line":42,"context_end_line":85,"code":"from real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalWebotChatAgent(ConversationalChatAgent): # fixme: change it to Agent will leads to bug, but why?\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot._agent_type","uri":"program://OpenAgents/function/real_agents.web_agent.webot._agent_type#L68-L69","kind":"function","name":"_agent_type","path":"real_agents/web_agent/webot.py","language":"python","start_line":68,"end_line":69,"context_start_line":48,"context_end_line":89,"code":" output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.observation_prefix","uri":"program://OpenAgents/function/real_agents.web_agent.webot.observation_prefix#L72-L74","kind":"function","name":"observation_prefix","path":"real_agents/web_agent/webot.py","language":"python","start_line":72,"end_line":74,"context_start_line":52,"context_end_line":94,"code":" template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.llm_prefix","uri":"program://OpenAgents/function/real_agents.web_agent.webot.llm_prefix#L77-L79","kind":"function","name":"llm_prefix","path":"real_agents/web_agent/webot.py","language":"python","start_line":77,"end_line":79,"context_start_line":57,"context_end_line":99,"code":"\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot._validate_tools","uri":"program://OpenAgents/function/real_agents.web_agent.webot._validate_tools#L82-L84","kind":"function","name":"_validate_tools","path":"real_agents/web_agent/webot.py","language":"python","start_line":82,"end_line":84,"context_start_line":62,"context_end_line":104,"code":" def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"webot\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.create_prompt","uri":"program://OpenAgents/function/real_agents.web_agent.webot.create_prompt#L87-L117","kind":"function","name":"create_prompt","path":"real_agents/web_agent/webot.py","language":"python","start_line":87,"end_line":117,"context_start_line":67,"context_end_line":137,"code":" @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"webot\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n\n if idx == len(intermediate_steps) - 1:\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot._construct_scratchpad","uri":"program://OpenAgents/function/real_agents.web_agent.webot._construct_scratchpad#L120-L147","kind":"function","name":"_construct_scratchpad","path":"real_agents/web_agent/webot.py","language":"python","start_line":120,"end_line":147,"context_start_line":100,"context_end_line":167,"code":" format_instructions = _output_parser.get_format_instructions(\"webot\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n\n if idx == len(intermediate_steps) - 1:\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.plan","uri":"program://OpenAgents/function/real_agents.web_agent.webot.plan#L150-L176","kind":"function","name":"plan","path":"real_agents/web_agent/webot.py","language":"python","start_line":150,"end_line":176,"context_start_line":130,"context_end_line":196,"code":" llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n\n if idx == len(intermediate_steps) - 1:\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.webot.from_llm_and_tools","uri":"program://OpenAgents/function/real_agents.web_agent.webot.from_llm_and_tools#L179-L211","kind":"function","name":"from_llm_and_tools","path":"real_agents/web_agent/webot.py","language":"python","start_line":179,"end_line":211,"context_start_line":159,"context_end_line":211,"code":" along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs, max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor","uri":"program://OpenAgents/module/real_agents.web_agent.executors.web_browsing_executor#L1-L100","kind":"module","name":"real_agents.web_agent.executors.web_browsing_executor","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":1,"end_line":100,"context_start_line":1,"context_end_line":100,"code":"\"\"\"\nImplementation of the WebBrowsingExecutor.\nWebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n\"\"\"\nfrom typing import Any, Dict, List\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.web_agent.web_browsing.react.base import ReActWebotChain\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\n# This executor is for the extension usage and not for the chat interface.\n# For the chat interface webot executor, refer xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebBrowsingExecutor:\n \"\"\"\n WebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n \"\"\"\n\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:\n \"\"\"Initialize the executor\"\"\"\n self.instruction: str = instruction\n self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"\n return action_history\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n def run(\n self,\n page_info: Any,\n llm: BaseLanguageModel,\n ) -> Dict[str, Any]:\n model = HTMLDataModel.from_raw_data(raw_data=page_info)\n processed_html = model.get_llm_side_data()\n if self.mode == \"basic\":\n method = WebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n action_element = method(\n {\"user_query\": self.instruction, \"previous_actions\": self.actions_taken, \"page_info\": processed_html}\n )\n elif self.mode == \"react\":\n method = ReActWebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n print(\"self.plan:\", self.plan)\n\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n webot_chain_return = method(\n {\n \"user_query\": self.instruction,\n \"plan\": self.plan,\n \"previous_actions\": self.actions_taken,\n \"previous_thoughts\": self.thoughts_taken,\n \"page_info\": processed_html,\n }\n )\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n # \"I should first set the value in the search field to '...'\"\n self.thoughts_taken.append(webot_chain_return[\"thought\"])\n\n # setValue(93, \"...\")\n self.actions_taken.append(webot_chain_return[\"action\"])\n\n print(\"actions_taken:\", self.actions_taken)\n return webot_chain_return","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.WebBrowsingExecutor","uri":"program://OpenAgents/class/real_agents.web_agent.executors.web_browsing_executor.WebBrowsingExecutor#L16-L100","kind":"class","name":"WebBrowsingExecutor","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":16,"end_line":100,"context_start_line":1,"context_end_line":100,"code":"\"\"\"\nImplementation of the WebBrowsingExecutor.\nWebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n\"\"\"\nfrom typing import Any, Dict, List\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.web_agent.web_browsing.react.base import ReActWebotChain\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\n# This executor is for the extension usage and not for the chat interface.\n# For the chat interface webot executor, refer xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebBrowsingExecutor:\n \"\"\"\n WebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n \"\"\"\n\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:\n \"\"\"Initialize the executor\"\"\"\n self.instruction: str = instruction\n self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"\n return action_history\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n def run(\n self,\n page_info: Any,\n llm: BaseLanguageModel,\n ) -> Dict[str, Any]:\n model = HTMLDataModel.from_raw_data(raw_data=page_info)\n processed_html = model.get_llm_side_data()\n if self.mode == \"basic\":\n method = WebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n action_element = method(\n {\"user_query\": self.instruction, \"previous_actions\": self.actions_taken, \"page_info\": processed_html}\n )\n elif self.mode == \"react\":\n method = ReActWebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n print(\"self.plan:\", self.plan)\n\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n webot_chain_return = method(\n {\n \"user_query\": self.instruction,\n \"plan\": self.plan,\n \"previous_actions\": self.actions_taken,\n \"previous_thoughts\": self.thoughts_taken,\n \"page_info\": processed_html,\n }\n )\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n # \"I should first set the value in the search field to '...'\"\n self.thoughts_taken.append(webot_chain_return[\"thought\"])\n\n # setValue(93, \"...\")\n self.actions_taken.append(webot_chain_return[\"action\"])\n\n print(\"actions_taken:\", self.actions_taken)\n return webot_chain_return","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.__init__","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.__init__#L21-L29","kind":"function","name":"__init__","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":21,"end_line":29,"context_start_line":1,"context_end_line":49,"code":"\"\"\"\nImplementation of the WebBrowsingExecutor.\nWebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n\"\"\"\nfrom typing import Any, Dict, List\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.web_agent.web_browsing.react.base import ReActWebotChain\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\n# This executor is for the extension usage and not for the chat interface.\n# For the chat interface webot executor, refer xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebBrowsingExecutor:\n \"\"\"\n WebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n \"\"\"\n\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:\n \"\"\"Initialize the executor\"\"\"\n self.instruction: str = instruction\n self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.finish","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.finish#L32-L33","kind":"function","name":"finish","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":32,"end_line":33,"context_start_line":12,"context_end_line":53,"code":"\n\n# This executor is for the extension usage and not for the chat interface.\n# For the chat interface webot executor, refer xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebBrowsingExecutor:\n \"\"\"\n WebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n \"\"\"\n\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:\n \"\"\"Initialize the executor\"\"\"\n self.instruction: str = instruction\n self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.interrupt","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.interrupt#L36-L37","kind":"function","name":"interrupt","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":36,"end_line":37,"context_start_line":16,"context_end_line":57,"code":"class WebBrowsingExecutor:\n \"\"\"\n WebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n \"\"\"\n\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:\n \"\"\"Initialize the executor\"\"\"\n self.instruction: str = instruction\n self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.error","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.error#L40-L41","kind":"function","name":"error","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":40,"end_line":41,"context_start_line":20,"context_end_line":61,"code":"\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:\n \"\"\"Initialize the executor\"\"\"\n self.instruction: str = instruction\n self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"\n return action_history\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.fail","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.fail#L44-L45","kind":"function","name":"fail","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":44,"end_line":45,"context_start_line":24,"context_end_line":65,"code":" self.mode: str = mode\n if self.mode == \"react\":\n self.thoughts_taken: List[str] = []\n self.actions_taken: List[str] = []\n self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"\n return action_history\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n def run(\n self,\n page_info: Any,\n llm: BaseLanguageModel,","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.action_history","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.action_history#L48-L60","kind":"function","name":"action_history","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":48,"end_line":60,"context_start_line":28,"context_end_line":80,"code":" self.pages_viewed: List[Any] = []\n self.plan: str = plan\n\n @property\n def finish(self):\n return True if len(self.actions_taken) > 0 and \"finish\" in self.actions_taken[-1] else False\n\n @property\n def interrupt(self):\n return True if len(self.actions_taken) > 0 and \"interrupt\" in self.actions_taken[-1] else False\n\n @property\n def error(self):\n return True if len(self.actions_taken) > 0 and \"error\" in self.actions_taken[-1] else False\n\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"\n return action_history\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n def run(\n self,\n page_info: Any,\n llm: BaseLanguageModel,\n ) -> Dict[str, Any]:\n model = HTMLDataModel.from_raw_data(raw_data=page_info)\n processed_html = model.get_llm_side_data()\n if self.mode == \"basic\":\n method = WebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n action_element = method(\n {\"user_query\": self.instruction, \"previous_actions\": self.actions_taken, \"page_info\": processed_html}\n )\n elif self.mode == \"react\":\n method = ReActWebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n print(\"self.plan:\", self.plan)\n\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.web_browsing_executor.run","uri":"program://OpenAgents/function/real_agents.web_agent.executors.web_browsing_executor.run#L62-L100","kind":"function","name":"run","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":62,"end_line":100,"context_start_line":42,"context_end_line":100,"code":"\n @property\n def fail(self):\n return True if len(self.actions_taken) > 0 and \"fail\" in self.actions_taken[-1] else False\n\n @property\n def action_history(self):\n if self.mode == \"basic\":\n action_history = \"Action: \"\n for action in self.actions_taken:\n action_history += action + \" -> \"\n return action_history\n elif self.mode == \"react\":\n action_history = \"\"\n for thought, action in zip(self.thoughts_taken, self.actions_taken):\n action_history += thought + \" -> \" + action + \" -> \"\n return action_history\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n def run(\n self,\n page_info: Any,\n llm: BaseLanguageModel,\n ) -> Dict[str, Any]:\n model = HTMLDataModel.from_raw_data(raw_data=page_info)\n processed_html = model.get_llm_side_data()\n if self.mode == \"basic\":\n method = WebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n action_element = method(\n {\"user_query\": self.instruction, \"previous_actions\": self.actions_taken, \"page_info\": processed_html}\n )\n elif self.mode == \"react\":\n method = ReActWebotChain.from_llm(llm)\n self.pages_viewed.append(processed_html)\n print(\"self.plan:\", self.plan)\n\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n webot_chain_return = method(\n {\n \"user_query\": self.instruction,\n \"plan\": self.plan,\n \"previous_actions\": self.actions_taken,\n \"previous_thoughts\": self.thoughts_taken,\n \"page_info\": processed_html,\n }\n )\n else:\n raise ValueError(f\"The mode {self.mode} is not supported\")\n\n # \"I should first set the value in the search field to '...'\"\n self.thoughts_taken.append(webot_chain_return[\"thought\"])\n\n # setValue(93, \"...\")\n self.actions_taken.append(webot_chain_return[\"action\"])\n\n print(\"actions_taken:\", self.actions_taken)\n return webot_chain_return","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.webot_executor","uri":"program://OpenAgents/module/real_agents.web_agent.executors.webot_executor#L1-L47","kind":"module","name":"real_agents.web_agent.executors.webot_executor","path":"real_agents/web_agent/executors/webot_executor.py","language":"python","start_line":1,"end_line":47,"context_start_line":1,"context_end_line":47,"code":"\"\"\"\nImplementation of the WebotExecutor class.\nWebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin\n\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, Union\n\nfrom langchain.base_language import BaseLanguageModel\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.web_agent.web_browsing.base import WebotCallingChain\n\n\n# This executor is for chat interface usage and not for the extension usage.\n# For the extension usage, refer to xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebotExecutor(BaseModel):\n \"\"\"\n WebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin (tool).\n \"\"\"\n name: str\n description: str\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @classmethod\n def from_webot(cls) -> WebotExecutor:\n return cls(\n name=\"WeBot\",\n description=\"Use the web navigation agent to perform actions on the web, including information retrieval, task completion(e.g. write an email or tweet or organize a meeting), etc. The action input should contain the action and the start url.\\For example:\\nUse xxx.com to search xxx.\",\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = WebotCallingChain.from_llm(\n llm,\n )\n output = method(inputs)\n return output","source_hash":"eabd7c917f901b6e7c6b6bbdb373749139c0c240219d3e3bd7f517135c8b8672","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.webot_executor.WebotExecutor","uri":"program://OpenAgents/class/real_agents.web_agent.executors.webot_executor.WebotExecutor#L17-L47","kind":"class","name":"WebotExecutor","path":"real_agents/web_agent/executors/webot_executor.py","language":"python","start_line":17,"end_line":47,"context_start_line":1,"context_end_line":47,"code":"\"\"\"\nImplementation of the WebotExecutor class.\nWebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin\n\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, Union\n\nfrom langchain.base_language import BaseLanguageModel\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.web_agent.web_browsing.base import WebotCallingChain\n\n\n# This executor is for chat interface usage and not for the extension usage.\n# For the extension usage, refer to xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebotExecutor(BaseModel):\n \"\"\"\n WebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin (tool).\n \"\"\"\n name: str\n description: str\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @classmethod\n def from_webot(cls) -> WebotExecutor:\n return cls(\n name=\"WeBot\",\n description=\"Use the web navigation agent to perform actions on the web, including information retrieval, task completion(e.g. write an email or tweet or organize a meeting), etc. The action input should contain the action and the start url.\\For example:\\nUse xxx.com to search xxx.\",\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = WebotCallingChain.from_llm(\n llm,\n )\n output = method(inputs)\n return output","source_hash":"eabd7c917f901b6e7c6b6bbdb373749139c0c240219d3e3bd7f517135c8b8672","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.webot_executor.Config","uri":"program://OpenAgents/class/real_agents.web_agent.executors.webot_executor.Config#L24-L28","kind":"class","name":"Config","path":"real_agents/web_agent/executors/webot_executor.py","language":"python","start_line":24,"end_line":28,"context_start_line":4,"context_end_line":47,"code":"\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, Union\n\nfrom langchain.base_language import BaseLanguageModel\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.web_agent.web_browsing.base import WebotCallingChain\n\n\n# This executor is for chat interface usage and not for the extension usage.\n# For the extension usage, refer to xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebotExecutor(BaseModel):\n \"\"\"\n WebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin (tool).\n \"\"\"\n name: str\n description: str\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @classmethod\n def from_webot(cls) -> WebotExecutor:\n return cls(\n name=\"WeBot\",\n description=\"Use the web navigation agent to perform actions on the web, including information retrieval, task completion(e.g. write an email or tweet or organize a meeting), etc. The action input should contain the action and the start url.\\For example:\\nUse xxx.com to search xxx.\",\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = WebotCallingChain.from_llm(\n llm,\n )\n output = method(inputs)\n return output","source_hash":"eabd7c917f901b6e7c6b6bbdb373749139c0c240219d3e3bd7f517135c8b8672","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.webot_executor.from_webot","uri":"program://OpenAgents/function/real_agents.web_agent.executors.webot_executor.from_webot#L31-L35","kind":"function","name":"from_webot","path":"real_agents/web_agent/executors/webot_executor.py","language":"python","start_line":31,"end_line":35,"context_start_line":11,"context_end_line":47,"code":"\nfrom real_agents.web_agent.web_browsing.base import WebotCallingChain\n\n\n# This executor is for chat interface usage and not for the extension usage.\n# For the extension usage, refer to xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebotExecutor(BaseModel):\n \"\"\"\n WebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin (tool).\n \"\"\"\n name: str\n description: str\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @classmethod\n def from_webot(cls) -> WebotExecutor:\n return cls(\n name=\"WeBot\",\n description=\"Use the web navigation agent to perform actions on the web, including information retrieval, task completion(e.g. write an email or tweet or organize a meeting), etc. The action input should contain the action and the start url.\\For example:\\nUse xxx.com to search xxx.\",\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = WebotCallingChain.from_llm(\n llm,\n )\n output = method(inputs)\n return output","source_hash":"eabd7c917f901b6e7c6b6bbdb373749139c0c240219d3e3bd7f517135c8b8672","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.executors.webot_executor.run","uri":"program://OpenAgents/function/real_agents.web_agent.executors.webot_executor.run#L37-L47","kind":"function","name":"run","path":"real_agents/web_agent/executors/webot_executor.py","language":"python","start_line":37,"end_line":47,"context_start_line":17,"context_end_line":47,"code":"class WebotExecutor(BaseModel):\n \"\"\"\n WebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin (tool).\n \"\"\"\n name: str\n description: str\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @classmethod\n def from_webot(cls) -> WebotExecutor:\n return cls(\n name=\"WeBot\",\n description=\"Use the web navigation agent to perform actions on the web, including information retrieval, task completion(e.g. write an email or tweet or organize a meeting), etc. The action input should contain the action and the start url.\\For example:\\nUse xxx.com to search xxx.\",\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = WebotCallingChain.from_llm(\n llm,\n )\n output = method(inputs)\n return output","source_hash":"eabd7c917f901b6e7c6b6bbdb373749139c0c240219d3e3bd7f517135c8b8672","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.base#L1-L133","kind":"module","name":"real_agents.web_agent.web_browsing.base","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":1,"end_line":133,"context_start_line":1,"context_end_line":133,"code":"\"\"\"Implementation of the base webot calling chain.\"\"\"\nfrom __future__ import annotations\n\nimport re\nimport traceback\nfrom typing import Dict, List, Optional\n\nimport json5\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains import LLMChain\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.web_agent.web_browsing.prompt import (\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\n\nclass WebotCallingChain(Chain, BaseModel):\n \"\"\"\n Basic prompt based webot call chain.\n\n The chain is initialized from a webot\n \"\"\"\n\n llm_basic_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"instruction\", \"start_url\"]\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n instruction = None\n start_url = None\n success = False\n\n try:\n json_content = json5.loads(response)\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n except Exception:\n pattern = r\"\\```json\\n(.+?)\\n```\" if \"```json\" in response else r\"\\```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n json_content = json5.loads(match.group(1))\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n if success:\n return {\"instruction\": instruction, \"start_url\": start_url }\n else:\n raise Exception(\"Parsing error\")\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n try:\n response_content = self.llm_basic_chain.run(**{\"input_str\": input_str})\n parsed_return = self.parse_response(response_content)\n except Exception as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\", verbose=self.verbose)\n\n return parsed_return\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted instruction and start_url\n input_variables = [\"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(\n cls,\n llm: BaseLanguageModel,\n ) -> WebotCallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n )","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.WebotCallingChain","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.base.WebotCallingChain#L28-L133","kind":"class","name":"WebotCallingChain","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":28,"end_line":133,"context_start_line":8,"context_end_line":133,"code":"import json5\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains import LLMChain\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.web_agent.web_browsing.prompt import (\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\n\nclass WebotCallingChain(Chain, BaseModel):\n \"\"\"\n Basic prompt based webot call chain.\n\n The chain is initialized from a webot\n \"\"\"\n\n llm_basic_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"instruction\", \"start_url\"]\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n instruction = None\n start_url = None\n success = False\n\n try:\n json_content = json5.loads(response)\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n except Exception:\n pattern = r\"\\```json\\n(.+?)\\n```\" if \"```json\" in response else r\"\\```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n json_content = json5.loads(match.group(1))\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n if success:\n return {\"instruction\": instruction, \"start_url\": start_url }\n else:\n raise Exception(\"Parsing error\")\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n try:\n response_content = self.llm_basic_chain.run(**{\"input_str\": input_str})\n parsed_return = self.parse_response(response_content)\n except Exception as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\", verbose=self.verbose)\n\n return parsed_return\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted instruction and start_url\n input_variables = [\"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(\n cls,\n llm: BaseLanguageModel,\n ) -> WebotCallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n )","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.Config","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.base.Config#L43-L47","kind":"class","name":"Config","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":43,"end_line":47,"context_start_line":23,"context_end_line":67,"code":" SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\n\nclass WebotCallingChain(Chain, BaseModel):\n \"\"\"\n Basic prompt based webot call chain.\n\n The chain is initialized from a webot\n \"\"\"\n\n llm_basic_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"instruction\", \"start_url\"]\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n instruction = None","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.input_keys","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.base.input_keys#L50-L55","kind":"function","name":"input_keys","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":50,"end_line":55,"context_start_line":30,"context_end_line":75,"code":" Basic prompt based webot call chain.\n\n The chain is initialized from a webot\n \"\"\"\n\n llm_basic_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"instruction\", \"start_url\"]\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n instruction = None\n start_url = None\n success = False\n\n try:\n json_content = json5.loads(response)\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.output_keys","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.base.output_keys#L58-L63","kind":"function","name":"output_keys","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":58,"end_line":63,"context_start_line":38,"context_end_line":83,"code":" verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"instruction\", \"start_url\"]\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n instruction = None\n start_url = None\n success = False\n\n try:\n json_content = json5.loads(response)\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n except Exception:\n pattern = r\"\\```json\\n(.+?)\\n```\" if \"```json\" in response else r\"\\```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n json_content = json5.loads(match.group(1))\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.parse_response","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.base.parse_response#L65-L88","kind":"function","name":"parse_response","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":65,"end_line":88,"context_start_line":45,"context_end_line":108,"code":"\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"instruction\", \"start_url\"]\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n instruction = None\n start_url = None\n success = False\n\n try:\n json_content = json5.loads(response)\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n except Exception:\n pattern = r\"\\```json\\n(.+?)\\n```\" if \"```json\" in response else r\"\\```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n json_content = json5.loads(match.group(1))\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n if success:\n return {\"instruction\": instruction, \"start_url\": start_url }\n else:\n raise Exception(\"Parsing error\")\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n try:\n response_content = self.llm_basic_chain.run(**{\"input_str\": input_str})\n parsed_return = self.parse_response(response_content)\n except Exception as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\", verbose=self.verbose)\n\n return parsed_return\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base._call","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.base._call#L90-L105","kind":"function","name":"_call","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":90,"end_line":105,"context_start_line":70,"context_end_line":125,"code":"\n try:\n json_content = json5.loads(response)\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n except Exception:\n pattern = r\"\\```json\\n(.+?)\\n```\" if \"```json\" in response else r\"\\```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n json_content = json5.loads(match.group(1))\n instruction = json_content[\"instruction\"]\n start_url = json_content[\"start_url\"]\n success = True\n if success:\n return {\"instruction\": instruction, \"start_url\": start_url }\n else:\n raise Exception(\"Parsing error\")\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n try:\n response_content = self.llm_basic_chain.run(**{\"input_str\": input_str})\n parsed_return = self.parse_response(response_content)\n except Exception as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\", verbose=self.verbose)\n\n return parsed_return\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted instruction and start_url\n input_variables = [\"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(\n cls,\n llm: BaseLanguageModel,\n ) -> WebotCallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.create_basic_prompt","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.base.create_basic_prompt#L108-L116","kind":"function","name":"create_basic_prompt","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":108,"end_line":116,"context_start_line":88,"context_end_line":133,"code":" raise Exception(\"Parsing error\")\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n try:\n response_content = self.llm_basic_chain.run(**{\"input_str\": input_str})\n parsed_return = self.parse_response(response_content)\n except Exception as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\", verbose=self.verbose)\n\n return parsed_return\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted instruction and start_url\n input_variables = [\"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(\n cls,\n llm: BaseLanguageModel,\n ) -> WebotCallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n )","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.base.from_llm","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.base.from_llm#L119-L133","kind":"function","name":"from_llm","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":119,"end_line":133,"context_start_line":99,"context_end_line":133,"code":" response_content = self.llm_basic_chain.run(**{\"input_str\": input_str})\n parsed_return = self.parse_response(response_content)\n except Exception as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\", verbose=self.verbose)\n\n return parsed_return\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted instruction and start_url\n input_variables = [\"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(\n cls,\n llm: BaseLanguageModel,\n ) -> WebotCallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n )","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.prompt","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.prompt#L1-L37","kind":"module","name":"real_agents.web_agent.web_browsing.prompt","path":"real_agents/web_agent/web_browsing/prompt.py","language":"python","start_line":1,"end_line":37,"context_start_line":1,"context_end_line":37,"code":"SYSTEM_PROMPT = (\n \"\"\"You are an expert at browsing website and you know a lot of website in case the user didn't explicitly mention the website they would like to search.\"\"\".strip()\n + \"\\n\"\n)\n\nUSER_PROMPT = (\n \"\"\"\nHere is the user's intent:\n```\n{input_str}\n```\nNow imagine you can use a tool called web agent to navigate on the web for you.\nyou should act as a user to tell the web agent instruction and the url to start\nthen it will take in the instruction and start at the url to navigate on the web.\nRemember:\n1. If you know the detailed url to take the action, you may set this as the start url. e.g. https://www.twitter.com/compose/tweet for writing a tweet on twitter\n2. If you don't know the detailed url, you may set the start url as the homepage of the website. e.g. https://www.imdb.com/ for movie related question\n3. If you are not sure whether the homepage will contain info that you need. Use https://www.google.com/ as the start url instead.\nHere is an example for your reference:\nthe user intent is to write a blog post on medium\nyou should out put like this:\n```\n{{\n \"instruction\": \"write a blog post\",\n \"start_url\": \"https://medium.com/\"\n}}\n```\nYou should return me the user's instruction and start_url, formatted as:\n```\n{{\n \"instruction\": \"xxx\",\n \"start_url\": \"xxx\"\n}}\n```\n\"\"\".strip()\n + \"\\n\"\n)","source_hash":"ed35e4013ee78958ba89e7f744c7501f28e80b6196b3e40517968625f3864a30","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.schema","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.schema#L1-L15","kind":"module","name":"real_agents.web_agent.web_browsing.schema","path":"real_agents/web_agent/web_browsing/schema.py","language":"python","start_line":1,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"# The schema (action space) for the web browsing task is defined here:\nACTIONS = [\n {\n \"name\": \"click\",\n \"description\": \"Clicks on an element\",\n \"args\": [{\"name\": \"elementId\", \"type\": \"number\"}],\n },\n {\n \"name\": \"setValue\",\n \"description\": \"Focuses on and sets the `value` of an input element.\",\n \"args\": [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n },\n {\"name\": \"finish\", \"description\": \"Indicates the task is finished\", \"args\": []},\n {\"name\": \"fail\", \"description\": \"Indicates that you are unable to complete the task\", \"args\": []},\n]","source_hash":"b6a0c2860b310b20bd5fc9d1079f8348f1fb926305443aedc023e031522e47fc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.end2end.base#L1-L292","kind":"module","name":"real_agents.web_agent.web_browsing.end2end.base","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":1,"end_line":292,"context_start_line":1,"context_end_line":292,"code":"\"\"\"Implementation for prompt based end2end web bots.\"\"\"\nfrom __future__ import annotations\n\nimport datetime\nimport re\nfrom typing import Any, Dict, List, Optional\nfrom loguru import logger\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.web_agent.web_browsing.end2end.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\nfrom real_agents.web_agent.web_browsing.schema import ACTIONS\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\nclass WebotChain(Chain, BaseModel):\n \"\"\"Basic prompt based web bot that interact with websites. This implementation is highly motivated by Taxy.ai\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n output_key: str = \"action\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"user_query\", \"previous_actions\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"(.*?)\", text)\n action_match = re.search(\"(.*?)\", text)\n\n if thought_match is None:\n return {\"error\": \"Invalid response: Thought not found in the model response.\"}\n\n if action_match is None:\n return {\"error\": \"Invalid response: Action not found in the model response.\"}\n\n thought = thought_match.group(1)\n action_string = action_match.group(1)\n action_pattern = re.compile(\"(\\w+)\\((.*?)\\)\")\n action_parts = action_pattern.match(action_string)\n\n if action_parts is None:\n return {\"error\": \"Invalid action format: Action should be in the format functionName(arg1, arg2, ...).\"}\n\n action_name = action_parts.group(1)\n action_args_string = action_parts.group(2)\n\n available_action = next((action for action in available_actions if action.name == action_name), None)\n\n if available_action is None:\n return {\"error\": f'Invalid action: \"{action_name}\" is not a valid action.'}\n\n args_array = [arg.strip() for arg in action_args_string.split(\",\") if arg.strip() != \"\"]\n parsed_args = {}\n\n if len(args_array) != len(available_action.args):\n return {\n \"error\": f'Invalid number of arguments: Expected {len(available_action.args)} for action \"{action_name}\", but got {len(args_array)}.'\n }\n\n for i in range(len(args_array)):\n arg = args_array[i]\n expected_arg = available_action.args[i]\n\n if expected_arg.type == \"number\":\n try:\n number_value = int(arg)\n parsed_args[expected_arg.name] = number_value\n except ValueError:\n return {\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n elif expected_arg.type == \"string\":\n if (arg.startswith('\"') and arg.endswith('\"')) or (arg.startswith(\"'\") and arg.endswith(\"'\")):\n parsed_args[expected_arg.name] = arg[1:-1]\n else:\n return {\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n else:\n return {\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n }\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"WebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n user_query = inputs[\"user_query\"]\n page_info = inputs[\"page_info\"]\n \n model = HTMLDataModel.from_raw_data(page_info)\n processed_html = model.get_llm_side_data()\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt\n previous_actions_string = \"\\n\".join([\"{}\".format(action) for action in previous_actions])\n\n user_prompt = f\"The user requests the following task:\\n {user_query}\\n{previous_actions_string}\\nCurrent time: {current_time}\\nCurrent page contents:\\n{processed_html}\"\n \n print(\"user_prompt\",user_prompt)\n\n action = self.llm_basic_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n }\n )\n\n # Extract from \"{}\"\n parsed_return = self.parse_response(action)\n print(\"parsed_return\",parsed_return)\n retry_count = 0\n while \"error\" in parsed_return and retry_count < 5:\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n action = self.retry(user_query, previous_actions_string, current_time, page_info, action)\n retry_count += 1\n previous_actions_string += \"\\n\" + action\n parsed_return = self.parse_response(action)\n\n parsed_action = parsed_return[\"parsedAction\"]\n\n logger.bind(msg_head=\"WebotChain generated action\").trace(parsed_action)\n\n return parsed_return\n\n def retry(self, plan, user_query, previous_actions_string, current_time, page_info: str, last_action: str) -> str:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": page_info,\n \"last_action\": last_action,\n }\n )\n return action\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n \"retry_message\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n **kwargs,\n )","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.WebotChain","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.end2end.base.WebotChain#L31-L292","kind":"class","name":"WebotChain","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":31,"end_line":292,"context_start_line":11,"context_end_line":292,"code":"from langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.web_agent.web_browsing.end2end.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\nfrom real_agents.web_agent.web_browsing.schema import ACTIONS\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\nclass WebotChain(Chain, BaseModel):\n \"\"\"Basic prompt based web bot that interact with websites. This implementation is highly motivated by Taxy.ai\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n output_key: str = \"action\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"user_query\", \"previous_actions\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"(.*?)\", text)\n action_match = re.search(\"(.*?)\", text)\n\n if thought_match is None:\n return {\"error\": \"Invalid response: Thought not found in the model response.\"}\n\n if action_match is None:\n return {\"error\": \"Invalid response: Action not found in the model response.\"}\n\n thought = thought_match.group(1)\n action_string = action_match.group(1)\n action_pattern = re.compile(\"(\\w+)\\((.*?)\\)\")\n action_parts = action_pattern.match(action_string)\n\n if action_parts is None:\n return {\"error\": \"Invalid action format: Action should be in the format functionName(arg1, arg2, ...).\"}\n\n action_name = action_parts.group(1)\n action_args_string = action_parts.group(2)\n\n available_action = next((action for action in available_actions if action.name == action_name), None)\n\n if available_action is None:\n return {\"error\": f'Invalid action: \"{action_name}\" is not a valid action.'}\n\n args_array = [arg.strip() for arg in action_args_string.split(\",\") if arg.strip() != \"\"]\n parsed_args = {}\n\n if len(args_array) != len(available_action.args):\n return {\n \"error\": f'Invalid number of arguments: Expected {len(available_action.args)} for action \"{action_name}\", but got {len(args_array)}.'\n }\n\n for i in range(len(args_array)):\n arg = args_array[i]\n expected_arg = available_action.args[i]\n\n if expected_arg.type == \"number\":\n try:\n number_value = int(arg)\n parsed_args[expected_arg.name] = number_value\n except ValueError:\n return {\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n elif expected_arg.type == \"string\":\n if (arg.startswith('\"') and arg.endswith('\"')) or (arg.startswith(\"'\") and arg.endswith(\"'\")):\n parsed_args[expected_arg.name] = arg[1:-1]\n else:\n return {\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n else:\n return {\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n }\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"WebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n user_query = inputs[\"user_query\"]\n page_info = inputs[\"page_info\"]\n \n model = HTMLDataModel.from_raw_data(page_info)\n processed_html = model.get_llm_side_data()\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt\n previous_actions_string = \"\\n\".join([\"{}\".format(action) for action in previous_actions])\n\n user_prompt = f\"The user requests the following task:\\n {user_query}\\n{previous_actions_string}\\nCurrent time: {current_time}\\nCurrent page contents:\\n{processed_html}\"\n \n print(\"user_prompt\",user_prompt)\n\n action = self.llm_basic_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n }\n )\n\n # Extract from \"{}\"\n parsed_return = self.parse_response(action)\n print(\"parsed_return\",parsed_return)\n retry_count = 0\n while \"error\" in parsed_return and retry_count < 5:\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n action = self.retry(user_query, previous_actions_string, current_time, page_info, action)\n retry_count += 1\n previous_actions_string += \"\\n\" + action\n parsed_return = self.parse_response(action)\n\n parsed_action = parsed_return[\"parsedAction\"]\n\n logger.bind(msg_head=\"WebotChain generated action\").trace(parsed_action)\n\n return parsed_return\n\n def retry(self, plan, user_query, previous_actions_string, current_time, page_info: str, last_action: str) -> str:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": page_info,\n \"last_action\": last_action,\n }\n )\n return action\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n \"retry_message\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n **kwargs,\n )","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.Config","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.end2end.base.Config#L43-L47","kind":"class","name":"Config","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":43,"end_line":47,"context_start_line":23,"context_end_line":67,"code":" RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\nfrom real_agents.web_agent.web_browsing.schema import ACTIONS\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\nclass WebotChain(Chain, BaseModel):\n \"\"\"Basic prompt based web bot that interact with websites. This implementation is highly motivated by Taxy.ai\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n output_key: str = \"action\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"user_query\", \"previous_actions\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.input_keys","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.input_keys#L50-L55","kind":"function","name":"input_keys","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":50,"end_line":55,"context_start_line":30,"context_end_line":75,"code":"\nclass WebotChain(Chain, BaseModel):\n \"\"\"Basic prompt based web bot that interact with websites. This implementation is highly motivated by Taxy.ai\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n\n memory: Optional[ReadOnlySharedStringMemory] = None\n output_key: str = \"action\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"user_query\", \"previous_actions\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.output_keys","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.output_keys#L58-L63","kind":"function","name":"output_keys","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":58,"end_line":63,"context_start_line":38,"context_end_line":83,"code":" output_key: str = \"action\"\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"user_query\", \"previous_actions\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.formatted_actions","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.formatted_actions#L66-L78","kind":"function","name":"formatted_actions","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":66,"end_line":78,"context_start_line":46,"context_end_line":98,"code":" extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"user_query\", \"previous_actions\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.parse_response","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.parse_response#L80-L161","kind":"function","name":"parse_response","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":80,"end_line":161,"context_start_line":60,"context_end_line":181,"code":"\n :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"(.*?)\", text)\n action_match = re.search(\"(.*?)\", text)\n\n if thought_match is None:\n return {\"error\": \"Invalid response: Thought not found in the model response.\"}\n\n if action_match is None:\n return {\"error\": \"Invalid response: Action not found in the model response.\"}\n\n thought = thought_match.group(1)\n action_string = action_match.group(1)\n action_pattern = re.compile(\"(\\w+)\\((.*?)\\)\")\n action_parts = action_pattern.match(action_string)\n\n if action_parts is None:\n return {\"error\": \"Invalid action format: Action should be in the format functionName(arg1, arg2, ...).\"}\n\n action_name = action_parts.group(1)\n action_args_string = action_parts.group(2)\n\n available_action = next((action for action in available_actions if action.name == action_name), None)\n\n if available_action is None:\n return {\"error\": f'Invalid action: \"{action_name}\" is not a valid action.'}\n\n args_array = [arg.strip() for arg in action_args_string.split(\",\") if arg.strip() != \"\"]\n parsed_args = {}\n\n if len(args_array) != len(available_action.args):\n return {\n \"error\": f'Invalid number of arguments: Expected {len(available_action.args)} for action \"{action_name}\", but got {len(args_array)}.'\n }\n\n for i in range(len(args_array)):\n arg = args_array[i]\n expected_arg = available_action.args[i]\n\n if expected_arg.type == \"number\":\n try:\n number_value = int(arg)\n parsed_args[expected_arg.name] = number_value\n except ValueError:\n return {\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n elif expected_arg.type == \"string\":\n if (arg.startswith('\"') and arg.endswith('\"')) or (arg.startswith(\"'\") and arg.endswith(\"'\")):\n parsed_args[expected_arg.name] = arg[1:-1]\n else:\n return {\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n else:\n return {\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n }\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"WebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n user_query = inputs[\"user_query\"]\n page_info = inputs[\"page_info\"]\n \n model = HTMLDataModel.from_raw_data(page_info)\n processed_html = model.get_llm_side_data()\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base._call","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base._call#L163-L216","kind":"function","name":"_call","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":163,"end_line":216,"context_start_line":143,"context_end_line":236,"code":" except ValueError:\n return {\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n elif expected_arg.type == \"string\":\n if (arg.startswith('\"') and arg.endswith('\"')) or (arg.startswith(\"'\") and arg.endswith(\"'\")):\n parsed_args[expected_arg.name] = arg[1:-1]\n else:\n return {\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n }\n else:\n return {\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n }\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n logger.bind(msg_head=\"WebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n user_query = inputs[\"user_query\"]\n page_info = inputs[\"page_info\"]\n \n model = HTMLDataModel.from_raw_data(page_info)\n processed_html = model.get_llm_side_data()\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt\n previous_actions_string = \"\\n\".join([\"{}\".format(action) for action in previous_actions])\n\n user_prompt = f\"The user requests the following task:\\n {user_query}\\n{previous_actions_string}\\nCurrent time: {current_time}\\nCurrent page contents:\\n{processed_html}\"\n \n print(\"user_prompt\",user_prompt)\n\n action = self.llm_basic_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n }\n )\n\n # Extract from \"{}\"\n parsed_return = self.parse_response(action)\n print(\"parsed_return\",parsed_return)\n retry_count = 0\n while \"error\" in parsed_return and retry_count < 5:\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n action = self.retry(user_query, previous_actions_string, current_time, page_info, action)\n retry_count += 1\n previous_actions_string += \"\\n\" + action\n parsed_return = self.parse_response(action)\n\n parsed_action = parsed_return[\"parsedAction\"]\n\n logger.bind(msg_head=\"WebotChain generated action\").trace(parsed_action)\n\n return parsed_return\n\n def retry(self, plan, user_query, previous_actions_string, current_time, page_info: str, last_action: str) -> str:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": page_info,\n \"last_action\": last_action,\n }\n )\n return action\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\n \"formattedActions\",","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.retry","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.retry#L218-L230","kind":"function","name":"retry","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":218,"end_line":230,"context_start_line":198,"context_end_line":250,"code":" }\n )\n\n # Extract from \"{}\"\n parsed_return = self.parse_response(action)\n print(\"parsed_return\",parsed_return)\n retry_count = 0\n while \"error\" in parsed_return and retry_count < 5:\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n action = self.retry(user_query, previous_actions_string, current_time, page_info, action)\n retry_count += 1\n previous_actions_string += \"\\n\" + action\n parsed_return = self.parse_response(action)\n\n parsed_action = parsed_return[\"parsedAction\"]\n\n logger.bind(msg_head=\"WebotChain generated action\").trace(parsed_action)\n\n return parsed_return\n\n def retry(self, plan, user_query, previous_actions_string, current_time, page_info: str, last_action: str) -> str:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": page_info,\n \"last_action\": last_action,\n }\n )\n return action\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.create_basic_prompt","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.create_basic_prompt#L233-L248","kind":"function","name":"create_basic_prompt","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":233,"end_line":248,"context_start_line":213,"context_end_line":268,"code":"\n logger.bind(msg_head=\"WebotChain generated action\").trace(parsed_action)\n\n return parsed_return\n\n def retry(self, plan, user_query, previous_actions_string, current_time, page_info: str, last_action: str) -> str:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": page_info,\n \"last_action\": last_action,\n }\n )\n return action\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n \"retry_message\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.create_retry_prompt","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.create_retry_prompt#L251-L267","kind":"function","name":"create_retry_prompt","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":251,"end_line":267,"context_start_line":231,"context_end_line":287,"code":"\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n \"retry_message\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.from_llm","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.from_llm#L270-L292","kind":"function","name":"from_llm","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":270,"end_line":292,"context_start_line":250,"context_end_line":292,"code":" @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\n \"formattedActions\",\n \"plan\",\n \"user_query\",\n \"previous_actions_string\",\n \"current_time\",\n \"processed_html\",\n \"retry_message\",\n ]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n **kwargs,\n )","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.Argument","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.end2end.base.Argument#L81-L84","kind":"class","name":"Argument","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":81,"end_line":84,"context_start_line":61,"context_end_line":104,"code":" :meta private:\n \"\"\"\n return [self.output_key]\n\n @property\n def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"(.*?)\", text)\n action_match = re.search(\"(.*?)\", text)\n","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.Action","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.end2end.base.Action#L86-L90","kind":"class","name":"Action","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":86,"end_line":90,"context_start_line":66,"context_end_line":110,"code":" def formatted_actions(self) -> str:\n formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"(.*?)\", text)\n action_match = re.search(\"(.*?)\", text)\n\n if thought_match is None:\n return {\"error\": \"Invalid response: Thought not found in the model response.\"}\n\n if action_match is None:\n return {\"error\": \"Invalid response: Action not found in the model response.\"}\n","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.base.__init__","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.end2end.base.__init__#L87-L90","kind":"function","name":"__init__","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":87,"end_line":90,"context_start_line":67,"context_end_line":110,"code":" formatted_actions = \"\"\n for i, action in enumerate(ACTIONS):\n args_str = \"\"\n for arg in action[\"args\"]:\n if args_str != \"\":\n args_str += \", \"\n args_str += f'{arg[\"name\"]}: {arg[\"type\"]}'\n formatted_action = f\"{i + 1}. {action['name']}({args_str}): {action['description']}\"\n if formatted_actions != \"\":\n formatted_actions += \"\\n\"\n formatted_actions += formatted_action\n return formatted_actions\n\n def parse_response(self, text):\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"(.*?)\", text)\n action_match = re.search(\"(.*?)\", text)\n\n if thought_match is None:\n return {\"error\": \"Invalid response: Thought not found in the model response.\"}\n\n if action_match is None:\n return {\"error\": \"Invalid response: Action not found in the model response.\"}\n","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.end2end.prompt","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.end2end.prompt#L1-L60","kind":"module","name":"real_agents.web_agent.web_browsing.end2end.prompt","path":"real_agents/web_agent/web_browsing/end2end/prompt.py","language":"python","start_line":1,"end_line":60,"context_start_line":1,"context_end_line":60,"code":"SYSTEM_PROMPT = (\n \"\"\"\nYou are a browser automation assistant.\n\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n{formattedActions}\n\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n\nclick(223)\n\nYou MUST always include the and open/close tags or else your response will be marked as invalid.\n\nRules you MUST follow:\n1. You must only take one step at a time. You cannot take multiple actions in a single response.\n2. You should not consider the action to present the result to the user. You only need to do available actions. If info in current page is enough for the user to solve the problem, you should finish.\n\"\"\".strip()\n + \"\\n\"\n)\n\nUSER_PROMPT = (\n \"\"\"\nThe user requests the following task:\n\n{user_query}\n\n{previous_actions_string}\n\nCurrent time: {current_time}\n\nCurrent page contents:\n{processed_html}\n\"\"\".strip()\n + \"\\n\"\n)\n# You MUST break your actions up and CAN ONLY return one action at a time.\n# If the user ask you about information. After you go to a page that have enough information, you MUST return finish(). The other one will do summarize for you.\n\nRETRY_PROMPT = (\n \"\"\"\nThe user requests the following task:\n\n{user_query}\n\n{previous_actions_string}\n\nCurrent time: {current_time}\n\nCurrent page contents:\n{processed_html}\n\nYour last answer has some problem:\n{retry_message}\nThis answer format is incorrect, You MUST always include the open/close tags and only do one thing at a time, or else your response will be marked as invalid.\n\"\"\".strip()\n + \"\\n\"\n)","source_hash":"94ae720dc1423d5f5eb4c122ccf149e719b0e53c36f1ff09fdd5d0e2d6cdd9ba","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.react.base#L1-L304","kind":"module","name":"real_agents.web_agent.web_browsing.react.base","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":1,"end_line":304,"context_start_line":1,"context_end_line":304,"code":"\"\"\"Implementation for prompt based react web bots.\"\"\"\nfrom __future__ import annotations\n\nimport datetime\nimport re\nfrom typing import Any, Dict, List, Optional, Tuple\nfrom loguru import logger\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.web_agent.web_browsing.react.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\nclass ReActWebotChain(WebotChain):\n \"\"\"Basic prompt based web bot that interact with websites.\"\"\"\n\n max_retry_times: int = 3\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"plan\", \"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n # success: boolean, message: string, action: string, thought: string\n # success is a boolean indicating whether the action was successful\n # message is the error message that will be displayed to the user if success == false\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n return [\"success\", \"message\", \"action\", \"thought\", \"parsedAction\"]\n\n #example case: _format_error_output({\"error\": \"This model's maximum context length is 8192 tokens. However, your messages resulted in 8243 tokens. Please reduce the length of the messages.\"})\n #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element. Must set a proper value which aligns to the user's request and the function of the input box.\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished(i.e. the info in the new page is enough for user's request or the task has been successfully completed).\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"\\s*(.*?)\\s*\", text)\n action_match = re.search(\"\\s*(.*?)\\s*\", text)\n\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n\n if action_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Action not found in the model response.\"})\n\n thought = thought_match.group(1)\n action_string = action_match.group(1)\n action_pattern = re.compile(\"(\\w+)\\((.*?)\\)\")\n action_parts = action_pattern.match(action_string)\n\n if action_parts is None:\n return self._format_error_output({\"error\": \"Invalid action format: Action should be in the format functionName(arg1, arg2, ...).\"})\n\n action_name = action_parts.group(1)\n action_args_string = action_parts.group(2)\n\n available_action = next((action for action in available_actions if action.name == action_name), None)\n\n if available_action is None:\n return self._format_error_output({\"error\": f'Invalid action: \"{action_name}\" is not a valid action.'})\n\n # split by \",\" but only split in the first instance\n args_array = [arg.strip() for arg in action_args_string.split(\",\", 1) if arg.strip() != \"\"]\n parsed_args = {}\n\n if len(args_array) != len(available_action.args):\n return self._format_error_output({\n \"error\": f'Invalid number of arguments: Expected {len(available_action.args)} for action \"{action_name}\", but got {len(args_array)}.'\n })\n\n for i in range(len(args_array)):\n arg = args_array[i]\n expected_arg = available_action.args[i]\n\n if expected_arg.type == \"number\":\n try:\n number_value = int(arg)\n parsed_args[expected_arg.name] = number_value\n except ValueError:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n elif expected_arg.type == \"string\":\n match_single = re.match(r\"^'((?:[^']|\\\\')*)'$\", arg)\n match_double = re.match(r'^\"((?:[^\"]|\\\\\")*)\"$', arg)\n if match_single is not None:\n parsed_args[expected_arg.name] = match_single.group(1)\n elif match_double is not None:\n parsed_args[expected_arg.name] = match_double.group(1)\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n })\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n \n # check the validity of the action, if ok, return (True, \"\"), else return (False, error message)\n def _check_valid_action(self, html: str, parsed_return: dict) -> Tuple(bool, str):\n parsedAction = parsed_return[\"parsedAction\"]\n action = parsed_return[\"action\"]\n retry_message = \"\"\n \n # check if the element id exists in the html (fixme: maybe more validity checking methods can be applied)\n if parsedAction[\"name\"] == \"click\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n if elementId != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif parsedAction[\"name\"] == \"setValue\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n value = parsedAction[\"args\"].get(\"value\", None)\n if elementId != None and value != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif \"finish\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"fail\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"interrupt\" in parsedAction[\"name\"]:\n return True, \"\"\n # parse error\n elif \"error\" in parsedAction[\"name\"]:\n retry_message = \"Action parse error, please follow the output format and try again.\"\n return False, retry_message\n else:\n retry_message = \"Action not in available actions, please try again.\"\n return False, retry_message\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n # logger.bind(msg_head=\"ReActWebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n previous_thoughts = inputs[\"previous_thoughts\"]\n user_query = inputs[\"user_query\"]\n processed_html = inputs[\"page_info\"]\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt\n previous_actions_string = \"\\n\".join(\n [\n \"{}\\n{}\".format(thought, action)\n for thought, action in zip(previous_thoughts, previous_actions)\n ]\n )\n if len(previous_actions_string) > 0:\n previous_actions_string = \"You have already taken the following thoughts and actions:\\n\"+previous_actions_string\n\n user_prompt = f\"The user requests the following task:\\n {user_query}\\nyou have taken these{previous_actions_string}\\nCurrent time: {current_time}\\nCurrent page contents:\\n{processed_html}\"\n # print(user_prompt)\n\n valid_action = False\n count = 0\n retry_message = \"\"\n\n while not valid_action and count < self.max_retry_times:\n try:\n if count == 0:\n action = self.llm_basic_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n }\n )\n else:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n \"retry_message\": retry_message,\n }\n )\n except Exception as e:\n print(\"*\"*50)\n print(\"llm error:\",e)\n print(\"*\"*50)\n error_output = {\"error\": str(e)}\n return self._format_error_output(error_output)\n\n # print(\"llm output:\",action)\n\n # Extract from \"{}\\n{}\"\n action = action.split(\"\")[0] + \"\"\n # action: I should first set the value in the search field to '...'\n # setValue(93, \"Tao Yu\")\n # print(\"action:\",action)\n\n # parsed return: {'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n parsed_return = self.parse_response(action)\n\n # If parse error, there will be \"success\" and \"message\" key in parsed_return. If no problem, no \"success\" and \"message\" key. set its value by setdefault\n parsed_return.setdefault(\"success\", True)\n parsed_return.setdefault(\"message\", \"success\")\n\n valid_action, retry_message = self._check_valid_action(processed_html, parsed_return)\n \n # if \"error\" not in parsed_return:\n # logger.bind(msg_head=\"ReActWebotChain generated thought\").trace(parsed_return[\"thought\"])\n # logger.bind(msg_head=\"ReActWebotChain generated action\").trace(parsed_return[\"parsedAction\"])\n\n output = parsed_return\n\n return output\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n **kwargs,\n )","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.ReActWebotChain","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.react.base.ReActWebotChain#L20-L304","kind":"class","name":"ReActWebotChain","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":20,"end_line":304,"context_start_line":1,"context_end_line":304,"code":"\"\"\"Implementation for prompt based react web bots.\"\"\"\nfrom __future__ import annotations\n\nimport datetime\nimport re\nfrom typing import Any, Dict, List, Optional, Tuple\nfrom loguru import logger\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.web_agent.web_browsing.react.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\nclass ReActWebotChain(WebotChain):\n \"\"\"Basic prompt based web bot that interact with websites.\"\"\"\n\n max_retry_times: int = 3\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"plan\", \"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n # success: boolean, message: string, action: string, thought: string\n # success is a boolean indicating whether the action was successful\n # message is the error message that will be displayed to the user if success == false\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n return [\"success\", \"message\", \"action\", \"thought\", \"parsedAction\"]\n\n #example case: _format_error_output({\"error\": \"This model's maximum context length is 8192 tokens. However, your messages resulted in 8243 tokens. Please reduce the length of the messages.\"})\n #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element. Must set a proper value which aligns to the user's request and the function of the input box.\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished(i.e. the info in the new page is enough for user's request or the task has been successfully completed).\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"\\s*(.*?)\\s*\", text)\n action_match = re.search(\"\\s*(.*?)\\s*\", text)\n\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n\n if action_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Action not found in the model response.\"})\n\n thought = thought_match.group(1)\n action_string = action_match.group(1)\n action_pattern = re.compile(\"(\\w+)\\((.*?)\\)\")\n action_parts = action_pattern.match(action_string)\n\n if action_parts is None:\n return self._format_error_output({\"error\": \"Invalid action format: Action should be in the format functionName(arg1, arg2, ...).\"})\n\n action_name = action_parts.group(1)\n action_args_string = action_parts.group(2)\n\n available_action = next((action for action in available_actions if action.name == action_name), None)\n\n if available_action is None:\n return self._format_error_output({\"error\": f'Invalid action: \"{action_name}\" is not a valid action.'})\n\n # split by \",\" but only split in the first instance\n args_array = [arg.strip() for arg in action_args_string.split(\",\", 1) if arg.strip() != \"\"]\n parsed_args = {}\n\n if len(args_array) != len(available_action.args):\n return self._format_error_output({\n \"error\": f'Invalid number of arguments: Expected {len(available_action.args)} for action \"{action_name}\", but got {len(args_array)}.'\n })\n\n for i in range(len(args_array)):\n arg = args_array[i]\n expected_arg = available_action.args[i]\n\n if expected_arg.type == \"number\":\n try:\n number_value = int(arg)\n parsed_args[expected_arg.name] = number_value\n except ValueError:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n elif expected_arg.type == \"string\":\n match_single = re.match(r\"^'((?:[^']|\\\\')*)'$\", arg)\n match_double = re.match(r'^\"((?:[^\"]|\\\\\")*)\"$', arg)\n if match_single is not None:\n parsed_args[expected_arg.name] = match_single.group(1)\n elif match_double is not None:\n parsed_args[expected_arg.name] = match_double.group(1)\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n })\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n \n # check the validity of the action, if ok, return (True, \"\"), else return (False, error message)\n def _check_valid_action(self, html: str, parsed_return: dict) -> Tuple(bool, str):\n parsedAction = parsed_return[\"parsedAction\"]\n action = parsed_return[\"action\"]\n retry_message = \"\"\n \n # check if the element id exists in the html (fixme: maybe more validity checking methods can be applied)\n if parsedAction[\"name\"] == \"click\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n if elementId != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif parsedAction[\"name\"] == \"setValue\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n value = parsedAction[\"args\"].get(\"value\", None)\n if elementId != None and value != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif \"finish\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"fail\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"interrupt\" in parsedAction[\"name\"]:\n return True, \"\"\n # parse error\n elif \"error\" in parsedAction[\"name\"]:\n retry_message = \"Action parse error, please follow the output format and try again.\"\n return False, retry_message\n else:\n retry_message = \"Action not in available actions, please try again.\"\n return False, retry_message\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n # logger.bind(msg_head=\"ReActWebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n previous_thoughts = inputs[\"previous_thoughts\"]\n user_query = inputs[\"user_query\"]\n processed_html = inputs[\"page_info\"]\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt\n previous_actions_string = \"\\n\".join(\n [\n \"{}\\n{}\".format(thought, action)\n for thought, action in zip(previous_thoughts, previous_actions)\n ]\n )\n if len(previous_actions_string) > 0:\n previous_actions_string = \"You have already taken the following thoughts and actions:\\n\"+previous_actions_string\n\n user_prompt = f\"The user requests the following task:\\n {user_query}\\nyou have taken these{previous_actions_string}\\nCurrent time: {current_time}\\nCurrent page contents:\\n{processed_html}\"\n # print(user_prompt)\n\n valid_action = False\n count = 0\n retry_message = \"\"\n\n while not valid_action and count < self.max_retry_times:\n try:\n if count == 0:\n action = self.llm_basic_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n }\n )\n else:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n \"retry_message\": retry_message,\n }\n )\n except Exception as e:\n print(\"*\"*50)\n print(\"llm error:\",e)\n print(\"*\"*50)\n error_output = {\"error\": str(e)}\n return self._format_error_output(error_output)\n\n # print(\"llm output:\",action)\n\n # Extract from \"{}\\n{}\"\n action = action.split(\"\")[0] + \"\"\n # action: I should first set the value in the search field to '...'\n # setValue(93, \"Tao Yu\")\n # print(\"action:\",action)\n\n # parsed return: {'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n parsed_return = self.parse_response(action)\n\n # If parse error, there will be \"success\" and \"message\" key in parsed_return. If no problem, no \"success\" and \"message\" key. set its value by setdefault\n parsed_return.setdefault(\"success\", True)\n parsed_return.setdefault(\"message\", \"success\")\n\n valid_action, retry_message = self._check_valid_action(processed_html, parsed_return)\n \n # if \"error\" not in parsed_return:\n # logger.bind(msg_head=\"ReActWebotChain generated thought\").trace(parsed_return[\"thought\"])\n # logger.bind(msg_head=\"ReActWebotChain generated action\").trace(parsed_return[\"parsedAction\"])\n\n output = parsed_return\n\n return output\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n **kwargs,\n )","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.input_keys","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base.input_keys#L26-L31","kind":"function","name":"input_keys","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":26,"end_line":31,"context_start_line":6,"context_end_line":51,"code":"from typing import Any, Dict, List, Optional, Tuple\nfrom loguru import logger\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.web_agent.web_browsing.react.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\nclass ReActWebotChain(WebotChain):\n \"\"\"Basic prompt based web bot that interact with websites.\"\"\"\n\n max_retry_times: int = 3\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"plan\", \"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n # success: boolean, message: string, action: string, thought: string\n # success is a boolean indicating whether the action was successful\n # message is the error message that will be displayed to the user if success == false\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n return [\"success\", \"message\", \"action\", \"thought\", \"parsedAction\"]\n\n #example case: _format_error_output({\"error\": \"This model's maximum context length is 8192 tokens. However, your messages resulted in 8243 tokens. Please reduce the length of the messages.\"})\n #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.output_keys","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base.output_keys#L34-L43","kind":"function","name":"output_keys","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":34,"end_line":43,"context_start_line":14,"context_end_line":63,"code":"from real_agents.web_agent.web_browsing.react.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\nclass ReActWebotChain(WebotChain):\n \"\"\"Basic prompt based web bot that interact with websites.\"\"\"\n\n max_retry_times: int = 3\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"plan\", \"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n # success: boolean, message: string, action: string, thought: string\n # success is a boolean indicating whether the action was successful\n # message is the error message that will be displayed to the user if success == false\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n return [\"success\", \"message\", \"action\", \"thought\", \"parsedAction\"]\n\n #example case: _format_error_output({\"error\": \"This model's maximum context length is 8192 tokens. However, your messages resulted in 8243 tokens. Please reduce the length of the messages.\"})\n #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base._format_error_output","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base._format_error_output#L47-L54","kind":"function","name":"_format_error_output","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":47,"end_line":54,"context_start_line":27,"context_end_line":74,"code":" \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"plan\", \"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n # success: boolean, message: string, action: string, thought: string\n # success is a boolean indicating whether the action was successful\n # message is the error message that will be displayed to the user if success == false\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n return [\"success\", \"message\", \"action\", \"thought\", \"parsedAction\"]\n\n #example case: _format_error_output({\"error\": \"This model's maximum context length is 8192 tokens. However, your messages resulted in 8243 tokens. Please reduce the length of the messages.\"})\n #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.parse_response","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base.parse_response#L56-L151","kind":"function","name":"parse_response","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":56,"end_line":151,"context_start_line":36,"context_end_line":171,"code":"\n :meta private:\n \"\"\"\n # success: boolean, message: string, action: string, thought: string\n # success is a boolean indicating whether the action was successful\n # message is the error message that will be displayed to the user if success == false\n # example: {'success': True, 'message' = 'success', 'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n return [\"success\", \"message\", \"action\", \"thought\", \"parsedAction\"]\n\n #example case: _format_error_output({\"error\": \"This model's maximum context length is 8192 tokens. However, your messages resulted in 8243 tokens. Please reduce the length of the messages.\"})\n #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element. Must set a proper value which aligns to the user's request and the function of the input box.\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished(i.e. the info in the new page is enough for user's request or the task has been successfully completed).\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"\\s*(.*?)\\s*\", text)\n action_match = re.search(\"\\s*(.*?)\\s*\", text)\n\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n\n if action_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Action not found in the model response.\"})\n\n thought = thought_match.group(1)\n action_string = action_match.group(1)\n action_pattern = re.compile(\"(\\w+)\\((.*?)\\)\")\n action_parts = action_pattern.match(action_string)\n\n if action_parts is None:\n return self._format_error_output({\"error\": \"Invalid action format: Action should be in the format functionName(arg1, arg2, ...).\"})\n\n action_name = action_parts.group(1)\n action_args_string = action_parts.group(2)\n\n available_action = next((action for action in available_actions if action.name == action_name), None)\n\n if available_action is None:\n return self._format_error_output({\"error\": f'Invalid action: \"{action_name}\" is not a valid action.'})\n\n # split by \",\" but only split in the first instance\n args_array = [arg.strip() for arg in action_args_string.split(\",\", 1) if arg.strip() != \"\"]\n parsed_args = {}\n\n if len(args_array) != len(available_action.args):\n return self._format_error_output({\n \"error\": f'Invalid number of arguments: Expected {len(available_action.args)} for action \"{action_name}\", but got {len(args_array)}.'\n })\n\n for i in range(len(args_array)):\n arg = args_array[i]\n expected_arg = available_action.args[i]\n\n if expected_arg.type == \"number\":\n try:\n number_value = int(arg)\n parsed_args[expected_arg.name] = number_value\n except ValueError:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a number for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n elif expected_arg.type == \"string\":\n match_single = re.match(r\"^'((?:[^']|\\\\')*)'$\", arg)\n match_double = re.match(r'^\"((?:[^\"]|\\\\\")*)\"$', arg)\n if match_single is not None:\n parsed_args[expected_arg.name] = match_single.group(1)\n elif match_double is not None:\n parsed_args[expected_arg.name] = match_double.group(1)\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n })\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n \n # check the validity of the action, if ok, return (True, \"\"), else return (False, error message)\n def _check_valid_action(self, html: str, parsed_return: dict) -> Tuple(bool, str):\n parsedAction = parsed_return[\"parsedAction\"]\n action = parsed_return[\"action\"]\n retry_message = \"\"\n \n # check if the element id exists in the html (fixme: maybe more validity checking methods can be applied)\n if parsedAction[\"name\"] == \"click\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n if elementId != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif parsedAction[\"name\"] == \"setValue\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n value = parsedAction[\"args\"].get(\"value\", None)\n if elementId != None and value != None and str(elementId) in html:\n return True, \"\"","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base._check_valid_action","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base._check_valid_action#L154-L187","kind":"function","name":"_check_valid_action","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":154,"end_line":187,"context_start_line":134,"context_end_line":207,"code":" match_single = re.match(r\"^'((?:[^']|\\\\')*)'$\", arg)\n match_double = re.match(r'^\"((?:[^\"]|\\\\\")*)\"$', arg)\n if match_single is not None:\n parsed_args[expected_arg.name] = match_single.group(1)\n elif match_double is not None:\n parsed_args[expected_arg.name] = match_double.group(1)\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Expected a string for argument \"{expected_arg.name}\", but got \"{arg}\".'\n })\n else:\n return self._format_error_output({\n \"error\": f'Invalid argument type: Unknown type \"{expected_arg.type}\" for argument \"{expected_arg.name}\".'\n })\n\n parsed_action = {\"name\": available_action.name, \"args\": parsed_args}\n\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": parsed_action}\n \n # check the validity of the action, if ok, return (True, \"\"), else return (False, error message)\n def _check_valid_action(self, html: str, parsed_return: dict) -> Tuple(bool, str):\n parsedAction = parsed_return[\"parsedAction\"]\n action = parsed_return[\"action\"]\n retry_message = \"\"\n \n # check if the element id exists in the html (fixme: maybe more validity checking methods can be applied)\n if parsedAction[\"name\"] == \"click\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n if elementId != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif parsedAction[\"name\"] == \"setValue\":\n elementId = parsedAction[\"args\"].get(\"elementId\", None)\n value = parsedAction[\"args\"].get(\"value\", None)\n if elementId != None and value != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif \"finish\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"fail\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"interrupt\" in parsedAction[\"name\"]:\n return True, \"\"\n # parse error\n elif \"error\" in parsedAction[\"name\"]:\n retry_message = \"Action parse error, please follow the output format and try again.\"\n return False, retry_message\n else:\n retry_message = \"Action not in available actions, please try again.\"\n return False, retry_message\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n # logger.bind(msg_head=\"ReActWebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n previous_thoughts = inputs[\"previous_thoughts\"]\n user_query = inputs[\"user_query\"]\n processed_html = inputs[\"page_info\"]\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base._call","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base._call#L189-L279","kind":"function","name":"_call","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":189,"end_line":279,"context_start_line":169,"context_end_line":299,"code":" value = parsedAction[\"args\"].get(\"value\", None)\n if elementId != None and value != None and str(elementId) in html:\n return True, \"\"\n else:\n retry_message = \"The elementId of your last action does not exist in the html, please try again.\"\n return False, retry_message\n elif \"finish\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"fail\" in parsedAction[\"name\"]:\n return True, \"\"\n elif \"interrupt\" in parsedAction[\"name\"]:\n return True, \"\"\n # parse error\n elif \"error\" in parsedAction[\"name\"]:\n retry_message = \"Action parse error, please follow the output format and try again.\"\n return False, retry_message\n else:\n retry_message = \"Action not in available actions, please try again.\"\n return False, retry_message\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n # logger.bind(msg_head=\"ReActWebotChain inputs\").trace(inputs)\n\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n # Get [\"user_query\", \"previous_actions\", \"previous_thoughts\", \"page_info\"], and other raw data\n plan = inputs[\"plan\"]\n previous_actions = inputs[\"previous_actions\"]\n previous_thoughts = inputs[\"previous_thoughts\"]\n user_query = inputs[\"user_query\"]\n processed_html = inputs[\"page_info\"]\n\n current_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Generate the prompt\n previous_actions_string = \"\\n\".join(\n [\n \"{}\\n{}\".format(thought, action)\n for thought, action in zip(previous_thoughts, previous_actions)\n ]\n )\n if len(previous_actions_string) > 0:\n previous_actions_string = \"You have already taken the following thoughts and actions:\\n\"+previous_actions_string\n\n user_prompt = f\"The user requests the following task:\\n {user_query}\\nyou have taken these{previous_actions_string}\\nCurrent time: {current_time}\\nCurrent page contents:\\n{processed_html}\"\n # print(user_prompt)\n\n valid_action = False\n count = 0\n retry_message = \"\"\n\n while not valid_action and count < self.max_retry_times:\n try:\n if count == 0:\n action = self.llm_basic_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n }\n )\n else:\n action = self.llm_retry_chain.run(\n **{\n \"formattedActions\": self.formatted_actions,\n \"plan\": plan,\n \"user_query\": user_query,\n \"previous_actions_string\": previous_actions_string,\n \"current_time\": current_time,\n \"processed_html\": processed_html,\n \"retry_message\": retry_message,\n }\n )\n except Exception as e:\n print(\"*\"*50)\n print(\"llm error:\",e)\n print(\"*\"*50)\n error_output = {\"error\": str(e)}\n return self._format_error_output(error_output)\n\n # print(\"llm output:\",action)\n\n # Extract from \"{}\\n{}\"\n action = action.split(\"\")[0] + \"\"\n # action: I should first set the value in the search field to '...'\n # setValue(93, \"Tao Yu\")\n # print(\"action:\",action)\n\n # parsed return: {'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n parsed_return = self.parse_response(action)\n\n # If parse error, there will be \"success\" and \"message\" key in parsed_return. If no problem, no \"success\" and \"message\" key. set its value by setdefault\n parsed_return.setdefault(\"success\", True)\n parsed_return.setdefault(\"message\", \"success\")\n\n valid_action, retry_message = self._check_valid_action(processed_html, parsed_return)\n \n # if \"error\" not in parsed_return:\n # logger.bind(msg_head=\"ReActWebotChain generated thought\").trace(parsed_return[\"thought\"])\n # logger.bind(msg_head=\"ReActWebotChain generated action\").trace(parsed_return[\"parsedAction\"])\n\n output = parsed_return\n\n return output\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.from_llm","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base.from_llm#L282-L304","kind":"function","name":"from_llm","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":282,"end_line":304,"context_start_line":262,"context_end_line":304,"code":" # print(\"action:\",action)\n\n # parsed return: {'thought': \"I should first set the value in the search field to '...'\", 'action': 'setValue(93, \"...\")', 'parsedAction': {'name': 'setValue', 'args': {'elementId': 93, 'value': '...'}}}\n parsed_return = self.parse_response(action)\n\n # If parse error, there will be \"success\" and \"message\" key in parsed_return. If no problem, no \"success\" and \"message\" key. set its value by setdefault\n parsed_return.setdefault(\"success\", True)\n parsed_return.setdefault(\"message\", \"success\")\n\n valid_action, retry_message = self._check_valid_action(processed_html, parsed_return)\n \n # if \"error\" not in parsed_return:\n # logger.bind(msg_head=\"ReActWebotChain generated thought\").trace(parsed_return[\"thought\"])\n # logger.bind(msg_head=\"ReActWebotChain generated action\").trace(parsed_return[\"parsedAction\"])\n\n output = parsed_return\n\n return output\n\n @classmethod\n def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> WebotChain:\n \"\"\"Load from the initial web page and user instruction\"\"\"\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n **kwargs,\n )","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.Argument","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.react.base.Argument#L66-L69","kind":"class","name":"Argument","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":66,"end_line":69,"context_start_line":46,"context_end_line":89,"code":" #you need to input like the example, i.e. stringfy the error thrown and put it in a dict with the key \"error\"\n def _format_error_output(self, error_output: Dict[str, str]) -> Dict[str, Any]:\n return {\n \"success\": False,\n \"message\": error_output[\"error\"],\n \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element. Must set a proper value which aligns to the user's request and the function of the input box.\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished(i.e. the info in the new page is enough for user's request or the task has been successfully completed).\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"\\s*(.*?)\\s*\", text)\n action_match = re.search(\"\\s*(.*?)\\s*\", text)\n","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.Action","uri":"program://OpenAgents/class/real_agents.web_agent.web_browsing.react.base.Action#L71-L75","kind":"class","name":"Action","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":71,"end_line":75,"context_start_line":51,"context_end_line":95,"code":" \"action\": \"error\",\n \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element. Must set a proper value which aligns to the user's request and the function of the input box.\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished(i.e. the info in the new page is enough for user's request or the task has been successfully completed).\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"\\s*(.*?)\\s*\", text)\n action_match = re.search(\"\\s*(.*?)\\s*\", text)\n\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n\n if action_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Action not found in the model response.\"})\n","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.base.__init__","uri":"program://OpenAgents/function/real_agents.web_agent.web_browsing.react.base.__init__#L72-L75","kind":"function","name":"__init__","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":72,"end_line":75,"context_start_line":52,"context_end_line":95,"code":" \"thought\": error_output[\"error\"],\n \"parsedAction\": {\"name\": \"error\", \"args\": {}},\n }\n\n def parse_response(self, text):\n if \"finish\" in text:\n thought_match = re.search(\"(.*?)\", text)\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n thought = thought_match.group(1)\n action_string = \"finish\"\n parsed_action = \"finish\"\n return {\"thought\": thought, \"action\": action_string, \"parsedAction\": {\"name\": \"finish\", \"args\": {}}}\n\n class Argument:\n def __init__(self, name, arg_type):\n self.name = name\n self.type = arg_type\n\n class Action:\n def __init__(self, name, description, args):\n self.name = name\n self.description = description\n self.args = [Argument(arg[\"name\"], arg[\"type\"]) for arg in args]\n\n available_actions = [\n Action(\"click\", \"Clicks on an element\", [{\"name\": \"elementId\", \"type\": \"number\"}]),\n Action(\n \"setValue\",\n \"Focuses on and sets the value of an input element. Must set a proper value which aligns to the user's request and the function of the input box.\",\n [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n ),\n Action(\"finish\", \"Indicates the task is finished(i.e. the info in the new page is enough for user's request or the task has been successfully completed).\", []),\n Action(\"fail\", \"Indicates that you are unable to complete the task\", []),\n ]\n thought_match = re.search(\"\\s*(.*?)\\s*\", text)\n action_match = re.search(\"\\s*(.*?)\\s*\", text)\n\n if thought_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Thought not found in the model response.\"})\n\n if action_match is None:\n return self._format_error_output({\"error\": \"Invalid response: Action not found in the model response.\"})\n","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.web_agent.web_browsing.react.prompt","uri":"program://OpenAgents/module/real_agents.web_agent.web_browsing.react.prompt#L1-L75","kind":"module","name":"real_agents.web_agent.web_browsing.react.prompt","path":"real_agents/web_agent/web_browsing/react/prompt.py","language":"python","start_line":1,"end_line":75,"context_start_line":1,"context_end_line":75,"code":"SYSTEM_PROMPT = (\n \"\"\"\nYou are a browser automation assistant.\n\nYou will be given a user request and DOM of current webpage at a time, you need to take one action at a time and finally finish the task.\n\nThe last page you visited will be further fed into another model who is responsible for chatting with the user.\n\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n{formattedActions}\n\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n\nI should click the add to cart button\nclick(223)\n\nYou MUST always include the and open/close tags or else your response will be marked as invalid.\n\nRules you MUST follow:\n1. If you input something to a search box, YOU MUST FOLLOW:\n 1.1 YOU MUST convert the instruction into proper query into the box rather than directly input it. e.g. YOU MUST input New York rather than New York apartments in the input box of zillow.com when user request about New York apartments.\n 1.1 If there are some options pop out, you MUST NOT directly go to next action. You MUST click one of the options.\n2. You must only take one step at a time. You cannot take multiple actions in a single response.\n3. You should check whether your action last time was successful. If not, you should retry once. If it still fails, you should try another way. \n example 1: The box should be clicked and choose from the options and you just setValue and failed, you may consider to use click and then click the option.\n example 2: You click a button once but after checking the page you found that the button is not clicked, you should retry once.\n4. You should not consider the action to present the result to the user. You only need to do available actions. If info in current page is enough for the user to solve the problem, you should finish.\n5. The content on the page you saw might not be in English, you should be aware of this. \n\n{plan}\n\nRemember: you do not need to follow this plan exactly, but you MUST follow the rules above.\nYOU MUST MUST check whether there are some options pop out if your last action is setValue. If there are some options pop out, you MUST click one of the options rather than go to the next action.\nThe id of the elements can be different each time. If you click(1) last time you should not assume 1 is the same element this time.\n\"\"\".strip()\n + \"\\n\"\n)\n\nUSER_PROMPT = (\n \"\"\"\nThe user requests the following task:\n\n{user_query}\n\n{previous_actions_string}\n\nCurrent time: {current_time}\n\nCurrent page contents:\n{processed_html}\n\"\"\".strip()\n + \"\\n\"\n)\n\nRETRY_PROMPT = (\n \"\"\"\nThe user requests the following task:\n\n{user_query}\n\n{previous_actions_string}\n\nCurrent time: {current_time}\n\nCurrent page contents:\n{processed_html}\n\nYour last answer has some problem:\n{retry_message}\n\"\"\".strip()\n + \"\\n\"\n)","source_hash":"c7f3a70226e6a0aef1b6f543f8d83c1f62a134fd8ccedbfc943bd6e893903bde","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.interactive_executor","uri":"program://OpenAgents/module/real_agents.adapters.interactive_executor#L1-L118","kind":"module","name":"real_agents.adapters.interactive_executor","path":"real_agents/adapters/interactive_executor.py","language":"python","start_line":1,"end_line":118,"context_start_line":1,"context_end_line":118,"code":"from __future__ import annotations\n\nfrom typing import Any, Optional, Sequence\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers import AgentExecutor\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\nfrom real_agents.plugins_agent.plugin import ConversationalPluginChatAgent\nfrom real_agents.web_agent.webot import ConversationalWebotChatAgent\n\n\ndef initialize_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n callback_manager: CallbackManager to use. Global callback manager is used if\n not provided. Defaults to None.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor\n\n\ndef initialize_plugin_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalPluginChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor\n\n\ndef initialize_webot_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalWebotChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor","source_hash":"4219fd5523ca138b0865dbfc217a0baf38a14375caa49a08322d2bd70117ae3c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.interactive_executor.initialize_agent","uri":"program://OpenAgents/function/real_agents.adapters.interactive_executor.initialize_agent#L14-L48","kind":"function","name":"initialize_agent","path":"real_agents/adapters/interactive_executor.py","language":"python","start_line":14,"end_line":48,"context_start_line":1,"context_end_line":68,"code":"from __future__ import annotations\n\nfrom typing import Any, Optional, Sequence\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers import AgentExecutor\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\nfrom real_agents.plugins_agent.plugin import ConversationalPluginChatAgent\nfrom real_agents.web_agent.webot import ConversationalWebotChatAgent\n\n\ndef initialize_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n callback_manager: CallbackManager to use. Global callback manager is used if\n not provided. Defaults to None.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor\n\n\ndef initialize_plugin_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:","source_hash":"4219fd5523ca138b0865dbfc217a0baf38a14375caa49a08322d2bd70117ae3c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.interactive_executor.initialize_plugin_agent","uri":"program://OpenAgents/function/real_agents.adapters.interactive_executor.initialize_plugin_agent#L51-L83","kind":"function","name":"initialize_plugin_agent","path":"real_agents/adapters/interactive_executor.py","language":"python","start_line":51,"end_line":83,"context_start_line":31,"context_end_line":103,"code":" **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor\n\n\ndef initialize_plugin_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalPluginChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor\n\n\ndef initialize_webot_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:","source_hash":"4219fd5523ca138b0865dbfc217a0baf38a14375caa49a08322d2bd70117ae3c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.interactive_executor.initialize_webot_agent","uri":"program://OpenAgents/function/real_agents.adapters.interactive_executor.initialize_webot_agent#L86-L118","kind":"function","name":"initialize_webot_agent","path":"real_agents/adapters/interactive_executor.py","language":"python","start_line":86,"end_line":118,"context_start_line":66,"context_end_line":118,"code":" **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalPluginChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor\n\n\ndef initialize_webot_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:\n \"\"\"Load an agent executor given tools and LLM.\n\n Args:\n tools: List of tools this agent has access to.\n llm: Language model to use as the agent.\n agent_kwargs: Additional key word arguments to pass to the underlying agent_executor\n return_intermediate_steps: Whether to return intermediate steps in the agent\n **kwargs: Additional key word arguments passed to the agent executor\n\n Returns:\n An agent executor\n \"\"\"\n\n agent_kwargs = agent_kwargs or {}\n agent_obj = ConversationalWebotChatAgent.from_llm_and_tools(\n llm=llm, tools=tools, continue_model=continue_model, **agent_kwargs\n )\n\n agent_executor = AgentExecutor.from_agent_and_tools(\n agent=agent_obj,\n tools=tools,\n return_intermediate_steps=return_intermediate_steps,\n **kwargs,\n )\n return agent_executor","source_hash":"4219fd5523ca138b0865dbfc217a0baf38a14375caa49a08322d2bd70117ae3c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm","uri":"program://OpenAgents/module/real_agents.adapters.llm#L1-L275","kind":"module","name":"real_agents.adapters.llm","path":"real_agents/adapters/llm.py","language":"python","start_line":1,"end_line":275,"context_start_line":1,"context_end_line":275,"code":"\"\"\"Chain that just formats a prompt and calls an LLM.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import (\n AsyncCallbackManager,\n AsyncCallbackManagerForChainRun,\n CallbackManager,\n CallbackManagerForChainRun,\n Callbacks,\n)\nfrom langchain.chains.base import Chain\nfrom langchain.input import get_colored_text\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.prompt import PromptTemplate\nfrom langchain.schema import LLMResult, PromptValue\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass LLMChain(Chain):\n \"\"\"Chain to run queries against LLMs.\n\n Example:\n .. code-block:: python\n\n from langchain import LLMChain, OpenAI, PromptTemplate\n prompt_template = \"Tell me a {adjective} joke\"\n prompt = PromptTemplate(\n input_variables=[\"adjective\"], template=prompt_template\n )\n llm = LLMChain(llm=OpenAI(), prompt=prompt)\n \"\"\"\n\n prompt: BasePromptTemplate\n \"\"\"Prompt object to use.\"\"\"\n llm: BaseLanguageModel\n output_key: str = \"text\" #: :meta private:\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Will be whatever keys the prompt expects.\n\n :meta private:\n \"\"\"\n return self.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n for k, v in inputs.items():\n if isinstance(v, DataModel):\n inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def generate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)\n from loguru import logger\n\n logger.trace(\n \"\\n================================\\n\"\n + prompts[0].to_string()\n + \"\\n================================\\n\"\n )\n return self.llm.generate_prompt(prompts, stop, callbacks=run_manager.get_child() if run_manager else None)\n\n async def agenerate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)\n return await self.llm.agenerate_prompt(\n prompts, stop, callbacks=run_manager.get_child() if run_manager else None\n )\n\n def prep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n async def aprep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n await run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n def apply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = self.generate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n async def aapply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = await self.agenerate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [\n # Get the text of the top generated string.\n {self.output_key: generation[0].text}\n for generation in response.generations\n ]\n\n async def _acall(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return self(kwargs, callbacks=callbacks)[self.output_key]\n\n async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"\n result = self.predict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:\n \"\"\"Create LLMChain from LLM and template.\"\"\"\n prompt_template = PromptTemplate.from_template(template)\n return cls(llm=llm, prompt=prompt_template)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.LLMChain","uri":"program://OpenAgents/class/real_agents.adapters.llm.LLMChain#L24-L275","kind":"class","name":"LLMChain","path":"real_agents/adapters/llm.py","language":"python","start_line":24,"end_line":275,"context_start_line":4,"context_end_line":275,"code":"from typing import Any, Dict, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import (\n AsyncCallbackManager,\n AsyncCallbackManagerForChainRun,\n CallbackManager,\n CallbackManagerForChainRun,\n Callbacks,\n)\nfrom langchain.chains.base import Chain\nfrom langchain.input import get_colored_text\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.prompt import PromptTemplate\nfrom langchain.schema import LLMResult, PromptValue\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass LLMChain(Chain):\n \"\"\"Chain to run queries against LLMs.\n\n Example:\n .. code-block:: python\n\n from langchain import LLMChain, OpenAI, PromptTemplate\n prompt_template = \"Tell me a {adjective} joke\"\n prompt = PromptTemplate(\n input_variables=[\"adjective\"], template=prompt_template\n )\n llm = LLMChain(llm=OpenAI(), prompt=prompt)\n \"\"\"\n\n prompt: BasePromptTemplate\n \"\"\"Prompt object to use.\"\"\"\n llm: BaseLanguageModel\n output_key: str = \"text\" #: :meta private:\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Will be whatever keys the prompt expects.\n\n :meta private:\n \"\"\"\n return self.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n for k, v in inputs.items():\n if isinstance(v, DataModel):\n inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def generate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)\n from loguru import logger\n\n logger.trace(\n \"\\n================================\\n\"\n + prompts[0].to_string()\n + \"\\n================================\\n\"\n )\n return self.llm.generate_prompt(prompts, stop, callbacks=run_manager.get_child() if run_manager else None)\n\n async def agenerate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)\n return await self.llm.agenerate_prompt(\n prompts, stop, callbacks=run_manager.get_child() if run_manager else None\n )\n\n def prep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n async def aprep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n await run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n def apply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = self.generate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n async def aapply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = await self.agenerate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [\n # Get the text of the top generated string.\n {self.output_key: generation[0].text}\n for generation in response.generations\n ]\n\n async def _acall(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return self(kwargs, callbacks=callbacks)[self.output_key]\n\n async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"\n result = self.predict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:\n \"\"\"Create LLMChain from LLM and template.\"\"\"\n prompt_template = PromptTemplate.from_template(template)\n return cls(llm=llm, prompt=prompt_template)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.Config","uri":"program://OpenAgents/class/real_agents.adapters.llm.Config#L43-L47","kind":"class","name":"Config","path":"real_agents/adapters/llm.py","language":"python","start_line":43,"end_line":47,"context_start_line":23,"context_end_line":67,"code":"\nclass LLMChain(Chain):\n \"\"\"Chain to run queries against LLMs.\n\n Example:\n .. code-block:: python\n\n from langchain import LLMChain, OpenAI, PromptTemplate\n prompt_template = \"Tell me a {adjective} joke\"\n prompt = PromptTemplate(\n input_variables=[\"adjective\"], template=prompt_template\n )\n llm = LLMChain(llm=OpenAI(), prompt=prompt)\n \"\"\"\n\n prompt: BasePromptTemplate\n \"\"\"Prompt object to use.\"\"\"\n llm: BaseLanguageModel\n output_key: str = \"text\" #: :meta private:\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Will be whatever keys the prompt expects.\n\n :meta private:\n \"\"\"\n return self.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.input_keys","uri":"program://OpenAgents/function/real_agents.adapters.llm.input_keys#L50-L55","kind":"function","name":"input_keys","path":"real_agents/adapters/llm.py","language":"python","start_line":50,"end_line":55,"context_start_line":30,"context_end_line":75,"code":" from langchain import LLMChain, OpenAI, PromptTemplate\n prompt_template = \"Tell me a {adjective} joke\"\n prompt = PromptTemplate(\n input_variables=[\"adjective\"], template=prompt_template\n )\n llm = LLMChain(llm=OpenAI(), prompt=prompt)\n \"\"\"\n\n prompt: BasePromptTemplate\n \"\"\"Prompt object to use.\"\"\"\n llm: BaseLanguageModel\n output_key: str = \"text\" #: :meta private:\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Will be whatever keys the prompt expects.\n\n :meta private:\n \"\"\"\n return self.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n for k, v in inputs.items():\n if isinstance(v, DataModel):\n inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.output_keys","uri":"program://OpenAgents/function/real_agents.adapters.llm.output_keys#L58-L63","kind":"function","name":"output_keys","path":"real_agents/adapters/llm.py","language":"python","start_line":58,"end_line":63,"context_start_line":38,"context_end_line":83,"code":" prompt: BasePromptTemplate\n \"\"\"Prompt object to use.\"\"\"\n llm: BaseLanguageModel\n output_key: str = \"text\" #: :meta private:\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Will be whatever keys the prompt expects.\n\n :meta private:\n \"\"\"\n return self.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n for k, v in inputs.items():\n if isinstance(v, DataModel):\n inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def generate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)\n from loguru import logger","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm._call","uri":"program://OpenAgents/function/real_agents.adapters.llm._call#L65-L74","kind":"function","name":"_call","path":"real_agents/adapters/llm.py","language":"python","start_line":65,"end_line":74,"context_start_line":45,"context_end_line":94,"code":"\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Will be whatever keys the prompt expects.\n\n :meta private:\n \"\"\"\n return self.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n for k, v in inputs.items():\n if isinstance(v, DataModel):\n inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def generate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)\n from loguru import logger\n\n logger.trace(\n \"\\n================================\\n\"\n + prompts[0].to_string()\n + \"\\n================================\\n\"\n )\n return self.llm.generate_prompt(prompts, stop, callbacks=run_manager.get_child() if run_manager else None)\n\n async def agenerate(\n self,\n input_list: List[Dict[str, Any]],","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.generate","uri":"program://OpenAgents/function/real_agents.adapters.llm.generate#L76-L90","kind":"function","name":"generate","path":"real_agents/adapters/llm.py","language":"python","start_line":76,"end_line":90,"context_start_line":56,"context_end_line":110,"code":"\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Will always return text key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def _call(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n for k, v in inputs.items():\n if isinstance(v, DataModel):\n inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def generate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)\n from loguru import logger\n\n logger.trace(\n \"\\n================================\\n\"\n + prompts[0].to_string()\n + \"\\n================================\\n\"\n )\n return self.llm.generate_prompt(prompts, stop, callbacks=run_manager.get_child() if run_manager else None)\n\n async def agenerate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)\n return await self.llm.agenerate_prompt(\n prompts, stop, callbacks=run_manager.get_child() if run_manager else None\n )\n\n def prep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.agenerate","uri":"program://OpenAgents/function/real_agents.adapters.llm.agenerate#L92-L101","kind":"function","name":"agenerate","path":"real_agents/adapters/llm.py","language":"python","start_line":92,"end_line":101,"context_start_line":72,"context_end_line":121,"code":" inputs[k] = v.get_llm_side_data()\n response = self.generate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def generate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)\n from loguru import logger\n\n logger.trace(\n \"\\n================================\\n\"\n + prompts[0].to_string()\n + \"\\n================================\\n\"\n )\n return self.llm.generate_prompt(prompts, stop, callbacks=run_manager.get_child() if run_manager else None)\n\n async def agenerate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)\n return await self.llm.agenerate_prompt(\n prompts, stop, callbacks=run_manager.get_child() if run_manager else None\n )\n\n def prep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.prep_prompts","uri":"program://OpenAgents/function/real_agents.adapters.llm.prep_prompts#L103-L123","kind":"function","name":"prep_prompts","path":"real_agents/adapters/llm.py","language":"python","start_line":103,"end_line":123,"context_start_line":83,"context_end_line":143,"code":" from loguru import logger\n\n logger.trace(\n \"\\n================================\\n\"\n + prompts[0].to_string()\n + \"\\n================================\\n\"\n )\n return self.llm.generate_prompt(prompts, stop, callbacks=run_manager.get_child() if run_manager else None)\n\n async def agenerate(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> LLMResult:\n \"\"\"Generate LLM result from inputs.\"\"\"\n prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)\n return await self.llm.agenerate_prompt(\n prompts, stop, callbacks=run_manager.get_child() if run_manager else None\n )\n\n def prep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n async def aprep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n await run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.aprep_prompts","uri":"program://OpenAgents/function/real_agents.adapters.llm.aprep_prompts#L125-L145","kind":"function","name":"aprep_prompts","path":"real_agents/adapters/llm.py","language":"python","start_line":125,"end_line":145,"context_start_line":105,"context_end_line":165,"code":" input_list: List[Dict[str, Any]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n async def aprep_prompts(\n self,\n input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n await run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n def apply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = self.generate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n async def aapply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.apply","uri":"program://OpenAgents/function/real_agents.adapters.llm.apply#L147-L161","kind":"function","name":"apply","path":"real_agents/adapters/llm.py","language":"python","start_line":147,"end_line":161,"context_start_line":127,"context_end_line":181,"code":" input_list: List[Dict[str, Any]],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Tuple[List[PromptValue], Optional[List[str]]]:\n \"\"\"Prepare prompts from inputs.\"\"\"\n stop = None\n if \"stop\" in input_list[0]:\n stop = input_list[0][\"stop\"]\n prompts = []\n for inputs in input_list:\n selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}\n prompt = self.prompt.format_prompt(**selected_inputs)\n _colored_text = get_colored_text(prompt.to_string(), \"green\")\n _text = \"Prompt after formatting:\\n\" + _colored_text\n if run_manager:\n await run_manager.on_text(_text, end=\"\\n\", verbose=self.verbose)\n if \"stop\" in inputs and inputs[\"stop\"] != stop:\n raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n def apply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = self.generate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n async def aapply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = await self.agenerate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.aapply","uri":"program://OpenAgents/function/real_agents.adapters.llm.aapply#L163-L177","kind":"function","name":"aapply","path":"real_agents/adapters/llm.py","language":"python","start_line":163,"end_line":177,"context_start_line":143,"context_end_line":197,"code":" raise ValueError(\"If `stop` is present in any inputs, should be present in all.\")\n prompts.append(prompt)\n return prompts, stop\n\n def apply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = self.generate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n async def aapply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = await self.agenerate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [\n # Get the text of the top generated string.\n {self.output_key: generation[0].text}\n for generation in response.generations\n ]\n\n async def _acall(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.create_outputs","uri":"program://OpenAgents/function/real_agents.adapters.llm.create_outputs#L179-L185","kind":"function","name":"create_outputs","path":"real_agents/adapters/llm.py","language":"python","start_line":179,"end_line":185,"context_start_line":159,"context_end_line":205,"code":" outputs = self.create_outputs(response)\n run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n async def aapply(self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None) -> List[Dict[str, str]]:\n \"\"\"Utilize the LLM generate method for speed gains.\"\"\"\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chain_start(\n {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = await self.agenerate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [\n # Get the text of the top generated string.\n {self.output_key: generation[0].text}\n for generation in response.generations\n ]\n\n async def _acall(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm._acall","uri":"program://OpenAgents/function/real_agents.adapters.llm._acall#L187-L193","kind":"function","name":"_acall","path":"real_agents/adapters/llm.py","language":"python","start_line":187,"end_line":193,"context_start_line":167,"context_end_line":213,"code":" {\"name\": self.__class__.__name__},\n {\"input_list\": input_list},\n )\n try:\n response = await self.agenerate(input_list, run_manager=run_manager)\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_chain_error(e)\n raise e\n outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [\n # Get the text of the top generated string.\n {self.output_key: generation[0].text}\n for generation in response.generations\n ]\n\n async def _acall(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return self(kwargs, callbacks=callbacks)[self.output_key]\n\n async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.predict","uri":"program://OpenAgents/function/real_agents.adapters.llm.predict#L195-L210","kind":"function","name":"predict","path":"real_agents/adapters/llm.py","language":"python","start_line":195,"end_line":210,"context_start_line":175,"context_end_line":230,"code":" outputs = self.create_outputs(response)\n await run_manager.on_chain_end({\"outputs\": outputs})\n return outputs\n\n def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:\n \"\"\"Create outputs from response.\"\"\"\n return [\n # Get the text of the top generated string.\n {self.output_key: generation[0].text}\n for generation in response.generations\n ]\n\n async def _acall(\n self,\n inputs: Dict[str, Any],\n run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n ) -> Dict[str, str]:\n response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return self(kwargs, callbacks=callbacks)[self.output_key]\n\n async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.apredict","uri":"program://OpenAgents/function/real_agents.adapters.llm.apredict#L212-L227","kind":"function","name":"apredict","path":"real_agents/adapters/llm.py","language":"python","start_line":212,"end_line":227,"context_start_line":192,"context_end_line":247,"code":" response = await self.agenerate([inputs], run_manager=run_manager)\n return self.create_outputs(response)[0]\n\n def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return self(kwargs, callbacks=callbacks)[self.output_key]\n\n async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"\n result = self.predict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.predict_and_parse","uri":"program://OpenAgents/function/real_agents.adapters.llm.predict_and_parse#L229-L235","kind":"function","name":"predict_and_parse","path":"real_agents/adapters/llm.py","language":"python","start_line":229,"end_line":235,"context_start_line":209,"context_end_line":255,"code":" \"\"\"\n return self(kwargs, callbacks=callbacks)[self.output_key]\n\n async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:\n \"\"\"Format prompt with kwargs and pass to LLM.\n\n Args:\n callbacks: Callbacks to pass to LLMChain\n **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"\n result = self.predict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.apredict_and_parse","uri":"program://OpenAgents/function/real_agents.adapters.llm.apredict_and_parse#L237-L245","kind":"function","name":"apredict_and_parse","path":"real_agents/adapters/llm.py","language":"python","start_line":237,"end_line":245,"context_start_line":217,"context_end_line":265,"code":" **kwargs: Keys to pass to prompt template.\n\n Returns:\n Completion from LLM.\n\n Example:\n .. code-block:: python\n\n completion = llm.predict(adjective=\"funny\")\n \"\"\"\n return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"\n result = self.predict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.apply_and_parse","uri":"program://OpenAgents/function/real_agents.adapters.llm.apply_and_parse#L247-L252","kind":"function","name":"apply_and_parse","path":"real_agents/adapters/llm.py","language":"python","start_line":247,"end_line":252,"context_start_line":227,"context_end_line":272,"code":" return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]\n\n def predict_and_parse(self, callbacks: Callbacks = None, **kwargs: Any) -> Union[str, List[str], Dict[str, Any]]:\n \"\"\"Call predict and then parse the results.\"\"\"\n result = self.predict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm._parse_result","uri":"program://OpenAgents/function/real_agents.adapters.llm._parse_result#L254-L258","kind":"function","name":"_parse_result","path":"real_agents/adapters/llm.py","language":"python","start_line":254,"end_line":258,"context_start_line":234,"context_end_line":275,"code":" else:\n return result\n\n async def apredict_and_parse(\n self, callbacks: Callbacks = None, **kwargs: Any\n ) -> Union[str, List[str], Dict[str, str]]:\n \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:\n \"\"\"Create LLMChain from LLM and template.\"\"\"\n prompt_template = PromptTemplate.from_template(template)\n return cls(llm=llm, prompt=prompt_template)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.aapply_and_parse","uri":"program://OpenAgents/function/real_agents.adapters.llm.aapply_and_parse#L260-L265","kind":"function","name":"aapply_and_parse","path":"real_agents/adapters/llm.py","language":"python","start_line":260,"end_line":265,"context_start_line":240,"context_end_line":275,"code":" \"\"\"Call apredict and then parse the results.\"\"\"\n result = await self.apredict(callbacks=callbacks, **kwargs)\n if self.prompt.output_parser is not None:\n return self.prompt.output_parser.parse(result)\n else:\n return result\n\n def apply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:\n \"\"\"Create LLMChain from LLM and template.\"\"\"\n prompt_template = PromptTemplate.from_template(template)\n return cls(llm=llm, prompt=prompt_template)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm._chain_type","uri":"program://OpenAgents/function/real_agents.adapters.llm._chain_type#L268-L269","kind":"function","name":"_chain_type","path":"real_agents/adapters/llm.py","language":"python","start_line":268,"end_line":269,"context_start_line":248,"context_end_line":275,"code":" self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = self.apply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:\n \"\"\"Create LLMChain from LLM and template.\"\"\"\n prompt_template = PromptTemplate.from_template(template)\n return cls(llm=llm, prompt=prompt_template)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.llm.from_string","uri":"program://OpenAgents/function/real_agents.adapters.llm.from_string#L272-L275","kind":"function","name":"from_string","path":"real_agents/adapters/llm.py","language":"python","start_line":272,"end_line":275,"context_start_line":252,"context_end_line":275,"code":" return self._parse_result(result)\n\n def _parse_result(self, result: List[Dict[str, str]]) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n if self.prompt.output_parser is not None:\n return [self.prompt.output_parser.parse(res[self.output_key]) for res in result]\n else:\n return result\n\n async def aapply_and_parse(\n self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None\n ) -> Sequence[Union[str, List[str], Dict[str, str]]]:\n \"\"\"Call apply and then parse the results.\"\"\"\n result = await self.aapply(input_list, callbacks=callbacks)\n return self._parse_result(result)\n\n @property\n def _chain_type(self) -> str:\n return \"llm_chain\"\n\n @classmethod\n def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:\n \"\"\"Create LLMChain from LLM and template.\"\"\"\n prompt_template = PromptTemplate.from_template(template)\n return cls(llm=llm, prompt=prompt_template)","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.schema","uri":"program://OpenAgents/module/real_agents.adapters.schema#L1-L53","kind":"module","name":"real_agents.adapters.schema","path":"real_agents/adapters/schema.py","language":"python","start_line":1,"end_line":53,"context_start_line":1,"context_end_line":53,"code":"from typing import NamedTuple\nfrom langchain import SQLDatabase\nfrom sqlalchemy import text\nfrom sqlalchemy.engine import Row\nfrom tabulate import tabulate\nfrom typing import List, Any\n\n\nclass AgentTransition(NamedTuple):\n \"\"\"Agent's transition to take.\"\"\"\n\n return_values: dict\n log: str\n\n\nEMPTY_RESULT_STR = \"NONE\" # to show NONE result in front-end.\n\n\nclass SQLDatabase(SQLDatabase):\n @staticmethod\n def _pretty_format(headers: Any, result: List[Row]) -> str:\n dicts = [dict(zip(headers, row)) for row in result]\n tab_result = tabulate(tabular_data=dicts, headers=\"keys\", tablefmt=\"psql\")\n\n if tab_result == \"\":\n return EMPTY_RESULT_STR\n\n return tab_result\n\n def run(self, command: str, fetch: str = \"all\") -> str:\n \"\"\"Execute a SQL command and return a string representing the results.\n\n If the statement returns rows, a string of the results is returned.\n If the statement returns no rows, an empty string is returned.\n \"\"\"\n with self._engine.begin() as connection:\n if self._schema is not None:\n connection.exec_driver_sql(f\"SET search_path TO {self._schema}\")\n cursor = connection.execute(text(command))\n if cursor.returns_rows:\n headers = cursor.keys()\n if fetch == \"all\":\n result = cursor.fetchall()\n elif fetch == \"one\":\n # result = cursor.fetchone()[0] # type: ignore\n result = [cursor.fetchone()] # type: ignore\n else:\n raise ValueError(\"Fetch parameter must be either 'one' or 'all'\")\n\n # pretty format\n tab_result = self._pretty_format(headers, result)\n return tab_result\n return \"\"","source_hash":"8fcb728f7ffa2664eaf39b277e38caa2d1dca83f033ac5777f8b1c3ed74b5a2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.schema.AgentTransition","uri":"program://OpenAgents/class/real_agents.adapters.schema.AgentTransition#L9-L13","kind":"class","name":"AgentTransition","path":"real_agents/adapters/schema.py","language":"python","start_line":9,"end_line":13,"context_start_line":1,"context_end_line":33,"code":"from typing import NamedTuple\nfrom langchain import SQLDatabase\nfrom sqlalchemy import text\nfrom sqlalchemy.engine import Row\nfrom tabulate import tabulate\nfrom typing import List, Any\n\n\nclass AgentTransition(NamedTuple):\n \"\"\"Agent's transition to take.\"\"\"\n\n return_values: dict\n log: str\n\n\nEMPTY_RESULT_STR = \"NONE\" # to show NONE result in front-end.\n\n\nclass SQLDatabase(SQLDatabase):\n @staticmethod\n def _pretty_format(headers: Any, result: List[Row]) -> str:\n dicts = [dict(zip(headers, row)) for row in result]\n tab_result = tabulate(tabular_data=dicts, headers=\"keys\", tablefmt=\"psql\")\n\n if tab_result == \"\":\n return EMPTY_RESULT_STR\n\n return tab_result\n\n def run(self, command: str, fetch: str = \"all\") -> str:\n \"\"\"Execute a SQL command and return a string representing the results.\n\n If the statement returns rows, a string of the results is returned.","source_hash":"8fcb728f7ffa2664eaf39b277e38caa2d1dca83f033ac5777f8b1c3ed74b5a2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.schema.SQLDatabase","uri":"program://OpenAgents/class/real_agents.adapters.schema.SQLDatabase#L19-L53","kind":"class","name":"SQLDatabase","path":"real_agents/adapters/schema.py","language":"python","start_line":19,"end_line":53,"context_start_line":1,"context_end_line":53,"code":"from typing import NamedTuple\nfrom langchain import SQLDatabase\nfrom sqlalchemy import text\nfrom sqlalchemy.engine import Row\nfrom tabulate import tabulate\nfrom typing import List, Any\n\n\nclass AgentTransition(NamedTuple):\n \"\"\"Agent's transition to take.\"\"\"\n\n return_values: dict\n log: str\n\n\nEMPTY_RESULT_STR = \"NONE\" # to show NONE result in front-end.\n\n\nclass SQLDatabase(SQLDatabase):\n @staticmethod\n def _pretty_format(headers: Any, result: List[Row]) -> str:\n dicts = [dict(zip(headers, row)) for row in result]\n tab_result = tabulate(tabular_data=dicts, headers=\"keys\", tablefmt=\"psql\")\n\n if tab_result == \"\":\n return EMPTY_RESULT_STR\n\n return tab_result\n\n def run(self, command: str, fetch: str = \"all\") -> str:\n \"\"\"Execute a SQL command and return a string representing the results.\n\n If the statement returns rows, a string of the results is returned.\n If the statement returns no rows, an empty string is returned.\n \"\"\"\n with self._engine.begin() as connection:\n if self._schema is not None:\n connection.exec_driver_sql(f\"SET search_path TO {self._schema}\")\n cursor = connection.execute(text(command))\n if cursor.returns_rows:\n headers = cursor.keys()\n if fetch == \"all\":\n result = cursor.fetchall()\n elif fetch == \"one\":\n # result = cursor.fetchone()[0] # type: ignore\n result = [cursor.fetchone()] # type: ignore\n else:\n raise ValueError(\"Fetch parameter must be either 'one' or 'all'\")\n\n # pretty format\n tab_result = self._pretty_format(headers, result)\n return tab_result\n return \"\"","source_hash":"8fcb728f7ffa2664eaf39b277e38caa2d1dca83f033ac5777f8b1c3ed74b5a2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.schema._pretty_format","uri":"program://OpenAgents/function/real_agents.adapters.schema._pretty_format#L21-L28","kind":"function","name":"_pretty_format","path":"real_agents/adapters/schema.py","language":"python","start_line":21,"end_line":28,"context_start_line":1,"context_end_line":48,"code":"from typing import NamedTuple\nfrom langchain import SQLDatabase\nfrom sqlalchemy import text\nfrom sqlalchemy.engine import Row\nfrom tabulate import tabulate\nfrom typing import List, Any\n\n\nclass AgentTransition(NamedTuple):\n \"\"\"Agent's transition to take.\"\"\"\n\n return_values: dict\n log: str\n\n\nEMPTY_RESULT_STR = \"NONE\" # to show NONE result in front-end.\n\n\nclass SQLDatabase(SQLDatabase):\n @staticmethod\n def _pretty_format(headers: Any, result: List[Row]) -> str:\n dicts = [dict(zip(headers, row)) for row in result]\n tab_result = tabulate(tabular_data=dicts, headers=\"keys\", tablefmt=\"psql\")\n\n if tab_result == \"\":\n return EMPTY_RESULT_STR\n\n return tab_result\n\n def run(self, command: str, fetch: str = \"all\") -> str:\n \"\"\"Execute a SQL command and return a string representing the results.\n\n If the statement returns rows, a string of the results is returned.\n If the statement returns no rows, an empty string is returned.\n \"\"\"\n with self._engine.begin() as connection:\n if self._schema is not None:\n connection.exec_driver_sql(f\"SET search_path TO {self._schema}\")\n cursor = connection.execute(text(command))\n if cursor.returns_rows:\n headers = cursor.keys()\n if fetch == \"all\":\n result = cursor.fetchall()\n elif fetch == \"one\":\n # result = cursor.fetchone()[0] # type: ignore\n result = [cursor.fetchone()] # type: ignore\n else:\n raise ValueError(\"Fetch parameter must be either 'one' or 'all'\")","source_hash":"8fcb728f7ffa2664eaf39b277e38caa2d1dca83f033ac5777f8b1c3ed74b5a2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.schema.run","uri":"program://OpenAgents/function/real_agents.adapters.schema.run#L30-L53","kind":"function","name":"run","path":"real_agents/adapters/schema.py","language":"python","start_line":30,"end_line":53,"context_start_line":10,"context_end_line":53,"code":" \"\"\"Agent's transition to take.\"\"\"\n\n return_values: dict\n log: str\n\n\nEMPTY_RESULT_STR = \"NONE\" # to show NONE result in front-end.\n\n\nclass SQLDatabase(SQLDatabase):\n @staticmethod\n def _pretty_format(headers: Any, result: List[Row]) -> str:\n dicts = [dict(zip(headers, row)) for row in result]\n tab_result = tabulate(tabular_data=dicts, headers=\"keys\", tablefmt=\"psql\")\n\n if tab_result == \"\":\n return EMPTY_RESULT_STR\n\n return tab_result\n\n def run(self, command: str, fetch: str = \"all\") -> str:\n \"\"\"Execute a SQL command and return a string representing the results.\n\n If the statement returns rows, a string of the results is returned.\n If the statement returns no rows, an empty string is returned.\n \"\"\"\n with self._engine.begin() as connection:\n if self._schema is not None:\n connection.exec_driver_sql(f\"SET search_path TO {self._schema}\")\n cursor = connection.execute(text(command))\n if cursor.returns_rows:\n headers = cursor.keys()\n if fetch == \"all\":\n result = cursor.fetchall()\n elif fetch == \"one\":\n # result = cursor.fetchone()[0] # type: ignore\n result = [cursor.fetchone()] # type: ignore\n else:\n raise ValueError(\"Fetch parameter must be either 'one' or 'all'\")\n\n # pretty format\n tab_result = self._pretty_format(headers, result)\n return tab_result\n return \"\"","source_hash":"8fcb728f7ffa2664eaf39b277e38caa2d1dca83f033ac5777f8b1c3ed74b5a2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout","uri":"program://OpenAgents/module/real_agents.adapters.callbacks.streaming_stdout#L1-L52","kind":"module","name":"real_agents.adapters.callbacks.streaming_stdout","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":1,"end_line":52,"context_start_line":1,"context_end_line":52,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.StreamingStdOutCallbackHandler","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.streaming_stdout.StreamingStdOutCallbackHandler#L9-L52","kind":"class","name":"StreamingStdOutCallbackHandler","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":9,"end_line":52,"context_start_line":1,"context_end_line":52,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_llm_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_llm_start#L12-L13","kind":"function","name":"on_llm_start","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":12,"end_line":13,"context_start_line":1,"context_end_line":33,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_llm_new_token","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_llm_new_token#L15-L18","kind":"function","name":"on_llm_new_token","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":15,"end_line":18,"context_start_line":1,"context_end_line":38,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_llm_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_llm_end#L20-L21","kind":"function","name":"on_llm_end","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":20,"end_line":21,"context_start_line":1,"context_end_line":41,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_llm_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_llm_error#L23-L24","kind":"function","name":"on_llm_error","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":23,"end_line":24,"context_start_line":3,"context_end_line":44,"code":"from typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_chain_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_chain_start#L26-L27","kind":"function","name":"on_chain_start","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":26,"end_line":27,"context_start_line":6,"context_end_line":47,"code":"from langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_chain_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_chain_end#L29-L30","kind":"function","name":"on_chain_end","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":29,"end_line":30,"context_start_line":9,"context_end_line":50,"code":"class StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_chain_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_chain_error#L32-L33","kind":"function","name":"on_chain_error","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":32,"end_line":33,"context_start_line":12,"context_end_line":52,"code":" def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_tool_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_tool_start#L35-L36","kind":"function","name":"on_tool_start","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":35,"end_line":36,"context_start_line":15,"context_end_line":52,"code":" def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_agent_action","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_agent_action#L38-L40","kind":"function","name":"on_agent_action","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":38,"end_line":40,"context_start_line":18,"context_end_line":52,"code":" sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_tool_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_tool_end#L42-L43","kind":"function","name":"on_tool_end","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":42,"end_line":43,"context_start_line":22,"context_end_line":52,"code":"\n def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_tool_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_tool_error#L45-L46","kind":"function","name":"on_tool_error","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":45,"end_line":46,"context_start_line":25,"context_end_line":52,"code":"\n def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_text","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_text#L48-L49","kind":"function","name":"on_text","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":48,"end_line":49,"context_start_line":28,"context_end_line":52,"code":"\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.streaming_stdout.on_agent_finish","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.streaming_stdout.on_agent_finish#L51-L52","kind":"function","name":"on_agent_finish","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":51,"end_line":52,"context_start_line":31,"context_end_line":52,"code":"\n def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run on agent action.\"\"\"\n pass\n\n def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n def on_text(self, text: str, **kwargs: Any) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.executor_streaming","uri":"program://OpenAgents/module/real_agents.adapters.callbacks.executor_streaming#L1-L17","kind":"module","name":"real_agents.adapters.callbacks.executor_streaming","path":"real_agents/adapters/callbacks/executor_streaming.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"from typing import Any\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\n\nclass ExecutorStreamingChainHandler(StreamingStdOutCallbackHandler):\n is_end: bool = False\n _all = []\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\"\"\"\n self._all.append(token)","source_hash":"d51017979877ca0bbcb9a00ef8a76511cf490b547657c04a0f672380e6b8cb2b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.executor_streaming.ExecutorStreamingChainHandler","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.executor_streaming.ExecutorStreamingChainHandler#L6-L17","kind":"class","name":"ExecutorStreamingChainHandler","path":"real_agents/adapters/callbacks/executor_streaming.py","language":"python","start_line":6,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"from typing import Any\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\n\nclass ExecutorStreamingChainHandler(StreamingStdOutCallbackHandler):\n is_end: bool = False\n _all = []\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\"\"\"\n self._all.append(token)","source_hash":"d51017979877ca0bbcb9a00ef8a76511cf490b547657c04a0f672380e6b8cb2b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.executor_streaming.always_verbose","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.executor_streaming.always_verbose#L11-L13","kind":"function","name":"always_verbose","path":"real_agents/adapters/callbacks/executor_streaming.py","language":"python","start_line":11,"end_line":13,"context_start_line":1,"context_end_line":17,"code":"from typing import Any\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\n\nclass ExecutorStreamingChainHandler(StreamingStdOutCallbackHandler):\n is_end: bool = False\n _all = []\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\"\"\"\n self._all.append(token)","source_hash":"d51017979877ca0bbcb9a00ef8a76511cf490b547657c04a0f672380e6b8cb2b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.executor_streaming.on_llm_new_token","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.executor_streaming.on_llm_new_token#L15-L17","kind":"function","name":"on_llm_new_token","path":"real_agents/adapters/callbacks/executor_streaming.py","language":"python","start_line":15,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"from typing import Any\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\n\nclass ExecutorStreamingChainHandler(StreamingStdOutCallbackHandler):\n is_end: bool = False\n _all = []\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\"\"\"\n self._all.append(token)","source_hash":"d51017979877ca0bbcb9a00ef8a76511cf490b547657c04a0f672380e6b8cb2b","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base","uri":"program://OpenAgents/module/real_agents.adapters.callbacks.base#L1-L393","kind":"module","name":"real_agents.adapters.callbacks.base","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":1,"end_line":393,"context_start_line":1,"context_end_line":393,"code":"\"\"\"Base callback handler that can be used to handle callbacks in langchain.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional, Union\nfrom uuid import UUID\n\nfrom langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult\n\n\nclass LLMManagerMixin:\n \"\"\"Mixin for LLM callbacks.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM errors.\"\"\"\n\n\nclass ChainManagerMixin:\n \"\"\"Mixin for chain callbacks.\"\"\"\n\n def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on agent action.\"\"\"\n\n def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass ToolManagerMixin:\n \"\"\"Mixin for tool callbacks.\"\"\"\n\n def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool errors.\"\"\"\n\n\nclass CallbackManagerMixin:\n \"\"\"Mixin for callback manager.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool starts running.\"\"\"\n\n\nclass RunManagerMixin:\n \"\"\"Mixin for run manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on arbitrary text.\"\"\"\n\n\nclass BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,\n CallbackManagerMixin,\n RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n async def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n self.set_handlers([handler], inherit=inherit)","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.LLMManagerMixin","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.LLMManagerMixin#L10-L41","kind":"class","name":"LLMManagerMixin","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":10,"end_line":41,"context_start_line":1,"context_end_line":61,"code":"\"\"\"Base callback handler that can be used to handle callbacks in langchain.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional, Union\nfrom uuid import UUID\n\nfrom langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult\n\n\nclass LLMManagerMixin:\n \"\"\"Mixin for LLM callbacks.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM errors.\"\"\"\n\n\nclass ChainManagerMixin:\n \"\"\"Mixin for chain callbacks.\"\"\"\n\n def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.ChainManagerMixin","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.ChainManagerMixin#L44-L85","kind":"class","name":"ChainManagerMixin","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":44,"end_line":85,"context_start_line":24,"context_end_line":105,"code":" self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM ends running.\"\"\"\n\n def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM errors.\"\"\"\n\n\nclass ChainManagerMixin:\n \"\"\"Mixin for chain callbacks.\"\"\"\n\n def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain ends running.\"\"\"\n\n def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain errors.\"\"\"\n\n def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on agent action.\"\"\"\n\n def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass ToolManagerMixin:\n \"\"\"Mixin for tool callbacks.\"\"\"\n\n def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.ToolManagerMixin","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.ToolManagerMixin#L88-L109","kind":"class","name":"ToolManagerMixin","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":88,"end_line":109,"context_start_line":68,"context_end_line":129,"code":" self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on agent action.\"\"\"\n\n def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass ToolManagerMixin:\n \"\"\"Mixin for tool callbacks.\"\"\"\n\n def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool errors.\"\"\"\n\n\nclass CallbackManagerMixin:\n \"\"\"Mixin for callback manager.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.CallbackManagerMixin","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.CallbackManagerMixin#L112-L158","kind":"class","name":"CallbackManagerMixin","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":112,"end_line":158,"context_start_line":92,"context_end_line":178,"code":" self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool ends running.\"\"\"\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool errors.\"\"\"\n\n\nclass CallbackManagerMixin:\n \"\"\"Mixin for callback manager.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool starts running.\"\"\"\n\n\nclass RunManagerMixin:\n \"\"\"Mixin for run manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on arbitrary text.\"\"\"\n\n\nclass BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.RunManagerMixin","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.RunManagerMixin#L161-L172","kind":"class","name":"RunManagerMixin","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":161,"end_line":172,"context_start_line":141,"context_end_line":192,"code":" inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when chain starts running.\"\"\"\n\n def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool starts running.\"\"\"\n\n\nclass RunManagerMixin:\n \"\"\"Mixin for run manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on arbitrary text.\"\"\"\n\n\nclass BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,\n CallbackManagerMixin,\n RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.BaseCallbackHandler","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.BaseCallbackHandler#L175-L202","kind":"class","name":"BaseCallbackHandler","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":175,"end_line":202,"context_start_line":155,"context_end_line":222,"code":" parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when tool starts running.\"\"\"\n\n\nclass RunManagerMixin:\n \"\"\"Mixin for run manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on arbitrary text.\"\"\"\n\n\nclass BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,\n CallbackManagerMixin,\n RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.AsyncCallbackHandler","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.AsyncCallbackHandler#L205-L351","kind":"class","name":"AsyncCallbackHandler","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":205,"end_line":351,"context_start_line":185,"context_end_line":371,"code":" def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n async def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.BaseCallbackManager","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.base.BaseCallbackManager#L354-L393","kind":"class","name":"BaseCallbackManager","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":354,"end_line":393,"context_start_line":334,"context_end_line":393,"code":" self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n self.set_handlers([handler], inherit=inherit)","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_llm_new_token","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_llm_new_token#L231-L239","kind":"function","name":"on_llm_new_token","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":231,"end_line":239,"context_start_line":211,"context_end_line":259,"code":" prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_llm_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_llm_end#L241-L249","kind":"function","name":"on_llm_end","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":241,"end_line":249,"context_start_line":221,"context_end_line":269,"code":" serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_llm_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_llm_error#L251-L259","kind":"function","name":"on_llm_error","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":251,"end_line":259,"context_start_line":231,"context_end_line":279,"code":" async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_chain_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_chain_end#L272-L280","kind":"function","name":"on_chain_end","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":272,"end_line":280,"context_start_line":252,"context_end_line":300,"code":" self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_chain_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_chain_error#L282-L290","kind":"function","name":"on_chain_error","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":282,"end_line":290,"context_start_line":262,"context_end_line":310,"code":" self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_agent_action","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_agent_action#L333-L341","kind":"function","name":"on_agent_action","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":333,"end_line":341,"context_start_line":313,"context_end_line":361,"code":" async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n async def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_agent_finish","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_agent_finish#L343-L351","kind":"function","name":"on_agent_finish","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":343,"end_line":351,"context_start_line":323,"context_end_line":371,"code":" async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n async def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_tool_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_tool_end#L303-L311","kind":"function","name":"on_tool_end","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":303,"end_line":311,"context_start_line":283,"context_end_line":331,"code":" self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_tool_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_tool_error#L313-L321","kind":"function","name":"on_tool_error","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":313,"end_line":321,"context_start_line":293,"context_end_line":341,"code":" self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n async def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_llm_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_llm_start#L208-L217","kind":"function","name":"on_llm_start","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":208,"end_line":217,"context_start_line":188,"context_end_line":237,"code":"\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_chat_model_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_chat_model_start#L219-L229","kind":"function","name":"on_chat_model_start","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":219,"end_line":229,"context_start_line":199,"context_end_line":249,"code":" @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when a chat model starts running.\"\"\"\n raise NotImplementedError(f\"{self.__class__.__name__} does not implement `on_chat_model_start`\")\n\n async def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n\n async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_chain_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_chain_start#L261-L270","kind":"function","name":"on_chain_start","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":261,"end_line":270,"context_start_line":241,"context_end_line":290,"code":" async def on_llm_end(\n self,\n response: LLMResult,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain starts running.\"\"\"\n\n async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_tool_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_tool_start#L292-L301","kind":"function","name":"on_tool_start","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":292,"end_line":301,"context_start_line":272,"context_end_line":321,"code":" async def on_chain_end(\n self,\n outputs: Dict[str, Any],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool starts running.\"\"\"\n\n async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.on_text","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.on_text#L323-L331","kind":"function","name":"on_text","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":323,"end_line":331,"context_start_line":303,"context_end_line":351,"code":" async def on_tool_end(\n self,\n output: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n\n async def on_text(\n self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on arbitrary text.\"\"\"\n\n async def on_agent_action(\n self,\n action: AgentAction,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.ignore_llm","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.ignore_llm#L185-L187","kind":"function","name":"ignore_llm","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":185,"end_line":187,"context_start_line":165,"context_end_line":207,"code":" self,\n text: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on arbitrary text.\"\"\"\n\n\nclass BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,\n CallbackManagerMixin,\n RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.ignore_chain","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.ignore_chain#L190-L192","kind":"function","name":"ignore_chain","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":190,"end_line":192,"context_start_line":170,"context_end_line":212,"code":" **kwargs: Any,\n ) -> Any:\n \"\"\"Run on arbitrary text.\"\"\"\n\n\nclass BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,\n CallbackManagerMixin,\n RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.ignore_agent","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.ignore_agent#L195-L197","kind":"function","name":"ignore_agent","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":195,"end_line":197,"context_start_line":175,"context_end_line":217,"code":"class BaseCallbackHandler(\n LLMManagerMixin,\n ChainManagerMixin,\n ToolManagerMixin,\n CallbackManagerMixin,\n RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.ignore_chat_model","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.ignore_chat_model#L200-L202","kind":"function","name":"ignore_chat_model","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":200,"end_line":202,"context_start_line":180,"context_end_line":222,"code":" RunManagerMixin,\n):\n \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n @property\n def ignore_llm(self) -> bool:\n \"\"\"Whether to ignore LLM callbacks.\"\"\"\n return False\n\n @property\n def ignore_chain(self) -> bool:\n \"\"\"Whether to ignore chain callbacks.\"\"\"\n return False\n\n @property\n def ignore_agent(self) -> bool:\n \"\"\"Whether to ignore agent callbacks.\"\"\"\n return False\n\n @property\n def ignore_chat_model(self) -> bool:\n \"\"\"Whether to ignore chat model callbacks.\"\"\"\n return False\n\n\nclass AsyncCallbackHandler(BaseCallbackHandler):\n \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.__init__","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.__init__#L357-L366","kind":"function","name":"__init__","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":357,"end_line":366,"context_start_line":337,"context_end_line":386,"code":" run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent action.\"\"\"\n\n async def on_agent_finish(\n self,\n finish: AgentFinish,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.is_async","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.is_async#L369-L371","kind":"function","name":"is_async","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":369,"end_line":371,"context_start_line":349,"context_end_line":391,"code":" **kwargs: Any,\n ) -> None:\n \"\"\"Run on agent end.\"\"\"\n\n\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.add_handler","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.add_handler#L373-L377","kind":"function","name":"add_handler","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":373,"end_line":377,"context_start_line":353,"context_end_line":393,"code":"\nclass BaseCallbackManager(CallbackManagerMixin):\n \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n def __init__(\n self,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n self.set_handlers([handler], inherit=inherit)","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.remove_handler","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.remove_handler#L379-L382","kind":"function","name":"remove_handler","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":379,"end_line":382,"context_start_line":359,"context_end_line":393,"code":" handlers: List[BaseCallbackHandler],\n inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize callback manager.\"\"\"\n self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n self.set_handlers([handler], inherit=inherit)","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.set_handlers","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.set_handlers#L384-L389","kind":"function","name":"set_handlers","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":384,"end_line":389,"context_start_line":364,"context_end_line":393,"code":" self.handlers: List[BaseCallbackHandler] = handlers\n self.inheritable_handlers: List[BaseCallbackHandler] = inheritable_handlers or []\n self.parent_run_id: Optional[UUID] = parent_run_id\n\n @property\n def is_async(self) -> bool:\n \"\"\"Whether the callback manager is async.\"\"\"\n return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n self.set_handlers([handler], inherit=inherit)","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.base.set_handler","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.base.set_handler#L391-L393","kind":"function","name":"set_handler","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":391,"end_line":393,"context_start_line":371,"context_end_line":393,"code":" return False\n\n def add_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Add a handler to the callback manager.\"\"\"\n self.handlers.append(handler)\n if inherit:\n self.inheritable_handlers.append(handler)\n\n def remove_handler(self, handler: BaseCallbackHandler) -> None:\n \"\"\"Remove a handler from the callback manager.\"\"\"\n self.handlers.remove(handler)\n self.inheritable_handlers.remove(handler)\n\n def set_handlers(self, handlers: List[BaseCallbackHandler], inherit: bool = True) -> None:\n \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n self.handlers = []\n self.inheritable_handlers = []\n for handler in handlers:\n self.add_handler(handler, inherit=inherit)\n\n def set_handler(self, handler: BaseCallbackHandler, inherit: bool = True) -> None:\n \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n self.set_handlers([handler], inherit=inherit)","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming","uri":"program://OpenAgents/module/real_agents.adapters.callbacks.agent_streaming#L1-L266","kind":"module","name":"real_agents.adapters.callbacks.agent_streaming","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":1,"end_line":266,"context_start_line":1,"context_end_line":266,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass JSON_PDA:\n def __init__(self):\n self.stack = []\n self.state = \"start\"\n self.json = {}\n self.current_key = \"\"\n self.current_value = \"\"\n self.escape_next = False\n\n def transition(self, char):\n if self.escape_next:\n # Add the escaped character to the current key or value and return\n if self.state == \"open_key_quote\":\n self.current_key += char\n elif self.state == \"open_value_quote\" or self.state == \"open_value_quote_brace\":\n self.current_value += char\n self.escape_next = False\n return\n\n if char == \"\\\\\":\n # The next character is an escaped character\n self.escape_next = True\n return\n\n if self.state == \"start\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"\n elif char == \"`\":\n self.state = \"open_one_backtick\"\n self.stack.append(\"`\")\n elif self.state == \"open_one_backtick\":\n if char == \"`\":\n if self.stack[-1] == \"`\":\n self.state = \"open_two_backticks\"\n self.stack.append(\"`\")\n else:\n while self.stack.pop() != \"`\":\n pass\n self.state = \"start\"\n else:\n self.stack.append(char)\n elif self.state == \"open_two_backticks\":\n if char == \"`\":\n if self.stack[-1] == \"`\":\n self.state = \"after_backtick\"\n self.stack.append(\"`\")\n else:\n while self.stack.pop() != \"`\":\n pass\n self.state = \"start\"\n else:\n self.stack.append(char)\n elif self.state == \"after_backtick\":\n if char == \"\\n\":\n self.state = \"after_backtick_newline\"\n elif self.state == \"after_backtick_newline\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"\n elif char == \"\\n\":\n self.state = \"after_backtick_newline\"\n else:\n self.state = \"in_block\"\n elif self.state == \"in_block\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif self.state == \"open_brace\" or self.state == \"comma\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n self.current_key = \"\"\n elif self.state == \"open_key_quote\" or self.state == \"open_value_quote\":\n if char != '\"':\n if self.state == \"open_key_quote\":\n self.current_key += char\n else:\n self.current_value += char\n else:\n self.stack.pop()\n if self.state == \"open_key_quote\":\n self.state = \"close_key_quote\"\n else:\n self.state = \"close_value_quote\"\n elif self.state == \"open_value_quote_brace\":\n if char == \"{\":\n self.stack.append(\"{\")\n elif char == \"}\":\n self.stack.pop()\n if self.stack[-1] == \"{\" and self.stack[-2] != \"{\":\n self.state = \"close_value_quote\"\n self.current_value += char\n elif self.state == \"close_key_quote\":\n if char == \":\":\n self.state = \"after_key\"\n elif self.state == \"after_key\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_value_quote\"\n self.current_value = \"\"\n elif char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_value_quote_brace\"\n self.current_value = \"{\"\n elif self.state == \"close_value_quote\":\n self.json[self.current_key] = self.current_value\n if char == \",\":\n self.state = \"after_value\"\n elif char == \"}\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif len(self.stack) == 3:\n self.state = \"close_brace\"\n elif self.state == \"after_value\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n elif self.state == \"close_brace\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n\n\nclass AgentStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):\n is_end = False\n generated_tokens: list = []\n for_display: list = []\n\n # Automata\n pda = JSON_PDA()\n llm_call_id = 0\n _in_json = False\n _in_key = False\n _in_value = False\n _direct_display = True\n _normal_json = False\n json_key: str = \"\"\n json_tmp_stack: list = []\n action_key_appear = False\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n self.is_end = False\n self.generated_tokens = []\n\n self.pda = JSON_PDA()\n self.llm_call_id += 1\n self._in_json = False\n self._in_key = False\n self._in_value = False\n self._direct_display = True\n self._normal_json = False\n self.json_key = \"\"\n self.json_tmp_stack = []\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\n Run on new LLM token. Only available when streaming is enabled.\n The tokens that we can decide their types ('plain', 'identifier', 'key', 'action', 'action_input') are stored in `self.for_display`.\n \"\"\"\n self.generated_tokens.append(token)\n\n # Automata that monitor json block\n for char in token:\n self.pda.transition(char)\n\n # Handle the logic of sentences and json blocks\n _type = \"plain\"\n\n if self.pda.state in [\"open_brace\", \"open_one_backtick\"]:\n self._in_json = True\n self._direct_display = False\n self._normal_json = False\n self.action_key_appear = False\n\n if self._in_json and not self._normal_json:\n _type = \"identifier\"\n\n if self.pda.state == \"in_block\":\n _type = \"plain\"\n self._normal_json = True\n\n if self.pda.state == \"open_key_quote\":\n if self._in_key:\n self.json_key += char\n _type = \"key\"\n self._in_key = True\n else:\n self._in_key = False\n\n if self.pda.state == \"open_value_quote\" or self.pda.state == \"open_value_quote_brace\":\n if self._in_value:\n _type = self.json_key\n self._in_value = True\n else:\n if self._in_value:\n self.json_key = \"\"\n self._in_value = False\n\n if self.pda.state == \"close_key_quote\":\n # Normal json block\n\n if self.json_key not in [\"action\", \"action_input\"]:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self.for_display.append({\"text\": char, \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n self._normal_json = True\n continue\n\n else:\n if self.json_key == \"action\":\n self.action_key_appear = True\n\n elif self.json_key == \"action_input\" and self.action_key_appear:\n # Action json block\n for char_item in self.json_tmp_stack:\n char_item[\"llm_call_id\"] = self.llm_call_id\n self.for_display.append(char_item)\n self.json_tmp_stack = []\n self._direct_display = True\n\n else:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self._direct_display = True\n\n if self.pda.state == \"start\":\n self._in_json = False\n\n self.for_display.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n ) if self._direct_display else self.json_tmp_stack.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n )\n\n def on_llm_end(self, response, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n self.is_end = True\n for char_item in self.json_tmp_stack:\n self.for_display.append({\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n\n def on_tool_end(self, output: Union[DataModel, str], **kwargs: Any) -> None:\n \"\"\"Run on tool end to add observation data model.\"\"\"\n self.for_display.append({\"text\": output, \"type\": \"block\", \"llm_call_id\": self.llm_call_id})","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.JSON_PDA","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.agent_streaming.JSON_PDA#L9-L133","kind":"class","name":"JSON_PDA","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":9,"end_line":133,"context_start_line":1,"context_end_line":153,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass JSON_PDA:\n def __init__(self):\n self.stack = []\n self.state = \"start\"\n self.json = {}\n self.current_key = \"\"\n self.current_value = \"\"\n self.escape_next = False\n\n def transition(self, char):\n if self.escape_next:\n # Add the escaped character to the current key or value and return\n if self.state == \"open_key_quote\":\n self.current_key += char\n elif self.state == \"open_value_quote\" or self.state == \"open_value_quote_brace\":\n self.current_value += char\n self.escape_next = False\n return\n\n if char == \"\\\\\":\n # The next character is an escaped character\n self.escape_next = True\n return\n\n if self.state == \"start\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"\n elif char == \"`\":\n self.state = \"open_one_backtick\"\n self.stack.append(\"`\")\n elif self.state == \"open_one_backtick\":\n if char == \"`\":\n if self.stack[-1] == \"`\":\n self.state = \"open_two_backticks\"\n self.stack.append(\"`\")\n else:\n while self.stack.pop() != \"`\":\n pass\n self.state = \"start\"\n else:\n self.stack.append(char)\n elif self.state == \"open_two_backticks\":\n if char == \"`\":\n if self.stack[-1] == \"`\":\n self.state = \"after_backtick\"\n self.stack.append(\"`\")\n else:\n while self.stack.pop() != \"`\":\n pass\n self.state = \"start\"\n else:\n self.stack.append(char)\n elif self.state == \"after_backtick\":\n if char == \"\\n\":\n self.state = \"after_backtick_newline\"\n elif self.state == \"after_backtick_newline\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"\n elif char == \"\\n\":\n self.state = \"after_backtick_newline\"\n else:\n self.state = \"in_block\"\n elif self.state == \"in_block\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif self.state == \"open_brace\" or self.state == \"comma\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n self.current_key = \"\"\n elif self.state == \"open_key_quote\" or self.state == \"open_value_quote\":\n if char != '\"':\n if self.state == \"open_key_quote\":\n self.current_key += char\n else:\n self.current_value += char\n else:\n self.stack.pop()\n if self.state == \"open_key_quote\":\n self.state = \"close_key_quote\"\n else:\n self.state = \"close_value_quote\"\n elif self.state == \"open_value_quote_brace\":\n if char == \"{\":\n self.stack.append(\"{\")\n elif char == \"}\":\n self.stack.pop()\n if self.stack[-1] == \"{\" and self.stack[-2] != \"{\":\n self.state = \"close_value_quote\"\n self.current_value += char\n elif self.state == \"close_key_quote\":\n if char == \":\":\n self.state = \"after_key\"\n elif self.state == \"after_key\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_value_quote\"\n self.current_value = \"\"\n elif char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_value_quote_brace\"\n self.current_value = \"{\"\n elif self.state == \"close_value_quote\":\n self.json[self.current_key] = self.current_value\n if char == \",\":\n self.state = \"after_value\"\n elif char == \"}\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif len(self.stack) == 3:\n self.state = \"close_brace\"\n elif self.state == \"after_value\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n elif self.state == \"close_brace\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n\n\nclass AgentStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):\n is_end = False\n generated_tokens: list = []\n for_display: list = []\n\n # Automata\n pda = JSON_PDA()\n llm_call_id = 0\n _in_json = False\n _in_key = False\n _in_value = False\n _direct_display = True\n _normal_json = False\n json_key: str = \"\"\n json_tmp_stack: list = []\n action_key_appear = False\n\n @property","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.AgentStreamingStdOutCallbackHandler","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.agent_streaming.AgentStreamingStdOutCallbackHandler#L136-L266","kind":"class","name":"AgentStreamingStdOutCallbackHandler","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":136,"end_line":266,"context_start_line":116,"context_end_line":266,"code":" self.json[self.current_key] = self.current_value\n if char == \",\":\n self.state = \"after_value\"\n elif char == \"}\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif len(self.stack) == 3:\n self.state = \"close_brace\"\n elif self.state == \"after_value\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n elif self.state == \"close_brace\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n\n\nclass AgentStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):\n is_end = False\n generated_tokens: list = []\n for_display: list = []\n\n # Automata\n pda = JSON_PDA()\n llm_call_id = 0\n _in_json = False\n _in_key = False\n _in_value = False\n _direct_display = True\n _normal_json = False\n json_key: str = \"\"\n json_tmp_stack: list = []\n action_key_appear = False\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n self.is_end = False\n self.generated_tokens = []\n\n self.pda = JSON_PDA()\n self.llm_call_id += 1\n self._in_json = False\n self._in_key = False\n self._in_value = False\n self._direct_display = True\n self._normal_json = False\n self.json_key = \"\"\n self.json_tmp_stack = []\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\n Run on new LLM token. Only available when streaming is enabled.\n The tokens that we can decide their types ('plain', 'identifier', 'key', 'action', 'action_input') are stored in `self.for_display`.\n \"\"\"\n self.generated_tokens.append(token)\n\n # Automata that monitor json block\n for char in token:\n self.pda.transition(char)\n\n # Handle the logic of sentences and json blocks\n _type = \"plain\"\n\n if self.pda.state in [\"open_brace\", \"open_one_backtick\"]:\n self._in_json = True\n self._direct_display = False\n self._normal_json = False\n self.action_key_appear = False\n\n if self._in_json and not self._normal_json:\n _type = \"identifier\"\n\n if self.pda.state == \"in_block\":\n _type = \"plain\"\n self._normal_json = True\n\n if self.pda.state == \"open_key_quote\":\n if self._in_key:\n self.json_key += char\n _type = \"key\"\n self._in_key = True\n else:\n self._in_key = False\n\n if self.pda.state == \"open_value_quote\" or self.pda.state == \"open_value_quote_brace\":\n if self._in_value:\n _type = self.json_key\n self._in_value = True\n else:\n if self._in_value:\n self.json_key = \"\"\n self._in_value = False\n\n if self.pda.state == \"close_key_quote\":\n # Normal json block\n\n if self.json_key not in [\"action\", \"action_input\"]:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self.for_display.append({\"text\": char, \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n self._normal_json = True\n continue\n\n else:\n if self.json_key == \"action\":\n self.action_key_appear = True\n\n elif self.json_key == \"action_input\" and self.action_key_appear:\n # Action json block\n for char_item in self.json_tmp_stack:\n char_item[\"llm_call_id\"] = self.llm_call_id\n self.for_display.append(char_item)\n self.json_tmp_stack = []\n self._direct_display = True\n\n else:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self._direct_display = True\n\n if self.pda.state == \"start\":\n self._in_json = False\n\n self.for_display.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n ) if self._direct_display else self.json_tmp_stack.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n )\n\n def on_llm_end(self, response, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n self.is_end = True\n for char_item in self.json_tmp_stack:\n self.for_display.append({\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n\n def on_tool_end(self, output: Union[DataModel, str], **kwargs: Any) -> None:\n \"\"\"Run on tool end to add observation data model.\"\"\"\n self.for_display.append({\"text\": output, \"type\": \"block\", \"llm_call_id\": self.llm_call_id})","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.__init__","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.__init__#L10-L16","kind":"function","name":"__init__","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":10,"end_line":16,"context_start_line":1,"context_end_line":36,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass JSON_PDA:\n def __init__(self):\n self.stack = []\n self.state = \"start\"\n self.json = {}\n self.current_key = \"\"\n self.current_value = \"\"\n self.escape_next = False\n\n def transition(self, char):\n if self.escape_next:\n # Add the escaped character to the current key or value and return\n if self.state == \"open_key_quote\":\n self.current_key += char\n elif self.state == \"open_value_quote\" or self.state == \"open_value_quote_brace\":\n self.current_value += char\n self.escape_next = False\n return\n\n if char == \"\\\\\":\n # The next character is an escaped character\n self.escape_next = True\n return\n\n if self.state == \"start\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.transition","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.transition#L18-L133","kind":"function","name":"transition","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":18,"end_line":133,"context_start_line":1,"context_end_line":153,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass JSON_PDA:\n def __init__(self):\n self.stack = []\n self.state = \"start\"\n self.json = {}\n self.current_key = \"\"\n self.current_value = \"\"\n self.escape_next = False\n\n def transition(self, char):\n if self.escape_next:\n # Add the escaped character to the current key or value and return\n if self.state == \"open_key_quote\":\n self.current_key += char\n elif self.state == \"open_value_quote\" or self.state == \"open_value_quote_brace\":\n self.current_value += char\n self.escape_next = False\n return\n\n if char == \"\\\\\":\n # The next character is an escaped character\n self.escape_next = True\n return\n\n if self.state == \"start\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"\n elif char == \"`\":\n self.state = \"open_one_backtick\"\n self.stack.append(\"`\")\n elif self.state == \"open_one_backtick\":\n if char == \"`\":\n if self.stack[-1] == \"`\":\n self.state = \"open_two_backticks\"\n self.stack.append(\"`\")\n else:\n while self.stack.pop() != \"`\":\n pass\n self.state = \"start\"\n else:\n self.stack.append(char)\n elif self.state == \"open_two_backticks\":\n if char == \"`\":\n if self.stack[-1] == \"`\":\n self.state = \"after_backtick\"\n self.stack.append(\"`\")\n else:\n while self.stack.pop() != \"`\":\n pass\n self.state = \"start\"\n else:\n self.stack.append(char)\n elif self.state == \"after_backtick\":\n if char == \"\\n\":\n self.state = \"after_backtick_newline\"\n elif self.state == \"after_backtick_newline\":\n if char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_brace\"\n elif char == \"\\n\":\n self.state = \"after_backtick_newline\"\n else:\n self.state = \"in_block\"\n elif self.state == \"in_block\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif self.state == \"open_brace\" or self.state == \"comma\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n self.current_key = \"\"\n elif self.state == \"open_key_quote\" or self.state == \"open_value_quote\":\n if char != '\"':\n if self.state == \"open_key_quote\":\n self.current_key += char\n else:\n self.current_value += char\n else:\n self.stack.pop()\n if self.state == \"open_key_quote\":\n self.state = \"close_key_quote\"\n else:\n self.state = \"close_value_quote\"\n elif self.state == \"open_value_quote_brace\":\n if char == \"{\":\n self.stack.append(\"{\")\n elif char == \"}\":\n self.stack.pop()\n if self.stack[-1] == \"{\" and self.stack[-2] != \"{\":\n self.state = \"close_value_quote\"\n self.current_value += char\n elif self.state == \"close_key_quote\":\n if char == \":\":\n self.state = \"after_key\"\n elif self.state == \"after_key\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_value_quote\"\n self.current_value = \"\"\n elif char == \"{\":\n self.stack.append(\"{\")\n self.state = \"open_value_quote_brace\"\n self.current_value = \"{\"\n elif self.state == \"close_value_quote\":\n self.json[self.current_key] = self.current_value\n if char == \",\":\n self.state = \"after_value\"\n elif char == \"}\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n elif len(self.stack) == 3:\n self.state = \"close_brace\"\n elif self.state == \"after_value\":\n if char == '\"':\n self.stack.append('\"')\n self.state = \"open_key_quote\"\n elif self.state == \"close_brace\":\n if char == \"`\":\n self.stack.pop()\n if len(self.stack) == 0:\n self.state = \"start\"\n\n\nclass AgentStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):\n is_end = False\n generated_tokens: list = []\n for_display: list = []\n\n # Automata\n pda = JSON_PDA()\n llm_call_id = 0\n _in_json = False\n _in_key = False\n _in_value = False\n _direct_display = True\n _normal_json = False\n json_key: str = \"\"\n json_tmp_stack: list = []\n action_key_appear = False\n\n @property","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.always_verbose","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.always_verbose#L154-L156","kind":"function","name":"always_verbose","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":154,"end_line":156,"context_start_line":134,"context_end_line":176,"code":"\n\nclass AgentStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):\n is_end = False\n generated_tokens: list = []\n for_display: list = []\n\n # Automata\n pda = JSON_PDA()\n llm_call_id = 0\n _in_json = False\n _in_key = False\n _in_value = False\n _direct_display = True\n _normal_json = False\n json_key: str = \"\"\n json_tmp_stack: list = []\n action_key_appear = False\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n self.is_end = False\n self.generated_tokens = []\n\n self.pda = JSON_PDA()\n self.llm_call_id += 1\n self._in_json = False\n self._in_key = False\n self._in_value = False\n self._direct_display = True\n self._normal_json = False\n self.json_key = \"\"\n self.json_tmp_stack = []\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\n Run on new LLM token. Only available when streaming is enabled.\n The tokens that we can decide their types ('plain', 'identifier', 'key', 'action', 'action_input') are stored in `self.for_display`.\n \"\"\"","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.on_llm_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.on_llm_start#L158-L170","kind":"function","name":"on_llm_start","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":158,"end_line":170,"context_start_line":138,"context_end_line":190,"code":" generated_tokens: list = []\n for_display: list = []\n\n # Automata\n pda = JSON_PDA()\n llm_call_id = 0\n _in_json = False\n _in_key = False\n _in_value = False\n _direct_display = True\n _normal_json = False\n json_key: str = \"\"\n json_tmp_stack: list = []\n action_key_appear = False\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n self.is_end = False\n self.generated_tokens = []\n\n self.pda = JSON_PDA()\n self.llm_call_id += 1\n self._in_json = False\n self._in_key = False\n self._in_value = False\n self._direct_display = True\n self._normal_json = False\n self.json_key = \"\"\n self.json_tmp_stack = []\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\n Run on new LLM token. Only available when streaming is enabled.\n The tokens that we can decide their types ('plain', 'identifier', 'key', 'action', 'action_input') are stored in `self.for_display`.\n \"\"\"\n self.generated_tokens.append(token)\n\n # Automata that monitor json block\n for char in token:\n self.pda.transition(char)\n\n # Handle the logic of sentences and json blocks\n _type = \"plain\"\n\n if self.pda.state in [\"open_brace\", \"open_one_backtick\"]:\n self._in_json = True\n self._direct_display = False\n self._normal_json = False\n self.action_key_appear = False","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.on_llm_new_token","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.on_llm_new_token#L172-L256","kind":"function","name":"on_llm_new_token","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":172,"end_line":256,"context_start_line":152,"context_end_line":266,"code":"\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n self.is_end = False\n self.generated_tokens = []\n\n self.pda = JSON_PDA()\n self.llm_call_id += 1\n self._in_json = False\n self._in_key = False\n self._in_value = False\n self._direct_display = True\n self._normal_json = False\n self.json_key = \"\"\n self.json_tmp_stack = []\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\n Run on new LLM token. Only available when streaming is enabled.\n The tokens that we can decide their types ('plain', 'identifier', 'key', 'action', 'action_input') are stored in `self.for_display`.\n \"\"\"\n self.generated_tokens.append(token)\n\n # Automata that monitor json block\n for char in token:\n self.pda.transition(char)\n\n # Handle the logic of sentences and json blocks\n _type = \"plain\"\n\n if self.pda.state in [\"open_brace\", \"open_one_backtick\"]:\n self._in_json = True\n self._direct_display = False\n self._normal_json = False\n self.action_key_appear = False\n\n if self._in_json and not self._normal_json:\n _type = \"identifier\"\n\n if self.pda.state == \"in_block\":\n _type = \"plain\"\n self._normal_json = True\n\n if self.pda.state == \"open_key_quote\":\n if self._in_key:\n self.json_key += char\n _type = \"key\"\n self._in_key = True\n else:\n self._in_key = False\n\n if self.pda.state == \"open_value_quote\" or self.pda.state == \"open_value_quote_brace\":\n if self._in_value:\n _type = self.json_key\n self._in_value = True\n else:\n if self._in_value:\n self.json_key = \"\"\n self._in_value = False\n\n if self.pda.state == \"close_key_quote\":\n # Normal json block\n\n if self.json_key not in [\"action\", \"action_input\"]:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self.for_display.append({\"text\": char, \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n self._normal_json = True\n continue\n\n else:\n if self.json_key == \"action\":\n self.action_key_appear = True\n\n elif self.json_key == \"action_input\" and self.action_key_appear:\n # Action json block\n for char_item in self.json_tmp_stack:\n char_item[\"llm_call_id\"] = self.llm_call_id\n self.for_display.append(char_item)\n self.json_tmp_stack = []\n self._direct_display = True\n\n else:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self._direct_display = True\n\n if self.pda.state == \"start\":\n self._in_json = False\n\n self.for_display.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n ) if self._direct_display else self.json_tmp_stack.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n )\n\n def on_llm_end(self, response, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n self.is_end = True\n for char_item in self.json_tmp_stack:\n self.for_display.append({\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n\n def on_tool_end(self, output: Union[DataModel, str], **kwargs: Any) -> None:\n \"\"\"Run on tool end to add observation data model.\"\"\"\n self.for_display.append({\"text\": output, \"type\": \"block\", \"llm_call_id\": self.llm_call_id})","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.on_llm_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.on_llm_end#L258-L262","kind":"function","name":"on_llm_end","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":258,"end_line":262,"context_start_line":238,"context_end_line":266,"code":" self.json_tmp_stack = []\n self._direct_display = True\n\n else:\n for char_item in self.json_tmp_stack:\n self.for_display.append(\n {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self._direct_display = True\n\n if self.pda.state == \"start\":\n self._in_json = False\n\n self.for_display.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n ) if self._direct_display else self.json_tmp_stack.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n )\n\n def on_llm_end(self, response, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n self.is_end = True\n for char_item in self.json_tmp_stack:\n self.for_display.append({\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n\n def on_tool_end(self, output: Union[DataModel, str], **kwargs: Any) -> None:\n \"\"\"Run on tool end to add observation data model.\"\"\"\n self.for_display.append({\"text\": output, \"type\": \"block\", \"llm_call_id\": self.llm_call_id})","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.agent_streaming.on_tool_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.agent_streaming.on_tool_end#L264-L266","kind":"function","name":"on_tool_end","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":264,"end_line":266,"context_start_line":244,"context_end_line":266,"code":" {\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id}\n )\n self.json_tmp_stack = []\n self._direct_display = True\n\n if self.pda.state == \"start\":\n self._in_json = False\n\n self.for_display.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n ) if self._direct_display else self.json_tmp_stack.append(\n {\"text\": char, \"type\": _type, \"llm_call_id\": self.llm_call_id}\n )\n\n def on_llm_end(self, response, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n self.is_end = True\n for char_item in self.json_tmp_stack:\n self.for_display.append({\"text\": char_item[\"text\"], \"type\": \"plain\", \"llm_call_id\": self.llm_call_id})\n\n def on_tool_end(self, output: Union[DataModel, str], **kwargs: Any) -> None:\n \"\"\"Run on tool end to add observation data model.\"\"\"\n self.for_display.append({\"text\": output, \"type\": \"block\", \"llm_call_id\": self.llm_call_id})","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager","uri":"program://OpenAgents/module/real_agents.adapters.callbacks.manager#L1-L867","kind":"module","name":"real_agents.adapters.callbacks.manager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":1,"end_line":867,"context_start_line":1,"context_end_line":867,"code":"from __future__ import annotations\n\nimport asyncio\nimport functools\nimport logging\nimport os\nimport warnings\nfrom contextlib import contextmanager\nfrom contextvars import ContextVar\nfrom typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast\nfrom uuid import UUID, uuid4\n\nimport langchain\nfrom langchain.callbacks.base import (\n BaseCallbackHandler,\n BaseCallbackManager,\n ChainManagerMixin,\n LLMManagerMixin,\n RunManagerMixin,\n ToolManagerMixin,\n)\nfrom langchain.callbacks.openai_info import OpenAICallbackHandler\nfrom langchain.callbacks.stdout import StdOutCallbackHandler\nfrom langchain.callbacks.tracers.langchain import LangChainTracer\nfrom langchain.callbacks.tracers.langchain_v1 import LangChainTracerV1, TracerSessionV1\nfrom langchain.callbacks.tracers.schemas import TracerSession\nfrom langchain.callbacks.tracers.stdout import ConsoleCallbackHandler\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n BaseMessage,\n LLMResult,\n get_buffer_string,\n)\n\nlogger = logging.getLogger(__name__)\nCallbacks = Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]\n\nopenai_callback_var: ContextVar[Optional[OpenAICallbackHandler]] = ContextVar(\"openai_callback\", default=None)\ntracing_callback_var: ContextVar[Optional[LangChainTracerV1]] = ContextVar( # noqa: E501\n \"tracing_callback\", default=None\n)\ntracing_v2_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar( # noqa: E501\n \"tracing_callback_v2\", default=None\n)\n\n\ndef _get_debug() -> bool:\n return langchain.debug\n\n\n@contextmanager\ndef get_openai_callback() -> Generator[OpenAICallbackHandler, None, None]:\n \"\"\"Get OpenAI callback handler in a context manager.\"\"\"\n cb = OpenAICallbackHandler()\n openai_callback_var.set(cb)\n yield cb\n openai_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_enabled(\n session_name: str = \"default\",\n) -> Generator[TracerSessionV1, None, None]:\n \"\"\"Get Tracer in a context manager.\"\"\"\n cb = LangChainTracerV1()\n session = cast(TracerSessionV1, cb.load_session(session_name))\n tracing_callback_var.set(cb)\n yield session\n tracing_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_v2_enabled(\n session_name: Optional[str] = None,\n *,\n example_id: Optional[Union[str, UUID]] = None,\n tenant_id: Optional[str] = None,\n session_extra: Optional[Dict[str, Any]] = None,\n) -> Generator[TracerSession, None, None]:\n \"\"\"Get the experimental tracer handler in a context manager.\"\"\"\n # Issue a warning that this is experimental\n warnings.warn(\n \"The experimental tracing v2 is in development. \" \"This is not yet stable and may change in the future.\"\n )\n if isinstance(example_id, str):\n example_id = UUID(example_id)\n cb = LangChainTracer(\n tenant_id=tenant_id,\n session_name=session_name,\n example_id=example_id,\n session_extra=session_extra,\n )\n session = cb.ensure_session()\n tracing_v2_callback_var.set(cb)\n yield session\n tracing_v2_callback_var.set(None)\n\n\ndef _handle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for CallbackManager.\"\"\"\n message_strings: Optional[List[str]] = None\n for handler in handlers:\n try:\n if ignore_condition_name is None or not getattr(handler, ignore_condition_name):\n getattr(handler, event_name)(*args, **kwargs)\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n if message_strings is None:\n message_strings = [get_buffer_string(m) for m in args[1]]\n _handle_event(\n [handler],\n \"on_llm_start\",\n \"ignore_llm\",\n args[0],\n message_strings,\n *args[2:],\n **kwargs,\n )\n else:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n except Exception as e:\n logging.warning(f\"Error in {event_name} callback: {e}\")\n\n\nasync def _ahandle_event_for_handler(\n handler: BaseCallbackHandler,\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n try:\n if ignore_condition_name is None or not getattr(handler, ignore_condition_name):\n event = getattr(handler, event_name)\n if asyncio.iscoroutinefunction(event):\n await event(*args, **kwargs)\n else:\n await asyncio.get_event_loop().run_in_executor(None, functools.partial(event, *args, **kwargs))\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n message_strings = [get_buffer_string(m) for m in args[1]]\n await _ahandle_event_for_handler(\n handler,\n \"on_llm_start\",\n \"ignore_llm\",\n args[0],\n message_strings,\n *args[2:],\n **kwargs,\n )\n else:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n except Exception as e:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n\n\nasync def _ahandle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for AsyncCallbackManager.\"\"\"\n await asyncio.gather(\n *(\n _ahandle_event_for_handler(handler, event_name, ignore_condition_name, *args, **kwargs)\n for handler in handlers\n )\n )\n\n\nBRM = TypeVar(\"BRM\", bound=\"BaseRunManager\")\n\n\nclass BaseRunManager(RunManagerMixin):\n \"\"\"Base class for run manager (a bound callback manager).\"\"\"\n\n def __init__(\n self,\n run_id: UUID,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: List[BaseCallbackHandler],\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize run manager.\"\"\"\n self.run_id = run_id\n self.handlers = handlers\n self.inheritable_handlers = inheritable_handlers\n self.parent_run_id = parent_run_id\n\n @classmethod\n def get_noop_manager(cls: Type[BRM]) -> BRM:\n \"\"\"Return a manager that doesn't perform any operations.\"\"\"\n return cls(uuid4(), [], [])\n\n\nclass RunManager(BaseRunManager):\n \"\"\"Sync Run Manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncRunManager(BaseRunManager):\n \"\"\"Async Run Manager.\"\"\"\n\n async def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForLLMRun(RunManager, LLMManagerMixin):\n \"\"\"Callback manager for LLM run.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token=token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):\n \"\"\"Async callback manager for LLM run.\"\"\"\n\n async def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForChainRun(RunManager, ChainManagerMixin):\n \"\"\"Callback manager for chain run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):\n \"\"\"Async callback manager for chain run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForToolRun(RunManager, ToolManagerMixin):\n \"\"\"Callback manager for tool run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_tool_end(\n self,\n output: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_tool_end_data_model(\n self,\n output,\n **kwargs: Any,\n ):\n \"\"\"Return the data model for the on_tool_end event.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end_data_model\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):\n \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManager(BaseCallbackManager):\n \"\"\"Callback manager that can be used to handle callbacks from langchain.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",\n serialized,\n prompts,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n _handle_event(\n self.handlers,\n \"on_chat_model_start\",\n \"ignore_chat_model\",\n serialized,\n messages,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n # Re-use the LLM Run Manager since the outputs are treated\n # the same for now\n return CallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForChainRun:\n \"\"\"Run when chain starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_chain_start\",\n \"ignore_chain\",\n serialized,\n inputs,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForChainRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n run_id: Optional[UUID] = None,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForToolRun:\n \"\"\"Run when tool starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_tool_start\",\n \"ignore_agent\",\n serialized,\n input_str,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> CallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nclass AsyncCallbackManager(BaseCallbackManager):\n \"\"\"Async callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n @property\n def is_async(self) -> bool:\n \"\"\"Return whether the handler is async.\"\"\"\n return True\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> A\n# ... truncated ...","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":true} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager._get_debug","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager._get_debug#L48-L49","kind":"function","name":"_get_debug","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":48,"end_line":49,"context_start_line":28,"context_end_line":69,"code":"from langchain.schema import (\n AgentAction,\n AgentFinish,\n BaseMessage,\n LLMResult,\n get_buffer_string,\n)\n\nlogger = logging.getLogger(__name__)\nCallbacks = Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]\n\nopenai_callback_var: ContextVar[Optional[OpenAICallbackHandler]] = ContextVar(\"openai_callback\", default=None)\ntracing_callback_var: ContextVar[Optional[LangChainTracerV1]] = ContextVar( # noqa: E501\n \"tracing_callback\", default=None\n)\ntracing_v2_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar( # noqa: E501\n \"tracing_callback_v2\", default=None\n)\n\n\ndef _get_debug() -> bool:\n return langchain.debug\n\n\n@contextmanager\ndef get_openai_callback() -> Generator[OpenAICallbackHandler, None, None]:\n \"\"\"Get OpenAI callback handler in a context manager.\"\"\"\n cb = OpenAICallbackHandler()\n openai_callback_var.set(cb)\n yield cb\n openai_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_enabled(\n session_name: str = \"default\",\n) -> Generator[TracerSessionV1, None, None]:\n \"\"\"Get Tracer in a context manager.\"\"\"\n cb = LangChainTracerV1()\n session = cast(TracerSessionV1, cb.load_session(session_name))\n tracing_callback_var.set(cb)\n yield session","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.get_openai_callback","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.get_openai_callback#L53-L58","kind":"function","name":"get_openai_callback","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":53,"end_line":58,"context_start_line":33,"context_end_line":78,"code":" get_buffer_string,\n)\n\nlogger = logging.getLogger(__name__)\nCallbacks = Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]\n\nopenai_callback_var: ContextVar[Optional[OpenAICallbackHandler]] = ContextVar(\"openai_callback\", default=None)\ntracing_callback_var: ContextVar[Optional[LangChainTracerV1]] = ContextVar( # noqa: E501\n \"tracing_callback\", default=None\n)\ntracing_v2_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar( # noqa: E501\n \"tracing_callback_v2\", default=None\n)\n\n\ndef _get_debug() -> bool:\n return langchain.debug\n\n\n@contextmanager\ndef get_openai_callback() -> Generator[OpenAICallbackHandler, None, None]:\n \"\"\"Get OpenAI callback handler in a context manager.\"\"\"\n cb = OpenAICallbackHandler()\n openai_callback_var.set(cb)\n yield cb\n openai_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_enabled(\n session_name: str = \"default\",\n) -> Generator[TracerSessionV1, None, None]:\n \"\"\"Get Tracer in a context manager.\"\"\"\n cb = LangChainTracerV1()\n session = cast(TracerSessionV1, cb.load_session(session_name))\n tracing_callback_var.set(cb)\n yield session\n tracing_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_v2_enabled(\n session_name: Optional[str] = None,\n *,\n example_id: Optional[Union[str, UUID]] = None,\n tenant_id: Optional[str] = None,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.tracing_enabled","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.tracing_enabled#L62-L70","kind":"function","name":"tracing_enabled","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":62,"end_line":70,"context_start_line":42,"context_end_line":90,"code":")\ntracing_v2_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar( # noqa: E501\n \"tracing_callback_v2\", default=None\n)\n\n\ndef _get_debug() -> bool:\n return langchain.debug\n\n\n@contextmanager\ndef get_openai_callback() -> Generator[OpenAICallbackHandler, None, None]:\n \"\"\"Get OpenAI callback handler in a context manager.\"\"\"\n cb = OpenAICallbackHandler()\n openai_callback_var.set(cb)\n yield cb\n openai_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_enabled(\n session_name: str = \"default\",\n) -> Generator[TracerSessionV1, None, None]:\n \"\"\"Get Tracer in a context manager.\"\"\"\n cb = LangChainTracerV1()\n session = cast(TracerSessionV1, cb.load_session(session_name))\n tracing_callback_var.set(cb)\n yield session\n tracing_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_v2_enabled(\n session_name: Optional[str] = None,\n *,\n example_id: Optional[Union[str, UUID]] = None,\n tenant_id: Optional[str] = None,\n session_extra: Optional[Dict[str, Any]] = None,\n) -> Generator[TracerSession, None, None]:\n \"\"\"Get the experimental tracer handler in a context manager.\"\"\"\n # Issue a warning that this is experimental\n warnings.warn(\n \"The experimental tracing v2 is in development. \" \"This is not yet stable and may change in the future.\"\n )\n if isinstance(example_id, str):\n example_id = UUID(example_id)\n cb = LangChainTracer(\n tenant_id=tenant_id,\n session_name=session_name,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.tracing_v2_enabled","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.tracing_v2_enabled#L74-L97","kind":"function","name":"tracing_v2_enabled","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":74,"end_line":97,"context_start_line":54,"context_end_line":117,"code":" \"\"\"Get OpenAI callback handler in a context manager.\"\"\"\n cb = OpenAICallbackHandler()\n openai_callback_var.set(cb)\n yield cb\n openai_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_enabled(\n session_name: str = \"default\",\n) -> Generator[TracerSessionV1, None, None]:\n \"\"\"Get Tracer in a context manager.\"\"\"\n cb = LangChainTracerV1()\n session = cast(TracerSessionV1, cb.load_session(session_name))\n tracing_callback_var.set(cb)\n yield session\n tracing_callback_var.set(None)\n\n\n@contextmanager\ndef tracing_v2_enabled(\n session_name: Optional[str] = None,\n *,\n example_id: Optional[Union[str, UUID]] = None,\n tenant_id: Optional[str] = None,\n session_extra: Optional[Dict[str, Any]] = None,\n) -> Generator[TracerSession, None, None]:\n \"\"\"Get the experimental tracer handler in a context manager.\"\"\"\n # Issue a warning that this is experimental\n warnings.warn(\n \"The experimental tracing v2 is in development. \" \"This is not yet stable and may change in the future.\"\n )\n if isinstance(example_id, str):\n example_id = UUID(example_id)\n cb = LangChainTracer(\n tenant_id=tenant_id,\n session_name=session_name,\n example_id=example_id,\n session_extra=session_extra,\n )\n session = cb.ensure_session()\n tracing_v2_callback_var.set(cb)\n yield session\n tracing_v2_callback_var.set(None)\n\n\ndef _handle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for CallbackManager.\"\"\"\n message_strings: Optional[List[str]] = None\n for handler in handlers:\n try:\n if ignore_condition_name is None or not getattr(handler, ignore_condition_name):\n getattr(handler, event_name)(*args, **kwargs)\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n if message_strings is None:\n message_strings = [get_buffer_string(m) for m in args[1]]\n _handle_event(","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager._handle_event","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager._handle_event#L100-L129","kind":"function","name":"_handle_event","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":100,"end_line":129,"context_start_line":80,"context_end_line":149,"code":") -> Generator[TracerSession, None, None]:\n \"\"\"Get the experimental tracer handler in a context manager.\"\"\"\n # Issue a warning that this is experimental\n warnings.warn(\n \"The experimental tracing v2 is in development. \" \"This is not yet stable and may change in the future.\"\n )\n if isinstance(example_id, str):\n example_id = UUID(example_id)\n cb = LangChainTracer(\n tenant_id=tenant_id,\n session_name=session_name,\n example_id=example_id,\n session_extra=session_extra,\n )\n session = cb.ensure_session()\n tracing_v2_callback_var.set(cb)\n yield session\n tracing_v2_callback_var.set(None)\n\n\ndef _handle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for CallbackManager.\"\"\"\n message_strings: Optional[List[str]] = None\n for handler in handlers:\n try:\n if ignore_condition_name is None or not getattr(handler, ignore_condition_name):\n getattr(handler, event_name)(*args, **kwargs)\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n if message_strings is None:\n message_strings = [get_buffer_string(m) for m in args[1]]\n _handle_event(\n [handler],\n \"on_llm_start\",\n \"ignore_llm\",\n args[0],\n message_strings,\n *args[2:],\n **kwargs,\n )\n else:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n except Exception as e:\n logging.warning(f\"Error in {event_name} callback: {e}\")\n\n\nasync def _ahandle_event_for_handler(\n handler: BaseCallbackHandler,\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n try:\n if ignore_condition_name is None or not getattr(handler, ignore_condition_name):\n event = getattr(handler, event_name)\n if asyncio.iscoroutinefunction(event):\n await event(*args, **kwargs)\n else:\n await asyncio.get_event_loop().run_in_executor(None, functools.partial(event, *args, **kwargs))\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n message_strings = [get_buffer_string(m) for m in args[1]]\n await _ahandle_event_for_handler(","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager._ahandle_event_for_handler","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager._ahandle_event_for_handler#L132-L161","kind":"function","name":"_ahandle_event_for_handler","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":132,"end_line":161,"context_start_line":112,"context_end_line":181,"code":" getattr(handler, event_name)(*args, **kwargs)\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n if message_strings is None:\n message_strings = [get_buffer_string(m) for m in args[1]]\n _handle_event(\n [handler],\n \"on_llm_start\",\n \"ignore_llm\",\n args[0],\n message_strings,\n *args[2:],\n **kwargs,\n )\n else:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n except Exception as e:\n logging.warning(f\"Error in {event_name} callback: {e}\")\n\n\nasync def _ahandle_event_for_handler(\n handler: BaseCallbackHandler,\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n try:\n if ignore_condition_name is None or not getattr(handler, ignore_condition_name):\n event = getattr(handler, event_name)\n if asyncio.iscoroutinefunction(event):\n await event(*args, **kwargs)\n else:\n await asyncio.get_event_loop().run_in_executor(None, functools.partial(event, *args, **kwargs))\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n message_strings = [get_buffer_string(m) for m in args[1]]\n await _ahandle_event_for_handler(\n handler,\n \"on_llm_start\",\n \"ignore_llm\",\n args[0],\n message_strings,\n *args[2:],\n **kwargs,\n )\n else:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n except Exception as e:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n\n\nasync def _ahandle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for AsyncCallbackManager.\"\"\"\n await asyncio.gather(\n *(\n _ahandle_event_for_handler(handler, event_name, ignore_condition_name, *args, **kwargs)\n for handler in handlers\n )\n )\n\n\nBRM = TypeVar(\"BRM\", bound=\"BaseRunManager\")\n","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager._ahandle_event","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager._ahandle_event#L164-L177","kind":"function","name":"_ahandle_event","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":164,"end_line":177,"context_start_line":144,"context_end_line":197,"code":" else:\n await asyncio.get_event_loop().run_in_executor(None, functools.partial(event, *args, **kwargs))\n except NotImplementedError as e:\n if event_name == \"on_chat_model_start\":\n message_strings = [get_buffer_string(m) for m in args[1]]\n await _ahandle_event_for_handler(\n handler,\n \"on_llm_start\",\n \"ignore_llm\",\n args[0],\n message_strings,\n *args[2:],\n **kwargs,\n )\n else:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n except Exception as e:\n logger.warning(f\"Error in {event_name} callback: {e}\")\n\n\nasync def _ahandle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for AsyncCallbackManager.\"\"\"\n await asyncio.gather(\n *(\n _ahandle_event_for_handler(handler, event_name, ignore_condition_name, *args, **kwargs)\n for handler in handlers\n )\n )\n\n\nBRM = TypeVar(\"BRM\", bound=\"BaseRunManager\")\n\n\nclass BaseRunManager(RunManagerMixin):\n \"\"\"Base class for run manager (a bound callback manager).\"\"\"\n\n def __init__(\n self,\n run_id: UUID,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: List[BaseCallbackHandler],\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize run manager.\"\"\"\n self.run_id = run_id\n self.handlers = handlers\n self.inheritable_handlers = inheritable_handlers\n self.parent_run_id = parent_run_id","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.BaseRunManager","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.BaseRunManager#L183-L202","kind":"class","name":"BaseRunManager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":183,"end_line":202,"context_start_line":163,"context_end_line":222,"code":"\nasync def _ahandle_event(\n handlers: List[BaseCallbackHandler],\n event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for AsyncCallbackManager.\"\"\"\n await asyncio.gather(\n *(\n _ahandle_event_for_handler(handler, event_name, ignore_condition_name, *args, **kwargs)\n for handler in handlers\n )\n )\n\n\nBRM = TypeVar(\"BRM\", bound=\"BaseRunManager\")\n\n\nclass BaseRunManager(RunManagerMixin):\n \"\"\"Base class for run manager (a bound callback manager).\"\"\"\n\n def __init__(\n self,\n run_id: UUID,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: List[BaseCallbackHandler],\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize run manager.\"\"\"\n self.run_id = run_id\n self.handlers = handlers\n self.inheritable_handlers = inheritable_handlers\n self.parent_run_id = parent_run_id\n\n @classmethod\n def get_noop_manager(cls: Type[BRM]) -> BRM:\n \"\"\"Return a manager that doesn't perform any operations.\"\"\"\n return cls(uuid4(), [], [])\n\n\nclass RunManager(BaseRunManager):\n \"\"\"Sync Run Manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.RunManager","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.RunManager#L205-L222","kind":"class","name":"RunManager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":205,"end_line":222,"context_start_line":185,"context_end_line":242,"code":"\n def __init__(\n self,\n run_id: UUID,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: List[BaseCallbackHandler],\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize run manager.\"\"\"\n self.run_id = run_id\n self.handlers = handlers\n self.inheritable_handlers = inheritable_handlers\n self.parent_run_id = parent_run_id\n\n @classmethod\n def get_noop_manager(cls: Type[BRM]) -> BRM:\n \"\"\"Return a manager that doesn't perform any operations.\"\"\"\n return cls(uuid4(), [], [])\n\n\nclass RunManager(BaseRunManager):\n \"\"\"Sync Run Manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncRunManager(BaseRunManager):\n \"\"\"Async Run Manager.\"\"\"\n\n async def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.AsyncRunManager","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.AsyncRunManager#L225-L242","kind":"class","name":"AsyncRunManager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":225,"end_line":242,"context_start_line":205,"context_end_line":262,"code":"class RunManager(BaseRunManager):\n \"\"\"Sync Run Manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncRunManager(BaseRunManager):\n \"\"\"Async Run Manager.\"\"\"\n\n async def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForLLMRun(RunManager, LLMManagerMixin):\n \"\"\"Callback manager for LLM run.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token=token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.CallbackManagerForLLMRun","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.CallbackManagerForLLMRun#L245-L290","kind":"class","name":"CallbackManagerForLLMRun","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":245,"end_line":290,"context_start_line":225,"context_end_line":310,"code":"class AsyncRunManager(BaseRunManager):\n \"\"\"Async Run Manager.\"\"\"\n\n async def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForLLMRun(RunManager, LLMManagerMixin):\n \"\"\"Callback manager for LLM run.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token=token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):\n \"\"\"Async callback manager for LLM run.\"\"\"\n\n async def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.AsyncCallbackManagerForLLMRun","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.AsyncCallbackManagerForLLMRun#L293-L338","kind":"class","name":"AsyncCallbackManagerForLLMRun","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":293,"end_line":338,"context_start_line":273,"context_end_line":358,"code":" **kwargs,\n )\n\n def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):\n \"\"\"Async callback manager for LLM run.\"\"\"\n\n async def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForChainRun(RunManager, ChainManagerMixin):\n \"\"\"Callback manager for chain run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.CallbackManagerForChainRun","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.CallbackManagerForChainRun#L341-L400","kind":"class","name":"CallbackManagerForChainRun","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":341,"end_line":400,"context_start_line":321,"context_end_line":420,"code":" **kwargs,\n )\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForChainRun(RunManager, ChainManagerMixin):\n \"\"\"Callback manager for chain run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):\n \"\"\"Async callback manager for chain run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.AsyncCallbackManagerForChainRun","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.AsyncCallbackManagerForChainRun#L403-L462","kind":"class","name":"AsyncCallbackManagerForChainRun","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":403,"end_line":462,"context_start_line":383,"context_end_line":482,"code":" \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):\n \"\"\"Async callback manager for chain run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForToolRun(RunManager, ToolManagerMixin):\n \"\"\"Callback manager for tool run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_tool_end(\n self,\n output: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end\",","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.CallbackManagerForToolRun","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.CallbackManagerForToolRun#L465-L520","kind":"class","name":"CallbackManagerForToolRun","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":465,"end_line":520,"context_start_line":445,"context_end_line":540,"code":" \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForToolRun(RunManager, ToolManagerMixin):\n \"\"\"Callback manager for tool run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_tool_end(\n self,\n output: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_tool_end_data_model(\n self,\n output,\n **kwargs: Any,\n ):\n \"\"\"Return the data model for the on_tool_end event.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end_data_model\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):\n \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.AsyncCallbackManagerForToolRun","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.AsyncCallbackManagerForToolRun#L523-L558","kind":"class","name":"AsyncCallbackManagerForToolRun","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":523,"end_line":558,"context_start_line":503,"context_end_line":578,"code":" **kwargs,\n )\n\n def on_tool_end_data_model(\n self,\n output,\n **kwargs: Any,\n ):\n \"\"\"Return the data model for the on_tool_end event.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end_data_model\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):\n \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManager(BaseCallbackManager):\n \"\"\"Callback manager that can be used to handle callbacks from langchain.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.CallbackManager","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.CallbackManager#L561-L670","kind":"class","name":"CallbackManager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":561,"end_line":670,"context_start_line":541,"context_end_line":690,"code":" **kwargs,\n )\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManager(BaseCallbackManager):\n \"\"\"Callback manager that can be used to handle callbacks from langchain.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",\n serialized,\n prompts,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n _handle_event(\n self.handlers,\n \"on_chat_model_start\",\n \"ignore_chat_model\",\n serialized,\n messages,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n # Re-use the LLM Run Manager since the outputs are treated\n # the same for now\n return CallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForChainRun:\n \"\"\"Run when chain starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_chain_start\",\n \"ignore_chain\",\n serialized,\n inputs,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForChainRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n run_id: Optional[UUID] = None,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForToolRun:\n \"\"\"Run when tool starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_tool_start\",\n \"ignore_agent\",\n serialized,\n input_str,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> CallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nclass AsyncCallbackManager(BaseCallbackManager):\n \"\"\"Async callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n @property\n def is_async(self) -> bool:\n \"\"\"Return whether the handler is async.\"\"\"\n return True\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.AsyncCallbackManager","uri":"program://OpenAgents/class/real_agents.adapters.callbacks.manager.AsyncCallbackManager#L673-L785","kind":"class","name":"AsyncCallbackManager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":673,"end_line":785,"context_start_line":653,"context_end_line":805,"code":" serialized,\n input_str,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return CallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> CallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nclass AsyncCallbackManager(BaseCallbackManager):\n \"\"\"Async callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n @property\n def is_async(self) -> bool:\n \"\"\"Return whether the handler is async.\"\"\"\n return True\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",\n serialized,\n prompts,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chat_model_start\",\n \"ignore_chat_model\",\n serialized,\n messages,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForChainRun:\n \"\"\"Run when chain starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chain_start\",\n \"ignore_chain\",\n serialized,\n inputs,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForChainRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n run_id: Optional[UUID] = None,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForToolRun:\n \"\"\"Run when tool starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_tool_start\",\n \"ignore_agent\",\n serialized,\n input_str,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> AsyncCallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nT = TypeVar(\"T\", CallbackManager, AsyncCallbackManager)\n\n\ndef _configure(\n callback_manager_cls: Type[T],\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n) -> T:\n \"\"\"Configure the callback manager.\"\"\"\n callback_manager = callback_manager_cls([])\n if inheritable_callbacks or local_callbacks:\n if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:\n inheritable_callbacks_ = inheritable_callbacks or []\n callback_manager = callback_manager_cls(\n handlers=inheritable_callbacks_.copy(),\n inheritable_handlers=inheritable_callbacks_.copy(),\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager._configure","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager._configure#L791-L867","kind":"function","name":"_configure","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":791,"end_line":867,"context_start_line":771,"context_end_line":867,"code":" parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> AsyncCallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nT = TypeVar(\"T\", CallbackManager, AsyncCallbackManager)\n\n\ndef _configure(\n callback_manager_cls: Type[T],\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n) -> T:\n \"\"\"Configure the callback manager.\"\"\"\n callback_manager = callback_manager_cls([])\n if inheritable_callbacks or local_callbacks:\n if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:\n inheritable_callbacks_ = inheritable_callbacks or []\n callback_manager = callback_manager_cls(\n handlers=inheritable_callbacks_.copy(),\n inheritable_handlers=inheritable_callbacks_.copy(),\n )\n else:\n callback_manager = callback_manager_cls(\n handlers=inheritable_callbacks.handlers,\n inheritable_handlers=inheritable_callbacks.inheritable_handlers,\n parent_run_id=inheritable_callbacks.parent_run_id,\n )\n local_handlers_ = (\n local_callbacks\n if isinstance(local_callbacks, list)\n else (local_callbacks.handlers if local_callbacks else [])\n )\n for handler in local_handlers_:\n callback_manager.add_handler(handler, False)\n\n tracer = tracing_callback_var.get()\n open_ai = openai_callback_var.get()\n tracing_enabled_ = (\n os.environ.get(\"LANGCHAIN_TRACING\") is not None\n or tracer is not None\n or os.environ.get(\"LANGCHAIN_HANDLER\") is not None\n )\n\n tracer_v2 = tracing_v2_callback_var.get()\n tracing_v2_enabled_ = os.environ.get(\"LANGCHAIN_TRACING_V2\") is not None or tracer_v2 is not None\n tracer_session = os.environ.get(\"LANGCHAIN_SESSION\")\n debug = _get_debug()\n if tracer_session is None:\n tracer_session = \"default\"\n if verbose or debug or tracing_enabled_ or tracing_v2_enabled_ or open_ai is not None:\n if verbose and not any(isinstance(handler, StdOutCallbackHandler) for handler in callback_manager.handlers):\n if debug:\n pass\n else:\n callback_manager.add_handler(StdOutCallbackHandler(), False)\n if debug and not any(isinstance(handler, ConsoleCallbackHandler) for handler in callback_manager.handlers):\n callback_manager.add_handler(ConsoleCallbackHandler(), True)\n if tracing_enabled_ and not any(\n isinstance(handler, LangChainTracerV1) for handler in callback_manager.handlers\n ):\n if tracer:\n callback_manager.add_handler(tracer, True)\n else:\n handler = LangChainTracerV1()\n handler.load_session(tracer_session)\n callback_manager.add_handler(handler, True)\n if tracing_v2_enabled_ and not any(\n isinstance(handler, LangChainTracer) for handler in callback_manager.handlers\n ):\n if tracer_v2:\n callback_manager.add_handler(tracer_v2, True)\n else:\n try:\n handler = LangChainTracer(session_name=tracer_session)\n handler.ensure_session()\n callback_manager.add_handler(handler, True)\n except Exception as e:\n logger.debug(\"Unable to load requested LangChainTracer\", e)\n if open_ai is not None and not any(\n isinstance(handler, OpenAICallbackHandler) for handler in callback_manager.handlers\n ):\n callback_manager.add_handler(open_ai, True)\n return callback_manager","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.__init__","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.__init__#L186-L197","kind":"function","name":"__init__","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":186,"end_line":197,"context_start_line":166,"context_end_line":217,"code":" event_name: str,\n ignore_condition_name: Optional[str],\n *args: Any,\n **kwargs: Any,\n) -> None:\n \"\"\"Generic event handler for AsyncCallbackManager.\"\"\"\n await asyncio.gather(\n *(\n _ahandle_event_for_handler(handler, event_name, ignore_condition_name, *args, **kwargs)\n for handler in handlers\n )\n )\n\n\nBRM = TypeVar(\"BRM\", bound=\"BaseRunManager\")\n\n\nclass BaseRunManager(RunManagerMixin):\n \"\"\"Base class for run manager (a bound callback manager).\"\"\"\n\n def __init__(\n self,\n run_id: UUID,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: List[BaseCallbackHandler],\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize run manager.\"\"\"\n self.run_id = run_id\n self.handlers = handlers\n self.inheritable_handlers = inheritable_handlers\n self.parent_run_id = parent_run_id\n\n @classmethod\n def get_noop_manager(cls: Type[BRM]) -> BRM:\n \"\"\"Return a manager that doesn't perform any operations.\"\"\"\n return cls(uuid4(), [], [])\n\n\nclass RunManager(BaseRunManager):\n \"\"\"Sync Run Manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.get_noop_manager","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.get_noop_manager#L200-L202","kind":"function","name":"get_noop_manager","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":200,"end_line":202,"context_start_line":180,"context_end_line":222,"code":"BRM = TypeVar(\"BRM\", bound=\"BaseRunManager\")\n\n\nclass BaseRunManager(RunManagerMixin):\n \"\"\"Base class for run manager (a bound callback manager).\"\"\"\n\n def __init__(\n self,\n run_id: UUID,\n handlers: List[BaseCallbackHandler],\n inheritable_handlers: List[BaseCallbackHandler],\n parent_run_id: Optional[UUID] = None,\n ) -> None:\n \"\"\"Initialize run manager.\"\"\"\n self.run_id = run_id\n self.handlers = handlers\n self.inheritable_handlers = inheritable_handlers\n self.parent_run_id = parent_run_id\n\n @classmethod\n def get_noop_manager(cls: Type[BRM]) -> BRM:\n \"\"\"Return a manager that doesn't perform any operations.\"\"\"\n return cls(uuid4(), [], [])\n\n\nclass RunManager(BaseRunManager):\n \"\"\"Sync Run Manager.\"\"\"\n\n def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_text","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_text#L228-L242","kind":"function","name":"on_text","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":228,"end_line":242,"context_start_line":208,"context_end_line":262,"code":" def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n _handle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncRunManager(BaseRunManager):\n \"\"\"Async Run Manager.\"\"\"\n\n async def on_text(\n self,\n text: str,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run when text is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_text\",\n None,\n text,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForLLMRun(RunManager, LLMManagerMixin):\n \"\"\"Callback manager for LLM run.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token=token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_llm_new_token","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_llm_new_token#L296-L310","kind":"function","name":"on_llm_new_token","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":296,"end_line":310,"context_start_line":276,"context_end_line":330,"code":" def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):\n \"\"\"Async callback manager for LLM run.\"\"\"\n\n async def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n await _ahandle_event(","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_llm_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_llm_end#L312-L322","kind":"function","name":"on_llm_end","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":312,"end_line":322,"context_start_line":292,"context_end_line":342,"code":"\nclass AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):\n \"\"\"Async callback manager for LLM run.\"\"\"\n\n async def on_llm_new_token(\n self,\n token: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM generates a new token.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_new_token\",\n \"ignore_llm\",\n token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForChainRun(RunManager, ChainManagerMixin):\n \"\"\"Callback manager for chain run.\"\"\"","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_llm_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_llm_error#L324-L338","kind":"function","name":"on_llm_error","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":324,"end_line":338,"context_start_line":304,"context_end_line":358,"code":" \"on_llm_new_token\",\n \"ignore_llm\",\n token,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_end\",\n \"ignore_llm\",\n response,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_llm_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when LLM errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_llm_error\",\n \"ignore_llm\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForChainRun(RunManager, ChainManagerMixin):\n \"\"\"Callback manager for chain run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.get_child","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.get_child#L526-L530","kind":"function","name":"get_child","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":526,"end_line":530,"context_start_line":506,"context_end_line":550,"code":" def on_tool_end_data_model(\n self,\n output,\n **kwargs: Any,\n ):\n \"\"\"Return the data model for the on_tool_end event.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end_data_model\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):\n \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n await _ahandle_event(","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_chain_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_chain_end#L412-L422","kind":"function","name":"on_chain_end","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":412,"end_line":422,"context_start_line":392,"context_end_line":442,"code":" _handle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):\n \"\"\"Async callback manager for chain run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n await _ahandle_event(","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_chain_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_chain_error#L424-L438","kind":"function","name":"on_chain_error","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":424,"end_line":438,"context_start_line":404,"context_end_line":458,"code":" \"\"\"Async callback manager for chain run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n \"\"\"Run when chain ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_end\",\n \"ignore_chain\",\n outputs,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_agent_action","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_agent_action#L440-L450","kind":"function","name":"on_agent_action","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":440,"end_line":450,"context_start_line":420,"context_end_line":470,"code":" parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_chain_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when chain errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForToolRun(RunManager, ToolManagerMixin):\n \"\"\"Callback manager for tool run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_agent_finish","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_agent_finish#L452-L462","kind":"function","name":"on_agent_finish","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":452,"end_line":462,"context_start_line":432,"context_end_line":482,"code":" \"on_chain_error\",\n \"ignore_chain\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n \"\"\"Run when agent action is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_action\",\n \"ignore_agent\",\n action,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n \"\"\"Run when agent finish is received.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_agent_finish\",\n \"ignore_agent\",\n finish,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManagerForToolRun(RunManager, ToolManagerMixin):\n \"\"\"Callback manager for tool run.\"\"\"\n\n def get_child(self) -> CallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = CallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n def on_tool_end(\n self,\n output: str,\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end\",","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_tool_end","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_tool_end#L532-L542","kind":"function","name":"on_tool_end","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":532,"end_line":542,"context_start_line":512,"context_end_line":562,"code":" _handle_event(\n self.handlers,\n \"on_tool_end_data_model\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):\n \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManager(BaseCallbackManager):\n \"\"\"Callback manager that can be used to handle callbacks from langchain.\"\"\"","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_tool_error","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_tool_error#L544-L558","kind":"function","name":"on_tool_error","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":544,"end_line":558,"context_start_line":524,"context_end_line":578,"code":" \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n async def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass CallbackManager(BaseCallbackManager):\n \"\"\"Callback manager that can be used to handle callbacks from langchain.\"\"\"\n\n def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> CallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n _handle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_tool_end_data_model","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_tool_end_data_model#L506-L520","kind":"function","name":"on_tool_end_data_model","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":506,"end_line":520,"context_start_line":486,"context_end_line":540,"code":" parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_tool_error(\n self,\n error: Union[Exception, KeyboardInterrupt],\n **kwargs: Any,\n ) -> None:\n \"\"\"Run when tool errors.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_error\",\n \"ignore_agent\",\n error,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n def on_tool_end_data_model(\n self,\n output,\n **kwargs: Any,\n ):\n \"\"\"Return the data model for the on_tool_end event.\"\"\"\n _handle_event(\n self.handlers,\n \"on_tool_end_data_model\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n\nclass AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):\n \"\"\"Async callback manager for tool run.\"\"\"\n\n def get_child(self) -> AsyncCallbackManager:\n \"\"\"Get a child callback manager.\"\"\"\n manager = AsyncCallbackManager([], parent_run_id=self.run_id)\n manager.set_handlers(self.inheritable_handlers)\n return manager\n\n async def on_tool_end(self, output: str, **kwargs: Any) -> None:\n \"\"\"Run when tool ends running.\"\"\"\n await _ahandle_event(\n self.handlers,\n \"on_tool_end\",\n \"ignore_agent\",\n output,\n run_id=self.run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_llm_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_llm_start#L681-L703","kind":"function","name":"on_llm_start","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":681,"end_line":703,"context_start_line":661,"context_end_line":723,"code":"\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> CallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nclass AsyncCallbackManager(BaseCallbackManager):\n \"\"\"Async callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n @property\n def is_async(self) -> bool:\n \"\"\"Return whether the handler is async.\"\"\"\n return True\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",\n serialized,\n prompts,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chat_model_start\",\n \"ignore_chat_model\",\n serialized,\n messages,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_chat_model_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_chat_model_start#L705-L726","kind":"function","name":"on_chat_model_start","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":705,"end_line":726,"context_start_line":685,"context_end_line":746,"code":" run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",\n serialized,\n prompts,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_chat_model_start(\n self,\n serialized: Dict[str, Any],\n messages: List[List[BaseMessage]],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chat_model_start\",\n \"ignore_chat_model\",\n serialized,\n messages,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForChainRun:\n \"\"\"Run when chain starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chain_start\",\n \"ignore_chain\",\n serialized,\n inputs,\n run_id=run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_chain_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_chain_start#L728-L750","kind":"function","name":"on_chain_start","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":728,"end_line":750,"context_start_line":708,"context_end_line":770,"code":" messages: List[List[BaseMessage]],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chat_model_start\",\n \"ignore_chat_model\",\n serialized,\n messages,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForLLMRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_chain_start(\n self,\n serialized: Dict[str, Any],\n inputs: Dict[str, Any],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForChainRun:\n \"\"\"Run when chain starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chain_start\",\n \"ignore_chain\",\n serialized,\n inputs,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForChainRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n run_id: Optional[UUID] = None,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForToolRun:\n \"\"\"Run when tool starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_tool_start\",\n \"ignore_agent\",\n serialized,\n input_str,\n run_id=run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.on_tool_start","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.on_tool_start#L752-L775","kind":"function","name":"on_tool_start","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":752,"end_line":775,"context_start_line":732,"context_end_line":795,"code":" run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForChainRun:\n \"\"\"Run when chain starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_chain_start\",\n \"ignore_chain\",\n serialized,\n inputs,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForChainRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n async def on_tool_start(\n self,\n serialized: Dict[str, Any],\n input_str: str,\n run_id: Optional[UUID] = None,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForToolRun:\n \"\"\"Run when tool starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_tool_start\",\n \"ignore_agent\",\n serialized,\n input_str,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> AsyncCallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nT = TypeVar(\"T\", CallbackManager, AsyncCallbackManager)\n\n\ndef _configure(\n callback_manager_cls: Type[T],\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.configure","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.configure#L778-L785","kind":"function","name":"configure","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":778,"end_line":785,"context_start_line":758,"context_end_line":805,"code":" **kwargs: Any,\n ) -> AsyncCallbackManagerForToolRun:\n \"\"\"Run when tool starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_tool_start\",\n \"ignore_agent\",\n serialized,\n input_str,\n run_id=run_id,\n parent_run_id=self.parent_run_id,\n **kwargs,\n )\n\n return AsyncCallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> AsyncCallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nT = TypeVar(\"T\", CallbackManager, AsyncCallbackManager)\n\n\ndef _configure(\n callback_manager_cls: Type[T],\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n) -> T:\n \"\"\"Configure the callback manager.\"\"\"\n callback_manager = callback_manager_cls([])\n if inheritable_callbacks or local_callbacks:\n if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:\n inheritable_callbacks_ = inheritable_callbacks or []\n callback_manager = callback_manager_cls(\n handlers=inheritable_callbacks_.copy(),\n inheritable_handlers=inheritable_callbacks_.copy(),\n )","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.callbacks.manager.is_async","uri":"program://OpenAgents/function/real_agents.adapters.callbacks.manager.is_async#L677-L679","kind":"function","name":"is_async","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":677,"end_line":679,"context_start_line":657,"context_end_line":699,"code":" **kwargs,\n )\n\n return CallbackManagerForToolRun(run_id, self.handlers, self.inheritable_handlers, self.parent_run_id)\n\n @classmethod\n def configure(\n cls,\n inheritable_callbacks: Callbacks = None,\n local_callbacks: Callbacks = None,\n verbose: bool = False,\n ) -> CallbackManager:\n \"\"\"Configure the callback manager.\"\"\"\n return _configure(cls, inheritable_callbacks, local_callbacks, verbose)\n\n\nclass AsyncCallbackManager(BaseCallbackManager):\n \"\"\"Async callback manager that can be used to handle callbacks from LangChain.\"\"\"\n\n @property\n def is_async(self) -> bool:\n \"\"\"Return whether the handler is async.\"\"\"\n return True\n\n async def on_llm_start(\n self,\n serialized: Dict[str, Any],\n prompts: List[str],\n run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> AsyncCallbackManagerForLLMRun:\n \"\"\"Run when LLM starts running.\"\"\"\n if run_id is None:\n run_id = uuid4()\n\n await _ahandle_event(\n self.handlers,\n \"on_llm_start\",\n \"ignore_llm\",\n serialized,\n prompts,\n run_id=run_id,\n parent_run_id=self.parent_run_id,","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base","uri":"program://OpenAgents/module/real_agents.adapters.models.base#L1-L205","kind":"module","name":"real_agents.adapters.models.base","path":"real_agents/adapters/models/base.py","language":"python","start_line":1,"end_line":205,"context_start_line":1,"context_end_line":205,"code":"import asyncio\nimport inspect\nimport warnings\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Dict, List, Mapping, Optional, Sequence\n\nimport langchain\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.base import BaseCallbackManager\nfrom langchain.callbacks.manager import (\n AsyncCallbackManager,\n AsyncCallbackManagerForLLMRun,\n CallbackManager,\n CallbackManagerForLLMRun,\n Callbacks,\n)\nfrom langchain.schema import (\n BaseMessage,\n ChatGeneration,\n ChatResult,\n HumanMessage,\n LLMResult,\n PromptValue,\n)\nfrom pydantic import Extra, Field, root_validator\n\n\ndef _get_verbosity() -> bool:\n return langchain.verbose\n\n\nclass BaseChatModel(BaseLanguageModel, ABC):\n verbose: bool = Field(default_factory=_get_verbosity)\n \"\"\"Whether to print out response text.\"\"\"\n callbacks: Callbacks = Field(default=None, exclude=True)\n callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)\n\n @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n return {}\n\n def generate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._generate).parameters.get(\"run_manager\")\n try:\n results = [\n self._generate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._generate(m, stop=stop)\n for m in messages\n ]\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n run_manager.on_llm_end(output)\n return output\n\n async def agenerate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._agenerate).parameters.get(\"run_manager\")\n try:\n results = await asyncio.gather(\n *[\n self._agenerate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._agenerate(m, stop=stop)\n for m in messages\n ]\n )\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n await run_manager.on_llm_end(output)\n return output\n\n def generate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return self.generate(prompt_messages, stop=stop, callbacks=callbacks)\n\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"\n starter_dict = dict(self._identifying_params)\n starter_dict[\"_type\"] = self._llm_type\n return starter_dict","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base._get_verbosity","uri":"program://OpenAgents/function/real_agents.adapters.models.base._get_verbosity#L28-L29","kind":"function","name":"_get_verbosity","path":"real_agents/adapters/models/base.py","language":"python","start_line":28,"end_line":29,"context_start_line":8,"context_end_line":49,"code":"from langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.base import BaseCallbackManager\nfrom langchain.callbacks.manager import (\n AsyncCallbackManager,\n AsyncCallbackManagerForLLMRun,\n CallbackManager,\n CallbackManagerForLLMRun,\n Callbacks,\n)\nfrom langchain.schema import (\n BaseMessage,\n ChatGeneration,\n ChatResult,\n HumanMessage,\n LLMResult,\n PromptValue,\n)\nfrom pydantic import Extra, Field, root_validator\n\n\ndef _get_verbosity() -> bool:\n return langchain.verbose\n\n\nclass BaseChatModel(BaseLanguageModel, ABC):\n verbose: bool = Field(default_factory=_get_verbosity)\n \"\"\"Whether to print out response text.\"\"\"\n callbacks: Callbacks = Field(default=None, exclude=True)\n callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)\n\n @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.BaseChatModel","uri":"program://OpenAgents/class/real_agents.adapters.models.base.BaseChatModel#L32-L205","kind":"class","name":"BaseChatModel","path":"real_agents/adapters/models/base.py","language":"python","start_line":32,"end_line":205,"context_start_line":12,"context_end_line":205,"code":" AsyncCallbackManagerForLLMRun,\n CallbackManager,\n CallbackManagerForLLMRun,\n Callbacks,\n)\nfrom langchain.schema import (\n BaseMessage,\n ChatGeneration,\n ChatResult,\n HumanMessage,\n LLMResult,\n PromptValue,\n)\nfrom pydantic import Extra, Field, root_validator\n\n\ndef _get_verbosity() -> bool:\n return langchain.verbose\n\n\nclass BaseChatModel(BaseLanguageModel, ABC):\n verbose: bool = Field(default_factory=_get_verbosity)\n \"\"\"Whether to print out response text.\"\"\"\n callbacks: Callbacks = Field(default=None, exclude=True)\n callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)\n\n @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n return {}\n\n def generate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._generate).parameters.get(\"run_manager\")\n try:\n results = [\n self._generate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._generate(m, stop=stop)\n for m in messages\n ]\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n run_manager.on_llm_end(output)\n return output\n\n async def agenerate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._agenerate).parameters.get(\"run_manager\")\n try:\n results = await asyncio.gather(\n *[\n self._agenerate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._agenerate(m, stop=stop)\n for m in messages\n ]\n )\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n await run_manager.on_llm_end(output)\n return output\n\n def generate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return self.generate(prompt_messages, stop=stop, callbacks=callbacks)\n\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"\n starter_dict = dict(self._identifying_params)\n starter_dict[\"_type\"] = self._llm_type\n return starter_dict","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.raise_deprecation","uri":"program://OpenAgents/function/real_agents.adapters.models.base.raise_deprecation#L39-L47","kind":"function","name":"raise_deprecation","path":"real_agents/adapters/models/base.py","language":"python","start_line":39,"end_line":47,"context_start_line":19,"context_end_line":67,"code":" ChatGeneration,\n ChatResult,\n HumanMessage,\n LLMResult,\n PromptValue,\n)\nfrom pydantic import Extra, Field, root_validator\n\n\ndef _get_verbosity() -> bool:\n return langchain.verbose\n\n\nclass BaseChatModel(BaseLanguageModel, ABC):\n verbose: bool = Field(default_factory=_get_verbosity)\n \"\"\"Whether to print out response text.\"\"\"\n callbacks: Callbacks = Field(default=None, exclude=True)\n callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)\n\n @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n return {}\n\n def generate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n\n params = self.dict()\n params[\"stop\"] = stop","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.Config","uri":"program://OpenAgents/class/real_agents.adapters.models.base.Config#L49-L53","kind":"class","name":"Config","path":"real_agents/adapters/models/base.py","language":"python","start_line":49,"end_line":53,"context_start_line":29,"context_end_line":73,"code":" return langchain.verbose\n\n\nclass BaseChatModel(BaseLanguageModel, ABC):\n verbose: bool = Field(default_factory=_get_verbosity)\n \"\"\"Whether to print out response text.\"\"\"\n callbacks: Callbacks = Field(default=None, exclude=True)\n callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)\n\n @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n return {}\n\n def generate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base._combine_llm_outputs","uri":"program://OpenAgents/function/real_agents.adapters.models.base._combine_llm_outputs#L55-L56","kind":"function","name":"_combine_llm_outputs","path":"real_agents/adapters/models/base.py","language":"python","start_line":55,"end_line":56,"context_start_line":35,"context_end_line":76,"code":" callbacks: Callbacks = Field(default=None, exclude=True)\n callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)\n\n @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n return {}\n\n def generate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._generate).parameters.get(\"run_manager\")\n try:\n results = [","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.generate","uri":"program://OpenAgents/function/real_agents.adapters.models.base.generate#L58-L89","kind":"function","name":"generate","path":"real_agents/adapters/models/base.py","language":"python","start_line":58,"end_line":89,"context_start_line":38,"context_end_line":109,"code":" @root_validator()\n def raise_deprecation(cls, values: Dict) -> Dict:\n \"\"\"Raise deprecation warning if callback_manager is used.\"\"\"\n if values.get(\"callback_manager\") is not None:\n warnings.warn(\n \"callback_manager is deprecated. Please use callbacks instead.\",\n DeprecationWarning,\n )\n values[\"callbacks\"] = values.pop(\"callback_manager\", None)\n return values\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n return {}\n\n def generate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._generate).parameters.get(\"run_manager\")\n try:\n results = [\n self._generate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._generate(m, stop=stop)\n for m in messages\n ]\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n run_manager.on_llm_end(output)\n return output\n\n async def agenerate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._agenerate).parameters.get(\"run_manager\")\n try:\n results = await asyncio.gather(\n *[","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.agenerate","uri":"program://OpenAgents/function/real_agents.adapters.models.base.agenerate#L91-L123","kind":"function","name":"agenerate","path":"real_agents/adapters/models/base.py","language":"python","start_line":91,"end_line":123,"context_start_line":71,"context_end_line":143,"code":" {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._generate).parameters.get(\"run_manager\")\n try:\n results = [\n self._generate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._generate(m, stop=stop)\n for m in messages\n ]\n except (KeyboardInterrupt, Exception) as e:\n run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n run_manager.on_llm_end(output)\n return output\n\n async def agenerate(\n self,\n messages: List[List[BaseMessage]],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n \"\"\"Top Level call\"\"\"\n params = self.dict()\n params[\"stop\"] = stop\n\n callback_manager = AsyncCallbackManager.configure(callbacks, self.callbacks, self.verbose)\n run_manager = await callback_manager.on_chat_model_start(\n {\"name\": self.__class__.__name__}, messages, invocation_params=params\n )\n\n new_arg_supported = inspect.signature(self._agenerate).parameters.get(\"run_manager\")\n try:\n results = await asyncio.gather(\n *[\n self._agenerate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._agenerate(m, stop=stop)\n for m in messages\n ]\n )\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n await run_manager.on_llm_end(output)\n return output\n\n def generate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return self.generate(prompt_messages, stop=stop, callbacks=callbacks)\n\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.generate_prompt","uri":"program://OpenAgents/function/real_agents.adapters.models.base.generate_prompt#L125-L132","kind":"function","name":"generate_prompt","path":"real_agents/adapters/models/base.py","language":"python","start_line":125,"end_line":132,"context_start_line":105,"context_end_line":152,"code":"\n new_arg_supported = inspect.signature(self._agenerate).parameters.get(\"run_manager\")\n try:\n results = await asyncio.gather(\n *[\n self._agenerate(m, stop=stop, run_manager=run_manager)\n if new_arg_supported\n else self._agenerate(m, stop=stop)\n for m in messages\n ]\n )\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n await run_manager.on_llm_end(output)\n return output\n\n def generate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return self.generate(prompt_messages, stop=stop, callbacks=callbacks)\n\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.agenerate_prompt","uri":"program://OpenAgents/function/real_agents.adapters.models.base.agenerate_prompt#L134-L141","kind":"function","name":"agenerate_prompt","path":"real_agents/adapters/models/base.py","language":"python","start_line":134,"end_line":141,"context_start_line":114,"context_end_line":161,"code":" ]\n )\n except (KeyboardInterrupt, Exception) as e:\n await run_manager.on_llm_error(e)\n raise e\n llm_output = self._combine_llm_outputs([res.llm_output for res in results])\n generations = [res.generations for res in results]\n output = LLMResult(generations=generations, llm_output=llm_output)\n await run_manager.on_llm_end(output)\n return output\n\n def generate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return self.generate(prompt_messages, stop=stop, callbacks=callbacks)\n\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base._generate","uri":"program://OpenAgents/function/real_agents.adapters.models.base._generate#L144-L150","kind":"function","name":"_generate","path":"real_agents/adapters/models/base.py","language":"python","start_line":144,"end_line":150,"context_start_line":124,"context_end_line":170,"code":"\n def generate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return self.generate(prompt_messages, stop=stop, callbacks=callbacks)\n\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base._agenerate","uri":"program://OpenAgents/function/real_agents.adapters.models.base._agenerate#L153-L159","kind":"function","name":"_agenerate","path":"real_agents/adapters/models/base.py","language":"python","start_line":153,"end_line":159,"context_start_line":133,"context_end_line":179,"code":"\n async def agenerate_prompt(\n self,\n prompts: List[PromptValue],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> LLMResult:\n prompt_messages = [p.to_messages() for p in prompts]\n return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.__call__","uri":"program://OpenAgents/function/real_agents.adapters.models.base.__call__#L161-L171","kind":"function","name":"__call__","path":"real_agents/adapters/models/base.py","language":"python","start_line":161,"end_line":171,"context_start_line":141,"context_end_line":191,"code":" return await self.agenerate(prompt_messages, stop=stop, callbacks=callbacks)\n\n @abstractmethod\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n @abstractmethod\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.call_as_llm","uri":"program://OpenAgents/function/real_agents.adapters.models.base.call_as_llm#L173-L174","kind":"function","name":"call_as_llm","path":"real_agents/adapters/models/base.py","language":"python","start_line":173,"end_line":174,"context_start_line":153,"context_end_line":194,"code":" async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.predict","uri":"program://OpenAgents/function/real_agents.adapters.models.base.predict#L176-L182","kind":"function","name":"predict","path":"real_agents/adapters/models/base.py","language":"python","start_line":176,"end_line":182,"context_start_line":156,"context_end_line":202,"code":" stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n \"\"\"Top Level call\"\"\"\n\n def __call__(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.predict_messages","uri":"program://OpenAgents/function/real_agents.adapters.models.base.predict_messages#L184-L189","kind":"function","name":"predict_messages","path":"real_agents/adapters/models/base.py","language":"python","start_line":184,"end_line":189,"context_start_line":164,"context_end_line":205,"code":" stop: Optional[List[str]] = None,\n callbacks: Callbacks = None,\n ) -> BaseMessage:\n generation = self.generate([messages], stop=stop, callbacks=callbacks).generations[0][0]\n if isinstance(generation, ChatGeneration):\n return generation.message\n else:\n raise ValueError(\"Unexpected generation type\")\n\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"\n starter_dict = dict(self._identifying_params)\n starter_dict[\"_type\"] = self._llm_type\n return starter_dict","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base._identifying_params","uri":"program://OpenAgents/function/real_agents.adapters.models.base._identifying_params#L192-L194","kind":"function","name":"_identifying_params","path":"real_agents/adapters/models/base.py","language":"python","start_line":192,"end_line":194,"context_start_line":172,"context_end_line":205,"code":"\n def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:\n return self.predict(message, stop=stop)\n\n def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"\n starter_dict = dict(self._identifying_params)\n starter_dict[\"_type\"] = self._llm_type\n return starter_dict","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base._llm_type","uri":"program://OpenAgents/function/real_agents.adapters.models.base._llm_type#L198-L199","kind":"function","name":"_llm_type","path":"real_agents/adapters/models/base.py","language":"python","start_line":198,"end_line":199,"context_start_line":178,"context_end_line":205,"code":" _stop = None\n else:\n _stop = list(stop)\n result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"\n starter_dict = dict(self._identifying_params)\n starter_dict[\"_type\"] = self._llm_type\n return starter_dict","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.base.dict","uri":"program://OpenAgents/function/real_agents.adapters.models.base.dict#L201-L205","kind":"function","name":"dict","path":"real_agents/adapters/models/base.py","language":"python","start_line":201,"end_line":205,"context_start_line":181,"context_end_line":205,"code":" result = self([HumanMessage(content=text)], stop=_stop)\n return result.content\n\n def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None) -> BaseMessage:\n if stop is None:\n _stop = None\n else:\n _stop = list(stop)\n return self(messages, stop=_stop)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {}\n\n @property\n @abstractmethod\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return a dictionary of the LLM.\"\"\"\n starter_dict = dict(self._identifying_params)\n starter_dict[\"_type\"] = self._llm_type\n return starter_dict","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic","uri":"program://OpenAgents/module/real_agents.adapters.models.anthropic#L1-L144","kind":"module","name":"real_agents.adapters.models.anthropic","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":1,"end_line":144,"context_start_line":1,"context_end_line":144,"code":"from typing import Any, Dict, List, Optional\nfrom pydantic import Extra\n\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForLLMRun,\n CallbackManagerForLLMRun,\n)\nfrom langchain.chat_models.base import BaseChatModel\nfrom langchain.llms.anthropic import _AnthropicCommon\nfrom langchain.schema import (\n AIMessage,\n BaseMessage,\n ChatGeneration,\n ChatMessage,\n ChatResult,\n HumanMessage,\n SystemMessage,\n)\n\n\nclass ChatAnthropic(BaseChatModel, _AnthropicCommon):\n r\"\"\"Wrapper around Anthropic's large language model.\n\n To use, you should have the ``anthropic`` python package installed, and the\n environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass\n it as a named parameter to the constructor.\n\n Example:\n .. code-block:: python\n import anthropic\n from langchain.llms import Anthropic\n model = ChatAnthropic(model=\"\", anthropic_api_key=\"my-api-key\")\n \"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"anthropic-chat\"\n\n def _convert_one_message_to_text(self, message: BaseMessage) -> str:\n if isinstance(message, ChatMessage):\n message_text = f\"\\n\\n{message.role.capitalize()}: {message.content}\"\n elif isinstance(message, HumanMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of strings into a single string with necessary newlines.\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary newlines.\n \"\"\"\n return \"\".join(self._convert_one_message_to_text(message) for message in messages)\n\n def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of messages into a full prompt for the Anthropic model\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.\n \"\"\"\n if not self.AI_PROMPT:\n raise NameError(\"Please ensure the anthropic package is loaded\")\n\n if not isinstance(messages[-1], AIMessage):\n messages.append(AIMessage(content=\"\"))\n text = self._convert_messages_to_text(messages)\n return text.rstrip() # trim off the trailing ' ' that might come from the \"Assistant: \"\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = self.client.completion_stream(**params)\n for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = self.client.completion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = await self.client.acompletion_stream(**params)\n async for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n await run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = await self.client.acompletion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic.ChatAnthropic","uri":"program://OpenAgents/class/real_agents.adapters.models.anthropic.ChatAnthropic#L21-L144","kind":"class","name":"ChatAnthropic","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":21,"end_line":144,"context_start_line":1,"context_end_line":144,"code":"from typing import Any, Dict, List, Optional\nfrom pydantic import Extra\n\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForLLMRun,\n CallbackManagerForLLMRun,\n)\nfrom langchain.chat_models.base import BaseChatModel\nfrom langchain.llms.anthropic import _AnthropicCommon\nfrom langchain.schema import (\n AIMessage,\n BaseMessage,\n ChatGeneration,\n ChatMessage,\n ChatResult,\n HumanMessage,\n SystemMessage,\n)\n\n\nclass ChatAnthropic(BaseChatModel, _AnthropicCommon):\n r\"\"\"Wrapper around Anthropic's large language model.\n\n To use, you should have the ``anthropic`` python package installed, and the\n environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass\n it as a named parameter to the constructor.\n\n Example:\n .. code-block:: python\n import anthropic\n from langchain.llms import Anthropic\n model = ChatAnthropic(model=\"\", anthropic_api_key=\"my-api-key\")\n \"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"anthropic-chat\"\n\n def _convert_one_message_to_text(self, message: BaseMessage) -> str:\n if isinstance(message, ChatMessage):\n message_text = f\"\\n\\n{message.role.capitalize()}: {message.content}\"\n elif isinstance(message, HumanMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of strings into a single string with necessary newlines.\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary newlines.\n \"\"\"\n return \"\".join(self._convert_one_message_to_text(message) for message in messages)\n\n def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of messages into a full prompt for the Anthropic model\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.\n \"\"\"\n if not self.AI_PROMPT:\n raise NameError(\"Please ensure the anthropic package is loaded\")\n\n if not isinstance(messages[-1], AIMessage):\n messages.append(AIMessage(content=\"\"))\n text = self._convert_messages_to_text(messages)\n return text.rstrip() # trim off the trailing ' ' that might come from the \"Assistant: \"\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = self.client.completion_stream(**params)\n for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = self.client.completion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = await self.client.acompletion_stream(**params)\n async for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n await run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = await self.client.acompletion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic.Config","uri":"program://OpenAgents/class/real_agents.adapters.models.anthropic.Config#L36-L39","kind":"class","name":"Config","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":36,"end_line":39,"context_start_line":16,"context_end_line":59,"code":" HumanMessage,\n SystemMessage,\n)\n\n\nclass ChatAnthropic(BaseChatModel, _AnthropicCommon):\n r\"\"\"Wrapper around Anthropic's large language model.\n\n To use, you should have the ``anthropic`` python package installed, and the\n environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass\n it as a named parameter to the constructor.\n\n Example:\n .. code-block:: python\n import anthropic\n from langchain.llms import Anthropic\n model = ChatAnthropic(model=\"\", anthropic_api_key=\"my-api-key\")\n \"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"anthropic-chat\"\n\n def _convert_one_message_to_text(self, message: BaseMessage) -> str:\n if isinstance(message, ChatMessage):\n message_text = f\"\\n\\n{message.role.capitalize()}: {message.content}\"\n elif isinstance(message, HumanMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic._llm_type","uri":"program://OpenAgents/function/real_agents.adapters.models.anthropic._llm_type#L42-L44","kind":"function","name":"_llm_type","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":42,"end_line":44,"context_start_line":22,"context_end_line":64,"code":" r\"\"\"Wrapper around Anthropic's large language model.\n\n To use, you should have the ``anthropic`` python package installed, and the\n environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass\n it as a named parameter to the constructor.\n\n Example:\n .. code-block:: python\n import anthropic\n from langchain.llms import Anthropic\n model = ChatAnthropic(model=\"\", anthropic_api_key=\"my-api-key\")\n \"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"anthropic-chat\"\n\n def _convert_one_message_to_text(self, message: BaseMessage) -> str:\n if isinstance(message, ChatMessage):\n message_text = f\"\\n\\n{message.role.capitalize()}: {message.content}\"\n elif isinstance(message, HumanMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of strings into a single string with necessary newlines.\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic._convert_one_message_to_text","uri":"program://OpenAgents/function/real_agents.adapters.models.anthropic._convert_one_message_to_text#L46-L57","kind":"function","name":"_convert_one_message_to_text","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":46,"end_line":57,"context_start_line":26,"context_end_line":77,"code":" it as a named parameter to the constructor.\n\n Example:\n .. code-block:: python\n import anthropic\n from langchain.llms import Anthropic\n model = ChatAnthropic(model=\"\", anthropic_api_key=\"my-api-key\")\n \"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"anthropic-chat\"\n\n def _convert_one_message_to_text(self, message: BaseMessage) -> str:\n if isinstance(message, ChatMessage):\n message_text = f\"\\n\\n{message.role.capitalize()}: {message.content}\"\n elif isinstance(message, HumanMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of strings into a single string with necessary newlines.\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary newlines.\n \"\"\"\n return \"\".join(self._convert_one_message_to_text(message) for message in messages)\n\n def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of messages into a full prompt for the Anthropic model\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic._convert_messages_to_text","uri":"program://OpenAgents/function/real_agents.adapters.models.anthropic._convert_messages_to_text#L59-L68","kind":"function","name":"_convert_messages_to_text","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":59,"end_line":68,"context_start_line":39,"context_end_line":88,"code":" extra = Extra.ignore\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"anthropic-chat\"\n\n def _convert_one_message_to_text(self, message: BaseMessage) -> str:\n if isinstance(message, ChatMessage):\n message_text = f\"\\n\\n{message.role.capitalize()}: {message.content}\"\n elif isinstance(message, HumanMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of strings into a single string with necessary newlines.\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary newlines.\n \"\"\"\n return \"\".join(self._convert_one_message_to_text(message) for message in messages)\n\n def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of messages into a full prompt for the Anthropic model\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.\n \"\"\"\n if not self.AI_PROMPT:\n raise NameError(\"Please ensure the anthropic package is loaded\")\n\n if not isinstance(messages[-1], AIMessage):\n messages.append(AIMessage(content=\"\"))\n text = self._convert_messages_to_text(messages)\n return text.rstrip() # trim off the trailing ' ' that might come from the \"Assistant: \"\n\n def _generate(\n self,","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic._convert_messages_to_prompt","uri":"program://OpenAgents/function/real_agents.adapters.models.anthropic._convert_messages_to_prompt#L70-L85","kind":"function","name":"_convert_messages_to_prompt","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":70,"end_line":85,"context_start_line":50,"context_end_line":105,"code":" message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n elif isinstance(message, AIMessage):\n message_text = f\"{self.AI_PROMPT} {message.content}\"\n elif isinstance(message, SystemMessage):\n message_text = f\"{self.HUMAN_PROMPT} {message.content}\"\n else:\n raise ValueError(f\"Got unknown type {message}\")\n return message_text\n\n def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of strings into a single string with necessary newlines.\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary newlines.\n \"\"\"\n return \"\".join(self._convert_one_message_to_text(message) for message in messages)\n\n def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of messages into a full prompt for the Anthropic model\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.\n \"\"\"\n if not self.AI_PROMPT:\n raise NameError(\"Please ensure the anthropic package is loaded\")\n\n if not isinstance(messages[-1], AIMessage):\n messages.append(AIMessage(content=\"\"))\n text = self._convert_messages_to_text(messages)\n return text.rstrip() # trim off the trailing ' ' that might come from the \"Assistant: \"\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = self.client.completion_stream(**params)","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic._generate","uri":"program://OpenAgents/function/real_agents.adapters.models.anthropic._generate#L87-L117","kind":"function","name":"_generate","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":87,"end_line":117,"context_start_line":67,"context_end_line":137,"code":" \"\"\"\n return \"\".join(self._convert_one_message_to_text(message) for message in messages)\n\n def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:\n \"\"\"Format a list of messages into a full prompt for the Anthropic model\n\n Args:\n messages (List[BaseMessage]): List of BaseMessage to combine.\n\n Returns:\n str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.\n \"\"\"\n if not self.AI_PROMPT:\n raise NameError(\"Please ensure the anthropic package is loaded\")\n\n if not isinstance(messages[-1], AIMessage):\n messages.append(AIMessage(content=\"\"))\n text = self._convert_messages_to_text(messages)\n return text.rstrip() # trim off the trailing ' ' that might come from the \"Assistant: \"\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = self.client.completion_stream(**params)\n for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = self.client.completion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = await self.client.acompletion_stream(**params)\n async for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n await run_manager.on_llm_new_token(","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.anthropic._agenerate","uri":"program://OpenAgents/function/real_agents.adapters.models.anthropic._agenerate#L119-L144","kind":"function","name":"_agenerate","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":119,"end_line":144,"context_start_line":99,"context_end_line":144,"code":" stop.extend(self.stop)\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = self.client.completion_stream(**params)\n for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = self.client.completion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n prompt = self._convert_messages_to_prompt(messages)\n params: Dict[str, Any] = {\"prompt\": prompt, **self._default_params}\n if stop:\n params[\"stop_sequences\"] = stop\n\n if self.streaming:\n completion = \"\"\n stream_resp = await self.client.acompletion_stream(**params)\n async for data in stream_resp:\n delta = data[\"completion\"][len(completion) :]\n completion = data[\"completion\"]\n if run_manager:\n await run_manager.on_llm_new_token(\n delta,\n )\n else:\n response = await self.client.acompletion(**params)\n completion = response[\"completion\"]\n message = AIMessage(content=completion)\n return ChatResult(generations=[ChatGeneration(message=message)])","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai","uri":"program://OpenAgents/module/real_agents.adapters.models.openai#L1-L460","kind":"module","name":"real_agents.adapters.models.openai","path":"real_agents/adapters/models/openai.py","language":"python","start_line":1,"end_line":460,"context_start_line":1,"context_end_line":460,"code":"\"\"\"OpenAI chat wrapper.\"\"\"\nfrom __future__ import annotations\n\nimport logging\nimport sys\nfrom typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union\nfrom pydantic import Extra, Field, root_validator\nfrom tenacity import (\n before_sleep_log,\n retry,\n retry_if_exception_type,\n stop_after_attempt,\n wait_exponential,\n)\n\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForLLMRun,\n CallbackManagerForLLMRun,\n)\nfrom langchain.chat_models.base import BaseChatModel\nfrom langchain.schema import (\n AIMessage,\n BaseMessage,\n ChatGeneration,\n ChatMessage,\n ChatResult,\n HumanMessage,\n SystemMessage,\n)\nfrom langchain.utils import get_from_dict_or_env\n\nlogger = logging.getLogger(__name__)\n\n\ndef _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:\n import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(llm.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n\nasync def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the async completion call.\"\"\"\n retry_decorator = _create_retry_decorator(llm)\n\n @retry_decorator\n async def _completion_with_retry(**kwargs: Any) -> Any:\n # Use OpenAI's async api https://github.com/openai/openai-python#async-api\n return await llm.client.acreate(**kwargs)\n\n return await _completion_with_retry(**kwargs)\n\n\ndef _convert_dict_to_message(_dict: dict) -> BaseMessage:\n role = _dict[\"role\"]\n if role == \"user\":\n return HumanMessage(content=_dict[\"content\"])\n elif role == \"assistant\":\n return AIMessage(content=_dict[\"content\"])\n elif role == \"system\":\n return SystemMessage(content=_dict[\"content\"])\n else:\n return ChatMessage(content=_dict[\"content\"], role=role)\n\n\ndef _convert_message_to_dict(message: BaseMessage) -> dict:\n if isinstance(message, ChatMessage):\n message_dict = {\"role\": message.role, \"content\": message.content}\n elif isinstance(message, HumanMessage):\n message_dict = {\"role\": \"user\", \"content\": message.content}\n elif isinstance(message, AIMessage):\n message_dict = {\"role\": \"assistant\", \"content\": message.content}\n elif isinstance(message, SystemMessage):\n message_dict = {\"role\": \"system\", \"content\": message.content}\n else:\n raise ValueError(f\"Got unknown type {message}\")\n if \"name\" in message.additional_kwargs:\n message_dict[\"name\"] = message.additional_kwargs[\"name\"]\n return message_dict\n\n\nclass ChatOpenAI(BaseChatModel):\n \"\"\"Wrapper around OpenAI Chat large language models.\n\n To use, you should have the ``openai`` python package installed, and the\n environment variable ``OPENAI_API_KEY`` set with your API key.\n\n Any parameters that are valid to be passed to the openai.create call can be passed\n in, even if not explicitly saved on this class.\n\n Example:\n .. code-block:: python\n\n from langchain.chat_models import ChatOpenAI\n openai = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n \"\"\"\n\n client: Any #: :meta private:\n model_name: str = \"gpt-3.5-turbo\"\n \"\"\"Model name to use.\"\"\"\n temperature: float = 0.7\n \"\"\"What sampling temperature to use.\"\"\"\n model_kwargs: Dict[str, Any] = Field(default_factory=dict)\n \"\"\"Holds any model parameters valid for `create` call not explicitly specified.\"\"\"\n openai_api_key: Optional[str] = None\n \"\"\"Base URL path for API requests,\n leave blank if not using a proxy or service emulator.\"\"\"\n openai_api_base: Optional[str] = None\n openai_organization: Optional[str] = None\n request_timeout: Optional[Union[float, Tuple[float, float]]] = None\n \"\"\"Timeout for requests to OpenAI completion API. Default is 600 seconds.\"\"\"\n max_retries: int = 6\n \"\"\"Maximum number of retries to make when generating.\"\"\"\n streaming: bool = False\n \"\"\"Whether to stream the results or not.\"\"\"\n n: int = 1\n \"\"\"Number of chat completions to generate for each prompt.\"\"\"\n max_tokens: Optional[int] = None\n \"\"\"Maximum number of tokens to generate.\"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @root_validator(pre=True)\n def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Build extra kwargs from additional params that were passed in.\"\"\"\n all_required_field_names = {field.alias for field in cls.__fields__.values()}\n\n extra = values.get(\"model_kwargs\", {})\n for field_name in list(values):\n if field_name in extra:\n raise ValueError(f\"Found {field_name} supplied twice.\")\n if field_name not in all_required_field_names:\n logger.warning(\n f\"\"\"WARNING! {field_name} is not default parameter.\n {field_name} was transferred to model_kwargs.\n Please confirm that {field_name} is what you intended.\"\"\"\n )\n extra[field_name] = values.pop(field_name)\n\n disallowed_model_kwargs = all_required_field_names | {\"model\"}\n invalid_model_kwargs = disallowed_model_kwargs.intersection(extra.keys())\n if invalid_model_kwargs:\n raise ValueError(\n f\"Parameters {invalid_model_kwargs} should be specified explicitly. \"\n f\"Instead they were passed in as part of `model_kwargs` parameter.\"\n )\n\n values[\"model_kwargs\"] = extra\n return values\n\n @root_validator()\n def validate_environment(cls, values: Dict) -> Dict:\n \"\"\"Validate that api key and python package exists in environment.\"\"\"\n openai_organization = get_from_dict_or_env(\n values,\n \"openai_organization\",\n \"OPENAI_ORGANIZATION\",\n default=\"\",\n )\n openai_api_base = get_from_dict_or_env(\n values,\n \"openai_api_base\",\n \"OPENAI_API_BASE\",\n default=\"\",\n )\n\n try:\n import openai\n\n except ImportError:\n raise ValueError(\n \"Could not import openai python package. \" \"Please install it with `pip install openai`.\")\n\n if openai_organization:\n openai.organization = openai_organization\n if openai_api_base:\n openai.api_base = openai_api_base\n\n try:\n values[\"client\"] = openai.ChatCompletion\n except AttributeError:\n raise ValueError(\n \"`openai` has no `ChatCompletion` attribute, this is likely \"\n \"due to an old version of the openai package. Try upgrading it \"\n \"with `pip install --upgrade openai`.\"\n )\n if values[\"n\"] < 1:\n raise ValueError(\"n must be at least 1.\")\n if values[\"n\"] > 1 and values[\"streaming\"]:\n raise ValueError(\"n must be 1 when streaming.\")\n return values\n\n @property\n def _default_params(self) -> Dict[str, Any]:\n \"\"\"Get the default parameters for calling OpenAI API.\"\"\"\n return {\n \"model\": self.model_name,\n \"request_timeout\": self.request_timeout,\n \"max_tokens\": self.max_tokens,\n \"stream\": self.streaming,\n \"n\": self.n,\n \"temperature\": self.temperature,\n **self.model_kwargs,\n }\n\n def _create_retry_decorator(self) -> Callable[[Any], Any]:\n import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(self.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n def completion_with_retry(self, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the completion call.\"\"\"\n retry_decorator = self._create_retry_decorator()\n\n @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:\n overall_token_usage[k] += v\n else:\n overall_token_usage[k] = v\n return {\"token_usage\": overall_token_usage, \"model_name\": self.model_name}\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import openai\n\n if self.openai_api_key:\n import os\n\n # Use the pass-in key, if the user provides\n openai.api_key = self.openai_api_key\n else:\n # Use the environment variable if neither is provided\n import os\n\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n default_role = \"assistant\"\n params[\"stream\"] = True\n for stream_resp in self.completion_with_retry(messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", default_role)\n if role is None:\n role = default_role\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n response = self.completion_with_retry(messages=message_dicts, **params)\n return self._create_chat_result(response)\n\n def _create_message_dicts(\n self, messages: List[BaseMessage], stop: Optional[List[str]]\n ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:\n params: Dict[str, Any] = {**{\"model\": self.model_name}, **self._default_params}\n if stop is not None:\n if \"stop\" in params:\n raise ValueError(\"`stop` found in both the input and default params.\")\n params[\"stop\"] = stop\n message_dicts = [_convert_message_to_dict(m) for m in messages]\n return message_dicts, params\n\n def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:\n generations = []\n for res in response[\"choices\"]:\n message = _convert_dict_to_message(res[\"message\"])\n gen = ChatGeneration(message=message)\n generations.append(gen)\n llm_output = {\"token_usage\": response[\"usage\"], \"model_name\": self.model_name}\n return ChatResult(generations=generations, llm_output=llm_output)\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import os\n\n import openai\n\n openai_api_base = os.environ.get(\"OPENAI_API_BASE\", \"https://api.openai.com/v1\")\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n openai.api_base = openai_api_base\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n role = \"assistant\"\n params[\"stream\"] = True\n async for stream_resp in await acompletion_with_retry(self,\n messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", role)\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n await run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n else:\n response = await acompletion_with_retry(self, messages=message_dicts,\n **params)\n return self._create_chat_result(response)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {**{\"model_name\": self.model_name}, **self._default_params}\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"openai-chat\"\n\n def get_num_tokens(self, text: str) -> int:\n \"\"\"Calculate num tokens with tiktoken package.\"\"\"\n # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n # create a GPT-3.5-Turbo encoder instance\n enc = tiktoken.encoding_for_model(self.model_name)\n\n # encode the text using the GPT-3.5-Turbo encoder\n tokenized_text = enc.encode(text)\n\n # calculate the number of tokens in the encoded text\n return len(tokenized_text)\n\n def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:\n \"\"\"Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.\n\n Official documentation: https://github.com/openai/openai-cookbook/blob/\n main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb\"\"\"\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n\n model = self.model_name\n if model == \"gpt-3.5-turbo\":\n # gpt-3.5-turbo may change over time.\n # Returning num tokens assuming gpt-3.5-turbo-0301.\n model = \"gpt-3.5-turbo-0301\"\n elif model == \"gpt-4\":\n # gpt-4 may change over time.\n # Returning num tokens assuming gpt-4-0314.\n model = \"gpt-4-0314\"\n\n # Returns the number of tokens used by a list of messages.\n try:\n encoding = tiktoken.encoding_for_model(model)\n except KeyError:\n logger.warning(\"Warning: model not found. Using cl100k_base encoding.\")\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n\n if model == \"gpt-3.5-turbo-0301\":\n # every message follows {role/name}\\n{content}\\n\n tokens_per_message = 4\n # if there's a name, the role is omitted\n tokens_per_name = -1\n elif model == \"gpt-4-0314\":\n tokens_per_message = 3\n tokens_per_name = 1\n else:\n raise NotImplementedError(\n f\"get_num_tokens_from_messages() is not presently implemented \"\n f\"for model {model}.\"\n \"See https://github.com/openai/openai-python/blob/main/chatml.md for \"\n \"information on how messages are converted to tokens.\"\n )\n num_tokens = 0\n messages_dict = [_convert_message_to_dict(m) for m in messages]\n for message in messages_dict:\n num_tokens += tokens_per_message\n for key, value in message.items():\n num_tokens += len(encoding.encode(value))\n if key == \"name\":\n num_tokens += tokens_per_name\n # every reply is primed with assistant\n num_tokens += 3\n return num_tokens","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._create_retry_decorator","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._create_retry_decorator#L225-L244","kind":"function","name":"_create_retry_decorator","path":"real_agents/adapters/models/openai.py","language":"python","start_line":225,"end_line":244,"context_start_line":205,"context_end_line":264,"code":" )\n if values[\"n\"] < 1:\n raise ValueError(\"n must be at least 1.\")\n if values[\"n\"] > 1 and values[\"streaming\"]:\n raise ValueError(\"n must be 1 when streaming.\")\n return values\n\n @property\n def _default_params(self) -> Dict[str, Any]:\n \"\"\"Get the default parameters for calling OpenAI API.\"\"\"\n return {\n \"model\": self.model_name,\n \"request_timeout\": self.request_timeout,\n \"max_tokens\": self.max_tokens,\n \"stream\": self.streaming,\n \"n\": self.n,\n \"temperature\": self.temperature,\n **self.model_kwargs,\n }\n\n def _create_retry_decorator(self) -> Callable[[Any], Any]:\n import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(self.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n def completion_with_retry(self, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the completion call.\"\"\"\n retry_decorator = self._create_retry_decorator()\n\n @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.acompletion_with_retry","uri":"program://OpenAgents/function/real_agents.adapters.models.openai.acompletion_with_retry#L57-L66","kind":"function","name":"acompletion_with_retry","path":"real_agents/adapters/models/openai.py","language":"python","start_line":57,"end_line":66,"context_start_line":37,"context_end_line":86,"code":"\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(llm.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n\nasync def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the async completion call.\"\"\"\n retry_decorator = _create_retry_decorator(llm)\n\n @retry_decorator\n async def _completion_with_retry(**kwargs: Any) -> Any:\n # Use OpenAI's async api https://github.com/openai/openai-python#async-api\n return await llm.client.acreate(**kwargs)\n\n return await _completion_with_retry(**kwargs)\n\n\ndef _convert_dict_to_message(_dict: dict) -> BaseMessage:\n role = _dict[\"role\"]\n if role == \"user\":\n return HumanMessage(content=_dict[\"content\"])\n elif role == \"assistant\":\n return AIMessage(content=_dict[\"content\"])\n elif role == \"system\":\n return SystemMessage(content=_dict[\"content\"])\n else:\n return ChatMessage(content=_dict[\"content\"], role=role)\n\n\ndef _convert_message_to_dict(message: BaseMessage) -> dict:\n if isinstance(message, ChatMessage):\n message_dict = {\"role\": message.role, \"content\": message.content}\n elif isinstance(message, HumanMessage):\n message_dict = {\"role\": \"user\", \"content\": message.content}\n elif isinstance(message, AIMessage):","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._convert_dict_to_message","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._convert_dict_to_message#L69-L78","kind":"function","name":"_convert_dict_to_message","path":"real_agents/adapters/models/openai.py","language":"python","start_line":69,"end_line":78,"context_start_line":49,"context_end_line":98,"code":" | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n\nasync def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the async completion call.\"\"\"\n retry_decorator = _create_retry_decorator(llm)\n\n @retry_decorator\n async def _completion_with_retry(**kwargs: Any) -> Any:\n # Use OpenAI's async api https://github.com/openai/openai-python#async-api\n return await llm.client.acreate(**kwargs)\n\n return await _completion_with_retry(**kwargs)\n\n\ndef _convert_dict_to_message(_dict: dict) -> BaseMessage:\n role = _dict[\"role\"]\n if role == \"user\":\n return HumanMessage(content=_dict[\"content\"])\n elif role == \"assistant\":\n return AIMessage(content=_dict[\"content\"])\n elif role == \"system\":\n return SystemMessage(content=_dict[\"content\"])\n else:\n return ChatMessage(content=_dict[\"content\"], role=role)\n\n\ndef _convert_message_to_dict(message: BaseMessage) -> dict:\n if isinstance(message, ChatMessage):\n message_dict = {\"role\": message.role, \"content\": message.content}\n elif isinstance(message, HumanMessage):\n message_dict = {\"role\": \"user\", \"content\": message.content}\n elif isinstance(message, AIMessage):\n message_dict = {\"role\": \"assistant\", \"content\": message.content}\n elif isinstance(message, SystemMessage):\n message_dict = {\"role\": \"system\", \"content\": message.content}\n else:\n raise ValueError(f\"Got unknown type {message}\")\n if \"name\" in message.additional_kwargs:\n message_dict[\"name\"] = message.additional_kwargs[\"name\"]\n return message_dict\n\n\nclass ChatOpenAI(BaseChatModel):\n \"\"\"Wrapper around OpenAI Chat large language models.","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._convert_message_to_dict","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._convert_message_to_dict#L81-L94","kind":"function","name":"_convert_message_to_dict","path":"real_agents/adapters/models/openai.py","language":"python","start_line":81,"end_line":94,"context_start_line":61,"context_end_line":114,"code":" @retry_decorator\n async def _completion_with_retry(**kwargs: Any) -> Any:\n # Use OpenAI's async api https://github.com/openai/openai-python#async-api\n return await llm.client.acreate(**kwargs)\n\n return await _completion_with_retry(**kwargs)\n\n\ndef _convert_dict_to_message(_dict: dict) -> BaseMessage:\n role = _dict[\"role\"]\n if role == \"user\":\n return HumanMessage(content=_dict[\"content\"])\n elif role == \"assistant\":\n return AIMessage(content=_dict[\"content\"])\n elif role == \"system\":\n return SystemMessage(content=_dict[\"content\"])\n else:\n return ChatMessage(content=_dict[\"content\"], role=role)\n\n\ndef _convert_message_to_dict(message: BaseMessage) -> dict:\n if isinstance(message, ChatMessage):\n message_dict = {\"role\": message.role, \"content\": message.content}\n elif isinstance(message, HumanMessage):\n message_dict = {\"role\": \"user\", \"content\": message.content}\n elif isinstance(message, AIMessage):\n message_dict = {\"role\": \"assistant\", \"content\": message.content}\n elif isinstance(message, SystemMessage):\n message_dict = {\"role\": \"system\", \"content\": message.content}\n else:\n raise ValueError(f\"Got unknown type {message}\")\n if \"name\" in message.additional_kwargs:\n message_dict[\"name\"] = message.additional_kwargs[\"name\"]\n return message_dict\n\n\nclass ChatOpenAI(BaseChatModel):\n \"\"\"Wrapper around OpenAI Chat large language models.\n\n To use, you should have the ``openai`` python package installed, and the\n environment variable ``OPENAI_API_KEY`` set with your API key.\n\n Any parameters that are valid to be passed to the openai.create call can be passed\n in, even if not explicitly saved on this class.\n\n Example:\n .. code-block:: python\n\n from langchain.chat_models import ChatOpenAI\n openai = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n \"\"\"\n\n client: Any #: :meta private:\n model_name: str = \"gpt-3.5-turbo\"","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.ChatOpenAI","uri":"program://OpenAgents/class/real_agents.adapters.models.openai.ChatOpenAI#L97-L460","kind":"class","name":"ChatOpenAI","path":"real_agents/adapters/models/openai.py","language":"python","start_line":97,"end_line":460,"context_start_line":77,"context_end_line":460,"code":" else:\n return ChatMessage(content=_dict[\"content\"], role=role)\n\n\ndef _convert_message_to_dict(message: BaseMessage) -> dict:\n if isinstance(message, ChatMessage):\n message_dict = {\"role\": message.role, \"content\": message.content}\n elif isinstance(message, HumanMessage):\n message_dict = {\"role\": \"user\", \"content\": message.content}\n elif isinstance(message, AIMessage):\n message_dict = {\"role\": \"assistant\", \"content\": message.content}\n elif isinstance(message, SystemMessage):\n message_dict = {\"role\": \"system\", \"content\": message.content}\n else:\n raise ValueError(f\"Got unknown type {message}\")\n if \"name\" in message.additional_kwargs:\n message_dict[\"name\"] = message.additional_kwargs[\"name\"]\n return message_dict\n\n\nclass ChatOpenAI(BaseChatModel):\n \"\"\"Wrapper around OpenAI Chat large language models.\n\n To use, you should have the ``openai`` python package installed, and the\n environment variable ``OPENAI_API_KEY`` set with your API key.\n\n Any parameters that are valid to be passed to the openai.create call can be passed\n in, even if not explicitly saved on this class.\n\n Example:\n .. code-block:: python\n\n from langchain.chat_models import ChatOpenAI\n openai = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n \"\"\"\n\n client: Any #: :meta private:\n model_name: str = \"gpt-3.5-turbo\"\n \"\"\"Model name to use.\"\"\"\n temperature: float = 0.7\n \"\"\"What sampling temperature to use.\"\"\"\n model_kwargs: Dict[str, Any] = Field(default_factory=dict)\n \"\"\"Holds any model parameters valid for `create` call not explicitly specified.\"\"\"\n openai_api_key: Optional[str] = None\n \"\"\"Base URL path for API requests,\n leave blank if not using a proxy or service emulator.\"\"\"\n openai_api_base: Optional[str] = None\n openai_organization: Optional[str] = None\n request_timeout: Optional[Union[float, Tuple[float, float]]] = None\n \"\"\"Timeout for requests to OpenAI completion API. Default is 600 seconds.\"\"\"\n max_retries: int = 6\n \"\"\"Maximum number of retries to make when generating.\"\"\"\n streaming: bool = False\n \"\"\"Whether to stream the results or not.\"\"\"\n n: int = 1\n \"\"\"Number of chat completions to generate for each prompt.\"\"\"\n max_tokens: Optional[int] = None\n \"\"\"Maximum number of tokens to generate.\"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @root_validator(pre=True)\n def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Build extra kwargs from additional params that were passed in.\"\"\"\n all_required_field_names = {field.alias for field in cls.__fields__.values()}\n\n extra = values.get(\"model_kwargs\", {})\n for field_name in list(values):\n if field_name in extra:\n raise ValueError(f\"Found {field_name} supplied twice.\")\n if field_name not in all_required_field_names:\n logger.warning(\n f\"\"\"WARNING! {field_name} is not default parameter.\n {field_name} was transferred to model_kwargs.\n Please confirm that {field_name} is what you intended.\"\"\"\n )\n extra[field_name] = values.pop(field_name)\n\n disallowed_model_kwargs = all_required_field_names | {\"model\"}\n invalid_model_kwargs = disallowed_model_kwargs.intersection(extra.keys())\n if invalid_model_kwargs:\n raise ValueError(\n f\"Parameters {invalid_model_kwargs} should be specified explicitly. \"\n f\"Instead they were passed in as part of `model_kwargs` parameter.\"\n )\n\n values[\"model_kwargs\"] = extra\n return values\n\n @root_validator()\n def validate_environment(cls, values: Dict) -> Dict:\n \"\"\"Validate that api key and python package exists in environment.\"\"\"\n openai_organization = get_from_dict_or_env(\n values,\n \"openai_organization\",\n \"OPENAI_ORGANIZATION\",\n default=\"\",\n )\n openai_api_base = get_from_dict_or_env(\n values,\n \"openai_api_base\",\n \"OPENAI_API_BASE\",\n default=\"\",\n )\n\n try:\n import openai\n\n except ImportError:\n raise ValueError(\n \"Could not import openai python package. \" \"Please install it with `pip install openai`.\")\n\n if openai_organization:\n openai.organization = openai_organization\n if openai_api_base:\n openai.api_base = openai_api_base\n\n try:\n values[\"client\"] = openai.ChatCompletion\n except AttributeError:\n raise ValueError(\n \"`openai` has no `ChatCompletion` attribute, this is likely \"\n \"due to an old version of the openai package. Try upgrading it \"\n \"with `pip install --upgrade openai`.\"\n )\n if values[\"n\"] < 1:\n raise ValueError(\"n must be at least 1.\")\n if values[\"n\"] > 1 and values[\"streaming\"]:\n raise ValueError(\"n must be 1 when streaming.\")\n return values\n\n @property\n def _default_params(self) -> Dict[str, Any]:\n \"\"\"Get the default parameters for calling OpenAI API.\"\"\"\n return {\n \"model\": self.model_name,\n \"request_timeout\": self.request_timeout,\n \"max_tokens\": self.max_tokens,\n \"stream\": self.streaming,\n \"n\": self.n,\n \"temperature\": self.temperature,\n **self.model_kwargs,\n }\n\n def _create_retry_decorator(self) -> Callable[[Any], Any]:\n import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(self.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n def completion_with_retry(self, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the completion call.\"\"\"\n retry_decorator = self._create_retry_decorator()\n\n @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:\n overall_token_usage[k] += v\n else:\n overall_token_usage[k] = v\n return {\"token_usage\": overall_token_usage, \"model_name\": self.model_name}\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import openai\n\n if self.openai_api_key:\n import os\n\n # Use the pass-in key, if the user provides\n openai.api_key = self.openai_api_key\n else:\n # Use the environment variable if neither is provided\n import os\n\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n default_role = \"assistant\"\n params[\"stream\"] = True\n for stream_resp in self.completion_with_retry(messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", default_role)\n if role is None:\n role = default_role\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n response = self.completion_with_retry(messages=message_dicts, **params)\n return self._create_chat_result(response)\n\n def _create_message_dicts(\n self, messages: List[BaseMessage], stop: Optional[List[str]]\n ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:\n params: Dict[str, Any] = {**{\"model\": self.model_name}, **self._default_params}\n if stop is not None:\n if \"stop\" in params:\n raise ValueError(\"`stop` found in both the input and default params.\")\n params[\"stop\"] = stop\n message_dicts = [_convert_message_to_dict(m) for m in messages]\n return message_dicts, params\n\n def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:\n generations = []\n for res in response[\"choices\"]:\n message = _convert_dict_to_message(res[\"message\"])\n gen = ChatGeneration(message=message)\n generations.append(gen)\n llm_output = {\"token_usage\": response[\"usage\"], \"model_name\": self.model_name}\n return ChatResult(generations=generations, llm_output=llm_output)\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import os\n\n import openai\n\n openai_api_base = os.environ.get(\"OPENAI_API_BASE\", \"https://api.openai.com/v1\")\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n openai.api_base = openai_api_base\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n role = \"assistant\"\n params[\"stream\"] = True\n async for stream_resp in await acompletion_with_retry(self,\n messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", role)\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n await run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n else:\n response = await acompletion_with_retry(self, messages=message_dicts,\n **params)\n return self._create_chat_result(response)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {**{\"model_name\": self.model_name}, **self._default_params}\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"openai-chat\"\n\n def get_num_tokens(self, text: str) -> int:\n \"\"\"Calculate num tokens with tiktoken package.\"\"\"\n # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n # create a GPT-3.5-Turbo encoder instance\n enc = tiktoken.encoding_for_model(self.model_name)\n\n # encode the text using the GPT-3.5-Turbo encoder\n tokenized_text = enc.encode(text)\n\n # calculate the number of tokens in the encoded text\n return len(tokenized_text)\n\n def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:\n \"\"\"Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.\n\n Official documentation: https://github.com/openai/openai-cookbook/blob/\n main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb\"\"\"\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n\n model = self.model_name\n if model == \"gpt-3.5-turbo\":\n # gpt-3.5-turbo may change over time.\n # Returning num tokens assuming gpt-3.5-turbo-0301.\n model = \"gpt-3.5-turbo-0301\"\n elif model == \"gpt-4\":\n # gpt-4 may change over time.\n # Returning num tokens assuming gpt-4-0314.\n model = \"gpt-4-0314\"\n\n # Returns the number of tokens used by a list of messages.\n try:\n encoding = tiktoken.encoding_for_model(model)\n except KeyError:\n logger.warning(\"Warning: model not found. Using cl100k_base encoding.\")\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n\n if model == \"gpt-3.5-turbo-0301\":\n # every message follows {role/name}\\n{content}\\n\n tokens_per_message = 4\n # if there's a name, the role is omitted\n tokens_per_name = -1\n elif model == \"gpt-4-0314\":\n tokens_per_message = 3\n tokens_per_name = 1\n else:\n raise NotImplementedError(\n f\"get_num_tokens_from_messages() is not presently implemented \"\n f\"for model {model}.\"\n \"See https://github.com/openai/openai-python/blob/main/chatml.md for \"\n \"information on how messages are converted to tokens.\"\n )\n num_tokens = 0\n messages_dict = [_convert_message_to_dict(m) for m in messages]\n for message in messages_dict:\n num_tokens += tokens_per_message\n for key, value in message.items():\n num_tokens += len(encoding.encode(value))\n if key == \"name\":\n num_tokens += tokens_per_name\n # every reply is primed with assistant\n num_tokens += 3\n return num_tokens","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._completion_with_retry","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._completion_with_retry#L251-L252","kind":"function","name":"_completion_with_retry","path":"real_agents/adapters/models/openai.py","language":"python","start_line":251,"end_line":252,"context_start_line":231,"context_end_line":272,"code":" # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(self.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n def completion_with_retry(self, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the completion call.\"\"\"\n retry_decorator = self._create_retry_decorator()\n\n @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:\n overall_token_usage[k] += v\n else:\n overall_token_usage[k] = v\n return {\"token_usage\": overall_token_usage, \"model_name\": self.model_name}\n\n def _generate(\n self,\n messages: List[BaseMessage],","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.Config","uri":"program://OpenAgents/class/real_agents.adapters.models.openai.Config#L137-L140","kind":"class","name":"Config","path":"real_agents/adapters/models/openai.py","language":"python","start_line":137,"end_line":140,"context_start_line":117,"context_end_line":160,"code":" \"\"\"What sampling temperature to use.\"\"\"\n model_kwargs: Dict[str, Any] = Field(default_factory=dict)\n \"\"\"Holds any model parameters valid for `create` call not explicitly specified.\"\"\"\n openai_api_key: Optional[str] = None\n \"\"\"Base URL path for API requests,\n leave blank if not using a proxy or service emulator.\"\"\"\n openai_api_base: Optional[str] = None\n openai_organization: Optional[str] = None\n request_timeout: Optional[Union[float, Tuple[float, float]]] = None\n \"\"\"Timeout for requests to OpenAI completion API. Default is 600 seconds.\"\"\"\n max_retries: int = 6\n \"\"\"Maximum number of retries to make when generating.\"\"\"\n streaming: bool = False\n \"\"\"Whether to stream the results or not.\"\"\"\n n: int = 1\n \"\"\"Number of chat completions to generate for each prompt.\"\"\"\n max_tokens: Optional[int] = None\n \"\"\"Maximum number of tokens to generate.\"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @root_validator(pre=True)\n def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Build extra kwargs from additional params that were passed in.\"\"\"\n all_required_field_names = {field.alias for field in cls.__fields__.values()}\n\n extra = values.get(\"model_kwargs\", {})\n for field_name in list(values):\n if field_name in extra:\n raise ValueError(f\"Found {field_name} supplied twice.\")\n if field_name not in all_required_field_names:\n logger.warning(\n f\"\"\"WARNING! {field_name} is not default parameter.\n {field_name} was transferred to model_kwargs.\n Please confirm that {field_name} is what you intended.\"\"\"\n )\n extra[field_name] = values.pop(field_name)\n\n disallowed_model_kwargs = all_required_field_names | {\"model\"}\n invalid_model_kwargs = disallowed_model_kwargs.intersection(extra.keys())","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.build_extra","uri":"program://OpenAgents/function/real_agents.adapters.models.openai.build_extra#L143-L168","kind":"function","name":"build_extra","path":"real_agents/adapters/models/openai.py","language":"python","start_line":143,"end_line":168,"context_start_line":123,"context_end_line":188,"code":" openai_api_base: Optional[str] = None\n openai_organization: Optional[str] = None\n request_timeout: Optional[Union[float, Tuple[float, float]]] = None\n \"\"\"Timeout for requests to OpenAI completion API. Default is 600 seconds.\"\"\"\n max_retries: int = 6\n \"\"\"Maximum number of retries to make when generating.\"\"\"\n streaming: bool = False\n \"\"\"Whether to stream the results or not.\"\"\"\n n: int = 1\n \"\"\"Number of chat completions to generate for each prompt.\"\"\"\n max_tokens: Optional[int] = None\n \"\"\"Maximum number of tokens to generate.\"\"\"\n stop: Optional[List[str]] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.ignore\n\n @root_validator(pre=True)\n def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Build extra kwargs from additional params that were passed in.\"\"\"\n all_required_field_names = {field.alias for field in cls.__fields__.values()}\n\n extra = values.get(\"model_kwargs\", {})\n for field_name in list(values):\n if field_name in extra:\n raise ValueError(f\"Found {field_name} supplied twice.\")\n if field_name not in all_required_field_names:\n logger.warning(\n f\"\"\"WARNING! {field_name} is not default parameter.\n {field_name} was transferred to model_kwargs.\n Please confirm that {field_name} is what you intended.\"\"\"\n )\n extra[field_name] = values.pop(field_name)\n\n disallowed_model_kwargs = all_required_field_names | {\"model\"}\n invalid_model_kwargs = disallowed_model_kwargs.intersection(extra.keys())\n if invalid_model_kwargs:\n raise ValueError(\n f\"Parameters {invalid_model_kwargs} should be specified explicitly. \"\n f\"Instead they were passed in as part of `model_kwargs` parameter.\"\n )\n\n values[\"model_kwargs\"] = extra\n return values\n\n @root_validator()\n def validate_environment(cls, values: Dict) -> Dict:\n \"\"\"Validate that api key and python package exists in environment.\"\"\"\n openai_organization = get_from_dict_or_env(\n values,\n \"openai_organization\",\n \"OPENAI_ORGANIZATION\",\n default=\"\",\n )\n openai_api_base = get_from_dict_or_env(\n values,\n \"openai_api_base\",\n \"OPENAI_API_BASE\",\n default=\"\",\n )\n\n try:\n import openai\n","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.validate_environment","uri":"program://OpenAgents/function/real_agents.adapters.models.openai.validate_environment#L171-L210","kind":"function","name":"validate_environment","path":"real_agents/adapters/models/openai.py","language":"python","start_line":171,"end_line":210,"context_start_line":151,"context_end_line":230,"code":" if field_name not in all_required_field_names:\n logger.warning(\n f\"\"\"WARNING! {field_name} is not default parameter.\n {field_name} was transferred to model_kwargs.\n Please confirm that {field_name} is what you intended.\"\"\"\n )\n extra[field_name] = values.pop(field_name)\n\n disallowed_model_kwargs = all_required_field_names | {\"model\"}\n invalid_model_kwargs = disallowed_model_kwargs.intersection(extra.keys())\n if invalid_model_kwargs:\n raise ValueError(\n f\"Parameters {invalid_model_kwargs} should be specified explicitly. \"\n f\"Instead they were passed in as part of `model_kwargs` parameter.\"\n )\n\n values[\"model_kwargs\"] = extra\n return values\n\n @root_validator()\n def validate_environment(cls, values: Dict) -> Dict:\n \"\"\"Validate that api key and python package exists in environment.\"\"\"\n openai_organization = get_from_dict_or_env(\n values,\n \"openai_organization\",\n \"OPENAI_ORGANIZATION\",\n default=\"\",\n )\n openai_api_base = get_from_dict_or_env(\n values,\n \"openai_api_base\",\n \"OPENAI_API_BASE\",\n default=\"\",\n )\n\n try:\n import openai\n\n except ImportError:\n raise ValueError(\n \"Could not import openai python package. \" \"Please install it with `pip install openai`.\")\n\n if openai_organization:\n openai.organization = openai_organization\n if openai_api_base:\n openai.api_base = openai_api_base\n\n try:\n values[\"client\"] = openai.ChatCompletion\n except AttributeError:\n raise ValueError(\n \"`openai` has no `ChatCompletion` attribute, this is likely \"\n \"due to an old version of the openai package. Try upgrading it \"\n \"with `pip install --upgrade openai`.\"\n )\n if values[\"n\"] < 1:\n raise ValueError(\"n must be at least 1.\")\n if values[\"n\"] > 1 and values[\"streaming\"]:\n raise ValueError(\"n must be 1 when streaming.\")\n return values\n\n @property\n def _default_params(self) -> Dict[str, Any]:\n \"\"\"Get the default parameters for calling OpenAI API.\"\"\"\n return {\n \"model\": self.model_name,\n \"request_timeout\": self.request_timeout,\n \"max_tokens\": self.max_tokens,\n \"stream\": self.streaming,\n \"n\": self.n,\n \"temperature\": self.temperature,\n **self.model_kwargs,\n }\n\n def _create_retry_decorator(self) -> Callable[[Any], Any]:\n import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._default_params","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._default_params#L213-L223","kind":"function","name":"_default_params","path":"real_agents/adapters/models/openai.py","language":"python","start_line":213,"end_line":223,"context_start_line":193,"context_end_line":243,"code":" if openai_organization:\n openai.organization = openai_organization\n if openai_api_base:\n openai.api_base = openai_api_base\n\n try:\n values[\"client\"] = openai.ChatCompletion\n except AttributeError:\n raise ValueError(\n \"`openai` has no `ChatCompletion` attribute, this is likely \"\n \"due to an old version of the openai package. Try upgrading it \"\n \"with `pip install --upgrade openai`.\"\n )\n if values[\"n\"] < 1:\n raise ValueError(\"n must be at least 1.\")\n if values[\"n\"] > 1 and values[\"streaming\"]:\n raise ValueError(\"n must be 1 when streaming.\")\n return values\n\n @property\n def _default_params(self) -> Dict[str, Any]:\n \"\"\"Get the default parameters for calling OpenAI API.\"\"\"\n return {\n \"model\": self.model_name,\n \"request_timeout\": self.request_timeout,\n \"max_tokens\": self.max_tokens,\n \"stream\": self.streaming,\n \"n\": self.n,\n \"temperature\": self.temperature,\n **self.model_kwargs,\n }\n\n def _create_retry_decorator(self) -> Callable[[Any], Any]:\n import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(self.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.completion_with_retry","uri":"program://OpenAgents/function/real_agents.adapters.models.openai.completion_with_retry#L246-L254","kind":"function","name":"completion_with_retry","path":"real_agents/adapters/models/openai.py","language":"python","start_line":246,"end_line":254,"context_start_line":226,"context_end_line":274,"code":" import openai\n\n min_seconds = 1\n max_seconds = 60\n # Wait 2^x * 1 second between each retry starting with\n # 4 seconds, then up to 10 seconds, then 10 seconds afterwards\n return retry(\n reraise=True,\n stop=stop_after_attempt(self.max_retries),\n wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),\n retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n def completion_with_retry(self, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the completion call.\"\"\"\n retry_decorator = self._create_retry_decorator()\n\n @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:\n overall_token_usage[k] += v\n else:\n overall_token_usage[k] = v\n return {\"token_usage\": overall_token_usage, \"model_name\": self.model_name}\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._combine_llm_outputs","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._combine_llm_outputs#L256-L268","kind":"function","name":"_combine_llm_outputs","path":"real_agents/adapters/models/openai.py","language":"python","start_line":256,"end_line":268,"context_start_line":236,"context_end_line":288,"code":" retry=(\n retry_if_exception_type(openai.error.Timeout)\n | retry_if_exception_type(openai.error.APIError)\n | retry_if_exception_type(openai.error.APIConnectionError)\n | retry_if_exception_type(openai.error.RateLimitError)\n | retry_if_exception_type(openai.error.ServiceUnavailableError)\n ),\n before_sleep=before_sleep_log(logger, logging.WARNING),\n )\n\n def completion_with_retry(self, **kwargs: Any) -> Any:\n \"\"\"Use tenacity to retry the completion call.\"\"\"\n retry_decorator = self._create_retry_decorator()\n\n @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:\n overall_token_usage[k] += v\n else:\n overall_token_usage[k] = v\n return {\"token_usage\": overall_token_usage, \"model_name\": self.model_name}\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import openai\n\n if self.openai_api_key:\n import os\n\n # Use the pass-in key, if the user provides\n openai.api_key = self.openai_api_key\n else:\n # Use the environment variable if neither is provided\n import os\n\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._generate","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._generate#L270-L314","kind":"function","name":"_generate","path":"real_agents/adapters/models/openai.py","language":"python","start_line":270,"end_line":314,"context_start_line":250,"context_end_line":334,"code":" @retry_decorator\n def _completion_with_retry(**kwargs: Any) -> Any:\n return self.client.create(**kwargs)\n\n return _completion_with_retry(**kwargs)\n\n def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:\n overall_token_usage: dict = {}\n for output in llm_outputs:\n if output is None:\n # Happens in streaming\n continue\n token_usage = output[\"token_usage\"]\n for k, v in token_usage.items():\n if k in overall_token_usage:\n overall_token_usage[k] += v\n else:\n overall_token_usage[k] = v\n return {\"token_usage\": overall_token_usage, \"model_name\": self.model_name}\n\n def _generate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[CallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import openai\n\n if self.openai_api_key:\n import os\n\n # Use the pass-in key, if the user provides\n openai.api_key = self.openai_api_key\n else:\n # Use the environment variable if neither is provided\n import os\n\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n\n if self.stop is not None:\n if stop is None:\n stop = self.stop\n else:\n stop.extend(self.stop)\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n default_role = \"assistant\"\n params[\"stream\"] = True\n for stream_resp in self.completion_with_retry(messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", default_role)\n if role is None:\n role = default_role\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n response = self.completion_with_retry(messages=message_dicts, **params)\n return self._create_chat_result(response)\n\n def _create_message_dicts(\n self, messages: List[BaseMessage], stop: Optional[List[str]]\n ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:\n params: Dict[str, Any] = {**{\"model\": self.model_name}, **self._default_params}\n if stop is not None:\n if \"stop\" in params:\n raise ValueError(\"`stop` found in both the input and default params.\")\n params[\"stop\"] = stop\n message_dicts = [_convert_message_to_dict(m) for m in messages]\n return message_dicts, params\n\n def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:\n generations = []\n for res in response[\"choices\"]:\n message = _convert_dict_to_message(res[\"message\"])\n gen = ChatGeneration(message=message)\n generations.append(gen)\n llm_output = {\"token_usage\": response[\"usage\"], \"model_name\": self.model_name}\n return ChatResult(generations=generations, llm_output=llm_output)","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._create_message_dicts","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._create_message_dicts#L316-L325","kind":"function","name":"_create_message_dicts","path":"real_agents/adapters/models/openai.py","language":"python","start_line":316,"end_line":325,"context_start_line":296,"context_end_line":345,"code":" message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n default_role = \"assistant\"\n params[\"stream\"] = True\n for stream_resp in self.completion_with_retry(messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", default_role)\n if role is None:\n role = default_role\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n response = self.completion_with_retry(messages=message_dicts, **params)\n return self._create_chat_result(response)\n\n def _create_message_dicts(\n self, messages: List[BaseMessage], stop: Optional[List[str]]\n ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:\n params: Dict[str, Any] = {**{\"model\": self.model_name}, **self._default_params}\n if stop is not None:\n if \"stop\" in params:\n raise ValueError(\"`stop` found in both the input and default params.\")\n params[\"stop\"] = stop\n message_dicts = [_convert_message_to_dict(m) for m in messages]\n return message_dicts, params\n\n def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:\n generations = []\n for res in response[\"choices\"]:\n message = _convert_dict_to_message(res[\"message\"])\n gen = ChatGeneration(message=message)\n generations.append(gen)\n llm_output = {\"token_usage\": response[\"usage\"], \"model_name\": self.model_name}\n return ChatResult(generations=generations, llm_output=llm_output)\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import os\n\n import openai\n","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._create_chat_result","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._create_chat_result#L327-L334","kind":"function","name":"_create_chat_result","path":"real_agents/adapters/models/openai.py","language":"python","start_line":327,"end_line":334,"context_start_line":307,"context_end_line":354,"code":" inner_completion += token\n if run_manager:\n run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n response = self.completion_with_retry(messages=message_dicts, **params)\n return self._create_chat_result(response)\n\n def _create_message_dicts(\n self, messages: List[BaseMessage], stop: Optional[List[str]]\n ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:\n params: Dict[str, Any] = {**{\"model\": self.model_name}, **self._default_params}\n if stop is not None:\n if \"stop\" in params:\n raise ValueError(\"`stop` found in both the input and default params.\")\n params[\"stop\"] = stop\n message_dicts = [_convert_message_to_dict(m) for m in messages]\n return message_dicts, params\n\n def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:\n generations = []\n for res in response[\"choices\"]:\n message = _convert_dict_to_message(res[\"message\"])\n gen = ChatGeneration(message=message)\n generations.append(gen)\n llm_output = {\"token_usage\": response[\"usage\"], \"model_name\": self.model_name}\n return ChatResult(generations=generations, llm_output=llm_output)\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import os\n\n import openai\n\n openai_api_base = os.environ.get(\"OPENAI_API_BASE\", \"https://api.openai.com/v1\")\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n openai.api_base = openai_api_base\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n role = \"assistant\"","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._agenerate","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._agenerate#L336-L370","kind":"function","name":"_agenerate","path":"real_agents/adapters/models/openai.py","language":"python","start_line":336,"end_line":370,"context_start_line":316,"context_end_line":390,"code":" def _create_message_dicts(\n self, messages: List[BaseMessage], stop: Optional[List[str]]\n ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:\n params: Dict[str, Any] = {**{\"model\": self.model_name}, **self._default_params}\n if stop is not None:\n if \"stop\" in params:\n raise ValueError(\"`stop` found in both the input and default params.\")\n params[\"stop\"] = stop\n message_dicts = [_convert_message_to_dict(m) for m in messages]\n return message_dicts, params\n\n def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:\n generations = []\n for res in response[\"choices\"]:\n message = _convert_dict_to_message(res[\"message\"])\n gen = ChatGeneration(message=message)\n generations.append(gen)\n llm_output = {\"token_usage\": response[\"usage\"], \"model_name\": self.model_name}\n return ChatResult(generations=generations, llm_output=llm_output)\n\n async def _agenerate(\n self,\n messages: List[BaseMessage],\n stop: Optional[List[str]] = None,\n run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,\n ) -> ChatResult:\n import os\n\n import openai\n\n openai_api_base = os.environ.get(\"OPENAI_API_BASE\", \"https://api.openai.com/v1\")\n openai_api_key = os.environ.get(\"OPENAI_API_KEY\", None)\n openai.api_key = openai_api_key\n openai.api_base = openai_api_base\n\n message_dicts, params = self._create_message_dicts(messages, stop)\n if self.streaming:\n inner_completion = \"\"\n role = \"assistant\"\n params[\"stream\"] = True\n async for stream_resp in await acompletion_with_retry(self,\n messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", role)\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n await run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n else:\n response = await acompletion_with_retry(self, messages=message_dicts,\n **params)\n return self._create_chat_result(response)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {**{\"model_name\": self.model_name}, **self._default_params}\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"openai-chat\"\n\n def get_num_tokens(self, text: str) -> int:\n \"\"\"Calculate num tokens with tiktoken package.\"\"\"\n # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._identifying_params","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._identifying_params#L373-L375","kind":"function","name":"_identifying_params","path":"real_agents/adapters/models/openai.py","language":"python","start_line":373,"end_line":375,"context_start_line":353,"context_end_line":395,"code":" inner_completion = \"\"\n role = \"assistant\"\n params[\"stream\"] = True\n async for stream_resp in await acompletion_with_retry(self,\n messages=message_dicts,\n **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", role)\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n await run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n else:\n response = await acompletion_with_retry(self, messages=message_dicts,\n **params)\n return self._create_chat_result(response)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {**{\"model_name\": self.model_name}, **self._default_params}\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"openai-chat\"\n\n def get_num_tokens(self, text: str) -> int:\n \"\"\"Calculate num tokens with tiktoken package.\"\"\"\n # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n # create a GPT-3.5-Turbo encoder instance","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai._llm_type","uri":"program://OpenAgents/function/real_agents.adapters.models.openai._llm_type#L378-L380","kind":"function","name":"_llm_type","path":"real_agents/adapters/models/openai.py","language":"python","start_line":378,"end_line":380,"context_start_line":358,"context_end_line":400,"code":" **params):\n role = stream_resp[\"choices\"][0][\"delta\"].get(\"role\", role)\n token = stream_resp[\"choices\"][0][\"delta\"].get(\"content\", \"\")\n inner_completion += token\n if run_manager:\n await run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n else:\n response = await acompletion_with_retry(self, messages=message_dicts,\n **params)\n return self._create_chat_result(response)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {**{\"model_name\": self.model_name}, **self._default_params}\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"openai-chat\"\n\n def get_num_tokens(self, text: str) -> int:\n \"\"\"Calculate num tokens with tiktoken package.\"\"\"\n # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n # create a GPT-3.5-Turbo encoder instance\n enc = tiktoken.encoding_for_model(self.model_name)\n\n # encode the text using the GPT-3.5-Turbo encoder\n tokenized_text = enc.encode(text)\n","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.get_num_tokens","uri":"program://OpenAgents/function/real_agents.adapters.models.openai.get_num_tokens#L382-L402","kind":"function","name":"get_num_tokens","path":"real_agents/adapters/models/openai.py","language":"python","start_line":382,"end_line":402,"context_start_line":362,"context_end_line":422,"code":" if run_manager:\n await run_manager.on_llm_new_token(token)\n message = _convert_dict_to_message(\n {\"content\": inner_completion, \"role\": role})\n return ChatResult(generations=[ChatGeneration(message=message)])\n else:\n response = await acompletion_with_retry(self, messages=message_dicts,\n **params)\n return self._create_chat_result(response)\n\n @property\n def _identifying_params(self) -> Mapping[str, Any]:\n \"\"\"Get the identifying parameters.\"\"\"\n return {**{\"model_name\": self.model_name}, **self._default_params}\n\n @property\n def _llm_type(self) -> str:\n \"\"\"Return type of chat model.\"\"\"\n return \"openai-chat\"\n\n def get_num_tokens(self, text: str) -> int:\n \"\"\"Calculate num tokens with tiktoken package.\"\"\"\n # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n # create a GPT-3.5-Turbo encoder instance\n enc = tiktoken.encoding_for_model(self.model_name)\n\n # encode the text using the GPT-3.5-Turbo encoder\n tokenized_text = enc.encode(text)\n\n # calculate the number of tokens in the encoded text\n return len(tokenized_text)\n\n def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:\n \"\"\"Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.\n\n Official documentation: https://github.com/openai/openai-cookbook/blob/\n main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb\"\"\"\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n\n model = self.model_name\n if model == \"gpt-3.5-turbo\":\n # gpt-3.5-turbo may change over time.\n # Returning num tokens assuming gpt-3.5-turbo-0301.\n model = \"gpt-3.5-turbo-0301\"","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.models.openai.get_num_tokens_from_messages","uri":"program://OpenAgents/function/real_agents.adapters.models.openai.get_num_tokens_from_messages#L404-L460","kind":"function","name":"get_num_tokens_from_messages","path":"real_agents/adapters/models/openai.py","language":"python","start_line":404,"end_line":460,"context_start_line":384,"context_end_line":460,"code":" # tiktoken NOT supported for Python 3.7 or below\n if sys.version_info[1] <= 7:\n return super().get_num_tokens(text)\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n # create a GPT-3.5-Turbo encoder instance\n enc = tiktoken.encoding_for_model(self.model_name)\n\n # encode the text using the GPT-3.5-Turbo encoder\n tokenized_text = enc.encode(text)\n\n # calculate the number of tokens in the encoded text\n return len(tokenized_text)\n\n def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:\n \"\"\"Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.\n\n Official documentation: https://github.com/openai/openai-cookbook/blob/\n main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb\"\"\"\n try:\n import tiktoken\n except ImportError:\n raise ValueError(\n \"Could not import tiktoken python package. \"\n \"This is needed in order to calculate get_num_tokens. \"\n \"Please install it with `pip install tiktoken`.\"\n )\n\n model = self.model_name\n if model == \"gpt-3.5-turbo\":\n # gpt-3.5-turbo may change over time.\n # Returning num tokens assuming gpt-3.5-turbo-0301.\n model = \"gpt-3.5-turbo-0301\"\n elif model == \"gpt-4\":\n # gpt-4 may change over time.\n # Returning num tokens assuming gpt-4-0314.\n model = \"gpt-4-0314\"\n\n # Returns the number of tokens used by a list of messages.\n try:\n encoding = tiktoken.encoding_for_model(model)\n except KeyError:\n logger.warning(\"Warning: model not found. Using cl100k_base encoding.\")\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n\n if model == \"gpt-3.5-turbo-0301\":\n # every message follows {role/name}\\n{content}\\n\n tokens_per_message = 4\n # if there's a name, the role is omitted\n tokens_per_name = -1\n elif model == \"gpt-4-0314\":\n tokens_per_message = 3\n tokens_per_name = 1\n else:\n raise NotImplementedError(\n f\"get_num_tokens_from_messages() is not presently implemented \"\n f\"for model {model}.\"\n \"See https://github.com/openai/openai-python/blob/main/chatml.md for \"\n \"information on how messages are converted to tokens.\"\n )\n num_tokens = 0\n messages_dict = [_convert_message_to_dict(m) for m in messages]\n for message in messages_dict:\n num_tokens += tokens_per_message\n for key, value in message.items():\n num_tokens += len(encoding.encode(value))\n if key == \"name\":\n num_tokens += tokens_per_name\n # every reply is primed with assistant\n num_tokens += 3\n return num_tokens","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.read_only_string_memory","uri":"program://OpenAgents/module/real_agents.adapters.memory.read_only_string_memory#L1-L30","kind":"module","name":"real_agents.adapters.memory.read_only_string_memory","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":1,"end_line":30,"context_start_line":1,"context_end_line":30,"code":"from typing import Any, Dict, List\n\nfrom langchain.schema import BaseMemory\n\n\nclass ReadOnlySharedStringMemory(BaseMemory):\n \"\"\"A memory wrapper that is read-only and cannot be changed.\"\"\"\n\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state\n return memory_string\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Nothing should be saved or changed\"\"\"\n pass\n\n def clear(self) -> None:\n \"\"\"Nothing to clear, got a memory like a vault.\"\"\"\n pass","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.read_only_string_memory.ReadOnlySharedStringMemory","uri":"program://OpenAgents/class/real_agents.adapters.memory.read_only_string_memory.ReadOnlySharedStringMemory#L6-L30","kind":"class","name":"ReadOnlySharedStringMemory","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":6,"end_line":30,"context_start_line":1,"context_end_line":30,"code":"from typing import Any, Dict, List\n\nfrom langchain.schema import BaseMemory\n\n\nclass ReadOnlySharedStringMemory(BaseMemory):\n \"\"\"A memory wrapper that is read-only and cannot be changed.\"\"\"\n\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state\n return memory_string\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Nothing should be saved or changed\"\"\"\n pass\n\n def clear(self) -> None:\n \"\"\"Nothing to clear, got a memory like a vault.\"\"\"\n pass","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.read_only_string_memory.memory_variables","uri":"program://OpenAgents/function/real_agents.adapters.memory.read_only_string_memory.memory_variables#L12-L14","kind":"function","name":"memory_variables","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":12,"end_line":14,"context_start_line":1,"context_end_line":30,"code":"from typing import Any, Dict, List\n\nfrom langchain.schema import BaseMemory\n\n\nclass ReadOnlySharedStringMemory(BaseMemory):\n \"\"\"A memory wrapper that is read-only and cannot be changed.\"\"\"\n\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state\n return memory_string\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Nothing should be saved or changed\"\"\"\n pass\n\n def clear(self) -> None:\n \"\"\"Nothing to clear, got a memory like a vault.\"\"\"\n pass","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.read_only_string_memory.load_memory_variables","uri":"program://OpenAgents/function/real_agents.adapters.memory.read_only_string_memory.load_memory_variables#L16-L22","kind":"function","name":"load_memory_variables","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":16,"end_line":22,"context_start_line":1,"context_end_line":30,"code":"from typing import Any, Dict, List\n\nfrom langchain.schema import BaseMemory\n\n\nclass ReadOnlySharedStringMemory(BaseMemory):\n \"\"\"A memory wrapper that is read-only and cannot be changed.\"\"\"\n\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state\n return memory_string\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Nothing should be saved or changed\"\"\"\n pass\n\n def clear(self) -> None:\n \"\"\"Nothing to clear, got a memory like a vault.\"\"\"\n pass","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.read_only_string_memory.save_context","uri":"program://OpenAgents/function/real_agents.adapters.memory.read_only_string_memory.save_context#L24-L26","kind":"function","name":"save_context","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":24,"end_line":26,"context_start_line":4,"context_end_line":30,"code":"\n\nclass ReadOnlySharedStringMemory(BaseMemory):\n \"\"\"A memory wrapper that is read-only and cannot be changed.\"\"\"\n\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state\n return memory_string\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Nothing should be saved or changed\"\"\"\n pass\n\n def clear(self) -> None:\n \"\"\"Nothing to clear, got a memory like a vault.\"\"\"\n pass","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.read_only_string_memory.clear","uri":"program://OpenAgents/function/real_agents.adapters.memory.read_only_string_memory.clear#L28-L30","kind":"function","name":"clear","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":28,"end_line":30,"context_start_line":8,"context_end_line":30,"code":"\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state\n return memory_string\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Nothing should be saved or changed\"\"\"\n pass\n\n def clear(self) -> None:\n \"\"\"Nothing to clear, got a memory like a vault.\"\"\"\n pass","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer","uri":"program://OpenAgents/module/real_agents.adapters.memory.buffer#L1-L197","kind":"module","name":"real_agents.adapters.memory.buffer","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":1,"end_line":197,"context_start_line":1,"context_end_line":197,"code":"from typing import Any, Dict, List, Optional, Tuple\nfrom pydantic import root_validator\n\nfrom langchain.memory.utils import get_prompt_input_key\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import BaseMessage, get_buffer_string\nfrom langchain.memory.chat_memory import BaseChatMemory, BaseMemory\n\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\n\n\nclass ConversationBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n\n @property\n def buffer(self) -> Any:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n\nclass ConversationStringBufferMemory(BaseMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n \"\"\"Prefix to use for AI generated responses.\"\"\"\n buffer: str = \"\"\n output_key: Optional[str] = None\n input_key: Optional[str] = None\n memory_key: str = \"history\" #: :meta private:\n\n @root_validator()\n def validate_chains(cls, values: Dict) -> Dict:\n \"\"\"Validate that return messages is not True.\"\"\"\n if values.get(\"return_messages\", False):\n raise ValueError(\"return_messages must be False for ConversationStringBufferMemory\")\n return values\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer.\"\"\"\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) != 1:\n raise ValueError(f\"One output key expected, got {outputs.keys()}\")\n output_key = list(outputs.keys())[0]\n else:\n output_key = self.output_key\n human = f\"{self.human_prefix}: \" + inputs[prompt_input_key]\n ai = f\"{self.ai_prefix}: \" + outputs[output_key]\n self.buffer += \"\\n\" + \"\\n\".join([human, ai])\n\n def clear(self) -> None:\n \"\"\"Clear memory contents.\"\"\"\n self.buffer = \"\"\n\n\nclass ConversationReActBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def _get_input_output(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Tuple[str, str]:\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) == 1:\n output_key = list(outputs.keys())[0]\n return inputs[prompt_input_key], outputs[output_key]\n else:\n assert \"intermediate_steps\" in outputs, \"intermediate_steps must in outputs when output_key length > 1\"\n intermediate_message = \"\"\n for action, full_observation in outputs[\"intermediate_steps\"]:\n intermediate_message += \"\\n{\\n\"\n intermediate_message += (\n '\\t\"action\": \"{}\"'.format(action.tool) + \"\\n\"\n ) # todo: move to schema, as well as the one in prompt\n intermediate_message += '\\t\"action_input\": \"{}\"'.format(action.tool_input) + \"\\n\"\n intermediate_message += \"}\\n\"\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.style\n )\n intermediate_message += \"{}\\n\".format(observation)\n output = intermediate_message + outputs[list(outputs.keys())[0]]\n\n return inputs[prompt_input_key], output\n else:\n output_key = self.output_key\n return inputs[prompt_input_key], outputs[output_key]\n\n def fit_max_token_limit(self):\n from real_agents.adapters.data_model import MessageDataModel\n\n # if self.llm != None:\n buffer = self.chat_memory.messages\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n if curr_buffer_length > self.max_token_limit:\n while curr_buffer_length > self.max_token_limit:\n buffer.pop(0)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n self.chat_memory.messages = buffer\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer. Pruned.\"\"\"\n super().save_context(inputs, outputs)\n self.fit_max_token_limit()","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.ConversationBufferMemory","uri":"program://OpenAgents/class/real_agents.adapters.memory.buffer.ConversationBufferMemory#L12-L41","kind":"class","name":"ConversationBufferMemory","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":12,"end_line":41,"context_start_line":1,"context_end_line":61,"code":"from typing import Any, Dict, List, Optional, Tuple\nfrom pydantic import root_validator\n\nfrom langchain.memory.utils import get_prompt_input_key\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import BaseMessage, get_buffer_string\nfrom langchain.memory.chat_memory import BaseChatMemory, BaseMemory\n\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\n\n\nclass ConversationBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n\n @property\n def buffer(self) -> Any:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n\nclass ConversationStringBufferMemory(BaseMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n \"\"\"Prefix to use for AI generated responses.\"\"\"\n buffer: str = \"\"\n output_key: Optional[str] = None\n input_key: Optional[str] = None\n memory_key: str = \"history\" #: :meta private:\n\n @root_validator()\n def validate_chains(cls, values: Dict) -> Dict:\n \"\"\"Validate that return messages is not True.\"\"\"\n if values.get(\"return_messages\", False):\n raise ValueError(\"return_messages must be False for ConversationStringBufferMemory\")\n return values\n","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.ConversationStringBufferMemory","uri":"program://OpenAgents/class/real_agents.adapters.memory.buffer.ConversationStringBufferMemory#L44-L91","kind":"class","name":"ConversationStringBufferMemory","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":44,"end_line":91,"context_start_line":24,"context_end_line":111,"code":" else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n\nclass ConversationStringBufferMemory(BaseMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n \"\"\"Prefix to use for AI generated responses.\"\"\"\n buffer: str = \"\"\n output_key: Optional[str] = None\n input_key: Optional[str] = None\n memory_key: str = \"history\" #: :meta private:\n\n @root_validator()\n def validate_chains(cls, values: Dict) -> Dict:\n \"\"\"Validate that return messages is not True.\"\"\"\n if values.get(\"return_messages\", False):\n raise ValueError(\"return_messages must be False for ConversationStringBufferMemory\")\n return values\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer.\"\"\"\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) != 1:\n raise ValueError(f\"One output key expected, got {outputs.keys()}\")\n output_key = list(outputs.keys())[0]\n else:\n output_key = self.output_key\n human = f\"{self.human_prefix}: \" + inputs[prompt_input_key]\n ai = f\"{self.ai_prefix}: \" + outputs[output_key]\n self.buffer += \"\\n\" + \"\\n\".join([human, ai])\n\n def clear(self) -> None:\n \"\"\"Clear memory contents.\"\"\"\n self.buffer = \"\"\n\n\nclass ConversationReActBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.ConversationReActBufferMemory","uri":"program://OpenAgents/class/real_agents.adapters.memory.buffer.ConversationReActBufferMemory#L94-L197","kind":"class","name":"ConversationReActBufferMemory","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":94,"end_line":197,"context_start_line":74,"context_end_line":197,"code":" \"\"\"Save context from this conversation to buffer.\"\"\"\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) != 1:\n raise ValueError(f\"One output key expected, got {outputs.keys()}\")\n output_key = list(outputs.keys())[0]\n else:\n output_key = self.output_key\n human = f\"{self.human_prefix}: \" + inputs[prompt_input_key]\n ai = f\"{self.ai_prefix}: \" + outputs[output_key]\n self.buffer += \"\\n\" + \"\\n\".join([human, ai])\n\n def clear(self) -> None:\n \"\"\"Clear memory contents.\"\"\"\n self.buffer = \"\"\n\n\nclass ConversationReActBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def _get_input_output(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Tuple[str, str]:\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) == 1:\n output_key = list(outputs.keys())[0]\n return inputs[prompt_input_key], outputs[output_key]\n else:\n assert \"intermediate_steps\" in outputs, \"intermediate_steps must in outputs when output_key length > 1\"\n intermediate_message = \"\"\n for action, full_observation in outputs[\"intermediate_steps\"]:\n intermediate_message += \"\\n{\\n\"\n intermediate_message += (\n '\\t\"action\": \"{}\"'.format(action.tool) + \"\\n\"\n ) # todo: move to schema, as well as the one in prompt\n intermediate_message += '\\t\"action_input\": \"{}\"'.format(action.tool_input) + \"\\n\"\n intermediate_message += \"}\\n\"\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.style\n )\n intermediate_message += \"{}\\n\".format(observation)\n output = intermediate_message + outputs[list(outputs.keys())[0]]\n\n return inputs[prompt_input_key], output\n else:\n output_key = self.output_key\n return inputs[prompt_input_key], outputs[output_key]\n\n def fit_max_token_limit(self):\n from real_agents.adapters.data_model import MessageDataModel\n\n # if self.llm != None:\n buffer = self.chat_memory.messages\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n if curr_buffer_length > self.max_token_limit:\n while curr_buffer_length > self.max_token_limit:\n buffer.pop(0)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n self.chat_memory.messages = buffer\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer. Pruned.\"\"\"\n super().save_context(inputs, outputs)\n self.fit_max_token_limit()","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.buffer","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.buffer#L124-L133","kind":"function","name":"buffer","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":124,"end_line":133,"context_start_line":104,"context_end_line":153,"code":" @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def _get_input_output(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Tuple[str, str]:\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) == 1:","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.memory_variables","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.memory_variables#L136-L141","kind":"function","name":"memory_variables","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":136,"end_line":141,"context_start_line":116,"context_end_line":161,"code":" \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def _get_input_output(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Tuple[str, str]:\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) == 1:\n output_key = list(outputs.keys())[0]\n return inputs[prompt_input_key], outputs[output_key]\n else:\n assert \"intermediate_steps\" in outputs, \"intermediate_steps must in outputs when output_key length > 1\"\n intermediate_message = \"\"\n for action, full_observation in outputs[\"intermediate_steps\"]:\n intermediate_message += \"\\n{\\n\"\n intermediate_message += (","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.load_memory_variables","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.load_memory_variables#L143-L145","kind":"function","name":"load_memory_variables","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":143,"end_line":145,"context_start_line":123,"context_end_line":165,"code":" @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def _get_input_output(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Tuple[str, str]:\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) == 1:\n output_key = list(outputs.keys())[0]\n return inputs[prompt_input_key], outputs[output_key]\n else:\n assert \"intermediate_steps\" in outputs, \"intermediate_steps must in outputs when output_key length > 1\"\n intermediate_message = \"\"\n for action, full_observation in outputs[\"intermediate_steps\"]:\n intermediate_message += \"\\n{\\n\"\n intermediate_message += (\n '\\t\"action\": \"{}\"'.format(action.tool) + \"\\n\"\n ) # todo: move to schema, as well as the one in prompt\n intermediate_message += '\\t\"action_input\": \"{}\"'.format(action.tool_input) + \"\\n\"\n intermediate_message += \"}\\n\"","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.validate_chains","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.validate_chains#L56-L60","kind":"function","name":"validate_chains","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":56,"end_line":60,"context_start_line":36,"context_end_line":80,"code":" \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n\nclass ConversationStringBufferMemory(BaseMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n \"\"\"Prefix to use for AI generated responses.\"\"\"\n buffer: str = \"\"\n output_key: Optional[str] = None\n input_key: Optional[str] = None\n memory_key: str = \"history\" #: :meta private:\n\n @root_validator()\n def validate_chains(cls, values: Dict) -> Dict:\n \"\"\"Validate that return messages is not True.\"\"\"\n if values.get(\"return_messages\", False):\n raise ValueError(\"return_messages must be False for ConversationStringBufferMemory\")\n return values\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer.\"\"\"\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) != 1:","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.save_context","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.save_context#L194-L197","kind":"function","name":"save_context","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":194,"end_line":197,"context_start_line":174,"context_end_line":197,"code":"\n return inputs[prompt_input_key], output\n else:\n output_key = self.output_key\n return inputs[prompt_input_key], outputs[output_key]\n\n def fit_max_token_limit(self):\n from real_agents.adapters.data_model import MessageDataModel\n\n # if self.llm != None:\n buffer = self.chat_memory.messages\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n if curr_buffer_length > self.max_token_limit:\n while curr_buffer_length > self.max_token_limit:\n buffer.pop(0)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n self.chat_memory.messages = buffer\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer. Pruned.\"\"\"\n super().save_context(inputs, outputs)\n self.fit_max_token_limit()","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.clear","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.clear#L89-L91","kind":"function","name":"clear","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":89,"end_line":91,"context_start_line":69,"context_end_line":111,"code":" def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer.\"\"\"\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) != 1:\n raise ValueError(f\"One output key expected, got {outputs.keys()}\")\n output_key = list(outputs.keys())[0]\n else:\n output_key = self.output_key\n human = f\"{self.human_prefix}: \" + inputs[prompt_input_key]\n ai = f\"{self.ai_prefix}: \" + outputs[output_key]\n self.buffer += \"\\n\" + \"\\n\".join([human, ai])\n\n def clear(self) -> None:\n \"\"\"Clear memory contents.\"\"\"\n self.buffer = \"\"\n\n\nclass ConversationReActBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.observation_prefix","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.observation_prefix#L105-L107","kind":"function","name":"observation_prefix","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":105,"end_line":107,"context_start_line":85,"context_end_line":127,"code":" human = f\"{self.human_prefix}: \" + inputs[prompt_input_key]\n ai = f\"{self.ai_prefix}: \" + outputs[output_key]\n self.buffer += \"\\n\" + \"\\n\".join([human, ai])\n\n def clear(self) -> None:\n \"\"\"Clear memory contents.\"\"\"\n self.buffer = \"\"\n\n\nclass ConversationReActBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.action_prefix","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.action_prefix#L110-L112","kind":"function","name":"action_prefix","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":110,"end_line":112,"context_start_line":90,"context_end_line":132,"code":" \"\"\"Clear memory contents.\"\"\"\n self.buffer = \"\"\n\n\nclass ConversationReActBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.llm_prefix","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.llm_prefix#L115-L117","kind":"function","name":"llm_prefix","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":115,"end_line":117,"context_start_line":95,"context_end_line":137,"code":" \"\"\"Buffer for storing conversational ReAct memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.llm_final","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.llm_final#L120-L121","kind":"function","name":"llm_final","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":120,"end_line":121,"context_start_line":100,"context_end_line":141,"code":" max_token_limit: int = 2000\n llm: BaseLanguageModel = None\n style: str = \"code\"\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def action_prefix(self) -> str:\n \"\"\"Prefix to append the action with.\"\"\"\n return \"Action:\"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @property\n def llm_final(self) -> str:\n \"\"\"Final Answer\"\"\"\n\n @property\n def buffer(self) -> List[BaseMessage]:\n \"\"\"String buffer of memory.\"\"\"\n if self.return_messages:\n return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer._get_input_output","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer._get_input_output#L147-L178","kind":"function","name":"_get_input_output","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":147,"end_line":178,"context_start_line":127,"context_end_line":197,"code":" return self.chat_memory.messages\n else:\n return get_buffer_string(\n self.chat_memory.messages,\n human_prefix=self.human_prefix,\n ai_prefix=self.ai_prefix,\n )\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Will always return list of memory variables.\n\n :meta private:\n \"\"\"\n return [self.memory_key]\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return history buffer.\"\"\"\n return {self.memory_key: self.buffer}\n\n def _get_input_output(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Tuple[str, str]:\n if self.input_key is None:\n prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)\n else:\n prompt_input_key = self.input_key\n if self.output_key is None:\n if len(outputs) == 1:\n output_key = list(outputs.keys())[0]\n return inputs[prompt_input_key], outputs[output_key]\n else:\n assert \"intermediate_steps\" in outputs, \"intermediate_steps must in outputs when output_key length > 1\"\n intermediate_message = \"\"\n for action, full_observation in outputs[\"intermediate_steps\"]:\n intermediate_message += \"\\n{\\n\"\n intermediate_message += (\n '\\t\"action\": \"{}\"'.format(action.tool) + \"\\n\"\n ) # todo: move to schema, as well as the one in prompt\n intermediate_message += '\\t\"action_input\": \"{}\"'.format(action.tool_input) + \"\\n\"\n intermediate_message += \"}\\n\"\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.style\n )\n intermediate_message += \"{}\\n\".format(observation)\n output = intermediate_message + outputs[list(outputs.keys())[0]]\n\n return inputs[prompt_input_key], output\n else:\n output_key = self.output_key\n return inputs[prompt_input_key], outputs[output_key]\n\n def fit_max_token_limit(self):\n from real_agents.adapters.data_model import MessageDataModel\n\n # if self.llm != None:\n buffer = self.chat_memory.messages\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n if curr_buffer_length > self.max_token_limit:\n while curr_buffer_length > self.max_token_limit:\n buffer.pop(0)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n self.chat_memory.messages = buffer\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer. Pruned.\"\"\"\n super().save_context(inputs, outputs)\n self.fit_max_token_limit()","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.memory.buffer.fit_max_token_limit","uri":"program://OpenAgents/function/real_agents.adapters.memory.buffer.fit_max_token_limit#L180-L192","kind":"function","name":"fit_max_token_limit","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":180,"end_line":192,"context_start_line":160,"context_end_line":197,"code":" intermediate_message += \"\\n{\\n\"\n intermediate_message += (\n '\\t\"action\": \"{}\"'.format(action.tool) + \"\\n\"\n ) # todo: move to schema, as well as the one in prompt\n intermediate_message += '\\t\"action_input\": \"{}\"'.format(action.tool_input) + \"\\n\"\n intermediate_message += \"}\\n\"\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.style\n )\n intermediate_message += \"{}\\n\".format(observation)\n output = intermediate_message + outputs[list(outputs.keys())[0]]\n\n return inputs[prompt_input_key], output\n else:\n output_key = self.output_key\n return inputs[prompt_input_key], outputs[output_key]\n\n def fit_max_token_limit(self):\n from real_agents.adapters.data_model import MessageDataModel\n\n # if self.llm != None:\n buffer = self.chat_memory.messages\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n if curr_buffer_length > self.max_token_limit:\n while curr_buffer_length > self.max_token_limit:\n buffer.pop(0)\n curr_buffer_length = MessageDataModel._count_tokens(\"\\n\".join([_.content for _ in buffer]))\n # curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)\n self.chat_memory.messages = buffer\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save context from this conversation to buffer. Pruned.\"\"\"\n super().save_context(inputs, outputs)\n self.fit_max_token_limit()","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message","uri":"program://OpenAgents/module/real_agents.adapters.data_model.message#L1-L205","kind":"module","name":"real_agents.adapters.data_model.message","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":1,"end_line":205,"context_start_line":1,"context_end_line":205,"code":"import re\nimport textwrap\nfrom typing import List, Dict, Any, Optional\nfrom langchain.schema import BaseMessage\nimport tiktoken\n\n# format of agent action\nACTION_FORMAT = \"\"\"```json\n{{\n \"action\": \"{_action}\",\n \"action_input\": \"{_action_input}\",\n}}\n```\"\"\"\n\n# format of tool call(code) & tool output(response)\nTOOL_FORMAT = {\n \"code\": \"\"\"\n{_intermediate_steps}\n\n\n\n{_result}\n\n\"\"\",\n \"plugin\": \"\"\"\n{_intermediate_steps}\n\n\n\n{_result}\n\n\"\"\",\n}\n\n# format to wrap tool call + tool output together\nTOOL_RESPONSE_FORMAT = \"\"\"[RESPONSE_BEGIN]\n{_response}\n[RESPONSE_END]\n\"\"\"\n\n\nclass MessageDataModel:\n \"\"\"A data model for Message Management, general purpose.\"\"\"\n\n @staticmethod\n def _count_tokens(test_string: str) -> int:\n \"\"\"copy of langchain _get_num_token_default_method\"\"\"\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(test_string))\n return tokens\n\n @classmethod\n def _get_num_tokens_from_messages(cls, buffer: List[BaseMessage]) -> int:\n return sum([cls._count_tokens(m.content) for m in buffer])\n\n @classmethod\n def truncate_text(cls, raw_text: str, max_token: Optional[int] = 250, trunc_ratio: int = 0.5) -> str:\n \"\"\"heuristic truncation for single long string & code\"\"\"\n tokens = cls._count_tokens(raw_text)\n if max_token is None or tokens <= max_token:\n return raw_text\n\n # assume we keep the first ratio * max_tokens and the (1 - ratio) * max_tokens\n half_tokens = int(max_token * trunc_ratio)\n lines = raw_text.strip().split(\"\\n\")\n lines = [\" \".join(line.split(\" \")[:100]) for line in lines]\n total_lines = len(lines)\n\n # first half\n left = 0\n right = total_lines // 2\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[0:mid])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n first_half = \"\\n\".join(lines[0:right])\n\n # last half\n left = total_lines // 2 + 1\n right = total_lines - 1\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[mid:])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n second_half = \"\\n\".join(lines[left:])\n\n if first_half != \"\" or second_half != \"\":\n return f\"{first_half}\\n...\\n[too long to show]\\n...\\n{second_half}\"\n else:\n # if len(first_half_list) == 0 and len(last_half_list) == 0:\n # if all lines >= max_token, return last 100 words as truncated results.\n return f\"...\\n[too long to show]\\n...\\n{raw_text[-100:]}\"\n\n @classmethod\n def truncate_chat_history(cls, full_inputs: Dict[str, Any], max_token: int = 2500) -> Dict[str, Any]:\n _input = full_inputs[\"input\"]\n agent_scratchpad = full_inputs[\"agent_scratchpad\"]\n agent_scratchpad = \"\\n\".join([_.content for _ in agent_scratchpad])\n _input_tokens = cls._count_tokens(_input)\n _scratchpad_tokens = cls._count_tokens(agent_scratchpad)\n\n left_tokens = max_token - _scratchpad_tokens - _input_tokens\n chat_history = full_inputs[\"chat_history\"]\n\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n while len(chat_history) != 0 and curr_buffer_length > left_tokens:\n chat_history.pop(0)\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n full_inputs[\"chat_history\"] = chat_history\n return full_inputs\n\n @staticmethod\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n result = match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\").strip()\n # result = f\"\\\"{result}\\\"\"\n return result\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n @staticmethod\n def _extract_response(\n chat_history: str,\n begin_marker: str = \"[RESPONSE_BEGIN]\",\n end_marker: str = \"[RESPONSE_END]\",\n ai_msg_marker: str = \"AI:\",\n ):\n code_blocks = chat_history.split(ai_msg_marker)\n pattern = r\"\\[RESPONSE_BEGIN\\](.*?)\\[RESPONSE_END\\]\"\n\n cleaned_output = []\n for code_block in code_blocks:\n matches = re.findall(pattern, code_block, re.DOTALL)\n if matches:\n cleaned_output.append(matches[0].strip())\n return \"\\n\".join(cleaned_output)\n\n @classmethod\n def extract_action_for_llm(cls, text, max_token: int = 500) -> str:\n \"\"\"Since Action should be fully inputted into an Agent, so we do not perform truncation here.\"\"\"\n action_format = ACTION_FORMAT\n cleaned_output = text.strip()\n try:\n _action = cls._extract_value(cleaned_output, \"action\")\n _action_input = cls._extract_value(cleaned_output, \"action_input\")\n return action_format.format(_action=_action, _action_input=_action_input)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n _action = lines[1].strip()\n _action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return action_format.format(_action=_action, _action_input=_action_input)\n else:\n _action_input = cleaned_output\n\n return action_format.format(_action=\"Final Answer\", _action_input=_action_input)\n\n @classmethod\n def extract_tool_response_for_llm(cls, text, tool_style: str = \"code\", max_token: int = 250) -> str:\n wrap_format = TOOL_RESPONSE_FORMAT\n tool_observation_format = TOOL_FORMAT[tool_style]\n cleaned_output = text.strip()\n if tool_style == \"plugin\":\n max_token = None\n\n try:\n _result = cls.truncate_text(cls._extract_value(cleaned_output, \"result\"), max_token)\n _intermediate_steps = cls.truncate_text(\n cls._extract_value(cleaned_output, \"intermediate_steps\"), max_token\n )\n _intermediate_steps = _intermediate_steps.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _result = _result.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _response = tool_observation_format.format(_intermediate_steps=_intermediate_steps, _result=_result)\n\n return wrap_format.format(_response=_response)\n except:\n if cleaned_output.startswith(\"Final Answer:\"):\n lines = cleaned_output.splitlines()\n _response = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n _response = cls.truncate_text(_response, max_token)\n return wrap_format.format(_response=_response)\n\n _response = cls.truncate_text(cleaned_output, max_token)\n return wrap_format.format(_response=_response)\n\n @classmethod\n def extract_code_for_python_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code\n\n @classmethod\n def extract_code_for_sql_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.MessageDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.message.MessageDataModel#L42-L205","kind":"class","name":"MessageDataModel","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":42,"end_line":205,"context_start_line":22,"context_end_line":205,"code":"{_result}\n\n\"\"\",\n \"plugin\": \"\"\"\n{_intermediate_steps}\n\n\n\n{_result}\n\n\"\"\",\n}\n\n# format to wrap tool call + tool output together\nTOOL_RESPONSE_FORMAT = \"\"\"[RESPONSE_BEGIN]\n{_response}\n[RESPONSE_END]\n\"\"\"\n\n\nclass MessageDataModel:\n \"\"\"A data model for Message Management, general purpose.\"\"\"\n\n @staticmethod\n def _count_tokens(test_string: str) -> int:\n \"\"\"copy of langchain _get_num_token_default_method\"\"\"\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(test_string))\n return tokens\n\n @classmethod\n def _get_num_tokens_from_messages(cls, buffer: List[BaseMessage]) -> int:\n return sum([cls._count_tokens(m.content) for m in buffer])\n\n @classmethod\n def truncate_text(cls, raw_text: str, max_token: Optional[int] = 250, trunc_ratio: int = 0.5) -> str:\n \"\"\"heuristic truncation for single long string & code\"\"\"\n tokens = cls._count_tokens(raw_text)\n if max_token is None or tokens <= max_token:\n return raw_text\n\n # assume we keep the first ratio * max_tokens and the (1 - ratio) * max_tokens\n half_tokens = int(max_token * trunc_ratio)\n lines = raw_text.strip().split(\"\\n\")\n lines = [\" \".join(line.split(\" \")[:100]) for line in lines]\n total_lines = len(lines)\n\n # first half\n left = 0\n right = total_lines // 2\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[0:mid])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n first_half = \"\\n\".join(lines[0:right])\n\n # last half\n left = total_lines // 2 + 1\n right = total_lines - 1\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[mid:])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n second_half = \"\\n\".join(lines[left:])\n\n if first_half != \"\" or second_half != \"\":\n return f\"{first_half}\\n...\\n[too long to show]\\n...\\n{second_half}\"\n else:\n # if len(first_half_list) == 0 and len(last_half_list) == 0:\n # if all lines >= max_token, return last 100 words as truncated results.\n return f\"...\\n[too long to show]\\n...\\n{raw_text[-100:]}\"\n\n @classmethod\n def truncate_chat_history(cls, full_inputs: Dict[str, Any], max_token: int = 2500) -> Dict[str, Any]:\n _input = full_inputs[\"input\"]\n agent_scratchpad = full_inputs[\"agent_scratchpad\"]\n agent_scratchpad = \"\\n\".join([_.content for _ in agent_scratchpad])\n _input_tokens = cls._count_tokens(_input)\n _scratchpad_tokens = cls._count_tokens(agent_scratchpad)\n\n left_tokens = max_token - _scratchpad_tokens - _input_tokens\n chat_history = full_inputs[\"chat_history\"]\n\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n while len(chat_history) != 0 and curr_buffer_length > left_tokens:\n chat_history.pop(0)\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n full_inputs[\"chat_history\"] = chat_history\n return full_inputs\n\n @staticmethod\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n result = match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\").strip()\n # result = f\"\\\"{result}\\\"\"\n return result\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n @staticmethod\n def _extract_response(\n chat_history: str,\n begin_marker: str = \"[RESPONSE_BEGIN]\",\n end_marker: str = \"[RESPONSE_END]\",\n ai_msg_marker: str = \"AI:\",\n ):\n code_blocks = chat_history.split(ai_msg_marker)\n pattern = r\"\\[RESPONSE_BEGIN\\](.*?)\\[RESPONSE_END\\]\"\n\n cleaned_output = []\n for code_block in code_blocks:\n matches = re.findall(pattern, code_block, re.DOTALL)\n if matches:\n cleaned_output.append(matches[0].strip())\n return \"\\n\".join(cleaned_output)\n\n @classmethod\n def extract_action_for_llm(cls, text, max_token: int = 500) -> str:\n \"\"\"Since Action should be fully inputted into an Agent, so we do not perform truncation here.\"\"\"\n action_format = ACTION_FORMAT\n cleaned_output = text.strip()\n try:\n _action = cls._extract_value(cleaned_output, \"action\")\n _action_input = cls._extract_value(cleaned_output, \"action_input\")\n return action_format.format(_action=_action, _action_input=_action_input)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n _action = lines[1].strip()\n _action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return action_format.format(_action=_action, _action_input=_action_input)\n else:\n _action_input = cleaned_output\n\n return action_format.format(_action=\"Final Answer\", _action_input=_action_input)\n\n @classmethod\n def extract_tool_response_for_llm(cls, text, tool_style: str = \"code\", max_token: int = 250) -> str:\n wrap_format = TOOL_RESPONSE_FORMAT\n tool_observation_format = TOOL_FORMAT[tool_style]\n cleaned_output = text.strip()\n if tool_style == \"plugin\":\n max_token = None\n\n try:\n _result = cls.truncate_text(cls._extract_value(cleaned_output, \"result\"), max_token)\n _intermediate_steps = cls.truncate_text(\n cls._extract_value(cleaned_output, \"intermediate_steps\"), max_token\n )\n _intermediate_steps = _intermediate_steps.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _result = _result.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _response = tool_observation_format.format(_intermediate_steps=_intermediate_steps, _result=_result)\n\n return wrap_format.format(_response=_response)\n except:\n if cleaned_output.startswith(\"Final Answer:\"):\n lines = cleaned_output.splitlines()\n _response = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n _response = cls.truncate_text(_response, max_token)\n return wrap_format.format(_response=_response)\n\n _response = cls.truncate_text(cleaned_output, max_token)\n return wrap_format.format(_response=_response)\n\n @classmethod\n def extract_code_for_python_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code\n\n @classmethod\n def extract_code_for_sql_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message._count_tokens","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message._count_tokens#L46-L50","kind":"function","name":"_count_tokens","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":46,"end_line":50,"context_start_line":26,"context_end_line":70,"code":"{_intermediate_steps}\n\n\n\n{_result}\n\n\"\"\",\n}\n\n# format to wrap tool call + tool output together\nTOOL_RESPONSE_FORMAT = \"\"\"[RESPONSE_BEGIN]\n{_response}\n[RESPONSE_END]\n\"\"\"\n\n\nclass MessageDataModel:\n \"\"\"A data model for Message Management, general purpose.\"\"\"\n\n @staticmethod\n def _count_tokens(test_string: str) -> int:\n \"\"\"copy of langchain _get_num_token_default_method\"\"\"\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(test_string))\n return tokens\n\n @classmethod\n def _get_num_tokens_from_messages(cls, buffer: List[BaseMessage]) -> int:\n return sum([cls._count_tokens(m.content) for m in buffer])\n\n @classmethod\n def truncate_text(cls, raw_text: str, max_token: Optional[int] = 250, trunc_ratio: int = 0.5) -> str:\n \"\"\"heuristic truncation for single long string & code\"\"\"\n tokens = cls._count_tokens(raw_text)\n if max_token is None or tokens <= max_token:\n return raw_text\n\n # assume we keep the first ratio * max_tokens and the (1 - ratio) * max_tokens\n half_tokens = int(max_token * trunc_ratio)\n lines = raw_text.strip().split(\"\\n\")\n lines = [\" \".join(line.split(\" \")[:100]) for line in lines]\n total_lines = len(lines)\n\n # first half\n left = 0","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message._get_num_tokens_from_messages","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message._get_num_tokens_from_messages#L53-L54","kind":"function","name":"_get_num_tokens_from_messages","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":53,"end_line":54,"context_start_line":33,"context_end_line":74,"code":"}\n\n# format to wrap tool call + tool output together\nTOOL_RESPONSE_FORMAT = \"\"\"[RESPONSE_BEGIN]\n{_response}\n[RESPONSE_END]\n\"\"\"\n\n\nclass MessageDataModel:\n \"\"\"A data model for Message Management, general purpose.\"\"\"\n\n @staticmethod\n def _count_tokens(test_string: str) -> int:\n \"\"\"copy of langchain _get_num_token_default_method\"\"\"\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(test_string))\n return tokens\n\n @classmethod\n def _get_num_tokens_from_messages(cls, buffer: List[BaseMessage]) -> int:\n return sum([cls._count_tokens(m.content) for m in buffer])\n\n @classmethod\n def truncate_text(cls, raw_text: str, max_token: Optional[int] = 250, trunc_ratio: int = 0.5) -> str:\n \"\"\"heuristic truncation for single long string & code\"\"\"\n tokens = cls._count_tokens(raw_text)\n if max_token is None or tokens <= max_token:\n return raw_text\n\n # assume we keep the first ratio * max_tokens and the (1 - ratio) * max_tokens\n half_tokens = int(max_token * trunc_ratio)\n lines = raw_text.strip().split(\"\\n\")\n lines = [\" \".join(line.split(\" \")[:100]) for line in lines]\n total_lines = len(lines)\n\n # first half\n left = 0\n right = total_lines // 2\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[0:mid])","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.truncate_text","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message.truncate_text#L57-L100","kind":"function","name":"truncate_text","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":57,"end_line":100,"context_start_line":37,"context_end_line":120,"code":"{_response}\n[RESPONSE_END]\n\"\"\"\n\n\nclass MessageDataModel:\n \"\"\"A data model for Message Management, general purpose.\"\"\"\n\n @staticmethod\n def _count_tokens(test_string: str) -> int:\n \"\"\"copy of langchain _get_num_token_default_method\"\"\"\n enc = tiktoken.get_encoding(\"cl100k_base\")\n tokens = len(enc.encode(test_string))\n return tokens\n\n @classmethod\n def _get_num_tokens_from_messages(cls, buffer: List[BaseMessage]) -> int:\n return sum([cls._count_tokens(m.content) for m in buffer])\n\n @classmethod\n def truncate_text(cls, raw_text: str, max_token: Optional[int] = 250, trunc_ratio: int = 0.5) -> str:\n \"\"\"heuristic truncation for single long string & code\"\"\"\n tokens = cls._count_tokens(raw_text)\n if max_token is None or tokens <= max_token:\n return raw_text\n\n # assume we keep the first ratio * max_tokens and the (1 - ratio) * max_tokens\n half_tokens = int(max_token * trunc_ratio)\n lines = raw_text.strip().split(\"\\n\")\n lines = [\" \".join(line.split(\" \")[:100]) for line in lines]\n total_lines = len(lines)\n\n # first half\n left = 0\n right = total_lines // 2\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[0:mid])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n first_half = \"\\n\".join(lines[0:right])\n\n # last half\n left = total_lines // 2 + 1\n right = total_lines - 1\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[mid:])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n second_half = \"\\n\".join(lines[left:])\n\n if first_half != \"\" or second_half != \"\":\n return f\"{first_half}\\n...\\n[too long to show]\\n...\\n{second_half}\"\n else:\n # if len(first_half_list) == 0 and len(last_half_list) == 0:\n # if all lines >= max_token, return last 100 words as truncated results.\n return f\"...\\n[too long to show]\\n...\\n{raw_text[-100:]}\"\n\n @classmethod\n def truncate_chat_history(cls, full_inputs: Dict[str, Any], max_token: int = 2500) -> Dict[str, Any]:\n _input = full_inputs[\"input\"]\n agent_scratchpad = full_inputs[\"agent_scratchpad\"]\n agent_scratchpad = \"\\n\".join([_.content for _ in agent_scratchpad])\n _input_tokens = cls._count_tokens(_input)\n _scratchpad_tokens = cls._count_tokens(agent_scratchpad)\n\n left_tokens = max_token - _scratchpad_tokens - _input_tokens\n chat_history = full_inputs[\"chat_history\"]\n\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n while len(chat_history) != 0 and curr_buffer_length > left_tokens:\n chat_history.pop(0)\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n full_inputs[\"chat_history\"] = chat_history\n return full_inputs\n\n @staticmethod","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.truncate_chat_history","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message.truncate_chat_history#L103-L118","kind":"function","name":"truncate_chat_history","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":103,"end_line":118,"context_start_line":83,"context_end_line":138,"code":" left = total_lines // 2 + 1\n right = total_lines - 1\n while left < right:\n mid = (left + right) >> 1\n text = \"\\n\".join(lines[mid:])\n token = cls._count_tokens(text)\n if token > half_tokens:\n right = mid\n else:\n left = mid + 1\n second_half = \"\\n\".join(lines[left:])\n\n if first_half != \"\" or second_half != \"\":\n return f\"{first_half}\\n...\\n[too long to show]\\n...\\n{second_half}\"\n else:\n # if len(first_half_list) == 0 and len(last_half_list) == 0:\n # if all lines >= max_token, return last 100 words as truncated results.\n return f\"...\\n[too long to show]\\n...\\n{raw_text[-100:]}\"\n\n @classmethod\n def truncate_chat_history(cls, full_inputs: Dict[str, Any], max_token: int = 2500) -> Dict[str, Any]:\n _input = full_inputs[\"input\"]\n agent_scratchpad = full_inputs[\"agent_scratchpad\"]\n agent_scratchpad = \"\\n\".join([_.content for _ in agent_scratchpad])\n _input_tokens = cls._count_tokens(_input)\n _scratchpad_tokens = cls._count_tokens(agent_scratchpad)\n\n left_tokens = max_token - _scratchpad_tokens - _input_tokens\n chat_history = full_inputs[\"chat_history\"]\n\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n while len(chat_history) != 0 and curr_buffer_length > left_tokens:\n chat_history.pop(0)\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n full_inputs[\"chat_history\"] = chat_history\n return full_inputs\n\n @staticmethod\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n result = match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\").strip()\n # result = f\"\\\"{result}\\\"\"\n return result\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n @staticmethod\n def _extract_response(\n chat_history: str,\n begin_marker: str = \"[RESPONSE_BEGIN]\",\n end_marker: str = \"[RESPONSE_END]\",\n ai_msg_marker: str = \"AI:\",\n ):\n code_blocks = chat_history.split(ai_msg_marker)\n pattern = r\"\\[RESPONSE_BEGIN\\](.*?)\\[RESPONSE_END\\]\"","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message._extract_value","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message._extract_value#L121-L128","kind":"function","name":"_extract_value","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":121,"end_line":128,"context_start_line":101,"context_end_line":148,"code":"\n @classmethod\n def truncate_chat_history(cls, full_inputs: Dict[str, Any], max_token: int = 2500) -> Dict[str, Any]:\n _input = full_inputs[\"input\"]\n agent_scratchpad = full_inputs[\"agent_scratchpad\"]\n agent_scratchpad = \"\\n\".join([_.content for _ in agent_scratchpad])\n _input_tokens = cls._count_tokens(_input)\n _scratchpad_tokens = cls._count_tokens(agent_scratchpad)\n\n left_tokens = max_token - _scratchpad_tokens - _input_tokens\n chat_history = full_inputs[\"chat_history\"]\n\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n while len(chat_history) != 0 and curr_buffer_length > left_tokens:\n chat_history.pop(0)\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n full_inputs[\"chat_history\"] = chat_history\n return full_inputs\n\n @staticmethod\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n result = match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\").strip()\n # result = f\"\\\"{result}\\\"\"\n return result\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n @staticmethod\n def _extract_response(\n chat_history: str,\n begin_marker: str = \"[RESPONSE_BEGIN]\",\n end_marker: str = \"[RESPONSE_END]\",\n ai_msg_marker: str = \"AI:\",\n ):\n code_blocks = chat_history.split(ai_msg_marker)\n pattern = r\"\\[RESPONSE_BEGIN\\](.*?)\\[RESPONSE_END\\]\"\n\n cleaned_output = []\n for code_block in code_blocks:\n matches = re.findall(pattern, code_block, re.DOTALL)\n if matches:\n cleaned_output.append(matches[0].strip())\n return \"\\n\".join(cleaned_output)\n\n @classmethod\n def extract_action_for_llm(cls, text, max_token: int = 500) -> str:","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message._extract_response","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message._extract_response#L131-L145","kind":"function","name":"_extract_response","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":131,"end_line":145,"context_start_line":111,"context_end_line":165,"code":" chat_history = full_inputs[\"chat_history\"]\n\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n while len(chat_history) != 0 and curr_buffer_length > left_tokens:\n chat_history.pop(0)\n curr_buffer_length = cls._get_num_tokens_from_messages(chat_history)\n full_inputs[\"chat_history\"] = chat_history\n return full_inputs\n\n @staticmethod\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n result = match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\").strip()\n # result = f\"\\\"{result}\\\"\"\n return result\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n @staticmethod\n def _extract_response(\n chat_history: str,\n begin_marker: str = \"[RESPONSE_BEGIN]\",\n end_marker: str = \"[RESPONSE_END]\",\n ai_msg_marker: str = \"AI:\",\n ):\n code_blocks = chat_history.split(ai_msg_marker)\n pattern = r\"\\[RESPONSE_BEGIN\\](.*?)\\[RESPONSE_END\\]\"\n\n cleaned_output = []\n for code_block in code_blocks:\n matches = re.findall(pattern, code_block, re.DOTALL)\n if matches:\n cleaned_output.append(matches[0].strip())\n return \"\\n\".join(cleaned_output)\n\n @classmethod\n def extract_action_for_llm(cls, text, max_token: int = 500) -> str:\n \"\"\"Since Action should be fully inputted into an Agent, so we do not perform truncation here.\"\"\"\n action_format = ACTION_FORMAT\n cleaned_output = text.strip()\n try:\n _action = cls._extract_value(cleaned_output, \"action\")\n _action_input = cls._extract_value(cleaned_output, \"action_input\")\n return action_format.format(_action=_action, _action_input=_action_input)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n _action = lines[1].strip()\n _action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return action_format.format(_action=_action, _action_input=_action_input)\n else:\n _action_input = cleaned_output\n\n return action_format.format(_action=\"Final Answer\", _action_input=_action_input)","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.extract_action_for_llm","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message.extract_action_for_llm#L148-L165","kind":"function","name":"extract_action_for_llm","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":148,"end_line":165,"context_start_line":128,"context_end_line":185,"code":" raise ValueError(f\"Could not find {key} in {json_string}\")\n\n @staticmethod\n def _extract_response(\n chat_history: str,\n begin_marker: str = \"[RESPONSE_BEGIN]\",\n end_marker: str = \"[RESPONSE_END]\",\n ai_msg_marker: str = \"AI:\",\n ):\n code_blocks = chat_history.split(ai_msg_marker)\n pattern = r\"\\[RESPONSE_BEGIN\\](.*?)\\[RESPONSE_END\\]\"\n\n cleaned_output = []\n for code_block in code_blocks:\n matches = re.findall(pattern, code_block, re.DOTALL)\n if matches:\n cleaned_output.append(matches[0].strip())\n return \"\\n\".join(cleaned_output)\n\n @classmethod\n def extract_action_for_llm(cls, text, max_token: int = 500) -> str:\n \"\"\"Since Action should be fully inputted into an Agent, so we do not perform truncation here.\"\"\"\n action_format = ACTION_FORMAT\n cleaned_output = text.strip()\n try:\n _action = cls._extract_value(cleaned_output, \"action\")\n _action_input = cls._extract_value(cleaned_output, \"action_input\")\n return action_format.format(_action=_action, _action_input=_action_input)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n _action = lines[1].strip()\n _action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return action_format.format(_action=_action, _action_input=_action_input)\n else:\n _action_input = cleaned_output\n\n return action_format.format(_action=\"Final Answer\", _action_input=_action_input)\n\n @classmethod\n def extract_tool_response_for_llm(cls, text, tool_style: str = \"code\", max_token: int = 250) -> str:\n wrap_format = TOOL_RESPONSE_FORMAT\n tool_observation_format = TOOL_FORMAT[tool_style]\n cleaned_output = text.strip()\n if tool_style == \"plugin\":\n max_token = None\n\n try:\n _result = cls.truncate_text(cls._extract_value(cleaned_output, \"result\"), max_token)\n _intermediate_steps = cls.truncate_text(\n cls._extract_value(cleaned_output, \"intermediate_steps\"), max_token\n )\n _intermediate_steps = _intermediate_steps.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _result = _result.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _response = tool_observation_format.format(_intermediate_steps=_intermediate_steps, _result=_result)\n\n return wrap_format.format(_response=_response)\n except:","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.extract_tool_response_for_llm","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message.extract_tool_response_for_llm#L168-L193","kind":"function","name":"extract_tool_response_for_llm","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":168,"end_line":193,"context_start_line":148,"context_end_line":205,"code":" def extract_action_for_llm(cls, text, max_token: int = 500) -> str:\n \"\"\"Since Action should be fully inputted into an Agent, so we do not perform truncation here.\"\"\"\n action_format = ACTION_FORMAT\n cleaned_output = text.strip()\n try:\n _action = cls._extract_value(cleaned_output, \"action\")\n _action_input = cls._extract_value(cleaned_output, \"action_input\")\n return action_format.format(_action=_action, _action_input=_action_input)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n _action = lines[1].strip()\n _action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return action_format.format(_action=_action, _action_input=_action_input)\n else:\n _action_input = cleaned_output\n\n return action_format.format(_action=\"Final Answer\", _action_input=_action_input)\n\n @classmethod\n def extract_tool_response_for_llm(cls, text, tool_style: str = \"code\", max_token: int = 250) -> str:\n wrap_format = TOOL_RESPONSE_FORMAT\n tool_observation_format = TOOL_FORMAT[tool_style]\n cleaned_output = text.strip()\n if tool_style == \"plugin\":\n max_token = None\n\n try:\n _result = cls.truncate_text(cls._extract_value(cleaned_output, \"result\"), max_token)\n _intermediate_steps = cls.truncate_text(\n cls._extract_value(cleaned_output, \"intermediate_steps\"), max_token\n )\n _intermediate_steps = _intermediate_steps.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _result = _result.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _response = tool_observation_format.format(_intermediate_steps=_intermediate_steps, _result=_result)\n\n return wrap_format.format(_response=_response)\n except:\n if cleaned_output.startswith(\"Final Answer:\"):\n lines = cleaned_output.splitlines()\n _response = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n _response = cls.truncate_text(_response, max_token)\n return wrap_format.format(_response=_response)\n\n _response = cls.truncate_text(cleaned_output, max_token)\n return wrap_format.format(_response=_response)\n\n @classmethod\n def extract_code_for_python_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code\n\n @classmethod\n def extract_code_for_sql_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.extract_code_for_python_tool","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message.extract_code_for_python_tool#L196-L199","kind":"function","name":"extract_code_for_python_tool","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":196,"end_line":199,"context_start_line":176,"context_end_line":205,"code":" _result = cls.truncate_text(cls._extract_value(cleaned_output, \"result\"), max_token)\n _intermediate_steps = cls.truncate_text(\n cls._extract_value(cleaned_output, \"intermediate_steps\"), max_token\n )\n _intermediate_steps = _intermediate_steps.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _result = _result.replace(\"\\\\n\", \"\\n\").strip(\"\\n\")\n _response = tool_observation_format.format(_intermediate_steps=_intermediate_steps, _result=_result)\n\n return wrap_format.format(_response=_response)\n except:\n if cleaned_output.startswith(\"Final Answer:\"):\n lines = cleaned_output.splitlines()\n _response = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n _response = cls.truncate_text(_response, max_token)\n return wrap_format.format(_response=_response)\n\n _response = cls.truncate_text(cleaned_output, max_token)\n return wrap_format.format(_response=_response)\n\n @classmethod\n def extract_code_for_python_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code\n\n @classmethod\n def extract_code_for_sql_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.message.extract_code_for_sql_tool","uri":"program://OpenAgents/function/real_agents.adapters.data_model.message.extract_code_for_sql_tool#L202-L205","kind":"function","name":"extract_code_for_sql_tool","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":202,"end_line":205,"context_start_line":182,"context_end_line":205,"code":" _response = tool_observation_format.format(_intermediate_steps=_intermediate_steps, _result=_result)\n\n return wrap_format.format(_response=_response)\n except:\n if cleaned_output.startswith(\"Final Answer:\"):\n lines = cleaned_output.splitlines()\n _response = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n _response = cls.truncate_text(_response, max_token)\n return wrap_format.format(_response=_response)\n\n _response = cls.truncate_text(cleaned_output, max_token)\n return wrap_format.format(_response=_response)\n\n @classmethod\n def extract_code_for_python_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code\n\n @classmethod\n def extract_code_for_sql_tool(cls, text: str, max_token: int = 2500, trunc_ratio: float = 0.2) -> str:\n whole_code = MessageDataModel._extract_response(text)\n trunc_code = cls.truncate_text(whole_code, max_token=max_token, trunc_ratio=trunc_ratio)\n return trunc_code","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base","uri":"program://OpenAgents/module/real_agents.adapters.data_model.base#L1-L42","kind":"module","name":"real_agents.adapters.data_model.base","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":1,"end_line":42,"context_start_line":1,"context_end_line":42,"code":"from __future__ import annotations\n\nimport uuid\nfrom typing import Any\n\nfrom pydantic import BaseModel\n\n\nclass DataModel(BaseModel):\n \"\"\"Base class for data models.\"\"\"\n\n id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.DataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.base.DataModel#L9-L42","kind":"class","name":"DataModel","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":9,"end_line":42,"context_start_line":1,"context_end_line":42,"code":"from __future__ import annotations\n\nimport uuid\nfrom typing import Any\n\nfrom pydantic import BaseModel\n\n\nclass DataModel(BaseModel):\n \"\"\"Base class for data models.\"\"\"\n\n id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.__hash__","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.__hash__#L19-L20","kind":"function","name":"__hash__","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":19,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"from __future__ import annotations\n\nimport uuid\nfrom typing import Any\n\nfrom pydantic import BaseModel\n\n\nclass DataModel(BaseModel):\n \"\"\"Base class for data models.\"\"\"\n\n id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.from_raw_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.from_raw_data#L23-L27","kind":"function","name":"from_raw_data","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":23,"end_line":27,"context_start_line":3,"context_end_line":42,"code":"import uuid\nfrom typing import Any\n\nfrom pydantic import BaseModel\n\n\nclass DataModel(BaseModel):\n \"\"\"Base class for data models.\"\"\"\n\n id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.get_id","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.get_id#L29-L30","kind":"function","name":"get_id","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":29,"end_line":30,"context_start_line":9,"context_end_line":42,"code":"class DataModel(BaseModel):\n \"\"\"Base class for data models.\"\"\"\n\n id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.get_raw_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.get_raw_data#L32-L33","kind":"function","name":"get_raw_data","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":32,"end_line":33,"context_start_line":12,"context_end_line":42,"code":" id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.get_llm_side_data#L35-L36","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":35,"end_line":36,"context_start_line":15,"context_end_line":42,"code":" raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.get_human_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.get_human_side_data#L38-L39","kind":"function","name":"get_human_side_data","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":38,"end_line":39,"context_start_line":18,"context_end_line":42,"code":"\n def __hash__(self) -> int:\n return hash(self.id)\n\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.base.__str__","uri":"program://OpenAgents/function/real_agents.adapters.data_model.base.__str__#L41-L42","kind":"function","name":"__str__","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":41,"end_line":42,"context_start_line":21,"context_end_line":42,"code":"\n @classmethod\n def from_raw_data(\n cls, raw_data: Any, raw_data_name: str = \"\", raw_data_path: str = \"\", **kwargs: Any\n ) -> DataModel:\n uid = str(uuid.uuid4())\n return cls(id=uid, raw_data=raw_data, raw_data_name=raw_data_name, raw_data_path=raw_data_path, **kwargs)\n\n def get_id(self) -> str:\n return self.id\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n return self.raw_data\n\n def get_human_side_data(self) -> Any:\n return self.raw_data\n\n def __str__(self) -> str:\n return self.get_llm_side_data()","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html","uri":"program://OpenAgents/module/real_agents.adapters.data_model.html#L1-L186","kind":"module","name":"real_agents.adapters.data_model.html","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":1,"end_line":186,"context_start_line":1,"context_end_line":186,"code":"import json\nfrom bs4 import BeautifulSoup\nfrom collections import defaultdict\nfrom typing import Any, Dict, List, Union\nfrom real_agents.adapters.data_model.base import DataModel\nimport requests\nimport re\nimport tiktoken\n\nJsonNode = Dict[str, Union[str, List[Any], int]]\nPossibleTemplate = Dict[str, Union[str, List[Any], int]]\nOptimizedTemplate = Dict[str, Union[str, List[Any], int, set]]\nPossibleTemplates = Dict[str, PossibleTemplate]\n\n\ndef find_potential_templates(node, possible_templates):\n \"\"\"Find all potential templates in the HTML tree.\"\"\"\n if node.name: # Element node\n attributes = {attr: node[attr] for attr in node.attrs}\n children = []\n for child in node.children:\n child_json = find_potential_templates(child, possible_templates)\n if child_json:\n children.append(child_json)\n\n # Max depth of the tree\n depth = max([c[\"depth\"] for c in children], default=0) + 1\n\n # Create a template hash\n template_hash = f\"{node.name}#{sorted(attributes.keys())}#{[c['template_hash'] for c in children]}\"\n\n # Gather template values\n template_values = list(attributes.values()) + [val for c in children for val in c[\"template_values\"]]\n\n json_node = {\n \"type\": \"ELEMENT\",\n \"tag_name\": node.name,\n \"attributes\": attributes,\n \"children\": children,\n \"template_hash\": template_hash,\n \"template_values\": template_values,\n \"depth\": depth,\n }\n\n # Add node to possible templates\n if template_hash in possible_templates:\n if possible_templates[template_hash][0][\"depth\"] != depth:\n raise ValueError(f\"Template depth mismatch for template {template_hash}\")\n possible_templates[template_hash].append(json_node)\n else:\n possible_templates[template_hash] = [json_node]\n\n return json_node\n elif isinstance(node, str): # Text node\n text = node.strip()\n if text:\n return {\"type\": \"TEXT\", \"content\": text, \"template_hash\": \"TEXT\", \"template_values\": [text], \"depth\": 0}\n return None\n\n\ndef optimize_template(template):\n \"\"\"Check and adjust the template in possible_templates to optimize style.\"\"\"\n values_to_inline = {\n i\n for i in range(len(template[\"nodes\"][0][\"templateValues\"]))\n if all(n[\"templateValues\"][i] == template[\"nodes\"][0][\"templateValues\"][i] for n in template[\"nodes\"])\n }\n return {**template, \"valuesToInline\": values_to_inline}\n\n\ndef is_string_a_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n\n\ndef get_placeholder(template, value_index):\n \"\"\"Get the placeholder for the value at the given index in the template.\"\"\"\n placeholder_index = value_index + 1 - len([i for i in template[\"valuesToInline\"] if i < value_index])\n return f\"${placeholder_index}\"\n\n\ndef create_template_tree(node, templates, render_for_template, current_value_index=0):\n \"\"\"Convert the DOM into processed template tree.\"\"\"\n if node[\"type\"] == \"TEXT\":\n if current_value_index in render_for_template[\"valuesToInline\"]:\n return {\n \"template\": node[\"content\"],\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n else:\n return {\n \"template\": get_placeholder(render_for_template, current_value_index),\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n\n else:\n updated_value_index = current_value_index\n consumed_templates = [node[\"templateHash\"]]\n\n attrs = \"\".join(\n [\n f' {k}=\"{v}\"'\n if updated_value_index + i in render_for_template[\"valuesToInline\"]\n else f\" {k}={get_placeholder(render_for_template, updated_value_index + i)}\"\n for i, (k, v) in enumerate(node[\"attributes\"].items())\n ]\n )\n updated_value_index += len(node[\"attributes\"])\n\n children = []\n for child in node[\"children\"]:\n child_template = create_template_tree(child, templates, render_for_template, updated_value_index)\n children.append(child_template[\"template\"])\n updated_value_index = child_template[\"valueIndex\"]\n consumed_templates.extend(child_template[\"consumedTemplates\"])\n\n return {\n \"template\": f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{''.join(children)}\",\n \"valueIndex\": updated_value_index,\n \"consumedTemplates\": consumed_templates,\n }\n\n\ndef serialize_tree(node, templates):\n \"\"\"Serialize the template tree into HTML string.\"\"\"\n if node[\"type\"] == \"TEXT\":\n return node[\"content\"]\n elif node[\"templateHash\"] in templates:\n template = templates[node[\"templateHash\"]]\n return f\"{{T{template['label']}({','.join([str(v) if is_string_a_number(v) else json.dumps(v) for i, v in enumerate(node['templateValues']) if i not in template['valuesToInline']])})}}\"\n else:\n attrs = \"\".join([f' {k}=\"{v}\"' for k, v in node[\"attributes\"].items()])\n children = \"\".join([serialize_tree(c, templates) for c in node[\"children\"]])\n return (\n f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{children}\"\n )\n\n\ndef truncate_html_by_tokens(html_string, max_tokens, model_name, num_tags_to_remove_each_time=10):\n tokens_count = count_tokens(html_string, model_name)\n num_tags_to_remove_each_time = round(tokens_count / 500)\n soup = BeautifulSoup(html_string, \"html.parser\")\n # Remove all iframe tags\n html_string = remove_iframes(html_string)\n while tokens_count > max_tokens:\n tags = soup.find_all(True) # find all tags\n # remove the last N tags\n for tag in tags[-num_tags_to_remove_each_time:]:\n tag.decompose()\n\n html_string = str(soup)\n\n # re-count the tokens\n tokens_count = count_tokens(html_string, model_name)\n\n return html_string\n\n\n# hacky way\ndef remove_iframes(html_string):\n # Remove all iframe tags using regex\n return re.sub(\"\", \"\", html_string, flags=re.DOTALL)\n\n\n# if you wanna change encoding schema, refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\ndef count_tokens(text, model_name):\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n return len(encoding.encode(text))\n\n\nclass HTMLDataModel(DataModel):\n \"\"\"A data model for HTML, for webot purpose.\"\"\"\n\n def get_llm_side_data(self) -> str:\n html_string = self.raw_data\n truncated_html_string = truncate_html_by_tokens(html_string, 5000, \"gpt-4\")\n return truncated_html_string","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.find_potential_templates","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.find_potential_templates#L16-L58","kind":"function","name":"find_potential_templates","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":16,"end_line":58,"context_start_line":1,"context_end_line":78,"code":"import json\nfrom bs4 import BeautifulSoup\nfrom collections import defaultdict\nfrom typing import Any, Dict, List, Union\nfrom real_agents.adapters.data_model.base import DataModel\nimport requests\nimport re\nimport tiktoken\n\nJsonNode = Dict[str, Union[str, List[Any], int]]\nPossibleTemplate = Dict[str, Union[str, List[Any], int]]\nOptimizedTemplate = Dict[str, Union[str, List[Any], int, set]]\nPossibleTemplates = Dict[str, PossibleTemplate]\n\n\ndef find_potential_templates(node, possible_templates):\n \"\"\"Find all potential templates in the HTML tree.\"\"\"\n if node.name: # Element node\n attributes = {attr: node[attr] for attr in node.attrs}\n children = []\n for child in node.children:\n child_json = find_potential_templates(child, possible_templates)\n if child_json:\n children.append(child_json)\n\n # Max depth of the tree\n depth = max([c[\"depth\"] for c in children], default=0) + 1\n\n # Create a template hash\n template_hash = f\"{node.name}#{sorted(attributes.keys())}#{[c['template_hash'] for c in children]}\"\n\n # Gather template values\n template_values = list(attributes.values()) + [val for c in children for val in c[\"template_values\"]]\n\n json_node = {\n \"type\": \"ELEMENT\",\n \"tag_name\": node.name,\n \"attributes\": attributes,\n \"children\": children,\n \"template_hash\": template_hash,\n \"template_values\": template_values,\n \"depth\": depth,\n }\n\n # Add node to possible templates\n if template_hash in possible_templates:\n if possible_templates[template_hash][0][\"depth\"] != depth:\n raise ValueError(f\"Template depth mismatch for template {template_hash}\")\n possible_templates[template_hash].append(json_node)\n else:\n possible_templates[template_hash] = [json_node]\n\n return json_node\n elif isinstance(node, str): # Text node\n text = node.strip()\n if text:\n return {\"type\": \"TEXT\", \"content\": text, \"template_hash\": \"TEXT\", \"template_values\": [text], \"depth\": 0}\n return None\n\n\ndef optimize_template(template):\n \"\"\"Check and adjust the template in possible_templates to optimize style.\"\"\"\n values_to_inline = {\n i\n for i in range(len(template[\"nodes\"][0][\"templateValues\"]))\n if all(n[\"templateValues\"][i] == template[\"nodes\"][0][\"templateValues\"][i] for n in template[\"nodes\"])\n }\n return {**template, \"valuesToInline\": values_to_inline}\n\n\ndef is_string_a_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n\n","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.optimize_template","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.optimize_template#L61-L68","kind":"function","name":"optimize_template","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":61,"end_line":68,"context_start_line":41,"context_end_line":88,"code":" \"template_values\": template_values,\n \"depth\": depth,\n }\n\n # Add node to possible templates\n if template_hash in possible_templates:\n if possible_templates[template_hash][0][\"depth\"] != depth:\n raise ValueError(f\"Template depth mismatch for template {template_hash}\")\n possible_templates[template_hash].append(json_node)\n else:\n possible_templates[template_hash] = [json_node]\n\n return json_node\n elif isinstance(node, str): # Text node\n text = node.strip()\n if text:\n return {\"type\": \"TEXT\", \"content\": text, \"template_hash\": \"TEXT\", \"template_values\": [text], \"depth\": 0}\n return None\n\n\ndef optimize_template(template):\n \"\"\"Check and adjust the template in possible_templates to optimize style.\"\"\"\n values_to_inline = {\n i\n for i in range(len(template[\"nodes\"][0][\"templateValues\"]))\n if all(n[\"templateValues\"][i] == template[\"nodes\"][0][\"templateValues\"][i] for n in template[\"nodes\"])\n }\n return {**template, \"valuesToInline\": values_to_inline}\n\n\ndef is_string_a_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n\n\ndef get_placeholder(template, value_index):\n \"\"\"Get the placeholder for the value at the given index in the template.\"\"\"\n placeholder_index = value_index + 1 - len([i for i in template[\"valuesToInline\"] if i < value_index])\n return f\"${placeholder_index}\"\n\n\ndef create_template_tree(node, templates, render_for_template, current_value_index=0):\n \"\"\"Convert the DOM into processed template tree.\"\"\"\n if node[\"type\"] == \"TEXT\":\n if current_value_index in render_for_template[\"valuesToInline\"]:","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.is_string_a_number","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.is_string_a_number#L71-L76","kind":"function","name":"is_string_a_number","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":71,"end_line":76,"context_start_line":51,"context_end_line":96,"code":" possible_templates[template_hash] = [json_node]\n\n return json_node\n elif isinstance(node, str): # Text node\n text = node.strip()\n if text:\n return {\"type\": \"TEXT\", \"content\": text, \"template_hash\": \"TEXT\", \"template_values\": [text], \"depth\": 0}\n return None\n\n\ndef optimize_template(template):\n \"\"\"Check and adjust the template in possible_templates to optimize style.\"\"\"\n values_to_inline = {\n i\n for i in range(len(template[\"nodes\"][0][\"templateValues\"]))\n if all(n[\"templateValues\"][i] == template[\"nodes\"][0][\"templateValues\"][i] for n in template[\"nodes\"])\n }\n return {**template, \"valuesToInline\": values_to_inline}\n\n\ndef is_string_a_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n\n\ndef get_placeholder(template, value_index):\n \"\"\"Get the placeholder for the value at the given index in the template.\"\"\"\n placeholder_index = value_index + 1 - len([i for i in template[\"valuesToInline\"] if i < value_index])\n return f\"${placeholder_index}\"\n\n\ndef create_template_tree(node, templates, render_for_template, current_value_index=0):\n \"\"\"Convert the DOM into processed template tree.\"\"\"\n if node[\"type\"] == \"TEXT\":\n if current_value_index in render_for_template[\"valuesToInline\"]:\n return {\n \"template\": node[\"content\"],\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n else:\n return {\n \"template\": get_placeholder(render_for_template, current_value_index),","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.get_placeholder","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.get_placeholder#L79-L82","kind":"function","name":"get_placeholder","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":79,"end_line":82,"context_start_line":59,"context_end_line":102,"code":"\n\ndef optimize_template(template):\n \"\"\"Check and adjust the template in possible_templates to optimize style.\"\"\"\n values_to_inline = {\n i\n for i in range(len(template[\"nodes\"][0][\"templateValues\"]))\n if all(n[\"templateValues\"][i] == template[\"nodes\"][0][\"templateValues\"][i] for n in template[\"nodes\"])\n }\n return {**template, \"valuesToInline\": values_to_inline}\n\n\ndef is_string_a_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n\n\ndef get_placeholder(template, value_index):\n \"\"\"Get the placeholder for the value at the given index in the template.\"\"\"\n placeholder_index = value_index + 1 - len([i for i in template[\"valuesToInline\"] if i < value_index])\n return f\"${placeholder_index}\"\n\n\ndef create_template_tree(node, templates, render_for_template, current_value_index=0):\n \"\"\"Convert the DOM into processed template tree.\"\"\"\n if node[\"type\"] == \"TEXT\":\n if current_value_index in render_for_template[\"valuesToInline\"]:\n return {\n \"template\": node[\"content\"],\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n else:\n return {\n \"template\": get_placeholder(render_for_template, current_value_index),\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n\n else:\n updated_value_index = current_value_index","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.create_template_tree","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.create_template_tree#L85-L128","kind":"function","name":"create_template_tree","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":85,"end_line":128,"context_start_line":65,"context_end_line":148,"code":" for i in range(len(template[\"nodes\"][0][\"templateValues\"]))\n if all(n[\"templateValues\"][i] == template[\"nodes\"][0][\"templateValues\"][i] for n in template[\"nodes\"])\n }\n return {**template, \"valuesToInline\": values_to_inline}\n\n\ndef is_string_a_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False\n\n\ndef get_placeholder(template, value_index):\n \"\"\"Get the placeholder for the value at the given index in the template.\"\"\"\n placeholder_index = value_index + 1 - len([i for i in template[\"valuesToInline\"] if i < value_index])\n return f\"${placeholder_index}\"\n\n\ndef create_template_tree(node, templates, render_for_template, current_value_index=0):\n \"\"\"Convert the DOM into processed template tree.\"\"\"\n if node[\"type\"] == \"TEXT\":\n if current_value_index in render_for_template[\"valuesToInline\"]:\n return {\n \"template\": node[\"content\"],\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n else:\n return {\n \"template\": get_placeholder(render_for_template, current_value_index),\n \"valueIndex\": current_value_index + 1,\n \"consumedTemplates\": [node[\"templateHash\"]],\n }\n\n else:\n updated_value_index = current_value_index\n consumed_templates = [node[\"templateHash\"]]\n\n attrs = \"\".join(\n [\n f' {k}=\"{v}\"'\n if updated_value_index + i in render_for_template[\"valuesToInline\"]\n else f\" {k}={get_placeholder(render_for_template, updated_value_index + i)}\"\n for i, (k, v) in enumerate(node[\"attributes\"].items())\n ]\n )\n updated_value_index += len(node[\"attributes\"])\n\n children = []\n for child in node[\"children\"]:\n child_template = create_template_tree(child, templates, render_for_template, updated_value_index)\n children.append(child_template[\"template\"])\n updated_value_index = child_template[\"valueIndex\"]\n consumed_templates.extend(child_template[\"consumedTemplates\"])\n\n return {\n \"template\": f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{''.join(children)}\",\n \"valueIndex\": updated_value_index,\n \"consumedTemplates\": consumed_templates,\n }\n\n\ndef serialize_tree(node, templates):\n \"\"\"Serialize the template tree into HTML string.\"\"\"\n if node[\"type\"] == \"TEXT\":\n return node[\"content\"]\n elif node[\"templateHash\"] in templates:\n template = templates[node[\"templateHash\"]]\n return f\"{{T{template['label']}({','.join([str(v) if is_string_a_number(v) else json.dumps(v) for i, v in enumerate(node['templateValues']) if i not in template['valuesToInline']])})}}\"\n else:\n attrs = \"\".join([f' {k}=\"{v}\"' for k, v in node[\"attributes\"].items()])\n children = \"\".join([serialize_tree(c, templates) for c in node[\"children\"]])\n return (\n f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{children}\"\n )\n\n\ndef truncate_html_by_tokens(html_string, max_tokens, model_name, num_tags_to_remove_each_time=10):","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.serialize_tree","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.serialize_tree#L131-L145","kind":"function","name":"serialize_tree","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":131,"end_line":145,"context_start_line":111,"context_end_line":165,"code":" ]\n )\n updated_value_index += len(node[\"attributes\"])\n\n children = []\n for child in node[\"children\"]:\n child_template = create_template_tree(child, templates, render_for_template, updated_value_index)\n children.append(child_template[\"template\"])\n updated_value_index = child_template[\"valueIndex\"]\n consumed_templates.extend(child_template[\"consumedTemplates\"])\n\n return {\n \"template\": f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{''.join(children)}\",\n \"valueIndex\": updated_value_index,\n \"consumedTemplates\": consumed_templates,\n }\n\n\ndef serialize_tree(node, templates):\n \"\"\"Serialize the template tree into HTML string.\"\"\"\n if node[\"type\"] == \"TEXT\":\n return node[\"content\"]\n elif node[\"templateHash\"] in templates:\n template = templates[node[\"templateHash\"]]\n return f\"{{T{template['label']}({','.join([str(v) if is_string_a_number(v) else json.dumps(v) for i, v in enumerate(node['templateValues']) if i not in template['valuesToInline']])})}}\"\n else:\n attrs = \"\".join([f' {k}=\"{v}\"' for k, v in node[\"attributes\"].items()])\n children = \"\".join([serialize_tree(c, templates) for c in node[\"children\"]])\n return (\n f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{children}\"\n )\n\n\ndef truncate_html_by_tokens(html_string, max_tokens, model_name, num_tags_to_remove_each_time=10):\n tokens_count = count_tokens(html_string, model_name)\n num_tags_to_remove_each_time = round(tokens_count / 500)\n soup = BeautifulSoup(html_string, \"html.parser\")\n # Remove all iframe tags\n html_string = remove_iframes(html_string)\n while tokens_count > max_tokens:\n tags = soup.find_all(True) # find all tags\n # remove the last N tags\n for tag in tags[-num_tags_to_remove_each_time:]:\n tag.decompose()\n\n html_string = str(soup)\n\n # re-count the tokens\n tokens_count = count_tokens(html_string, model_name)\n\n return html_string","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.truncate_html_by_tokens","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.truncate_html_by_tokens#L148-L165","kind":"function","name":"truncate_html_by_tokens","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":148,"end_line":165,"context_start_line":128,"context_end_line":185,"code":" }\n\n\ndef serialize_tree(node, templates):\n \"\"\"Serialize the template tree into HTML string.\"\"\"\n if node[\"type\"] == \"TEXT\":\n return node[\"content\"]\n elif node[\"templateHash\"] in templates:\n template = templates[node[\"templateHash\"]]\n return f\"{{T{template['label']}({','.join([str(v) if is_string_a_number(v) else json.dumps(v) for i, v in enumerate(node['templateValues']) if i not in template['valuesToInline']])})}}\"\n else:\n attrs = \"\".join([f' {k}=\"{v}\"' for k, v in node[\"attributes\"].items()])\n children = \"\".join([serialize_tree(c, templates) for c in node[\"children\"]])\n return (\n f\"<{node['tagName'].lower()}{attrs}/>\"\n if not children\n else f\"<{node['tagName'].lower()}{attrs}>{children}\"\n )\n\n\ndef truncate_html_by_tokens(html_string, max_tokens, model_name, num_tags_to_remove_each_time=10):\n tokens_count = count_tokens(html_string, model_name)\n num_tags_to_remove_each_time = round(tokens_count / 500)\n soup = BeautifulSoup(html_string, \"html.parser\")\n # Remove all iframe tags\n html_string = remove_iframes(html_string)\n while tokens_count > max_tokens:\n tags = soup.find_all(True) # find all tags\n # remove the last N tags\n for tag in tags[-num_tags_to_remove_each_time:]:\n tag.decompose()\n\n html_string = str(soup)\n\n # re-count the tokens\n tokens_count = count_tokens(html_string, model_name)\n\n return html_string\n\n\n# hacky way\ndef remove_iframes(html_string):\n # Remove all iframe tags using regex\n return re.sub(\"\", \"\", html_string, flags=re.DOTALL)\n\n\n# if you wanna change encoding schema, refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\ndef count_tokens(text, model_name):\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n return len(encoding.encode(text))\n\n\nclass HTMLDataModel(DataModel):\n \"\"\"A data model for HTML, for webot purpose.\"\"\"\n\n def get_llm_side_data(self) -> str:\n html_string = self.raw_data\n truncated_html_string = truncate_html_by_tokens(html_string, 5000, \"gpt-4\")","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.remove_iframes","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.remove_iframes#L169-L171","kind":"function","name":"remove_iframes","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":169,"end_line":171,"context_start_line":149,"context_end_line":186,"code":" tokens_count = count_tokens(html_string, model_name)\n num_tags_to_remove_each_time = round(tokens_count / 500)\n soup = BeautifulSoup(html_string, \"html.parser\")\n # Remove all iframe tags\n html_string = remove_iframes(html_string)\n while tokens_count > max_tokens:\n tags = soup.find_all(True) # find all tags\n # remove the last N tags\n for tag in tags[-num_tags_to_remove_each_time:]:\n tag.decompose()\n\n html_string = str(soup)\n\n # re-count the tokens\n tokens_count = count_tokens(html_string, model_name)\n\n return html_string\n\n\n# hacky way\ndef remove_iframes(html_string):\n # Remove all iframe tags using regex\n return re.sub(\"\", \"\", html_string, flags=re.DOTALL)\n\n\n# if you wanna change encoding schema, refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\ndef count_tokens(text, model_name):\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n return len(encoding.encode(text))\n\n\nclass HTMLDataModel(DataModel):\n \"\"\"A data model for HTML, for webot purpose.\"\"\"\n\n def get_llm_side_data(self) -> str:\n html_string = self.raw_data\n truncated_html_string = truncate_html_by_tokens(html_string, 5000, \"gpt-4\")\n return truncated_html_string","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.count_tokens","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.count_tokens#L175-L177","kind":"function","name":"count_tokens","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":175,"end_line":177,"context_start_line":155,"context_end_line":186,"code":" tags = soup.find_all(True) # find all tags\n # remove the last N tags\n for tag in tags[-num_tags_to_remove_each_time:]:\n tag.decompose()\n\n html_string = str(soup)\n\n # re-count the tokens\n tokens_count = count_tokens(html_string, model_name)\n\n return html_string\n\n\n# hacky way\ndef remove_iframes(html_string):\n # Remove all iframe tags using regex\n return re.sub(\"\", \"\", html_string, flags=re.DOTALL)\n\n\n# if you wanna change encoding schema, refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\ndef count_tokens(text, model_name):\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n return len(encoding.encode(text))\n\n\nclass HTMLDataModel(DataModel):\n \"\"\"A data model for HTML, for webot purpose.\"\"\"\n\n def get_llm_side_data(self) -> str:\n html_string = self.raw_data\n truncated_html_string = truncate_html_by_tokens(html_string, 5000, \"gpt-4\")\n return truncated_html_string","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.HTMLDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.html.HTMLDataModel#L180-L186","kind":"class","name":"HTMLDataModel","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":180,"end_line":186,"context_start_line":160,"context_end_line":186,"code":" html_string = str(soup)\n\n # re-count the tokens\n tokens_count = count_tokens(html_string, model_name)\n\n return html_string\n\n\n# hacky way\ndef remove_iframes(html_string):\n # Remove all iframe tags using regex\n return re.sub(\"\", \"\", html_string, flags=re.DOTALL)\n\n\n# if you wanna change encoding schema, refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\ndef count_tokens(text, model_name):\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n return len(encoding.encode(text))\n\n\nclass HTMLDataModel(DataModel):\n \"\"\"A data model for HTML, for webot purpose.\"\"\"\n\n def get_llm_side_data(self) -> str:\n html_string = self.raw_data\n truncated_html_string = truncate_html_by_tokens(html_string, 5000, \"gpt-4\")\n return truncated_html_string","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.html.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.html.get_llm_side_data#L183-L186","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":183,"end_line":186,"context_start_line":163,"context_end_line":186,"code":" tokens_count = count_tokens(html_string, model_name)\n\n return html_string\n\n\n# hacky way\ndef remove_iframes(html_string):\n # Remove all iframe tags using regex\n return re.sub(\"\", \"\", html_string, flags=re.DOTALL)\n\n\n# if you wanna change encoding schema, refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\ndef count_tokens(text, model_name):\n encoding = tiktoken.get_encoding(\"cl100k_base\")\n return len(encoding.encode(text))\n\n\nclass HTMLDataModel(DataModel):\n \"\"\"A data model for HTML, for webot purpose.\"\"\"\n\n def get_llm_side_data(self) -> str:\n html_string = self.raw_data\n truncated_html_string = truncate_html_by_tokens(html_string, 5000, \"gpt-4\")\n return truncated_html_string","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.database","uri":"program://OpenAgents/module/real_agents.adapters.data_model.database#L1-L50","kind":"module","name":"real_agents.adapters.data_model.database","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":1,"end_line":50,"context_start_line":1,"context_end_line":50,"code":"from __future__ import annotations\n\nimport os\nfrom typing import Any\n\nimport pandas as pd\nfrom sqlalchemy import create_engine, inspect\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.table import TableDataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass DatabaseDataModel(DataModel):\n \"\"\"A data model for database.\"\"\"\n\n @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(\n f\".db_cache/{table_data_model.id}\", os.path.splitext(table_data_model.raw_data_name)[0] + \".db\"\n )\n engine = create_engine(f\"sqlite:///{db_path}\")\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine, if_exists=\"replace\")\n db = SQLDatabase(engine)\n return cls.from_raw_data(raw_data=db, raw_data_name=table_data_model.raw_data_name)\n\n def insert_table_data_model(self, table_data_model: TableDataModel) -> None:\n engine = self.raw_data.engine\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine)\n\n def get_llm_side_data(self, serialize_method: str = \"database\", num_visible_rows: int = 3) -> Any:\n db = self.raw_data\n formatted_db = serialize_db(db, serialize_method, num_visible_rows)\n return formatted_db\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n engine = self.raw_data.engine\n inspector = inspect(engine)\n table_names = inspector.get_table_names()\n\n # Loop through each table name, creating a DataFrame from the first three rows of each table\n df_dict = {}\n for table_name in table_names:\n query = f\"SELECT * FROM {table_name} LIMIT 3\"\n df = pd.read_sql(query, engine)\n df_dict[table_name] = df\n return df_dict","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.database.DatabaseDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.database.DatabaseDataModel#L15-L50","kind":"class","name":"DatabaseDataModel","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":15,"end_line":50,"context_start_line":1,"context_end_line":50,"code":"from __future__ import annotations\n\nimport os\nfrom typing import Any\n\nimport pandas as pd\nfrom sqlalchemy import create_engine, inspect\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.table import TableDataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass DatabaseDataModel(DataModel):\n \"\"\"A data model for database.\"\"\"\n\n @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(\n f\".db_cache/{table_data_model.id}\", os.path.splitext(table_data_model.raw_data_name)[0] + \".db\"\n )\n engine = create_engine(f\"sqlite:///{db_path}\")\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine, if_exists=\"replace\")\n db = SQLDatabase(engine)\n return cls.from_raw_data(raw_data=db, raw_data_name=table_data_model.raw_data_name)\n\n def insert_table_data_model(self, table_data_model: TableDataModel) -> None:\n engine = self.raw_data.engine\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine)\n\n def get_llm_side_data(self, serialize_method: str = \"database\", num_visible_rows: int = 3) -> Any:\n db = self.raw_data\n formatted_db = serialize_db(db, serialize_method, num_visible_rows)\n return formatted_db\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n engine = self.raw_data.engine\n inspector = inspect(engine)\n table_names = inspector.get_table_names()\n\n # Loop through each table name, creating a DataFrame from the first three rows of each table\n df_dict = {}\n for table_name in table_names:\n query = f\"SELECT * FROM {table_name} LIMIT 3\"\n df = pd.read_sql(query, engine)\n df_dict[table_name] = df\n return df_dict","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.database.from_table_data_model","uri":"program://OpenAgents/function/real_agents.adapters.data_model.database.from_table_data_model#L19-L27","kind":"function","name":"from_table_data_model","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":19,"end_line":27,"context_start_line":1,"context_end_line":47,"code":"from __future__ import annotations\n\nimport os\nfrom typing import Any\n\nimport pandas as pd\nfrom sqlalchemy import create_engine, inspect\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.table import TableDataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass DatabaseDataModel(DataModel):\n \"\"\"A data model for database.\"\"\"\n\n @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(\n f\".db_cache/{table_data_model.id}\", os.path.splitext(table_data_model.raw_data_name)[0] + \".db\"\n )\n engine = create_engine(f\"sqlite:///{db_path}\")\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine, if_exists=\"replace\")\n db = SQLDatabase(engine)\n return cls.from_raw_data(raw_data=db, raw_data_name=table_data_model.raw_data_name)\n\n def insert_table_data_model(self, table_data_model: TableDataModel) -> None:\n engine = self.raw_data.engine\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine)\n\n def get_llm_side_data(self, serialize_method: str = \"database\", num_visible_rows: int = 3) -> Any:\n db = self.raw_data\n formatted_db = serialize_db(db, serialize_method, num_visible_rows)\n return formatted_db\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n engine = self.raw_data.engine\n inspector = inspect(engine)\n table_names = inspector.get_table_names()\n\n # Loop through each table name, creating a DataFrame from the first three rows of each table\n df_dict = {}\n for table_name in table_names:\n query = f\"SELECT * FROM {table_name} LIMIT 3\"","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.database.insert_table_data_model","uri":"program://OpenAgents/function/real_agents.adapters.data_model.database.insert_table_data_model#L29-L31","kind":"function","name":"insert_table_data_model","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":29,"end_line":31,"context_start_line":9,"context_end_line":50,"code":"from real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.table import TableDataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass DatabaseDataModel(DataModel):\n \"\"\"A data model for database.\"\"\"\n\n @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(\n f\".db_cache/{table_data_model.id}\", os.path.splitext(table_data_model.raw_data_name)[0] + \".db\"\n )\n engine = create_engine(f\"sqlite:///{db_path}\")\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine, if_exists=\"replace\")\n db = SQLDatabase(engine)\n return cls.from_raw_data(raw_data=db, raw_data_name=table_data_model.raw_data_name)\n\n def insert_table_data_model(self, table_data_model: TableDataModel) -> None:\n engine = self.raw_data.engine\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine)\n\n def get_llm_side_data(self, serialize_method: str = \"database\", num_visible_rows: int = 3) -> Any:\n db = self.raw_data\n formatted_db = serialize_db(db, serialize_method, num_visible_rows)\n return formatted_db\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n engine = self.raw_data.engine\n inspector = inspect(engine)\n table_names = inspector.get_table_names()\n\n # Loop through each table name, creating a DataFrame from the first three rows of each table\n df_dict = {}\n for table_name in table_names:\n query = f\"SELECT * FROM {table_name} LIMIT 3\"\n df = pd.read_sql(query, engine)\n df_dict[table_name] = df\n return df_dict","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.database.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.database.get_llm_side_data#L33-L36","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":33,"end_line":36,"context_start_line":13,"context_end_line":50,"code":"\n\nclass DatabaseDataModel(DataModel):\n \"\"\"A data model for database.\"\"\"\n\n @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(\n f\".db_cache/{table_data_model.id}\", os.path.splitext(table_data_model.raw_data_name)[0] + \".db\"\n )\n engine = create_engine(f\"sqlite:///{db_path}\")\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine, if_exists=\"replace\")\n db = SQLDatabase(engine)\n return cls.from_raw_data(raw_data=db, raw_data_name=table_data_model.raw_data_name)\n\n def insert_table_data_model(self, table_data_model: TableDataModel) -> None:\n engine = self.raw_data.engine\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine)\n\n def get_llm_side_data(self, serialize_method: str = \"database\", num_visible_rows: int = 3) -> Any:\n db = self.raw_data\n formatted_db = serialize_db(db, serialize_method, num_visible_rows)\n return formatted_db\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n engine = self.raw_data.engine\n inspector = inspect(engine)\n table_names = inspector.get_table_names()\n\n # Loop through each table name, creating a DataFrame from the first three rows of each table\n df_dict = {}\n for table_name in table_names:\n query = f\"SELECT * FROM {table_name} LIMIT 3\"\n df = pd.read_sql(query, engine)\n df_dict[table_name] = df\n return df_dict","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.database.get_human_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.database.get_human_side_data#L38-L50","kind":"function","name":"get_human_side_data","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":38,"end_line":50,"context_start_line":18,"context_end_line":50,"code":" @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(\n f\".db_cache/{table_data_model.id}\", os.path.splitext(table_data_model.raw_data_name)[0] + \".db\"\n )\n engine = create_engine(f\"sqlite:///{db_path}\")\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine, if_exists=\"replace\")\n db = SQLDatabase(engine)\n return cls.from_raw_data(raw_data=db, raw_data_name=table_data_model.raw_data_name)\n\n def insert_table_data_model(self, table_data_model: TableDataModel) -> None:\n engine = self.raw_data.engine\n table_data_model.raw_data.to_sql(table_data_model.raw_data_name, engine)\n\n def get_llm_side_data(self, serialize_method: str = \"database\", num_visible_rows: int = 3) -> Any:\n db = self.raw_data\n formatted_db = serialize_db(db, serialize_method, num_visible_rows)\n return formatted_db\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n engine = self.raw_data.engine\n inspector = inspect(engine)\n table_names = inspector.get_table_names()\n\n # Loop through each table name, creating a DataFrame from the first three rows of each table\n df_dict = {}\n for table_name in table_names:\n query = f\"SELECT * FROM {table_name} LIMIT 3\"\n df = pd.read_sql(query, engine)\n df_dict[table_name] = df\n return df_dict","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.utils","uri":"program://OpenAgents/module/real_agents.adapters.data_model.utils#L1-L2","kind":"module","name":"real_agents.adapters.data_model.utils","path":"real_agents/adapters/data_model/utils.py","language":"python","start_line":1,"end_line":2,"context_start_line":1,"context_end_line":2,"code":"def indent_multiline_string(multiline_string: str, indent: int = 1) -> str:\n return \"\\n\".join(\"\\t\" * indent + line for line in multiline_string.split(\"\\n\"))","source_hash":"d5702b5bcd10e6b7985d17b0e0a913e4e52ed5045ea93994c5ba748f7ea0a3f1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.utils.indent_multiline_string","uri":"program://OpenAgents/function/real_agents.adapters.data_model.utils.indent_multiline_string#L1-L2","kind":"function","name":"indent_multiline_string","path":"real_agents/adapters/data_model/utils.py","language":"python","start_line":1,"end_line":2,"context_start_line":1,"context_end_line":2,"code":"def indent_multiline_string(multiline_string: str, indent: int = 1) -> str:\n return \"\\n\".join(\"\\t\" * indent + line for line in multiline_string.split(\"\\n\"))","source_hash":"d5702b5bcd10e6b7985d17b0e0a913e4e52ed5045ea93994c5ba748f7ea0a3f1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.text","uri":"program://OpenAgents/module/real_agents.adapters.data_model.text#L1-L9","kind":"module","name":"real_agents.adapters.data_model.text","path":"real_agents/adapters/data_model/text.py","language":"python","start_line":1,"end_line":9,"context_start_line":1,"context_end_line":9,"code":"from real_agents.adapters.data_model.base import DataModel\n\n\nclass TextDataModel(DataModel):\n \"\"\"A data model for text, general purpose.\"\"\"\n\n def get_llm_side_data(self, max_chars: int = 5000) -> str:\n assert isinstance(self.raw_data, str)\n return self.raw_data[:max_chars]","source_hash":"54af962e1cd4a83217ea02a2068c3e45c3b1d9e54e6993bd5e8e4743e93015d1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.text.TextDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.text.TextDataModel#L4-L9","kind":"class","name":"TextDataModel","path":"real_agents/adapters/data_model/text.py","language":"python","start_line":4,"end_line":9,"context_start_line":1,"context_end_line":9,"code":"from real_agents.adapters.data_model.base import DataModel\n\n\nclass TextDataModel(DataModel):\n \"\"\"A data model for text, general purpose.\"\"\"\n\n def get_llm_side_data(self, max_chars: int = 5000) -> str:\n assert isinstance(self.raw_data, str)\n return self.raw_data[:max_chars]","source_hash":"54af962e1cd4a83217ea02a2068c3e45c3b1d9e54e6993bd5e8e4743e93015d1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.text.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.text.get_llm_side_data#L7-L9","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/text.py","language":"python","start_line":7,"end_line":9,"context_start_line":1,"context_end_line":9,"code":"from real_agents.adapters.data_model.base import DataModel\n\n\nclass TextDataModel(DataModel):\n \"\"\"A data model for text, general purpose.\"\"\"\n\n def get_llm_side_data(self, max_chars: int = 5000) -> str:\n assert isinstance(self.raw_data, str)\n return self.raw_data[:max_chars]","source_hash":"54af962e1cd4a83217ea02a2068c3e45c3b1d9e54e6993bd5e8e4743e93015d1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.table","uri":"program://OpenAgents/module/real_agents.adapters.data_model.table#L1-L44","kind":"module","name":"real_agents.adapters.data_model.table","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":1,"end_line":44,"context_start_line":1,"context_end_line":44,"code":"from __future__ import annotations\n\nimport json\nfrom typing import Any\n\nfrom pandas import DataFrame\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\n\n\nclass TableDataModel(DataModel):\n \"\"\"A data model for table.\"\"\"\n\n db_view: DataModel = None\n\n def set_db_view(self, db_data_model: DataModel) -> None:\n self.db_view = db_data_model\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.\n table_data = self.raw_data\n table_name = self.raw_data_name\n table_path = self.raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n return formatted_table\n\n def get_human_side_data(self, mode: str = \"HEAD\") -> Any:\n # We support different mode for the front-end display.\n # For `HEAD` mode, we show the first few rows for observation.\n if mode == \"HEAD\":\n return self.raw_data.head()\n elif mode == \"FULL\":\n return self.raw_data\n else:\n raise ValueError(f\"Unsupported mode: {mode}\")\n\n @staticmethod\n def to_react_table(table: DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.table.TableDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.table.TableDataModel#L12-L44","kind":"class","name":"TableDataModel","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":12,"end_line":44,"context_start_line":1,"context_end_line":44,"code":"from __future__ import annotations\n\nimport json\nfrom typing import Any\n\nfrom pandas import DataFrame\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\n\n\nclass TableDataModel(DataModel):\n \"\"\"A data model for table.\"\"\"\n\n db_view: DataModel = None\n\n def set_db_view(self, db_data_model: DataModel) -> None:\n self.db_view = db_data_model\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.\n table_data = self.raw_data\n table_name = self.raw_data_name\n table_path = self.raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n return formatted_table\n\n def get_human_side_data(self, mode: str = \"HEAD\") -> Any:\n # We support different mode for the front-end display.\n # For `HEAD` mode, we show the first few rows for observation.\n if mode == \"HEAD\":\n return self.raw_data.head()\n elif mode == \"FULL\":\n return self.raw_data\n else:\n raise ValueError(f\"Unsupported mode: {mode}\")\n\n @staticmethod\n def to_react_table(table: DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.table.set_db_view","uri":"program://OpenAgents/function/real_agents.adapters.data_model.table.set_db_view#L17-L18","kind":"function","name":"set_db_view","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":17,"end_line":18,"context_start_line":1,"context_end_line":38,"code":"from __future__ import annotations\n\nimport json\nfrom typing import Any\n\nfrom pandas import DataFrame\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\n\n\nclass TableDataModel(DataModel):\n \"\"\"A data model for table.\"\"\"\n\n db_view: DataModel = None\n\n def set_db_view(self, db_data_model: DataModel) -> None:\n self.db_view = db_data_model\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.\n table_data = self.raw_data\n table_name = self.raw_data_name\n table_path = self.raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n return formatted_table\n\n def get_human_side_data(self, mode: str = \"HEAD\") -> Any:\n # We support different mode for the front-end display.\n # For `HEAD` mode, we show the first few rows for observation.\n if mode == \"HEAD\":\n return self.raw_data.head()\n elif mode == \"FULL\":\n return self.raw_data\n else:\n raise ValueError(f\"Unsupported mode: {mode}\")\n\n @staticmethod","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.table.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.table.get_llm_side_data#L20-L26","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":20,"end_line":26,"context_start_line":1,"context_end_line":44,"code":"from __future__ import annotations\n\nimport json\nfrom typing import Any\n\nfrom pandas import DataFrame\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\n\n\nclass TableDataModel(DataModel):\n \"\"\"A data model for table.\"\"\"\n\n db_view: DataModel = None\n\n def set_db_view(self, db_data_model: DataModel) -> None:\n self.db_view = db_data_model\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.\n table_data = self.raw_data\n table_name = self.raw_data_name\n table_path = self.raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n return formatted_table\n\n def get_human_side_data(self, mode: str = \"HEAD\") -> Any:\n # We support different mode for the front-end display.\n # For `HEAD` mode, we show the first few rows for observation.\n if mode == \"HEAD\":\n return self.raw_data.head()\n elif mode == \"FULL\":\n return self.raw_data\n else:\n raise ValueError(f\"Unsupported mode: {mode}\")\n\n @staticmethod\n def to_react_table(table: DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.table.get_human_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.table.get_human_side_data#L28-L36","kind":"function","name":"get_human_side_data","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":28,"end_line":36,"context_start_line":8,"context_end_line":44,"code":"from real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\n\n\nclass TableDataModel(DataModel):\n \"\"\"A data model for table.\"\"\"\n\n db_view: DataModel = None\n\n def set_db_view(self, db_data_model: DataModel) -> None:\n self.db_view = db_data_model\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.\n table_data = self.raw_data\n table_name = self.raw_data_name\n table_path = self.raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n return formatted_table\n\n def get_human_side_data(self, mode: str = \"HEAD\") -> Any:\n # We support different mode for the front-end display.\n # For `HEAD` mode, we show the first few rows for observation.\n if mode == \"HEAD\":\n return self.raw_data.head()\n elif mode == \"FULL\":\n return self.raw_data\n else:\n raise ValueError(f\"Unsupported mode: {mode}\")\n\n @staticmethod\n def to_react_table(table: DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.table.to_react_table","uri":"program://OpenAgents/function/real_agents.adapters.data_model.table.to_react_table#L39-L44","kind":"function","name":"to_react_table","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":39,"end_line":44,"context_start_line":19,"context_end_line":44,"code":"\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.\n table_data = self.raw_data\n table_name = self.raw_data_name\n table_path = self.raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n return formatted_table\n\n def get_human_side_data(self, mode: str = \"HEAD\") -> Any:\n # We support different mode for the front-end display.\n # For `HEAD` mode, we show the first few rows for observation.\n if mode == \"HEAD\":\n return self.raw_data.head()\n elif mode == \"FULL\":\n return self.raw_data\n else:\n raise ValueError(f\"Unsupported mode: {mode}\")\n\n @staticmethod\n def to_react_table(table: DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.json","uri":"program://OpenAgents/module/real_agents.adapters.data_model.json#L1-L29","kind":"module","name":"real_agents.adapters.data_model.json","path":"real_agents/adapters/data_model/json.py","language":"python","start_line":1,"end_line":29,"context_start_line":1,"context_end_line":29,"code":"import json\nfrom copy import deepcopy\nfrom typing import Dict, List\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass JsonDataModel(DataModel):\n \"\"\"A data model for json, general purpose.\"\"\"\n\n filter_keys: List[str] = []\n\n def get_llm_side_data(self, json_format: str = \"json\") -> str:\n if json_format == \"json\":\n assert isinstance(self.raw_data, Dict)\n llm_side_data = deepcopy(self.raw_data)\n for key, value in self.raw_data.items():\n if key in self.filter_keys:\n llm_side_data[key] = \"...\"\n continue\n\n if isinstance(value, DataModel):\n llm_side_data[key] = value.get_llm_side_data()\n else:\n llm_side_data[key] = str(value)\n\n return json.dumps(llm_side_data, indent=4)\n else:\n raise NotImplementedError","source_hash":"9637032777ecb623c69f596505ff900c488dd0ed03c88c959d3548e31842a256","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.json.JsonDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.json.JsonDataModel#L8-L29","kind":"class","name":"JsonDataModel","path":"real_agents/adapters/data_model/json.py","language":"python","start_line":8,"end_line":29,"context_start_line":1,"context_end_line":29,"code":"import json\nfrom copy import deepcopy\nfrom typing import Dict, List\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass JsonDataModel(DataModel):\n \"\"\"A data model for json, general purpose.\"\"\"\n\n filter_keys: List[str] = []\n\n def get_llm_side_data(self, json_format: str = \"json\") -> str:\n if json_format == \"json\":\n assert isinstance(self.raw_data, Dict)\n llm_side_data = deepcopy(self.raw_data)\n for key, value in self.raw_data.items():\n if key in self.filter_keys:\n llm_side_data[key] = \"...\"\n continue\n\n if isinstance(value, DataModel):\n llm_side_data[key] = value.get_llm_side_data()\n else:\n llm_side_data[key] = str(value)\n\n return json.dumps(llm_side_data, indent=4)\n else:\n raise NotImplementedError","source_hash":"9637032777ecb623c69f596505ff900c488dd0ed03c88c959d3548e31842a256","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.json.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.json.get_llm_side_data#L13-L29","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/json.py","language":"python","start_line":13,"end_line":29,"context_start_line":1,"context_end_line":29,"code":"import json\nfrom copy import deepcopy\nfrom typing import Dict, List\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass JsonDataModel(DataModel):\n \"\"\"A data model for json, general purpose.\"\"\"\n\n filter_keys: List[str] = []\n\n def get_llm_side_data(self, json_format: str = \"json\") -> str:\n if json_format == \"json\":\n assert isinstance(self.raw_data, Dict)\n llm_side_data = deepcopy(self.raw_data)\n for key, value in self.raw_data.items():\n if key in self.filter_keys:\n llm_side_data[key] = \"...\"\n continue\n\n if isinstance(value, DataModel):\n llm_side_data[key] = value.get_llm_side_data()\n else:\n llm_side_data[key] = str(value)\n\n return json.dumps(llm_side_data, indent=4)\n else:\n raise NotImplementedError","source_hash":"9637032777ecb623c69f596505ff900c488dd0ed03c88c959d3548e31842a256","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.kaggle","uri":"program://OpenAgents/module/real_agents.adapters.data_model.kaggle#L1-L46","kind":"module","name":"real_agents.adapters.data_model.kaggle","path":"real_agents/adapters/data_model/kaggle.py","language":"python","start_line":1,"end_line":46,"context_start_line":1,"context_end_line":46,"code":"from __future__ import annotations\n\nfrom typing import Any, Dict\n\nimport pandas as pd\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\nimport json\n\n\nclass KaggleDataModel(DataModel):\n \"\"\"A data model for KaggleDataModel.\n We only support the csv and sqlite format for now.\n raw_data is a Dict[str, TableDataModel]\n raw_data_path is List[str]\n raw_data_name is Dict[str, str]\n \"\"\"\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n formatted_tables = []\n for _raw_data_path in self.raw_data_path:\n table_data = self.raw_data[_raw_data_path]\n table_name = self.raw_data_name[_raw_data_path]\n table_path = _raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n formatted_tables.append(formatted_table)\n return \"\\n\".join(formatted_tables)\n\n @staticmethod\n def to_react_table(table: pd.DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n react_tables = {}\n for table_path in self.raw_data_path:\n table_name = self.raw_data_name[table_path]\n table = self.raw_data[table_path]\n react_tables[table_name] = self.to_react_table(table)\n return json.dumps(react_tables)","source_hash":"6832dc4b9f8a780eae9eeee72222498271da9973af41a6e894ff56b860b89050","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.kaggle.KaggleDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.kaggle.KaggleDataModel#L13-L46","kind":"class","name":"KaggleDataModel","path":"real_agents/adapters/data_model/kaggle.py","language":"python","start_line":13,"end_line":46,"context_start_line":1,"context_end_line":46,"code":"from __future__ import annotations\n\nfrom typing import Any, Dict\n\nimport pandas as pd\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\nimport json\n\n\nclass KaggleDataModel(DataModel):\n \"\"\"A data model for KaggleDataModel.\n We only support the csv and sqlite format for now.\n raw_data is a Dict[str, TableDataModel]\n raw_data_path is List[str]\n raw_data_name is Dict[str, str]\n \"\"\"\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n formatted_tables = []\n for _raw_data_path in self.raw_data_path:\n table_data = self.raw_data[_raw_data_path]\n table_name = self.raw_data_name[_raw_data_path]\n table_path = _raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n formatted_tables.append(formatted_table)\n return \"\\n\".join(formatted_tables)\n\n @staticmethod\n def to_react_table(table: pd.DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n react_tables = {}\n for table_path in self.raw_data_path:\n table_name = self.raw_data_name[table_path]\n table = self.raw_data[table_path]\n react_tables[table_name] = self.to_react_table(table)\n return json.dumps(react_tables)","source_hash":"6832dc4b9f8a780eae9eeee72222498271da9973af41a6e894ff56b860b89050","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.kaggle.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.kaggle.get_llm_side_data#L21-L29","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/kaggle.py","language":"python","start_line":21,"end_line":29,"context_start_line":1,"context_end_line":46,"code":"from __future__ import annotations\n\nfrom typing import Any, Dict\n\nimport pandas as pd\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\nimport json\n\n\nclass KaggleDataModel(DataModel):\n \"\"\"A data model for KaggleDataModel.\n We only support the csv and sqlite format for now.\n raw_data is a Dict[str, TableDataModel]\n raw_data_path is List[str]\n raw_data_name is Dict[str, str]\n \"\"\"\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n formatted_tables = []\n for _raw_data_path in self.raw_data_path:\n table_data = self.raw_data[_raw_data_path]\n table_name = self.raw_data_name[_raw_data_path]\n table_path = _raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n formatted_tables.append(formatted_table)\n return \"\\n\".join(formatted_tables)\n\n @staticmethod\n def to_react_table(table: pd.DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n react_tables = {}\n for table_path in self.raw_data_path:\n table_name = self.raw_data_name[table_path]\n table = self.raw_data[table_path]\n react_tables[table_name] = self.to_react_table(table)\n return json.dumps(react_tables)","source_hash":"6832dc4b9f8a780eae9eeee72222498271da9973af41a6e894ff56b860b89050","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.kaggle.to_react_table","uri":"program://OpenAgents/function/real_agents.adapters.data_model.kaggle.to_react_table#L32-L37","kind":"function","name":"to_react_table","path":"real_agents/adapters/data_model/kaggle.py","language":"python","start_line":32,"end_line":37,"context_start_line":12,"context_end_line":46,"code":"\nclass KaggleDataModel(DataModel):\n \"\"\"A data model for KaggleDataModel.\n We only support the csv and sqlite format for now.\n raw_data is a Dict[str, TableDataModel]\n raw_data_path is List[str]\n raw_data_name is Dict[str, str]\n \"\"\"\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n formatted_tables = []\n for _raw_data_path in self.raw_data_path:\n table_data = self.raw_data[_raw_data_path]\n table_name = self.raw_data_name[_raw_data_path]\n table_path = _raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n formatted_tables.append(formatted_table)\n return \"\\n\".join(formatted_tables)\n\n @staticmethod\n def to_react_table(table: pd.DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n react_tables = {}\n for table_path in self.raw_data_path:\n table_name = self.raw_data_name[table_path]\n table = self.raw_data[table_path]\n react_tables[table_name] = self.to_react_table(table)\n return json.dumps(react_tables)","source_hash":"6832dc4b9f8a780eae9eeee72222498271da9973af41a6e894ff56b860b89050","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.kaggle.get_human_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.kaggle.get_human_side_data#L39-L46","kind":"function","name":"get_human_side_data","path":"real_agents/adapters/data_model/kaggle.py","language":"python","start_line":39,"end_line":46,"context_start_line":19,"context_end_line":46,"code":" \"\"\"\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n formatted_tables = []\n for _raw_data_path in self.raw_data_path:\n table_data = self.raw_data[_raw_data_path]\n table_name = self.raw_data_name[_raw_data_path]\n table_path = _raw_data_path\n formatted_table = serialize_df(table_data, table_name, table_path, serialize_method, num_visible_rows)\n formatted_tables.append(formatted_table)\n return \"\\n\".join(formatted_tables)\n\n @staticmethod\n def to_react_table(table: pd.DataFrame) -> str:\n columns = list(map(lambda item: {\"accessorKey\": item, \"header\": item}, table.columns.tolist()))\n # FIXME: NaN may not be handled here.\n data = table.fillna(\"\").to_dict(orient=\"records\")\n table = json.dumps({\"columns\": columns, \"data\": data})\n return table\n\n def get_human_side_data(self) -> Any:\n # In the frontend, we show the first few rows of each table\n react_tables = {}\n for table_path in self.raw_data_path:\n table_name = self.raw_data_name[table_path]\n table = self.raw_data[table_path]\n react_tables[table_name] = self.to_react_table(table)\n return json.dumps(react_tables)","source_hash":"6832dc4b9f8a780eae9eeee72222498271da9973af41a6e894ff56b860b89050","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.image","uri":"program://OpenAgents/module/real_agents.adapters.data_model.image#L1-L23","kind":"module","name":"real_agents.adapters.data_model.image","path":"real_agents/adapters/data_model/image.py","language":"python","start_line":1,"end_line":23,"context_start_line":1,"context_end_line":23,"code":"from typing import Any\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass ImageDataModel(DataModel):\n \"\"\"A data model for image.\"\"\"\n\n simple_filename = \"\"\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n if self.simple_filename == \"\":\n import os\n\n self.simple_filename = os.path.basename(self.raw_data_path)\n string = \"image: \" + self.simple_filename\n return string\n\n def get_human_side_data(self) -> Any:\n return self.raw_data[\"base64_string\"]","source_hash":"1bacb2e976e325e2d0fa2e4d612593edb97d83fccabdcf0217bbfbc69e9eb302","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.image.ImageDataModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.image.ImageDataModel#L6-L23","kind":"class","name":"ImageDataModel","path":"real_agents/adapters/data_model/image.py","language":"python","start_line":6,"end_line":23,"context_start_line":1,"context_end_line":23,"code":"from typing import Any\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass ImageDataModel(DataModel):\n \"\"\"A data model for image.\"\"\"\n\n simple_filename = \"\"\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n if self.simple_filename == \"\":\n import os\n\n self.simple_filename = os.path.basename(self.raw_data_path)\n string = \"image: \" + self.simple_filename\n return string\n\n def get_human_side_data(self) -> Any:\n return self.raw_data[\"base64_string\"]","source_hash":"1bacb2e976e325e2d0fa2e4d612593edb97d83fccabdcf0217bbfbc69e9eb302","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.image.get_raw_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.image.get_raw_data#L11-L12","kind":"function","name":"get_raw_data","path":"real_agents/adapters/data_model/image.py","language":"python","start_line":11,"end_line":12,"context_start_line":1,"context_end_line":23,"code":"from typing import Any\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass ImageDataModel(DataModel):\n \"\"\"A data model for image.\"\"\"\n\n simple_filename = \"\"\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n if self.simple_filename == \"\":\n import os\n\n self.simple_filename = os.path.basename(self.raw_data_path)\n string = \"image: \" + self.simple_filename\n return string\n\n def get_human_side_data(self) -> Any:\n return self.raw_data[\"base64_string\"]","source_hash":"1bacb2e976e325e2d0fa2e4d612593edb97d83fccabdcf0217bbfbc69e9eb302","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.image.get_llm_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.image.get_llm_side_data#L14-L20","kind":"function","name":"get_llm_side_data","path":"real_agents/adapters/data_model/image.py","language":"python","start_line":14,"end_line":20,"context_start_line":1,"context_end_line":23,"code":"from typing import Any\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass ImageDataModel(DataModel):\n \"\"\"A data model for image.\"\"\"\n\n simple_filename = \"\"\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n if self.simple_filename == \"\":\n import os\n\n self.simple_filename = os.path.basename(self.raw_data_path)\n string = \"image: \" + self.simple_filename\n return string\n\n def get_human_side_data(self) -> Any:\n return self.raw_data[\"base64_string\"]","source_hash":"1bacb2e976e325e2d0fa2e4d612593edb97d83fccabdcf0217bbfbc69e9eb302","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.image.get_human_side_data","uri":"program://OpenAgents/function/real_agents.adapters.data_model.image.get_human_side_data#L22-L23","kind":"function","name":"get_human_side_data","path":"real_agents/adapters/data_model/image.py","language":"python","start_line":22,"end_line":23,"context_start_line":2,"context_end_line":23,"code":"\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass ImageDataModel(DataModel):\n \"\"\"A data model for image.\"\"\"\n\n simple_filename = \"\"\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n if self.simple_filename == \"\":\n import os\n\n self.simple_filename = os.path.basename(self.raw_data_path)\n string = \"image: \" + self.simple_filename\n return string\n\n def get_human_side_data(self) -> Any:\n return self.raw_data[\"base64_string\"]","source_hash":"1bacb2e976e325e2d0fa2e4d612593edb97d83fccabdcf0217bbfbc69e9eb302","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec","uri":"program://OpenAgents/module/real_agents.adapters.data_model.plugin.spec#L1-L164","kind":"module","name":"real_agents.adapters.data_model.plugin.spec","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":1,"end_line":164,"context_start_line":1,"context_end_line":164,"code":"import os\nfrom typing import Any, Dict\nimport importlib.util\nimport tiktoken\n\nfrom real_agents.adapters.data_model.plugin.base import APIYamlModel\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\ndef import_function_from_file(filepath, function_name):\n spec = importlib.util.spec_from_file_location(\"module.name\", filepath)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module)\n\n function = getattr(module, function_name)\n\n return function\n\n\ndef process_one_param(param_dict: Dict[str, Any]) -> str:\n name = param_dict.get(\"name\", None)\n description = param_dict.get(\"description\", None)\n required = param_dict.get(\"required\", None)\n\n schema = param_dict.get(\"schema\", {})\n type = schema.get(\"type\", \"UnknownType\")\n value_choices = schema.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join([str(_) for _ in value_choices]) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\ndef process_one_property(name: str, value_dict: Dict[str, Any]) -> str:\n description = value_dict.get(\"description\", None)\n required = value_dict.get(\"required\", None)\n\n type = value_dict.get(\"type\", \"UnknownType\")\n value_choices = value_dict.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join(value_choices) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\nclass SpecModel:\n def __init__(self, yaml_path: str, model_name: str = \"gpt-4\") -> None:\n # fixme: Must move out the logic of yaml path\n self.yaml_path = yaml_path\n self.full_spec = APIYamlModel.from_yaml(yaml_path).to_json()\n self.paths = self.full_spec[\"paths\"]\n\n # Process the description\n enc = tiktoken.encoding_for_model(model_name)\n if \"description\" in self.full_spec[\"info\"]:\n if len(self.full_spec[\"info\"][\"description\"]) > 200:\n self.full_spec[\"info\"][\"description\"] = enc.decode(\n enc.encode(self.full_spec[\"info\"][\"description\"])[:200]\n )\n\n def load_personnel_info(self, api_key: str):\n # Get the dir of the yaml file\n yaml_dir = os.path.dirname(self.yaml_path)\n personnel_load_dir = os.path.join(yaml_dir, \"personnel.py\")\n\n if not os.path.exists(personnel_load_dir):\n return {}, {}\n\n # Reload openapi.yaml\n reload_openapi = import_function_from_file(personnel_load_dir, \"reload_openapi\")\n resolved_json, new_paths_json = reload_openapi(api_key, self.full_spec)\n self.full_spec = resolved_json\n self.full_spec[\"info\"] = resolved_json[\"info\"]\n self.paths = resolved_json[\"paths\"]\n\n # Reload the endpoints functions\n reload_endpoints = import_function_from_file(personnel_load_dir, \"reload_endpoints\")\n new_endpoint2caller = reload_endpoints(new_paths_json)\n\n # Reload the endpoints datamodels\n # todo: Add reload datamodels function\n new_endpoints2output_model = {k: lambda x: x for k in new_paths_json}\n\n return new_endpoint2caller, new_endpoints2output_model\n\n def prepare_spec(self, include_params: bool = True) -> str:\n path_names = list(self.paths.keys())\n ret = self.prepare_spec_for_one_path(path_names[0], include_params=include_params)\n\n if len(path_names) > 1:\n ret += \"\\n\"\n\n for path_name in path_names[1:]:\n ret += (\n self.prepare_spec_for_one_path(path_name, include_api_info=False, include_params=include_params) + \"\\n\"\n )\n\n return ret\n\n def list_endpoints(self) -> str:\n ret = \"\"\n for ep, ep_spec in self.paths.items():\n assert len(ep_spec) == 1, \"Support two request methods!\"\n request_method = list(ep_spec.keys())[0]\n func_spec = ep_spec[request_method]\n desc = func_spec.get(\"summary\", None)\n ret += f\"`{ep}`: {desc}\\n\"\n return ret.strip()\n\n def prepare_spec_for_one_path(\n self,\n path_name: str,\n include_api_info: bool = True,\n include_params: bool = True,\n ) -> str:\n func_dict = self.paths[path_name]\n if \"servers\" in func_dict:\n del func_dict[\"servers\"]\n\n rets = []\n for request_method in list(func_dict.keys()):\n candidate_inputs_str = \"\"\n func_spec = func_dict[request_method]\n\n # Only GET and DELETE are processed, others are not properly processed\n if request_method.lower() not in [\"get\", \"post\", \"put\", \"patch\", \"delete\"]:\n raise ValueError(\"Unknown request method\")\n\n # TODO: not sure how to arrange input when post method has \"parameters\"\n func_summary = func_spec.get(\"summary\", None)\n func_description = func_spec.get(\"description\", None)\n candidate_inputs = func_spec.get(\"parameters\", [])\n candidate_inputs_str += \"\\n\".join(process_one_param(p) for p in candidate_inputs)\n\n if request_method.lower() == \"post\" and \"requestBody\" in func_spec:\n request_body = func_spec[\"requestBody\"]\n assert \"content\" in request_body, \"Must have content in requestBody\"\n content_dict = request_body[\"content\"]\n assert len(content_dict) == 1, \"Support one content type\"\n content_type = list(content_dict.keys())[0]\n content = content_dict[content_type]\n assert \"schema\" in content, \"Must have schema in requestBody\"\n if \"properties\" in content[\"schema\"]:\n properties = content[\"schema\"][\"properties\"]\n candidate_inputs_str += \"\\n\".join(process_one_property(n, vd) for n, vd in properties.items())\n\n ret = \"\"\n if include_api_info:\n ret += f\"\"\"Name: {self.full_spec[\"info\"][\"title\"]}\\n{'Description: ' + self.full_spec[\"info\"]['description'] if\n \"description\" in self.full_spec[\"info\"] else \"\"}\\n\"\"\"\n\n ret += f\"\"\"\\tSummary: {func_summary}\\n\"\"\"\n ret += f\"\"\"\\tDescription: {func_description}\\n\"\"\"\n candidate_inputs_str = \"None\" if len(candidate_inputs_str) == 0 else candidate_inputs_str\n ret += (\n f\"\"\"\\tInput: \\n{indent_multiline_string(candidate_inputs_str, indent=2)}\\n\"\"\" if include_params else \"\"\n )\n rets.append(ret)\n\n return f\"\"\"Endpoint: {path_name}\\n\"\"\" + \"\\n\".join(rets)","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.import_function_from_file","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.import_function_from_file#L10-L17","kind":"function","name":"import_function_from_file","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":10,"end_line":17,"context_start_line":1,"context_end_line":37,"code":"import os\nfrom typing import Any, Dict\nimport importlib.util\nimport tiktoken\n\nfrom real_agents.adapters.data_model.plugin.base import APIYamlModel\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\ndef import_function_from_file(filepath, function_name):\n spec = importlib.util.spec_from_file_location(\"module.name\", filepath)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module)\n\n function = getattr(module, function_name)\n\n return function\n\n\ndef process_one_param(param_dict: Dict[str, Any]) -> str:\n name = param_dict.get(\"name\", None)\n description = param_dict.get(\"description\", None)\n required = param_dict.get(\"required\", None)\n\n schema = param_dict.get(\"schema\", {})\n type = schema.get(\"type\", \"UnknownType\")\n value_choices = schema.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join([str(_) for _ in value_choices]) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\ndef process_one_property(name: str, value_dict: Dict[str, Any]) -> str:\n description = value_dict.get(\"description\", None)","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.process_one_param","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.process_one_param#L20-L33","kind":"function","name":"process_one_param","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":20,"end_line":33,"context_start_line":1,"context_end_line":53,"code":"import os\nfrom typing import Any, Dict\nimport importlib.util\nimport tiktoken\n\nfrom real_agents.adapters.data_model.plugin.base import APIYamlModel\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\ndef import_function_from_file(filepath, function_name):\n spec = importlib.util.spec_from_file_location(\"module.name\", filepath)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module)\n\n function = getattr(module, function_name)\n\n return function\n\n\ndef process_one_param(param_dict: Dict[str, Any]) -> str:\n name = param_dict.get(\"name\", None)\n description = param_dict.get(\"description\", None)\n required = param_dict.get(\"required\", None)\n\n schema = param_dict.get(\"schema\", {})\n type = schema.get(\"type\", \"UnknownType\")\n value_choices = schema.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join([str(_) for _ in value_choices]) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\ndef process_one_property(name: str, value_dict: Dict[str, Any]) -> str:\n description = value_dict.get(\"description\", None)\n required = value_dict.get(\"required\", None)\n\n type = value_dict.get(\"type\", \"UnknownType\")\n value_choices = value_dict.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join(value_choices) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\nclass SpecModel:\n def __init__(self, yaml_path: str, model_name: str = \"gpt-4\") -> None:\n # fixme: Must move out the logic of yaml path\n self.yaml_path = yaml_path","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.process_one_property","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.process_one_property#L36-L47","kind":"function","name":"process_one_property","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":36,"end_line":47,"context_start_line":16,"context_end_line":67,"code":"\n return function\n\n\ndef process_one_param(param_dict: Dict[str, Any]) -> str:\n name = param_dict.get(\"name\", None)\n description = param_dict.get(\"description\", None)\n required = param_dict.get(\"required\", None)\n\n schema = param_dict.get(\"schema\", {})\n type = schema.get(\"type\", \"UnknownType\")\n value_choices = schema.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join([str(_) for _ in value_choices]) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\ndef process_one_property(name: str, value_dict: Dict[str, Any]) -> str:\n description = value_dict.get(\"description\", None)\n required = value_dict.get(\"required\", None)\n\n type = value_dict.get(\"type\", \"UnknownType\")\n value_choices = value_dict.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join(value_choices) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\nclass SpecModel:\n def __init__(self, yaml_path: str, model_name: str = \"gpt-4\") -> None:\n # fixme: Must move out the logic of yaml path\n self.yaml_path = yaml_path\n self.full_spec = APIYamlModel.from_yaml(yaml_path).to_json()\n self.paths = self.full_spec[\"paths\"]\n\n # Process the description\n enc = tiktoken.encoding_for_model(model_name)\n if \"description\" in self.full_spec[\"info\"]:\n if len(self.full_spec[\"info\"][\"description\"]) > 200:\n self.full_spec[\"info\"][\"description\"] = enc.decode(\n enc.encode(self.full_spec[\"info\"][\"description\"])[:200]\n )\n\n def load_personnel_info(self, api_key: str):\n # Get the dir of the yaml file\n yaml_dir = os.path.dirname(self.yaml_path)","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.SpecModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.plugin.spec.SpecModel#L50-L164","kind":"class","name":"SpecModel","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":50,"end_line":164,"context_start_line":30,"context_end_line":164,"code":" f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join([str(_) for _ in value_choices]) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\ndef process_one_property(name: str, value_dict: Dict[str, Any]) -> str:\n description = value_dict.get(\"description\", None)\n required = value_dict.get(\"required\", None)\n\n type = value_dict.get(\"type\", \"UnknownType\")\n value_choices = value_dict.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join(value_choices) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\nclass SpecModel:\n def __init__(self, yaml_path: str, model_name: str = \"gpt-4\") -> None:\n # fixme: Must move out the logic of yaml path\n self.yaml_path = yaml_path\n self.full_spec = APIYamlModel.from_yaml(yaml_path).to_json()\n self.paths = self.full_spec[\"paths\"]\n\n # Process the description\n enc = tiktoken.encoding_for_model(model_name)\n if \"description\" in self.full_spec[\"info\"]:\n if len(self.full_spec[\"info\"][\"description\"]) > 200:\n self.full_spec[\"info\"][\"description\"] = enc.decode(\n enc.encode(self.full_spec[\"info\"][\"description\"])[:200]\n )\n\n def load_personnel_info(self, api_key: str):\n # Get the dir of the yaml file\n yaml_dir = os.path.dirname(self.yaml_path)\n personnel_load_dir = os.path.join(yaml_dir, \"personnel.py\")\n\n if not os.path.exists(personnel_load_dir):\n return {}, {}\n\n # Reload openapi.yaml\n reload_openapi = import_function_from_file(personnel_load_dir, \"reload_openapi\")\n resolved_json, new_paths_json = reload_openapi(api_key, self.full_spec)\n self.full_spec = resolved_json\n self.full_spec[\"info\"] = resolved_json[\"info\"]\n self.paths = resolved_json[\"paths\"]\n\n # Reload the endpoints functions\n reload_endpoints = import_function_from_file(personnel_load_dir, \"reload_endpoints\")\n new_endpoint2caller = reload_endpoints(new_paths_json)\n\n # Reload the endpoints datamodels\n # todo: Add reload datamodels function\n new_endpoints2output_model = {k: lambda x: x for k in new_paths_json}\n\n return new_endpoint2caller, new_endpoints2output_model\n\n def prepare_spec(self, include_params: bool = True) -> str:\n path_names = list(self.paths.keys())\n ret = self.prepare_spec_for_one_path(path_names[0], include_params=include_params)\n\n if len(path_names) > 1:\n ret += \"\\n\"\n\n for path_name in path_names[1:]:\n ret += (\n self.prepare_spec_for_one_path(path_name, include_api_info=False, include_params=include_params) + \"\\n\"\n )\n\n return ret\n\n def list_endpoints(self) -> str:\n ret = \"\"\n for ep, ep_spec in self.paths.items():\n assert len(ep_spec) == 1, \"Support two request methods!\"\n request_method = list(ep_spec.keys())[0]\n func_spec = ep_spec[request_method]\n desc = func_spec.get(\"summary\", None)\n ret += f\"`{ep}`: {desc}\\n\"\n return ret.strip()\n\n def prepare_spec_for_one_path(\n self,\n path_name: str,\n include_api_info: bool = True,\n include_params: bool = True,\n ) -> str:\n func_dict = self.paths[path_name]\n if \"servers\" in func_dict:\n del func_dict[\"servers\"]\n\n rets = []\n for request_method in list(func_dict.keys()):\n candidate_inputs_str = \"\"\n func_spec = func_dict[request_method]\n\n # Only GET and DELETE are processed, others are not properly processed\n if request_method.lower() not in [\"get\", \"post\", \"put\", \"patch\", \"delete\"]:\n raise ValueError(\"Unknown request method\")\n\n # TODO: not sure how to arrange input when post method has \"parameters\"\n func_summary = func_spec.get(\"summary\", None)\n func_description = func_spec.get(\"description\", None)\n candidate_inputs = func_spec.get(\"parameters\", [])\n candidate_inputs_str += \"\\n\".join(process_one_param(p) for p in candidate_inputs)\n\n if request_method.lower() == \"post\" and \"requestBody\" in func_spec:\n request_body = func_spec[\"requestBody\"]\n assert \"content\" in request_body, \"Must have content in requestBody\"\n content_dict = request_body[\"content\"]\n assert len(content_dict) == 1, \"Support one content type\"\n content_type = list(content_dict.keys())[0]\n content = content_dict[content_type]\n assert \"schema\" in content, \"Must have schema in requestBody\"\n if \"properties\" in content[\"schema\"]:\n properties = content[\"schema\"][\"properties\"]\n candidate_inputs_str += \"\\n\".join(process_one_property(n, vd) for n, vd in properties.items())\n\n ret = \"\"\n if include_api_info:\n ret += f\"\"\"Name: {self.full_spec[\"info\"][\"title\"]}\\n{'Description: ' + self.full_spec[\"info\"]['description'] if\n \"description\" in self.full_spec[\"info\"] else \"\"}\\n\"\"\"\n\n ret += f\"\"\"\\tSummary: {func_summary}\\n\"\"\"\n ret += f\"\"\"\\tDescription: {func_description}\\n\"\"\"\n candidate_inputs_str = \"None\" if len(candidate_inputs_str) == 0 else candidate_inputs_str\n ret += (\n f\"\"\"\\tInput: \\n{indent_multiline_string(candidate_inputs_str, indent=2)}\\n\"\"\" if include_params else \"\"\n )\n rets.append(ret)\n\n return f\"\"\"Endpoint: {path_name}\\n\"\"\" + \"\\n\".join(rets)","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.__init__","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.__init__#L51-L63","kind":"function","name":"__init__","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":51,"end_line":63,"context_start_line":31,"context_end_line":83,"code":" f\"{'Examples:' + ','.join([str(_) for _ in value_choices]) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\ndef process_one_property(name: str, value_dict: Dict[str, Any]) -> str:\n description = value_dict.get(\"description\", None)\n required = value_dict.get(\"required\", None)\n\n type = value_dict.get(\"type\", \"UnknownType\")\n value_choices = value_dict.get(\"enum\", [])\n\n ret = (\n f\"`{name}` ({type}, {'required' if required else 'optional'}): {description}.\"\n f\"{'Examples:' + ','.join(value_choices) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\nclass SpecModel:\n def __init__(self, yaml_path: str, model_name: str = \"gpt-4\") -> None:\n # fixme: Must move out the logic of yaml path\n self.yaml_path = yaml_path\n self.full_spec = APIYamlModel.from_yaml(yaml_path).to_json()\n self.paths = self.full_spec[\"paths\"]\n\n # Process the description\n enc = tiktoken.encoding_for_model(model_name)\n if \"description\" in self.full_spec[\"info\"]:\n if len(self.full_spec[\"info\"][\"description\"]) > 200:\n self.full_spec[\"info\"][\"description\"] = enc.decode(\n enc.encode(self.full_spec[\"info\"][\"description\"])[:200]\n )\n\n def load_personnel_info(self, api_key: str):\n # Get the dir of the yaml file\n yaml_dir = os.path.dirname(self.yaml_path)\n personnel_load_dir = os.path.join(yaml_dir, \"personnel.py\")\n\n if not os.path.exists(personnel_load_dir):\n return {}, {}\n\n # Reload openapi.yaml\n reload_openapi = import_function_from_file(personnel_load_dir, \"reload_openapi\")\n resolved_json, new_paths_json = reload_openapi(api_key, self.full_spec)\n self.full_spec = resolved_json\n self.full_spec[\"info\"] = resolved_json[\"info\"]\n self.paths = resolved_json[\"paths\"]\n\n # Reload the endpoints functions\n reload_endpoints = import_function_from_file(personnel_load_dir, \"reload_endpoints\")\n new_endpoint2caller = reload_endpoints(new_paths_json)\n","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.load_personnel_info","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.load_personnel_info#L65-L88","kind":"function","name":"load_personnel_info","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":65,"end_line":88,"context_start_line":45,"context_end_line":108,"code":" f\"{'Examples:' + ','.join(value_choices) if len(value_choices) > 0 else ''}\"\n )\n return ret\n\n\nclass SpecModel:\n def __init__(self, yaml_path: str, model_name: str = \"gpt-4\") -> None:\n # fixme: Must move out the logic of yaml path\n self.yaml_path = yaml_path\n self.full_spec = APIYamlModel.from_yaml(yaml_path).to_json()\n self.paths = self.full_spec[\"paths\"]\n\n # Process the description\n enc = tiktoken.encoding_for_model(model_name)\n if \"description\" in self.full_spec[\"info\"]:\n if len(self.full_spec[\"info\"][\"description\"]) > 200:\n self.full_spec[\"info\"][\"description\"] = enc.decode(\n enc.encode(self.full_spec[\"info\"][\"description\"])[:200]\n )\n\n def load_personnel_info(self, api_key: str):\n # Get the dir of the yaml file\n yaml_dir = os.path.dirname(self.yaml_path)\n personnel_load_dir = os.path.join(yaml_dir, \"personnel.py\")\n\n if not os.path.exists(personnel_load_dir):\n return {}, {}\n\n # Reload openapi.yaml\n reload_openapi = import_function_from_file(personnel_load_dir, \"reload_openapi\")\n resolved_json, new_paths_json = reload_openapi(api_key, self.full_spec)\n self.full_spec = resolved_json\n self.full_spec[\"info\"] = resolved_json[\"info\"]\n self.paths = resolved_json[\"paths\"]\n\n # Reload the endpoints functions\n reload_endpoints = import_function_from_file(personnel_load_dir, \"reload_endpoints\")\n new_endpoint2caller = reload_endpoints(new_paths_json)\n\n # Reload the endpoints datamodels\n # todo: Add reload datamodels function\n new_endpoints2output_model = {k: lambda x: x for k in new_paths_json}\n\n return new_endpoint2caller, new_endpoints2output_model\n\n def prepare_spec(self, include_params: bool = True) -> str:\n path_names = list(self.paths.keys())\n ret = self.prepare_spec_for_one_path(path_names[0], include_params=include_params)\n\n if len(path_names) > 1:\n ret += \"\\n\"\n\n for path_name in path_names[1:]:\n ret += (\n self.prepare_spec_for_one_path(path_name, include_api_info=False, include_params=include_params) + \"\\n\"\n )\n\n return ret\n\n def list_endpoints(self) -> str:\n ret = \"\"\n for ep, ep_spec in self.paths.items():\n assert len(ep_spec) == 1, \"Support two request methods!\"\n request_method = list(ep_spec.keys())[0]","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.prepare_spec","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.prepare_spec#L90-L102","kind":"function","name":"prepare_spec","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":90,"end_line":102,"context_start_line":70,"context_end_line":122,"code":" if not os.path.exists(personnel_load_dir):\n return {}, {}\n\n # Reload openapi.yaml\n reload_openapi = import_function_from_file(personnel_load_dir, \"reload_openapi\")\n resolved_json, new_paths_json = reload_openapi(api_key, self.full_spec)\n self.full_spec = resolved_json\n self.full_spec[\"info\"] = resolved_json[\"info\"]\n self.paths = resolved_json[\"paths\"]\n\n # Reload the endpoints functions\n reload_endpoints = import_function_from_file(personnel_load_dir, \"reload_endpoints\")\n new_endpoint2caller = reload_endpoints(new_paths_json)\n\n # Reload the endpoints datamodels\n # todo: Add reload datamodels function\n new_endpoints2output_model = {k: lambda x: x for k in new_paths_json}\n\n return new_endpoint2caller, new_endpoints2output_model\n\n def prepare_spec(self, include_params: bool = True) -> str:\n path_names = list(self.paths.keys())\n ret = self.prepare_spec_for_one_path(path_names[0], include_params=include_params)\n\n if len(path_names) > 1:\n ret += \"\\n\"\n\n for path_name in path_names[1:]:\n ret += (\n self.prepare_spec_for_one_path(path_name, include_api_info=False, include_params=include_params) + \"\\n\"\n )\n\n return ret\n\n def list_endpoints(self) -> str:\n ret = \"\"\n for ep, ep_spec in self.paths.items():\n assert len(ep_spec) == 1, \"Support two request methods!\"\n request_method = list(ep_spec.keys())[0]\n func_spec = ep_spec[request_method]\n desc = func_spec.get(\"summary\", None)\n ret += f\"`{ep}`: {desc}\\n\"\n return ret.strip()\n\n def prepare_spec_for_one_path(\n self,\n path_name: str,\n include_api_info: bool = True,\n include_params: bool = True,\n ) -> str:\n func_dict = self.paths[path_name]\n if \"servers\" in func_dict:\n del func_dict[\"servers\"]","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.list_endpoints","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.list_endpoints#L104-L112","kind":"function","name":"list_endpoints","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":104,"end_line":112,"context_start_line":84,"context_end_line":132,"code":" # Reload the endpoints datamodels\n # todo: Add reload datamodels function\n new_endpoints2output_model = {k: lambda x: x for k in new_paths_json}\n\n return new_endpoint2caller, new_endpoints2output_model\n\n def prepare_spec(self, include_params: bool = True) -> str:\n path_names = list(self.paths.keys())\n ret = self.prepare_spec_for_one_path(path_names[0], include_params=include_params)\n\n if len(path_names) > 1:\n ret += \"\\n\"\n\n for path_name in path_names[1:]:\n ret += (\n self.prepare_spec_for_one_path(path_name, include_api_info=False, include_params=include_params) + \"\\n\"\n )\n\n return ret\n\n def list_endpoints(self) -> str:\n ret = \"\"\n for ep, ep_spec in self.paths.items():\n assert len(ep_spec) == 1, \"Support two request methods!\"\n request_method = list(ep_spec.keys())[0]\n func_spec = ep_spec[request_method]\n desc = func_spec.get(\"summary\", None)\n ret += f\"`{ep}`: {desc}\\n\"\n return ret.strip()\n\n def prepare_spec_for_one_path(\n self,\n path_name: str,\n include_api_info: bool = True,\n include_params: bool = True,\n ) -> str:\n func_dict = self.paths[path_name]\n if \"servers\" in func_dict:\n del func_dict[\"servers\"]\n\n rets = []\n for request_method in list(func_dict.keys()):\n candidate_inputs_str = \"\"\n func_spec = func_dict[request_method]\n\n # Only GET and DELETE are processed, others are not properly processed\n if request_method.lower() not in [\"get\", \"post\", \"put\", \"patch\", \"delete\"]:\n raise ValueError(\"Unknown request method\")\n","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.spec.prepare_spec_for_one_path","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.spec.prepare_spec_for_one_path#L114-L164","kind":"function","name":"prepare_spec_for_one_path","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":114,"end_line":164,"context_start_line":94,"context_end_line":164,"code":" if len(path_names) > 1:\n ret += \"\\n\"\n\n for path_name in path_names[1:]:\n ret += (\n self.prepare_spec_for_one_path(path_name, include_api_info=False, include_params=include_params) + \"\\n\"\n )\n\n return ret\n\n def list_endpoints(self) -> str:\n ret = \"\"\n for ep, ep_spec in self.paths.items():\n assert len(ep_spec) == 1, \"Support two request methods!\"\n request_method = list(ep_spec.keys())[0]\n func_spec = ep_spec[request_method]\n desc = func_spec.get(\"summary\", None)\n ret += f\"`{ep}`: {desc}\\n\"\n return ret.strip()\n\n def prepare_spec_for_one_path(\n self,\n path_name: str,\n include_api_info: bool = True,\n include_params: bool = True,\n ) -> str:\n func_dict = self.paths[path_name]\n if \"servers\" in func_dict:\n del func_dict[\"servers\"]\n\n rets = []\n for request_method in list(func_dict.keys()):\n candidate_inputs_str = \"\"\n func_spec = func_dict[request_method]\n\n # Only GET and DELETE are processed, others are not properly processed\n if request_method.lower() not in [\"get\", \"post\", \"put\", \"patch\", \"delete\"]:\n raise ValueError(\"Unknown request method\")\n\n # TODO: not sure how to arrange input when post method has \"parameters\"\n func_summary = func_spec.get(\"summary\", None)\n func_description = func_spec.get(\"description\", None)\n candidate_inputs = func_spec.get(\"parameters\", [])\n candidate_inputs_str += \"\\n\".join(process_one_param(p) for p in candidate_inputs)\n\n if request_method.lower() == \"post\" and \"requestBody\" in func_spec:\n request_body = func_spec[\"requestBody\"]\n assert \"content\" in request_body, \"Must have content in requestBody\"\n content_dict = request_body[\"content\"]\n assert len(content_dict) == 1, \"Support one content type\"\n content_type = list(content_dict.keys())[0]\n content = content_dict[content_type]\n assert \"schema\" in content, \"Must have schema in requestBody\"\n if \"properties\" in content[\"schema\"]:\n properties = content[\"schema\"][\"properties\"]\n candidate_inputs_str += \"\\n\".join(process_one_property(n, vd) for n, vd in properties.items())\n\n ret = \"\"\n if include_api_info:\n ret += f\"\"\"Name: {self.full_spec[\"info\"][\"title\"]}\\n{'Description: ' + self.full_spec[\"info\"]['description'] if\n \"description\" in self.full_spec[\"info\"] else \"\"}\\n\"\"\"\n\n ret += f\"\"\"\\tSummary: {func_summary}\\n\"\"\"\n ret += f\"\"\"\\tDescription: {func_description}\\n\"\"\"\n candidate_inputs_str = \"None\" if len(candidate_inputs_str) == 0 else candidate_inputs_str\n ret += (\n f\"\"\"\\tInput: \\n{indent_multiline_string(candidate_inputs_str, indent=2)}\\n\"\"\" if include_params else \"\"\n )\n rets.append(ret)\n\n return f\"\"\"Endpoint: {path_name}\\n\"\"\" + \"\\n\".join(rets)","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base","uri":"program://OpenAgents/module/real_agents.adapters.data_model.plugin.base#L1-L51","kind":"module","name":"real_agents.adapters.data_model.plugin.base","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":1,"end_line":51,"context_start_line":1,"context_end_line":51,"code":"from __future__ import annotations\n\nimport json\nimport os\nfrom typing import Any, Dict\n\nimport yaml\nfrom prance import ResolvingParser\nfrom pydantic import BaseModel\n\n# get the absolute path of the current file\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:\n # Open the OpenAPI JSON file\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n yaml_data = yaml.dump(json_data)\n return yaml_data","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.APIYamlModel","uri":"program://OpenAgents/class/real_agents.adapters.data_model.plugin.base.APIYamlModel#L15-L51","kind":"class","name":"APIYamlModel","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":15,"end_line":51,"context_start_line":1,"context_end_line":51,"code":"from __future__ import annotations\n\nimport json\nimport os\nfrom typing import Any, Dict\n\nimport yaml\nfrom prance import ResolvingParser\nfrom pydantic import BaseModel\n\n# get the absolute path of the current file\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:\n # Open the OpenAPI JSON file\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n yaml_data = yaml.dump(json_data)\n return yaml_data","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.from_yaml","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.base.from_yaml#L19-L20","kind":"function","name":"from_yaml","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":19,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"from __future__ import annotations\n\nimport json\nimport os\nfrom typing import Any, Dict\n\nimport yaml\nfrom prance import ResolvingParser\nfrom pydantic import BaseModel\n\n# get the absolute path of the current file\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.from_json","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.base.from_json#L23-L26","kind":"function","name":"from_json","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":23,"end_line":26,"context_start_line":3,"context_end_line":46,"code":"import json\nimport os\nfrom typing import Any, Dict\n\nimport yaml\nfrom prance import ResolvingParser\nfrom pydantic import BaseModel\n\n# get the absolute path of the current file\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.to_yaml","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.base.to_yaml#L28-L30","kind":"function","name":"to_yaml","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":28,"end_line":30,"context_start_line":8,"context_end_line":50,"code":"from prance import ResolvingParser\nfrom pydantic import BaseModel\n\n# get the absolute path of the current file\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:\n # Open the OpenAPI JSON file\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n yaml_data = yaml.dump(json_data)","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.to_json","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.base.to_json#L32-L33","kind":"function","name":"to_json","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":32,"end_line":33,"context_start_line":12,"context_end_line":51,"code":"CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:\n # Open the OpenAPI JSON file\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n yaml_data = yaml.dump(json_data)\n return yaml_data","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.yaml_to_json","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.base.yaml_to_json#L36-L43","kind":"function","name":"yaml_to_json","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":36,"end_line":43,"context_start_line":16,"context_end_line":51,"code":" info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n\n @classmethod\n def from_json(cls, json_path: str) -> APIYamlModel:\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:\n # Open the OpenAPI JSON file\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n yaml_data = yaml.dump(json_data)\n return yaml_data","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.base.json_to_yaml","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.base.json_to_yaml#L46-L51","kind":"function","name":"json_to_yaml","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":46,"end_line":51,"context_start_line":26,"context_end_line":51,"code":" return cls(info=json_data)\n\n def to_yaml(self) -> Dict:\n yaml_data = yaml.safe_dump(self.info, sort_keys=False)\n return yaml_data\n\n def to_json(self) -> Dict:\n return self.info\n\n @staticmethod\n def yaml_to_json(yaml_path: str) -> Dict:\n # Open the OpenAPI YAML file\n # Load the YAML contents into a Python dictionary\n # json_data = yaml.safe_load(yaml_file)\n # there are #/xxxx/yyyy reference in openapi.yaml\n parsed = ResolvingParser(yaml_path, backend=\"openapi-spec-validator\", strict=False)\n json_data = json.loads(parsed.json())\n return json_data\n\n @staticmethod\n def json_to_yaml(json_path: str) -> Any:\n # Open the OpenAPI JSON file\n with open(json_path, \"r\") as json_file:\n json_data = json.load(json_file)\n yaml_data = yaml.dump(json_data)\n return yaml_data","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.newsapi.everything","uri":"program://OpenAgents/module/real_agents.adapters.data_model.plugin.newsapi.everything#L1-L10","kind":"module","name":"real_agents.adapters.data_model.plugin.newsapi.everything","path":"real_agents/adapters/data_model/plugin/newsapi/everything.py","language":"python","start_line":1,"end_line":10,"context_start_line":1,"context_end_line":10,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"][\"articles\"] = input_json[\"out\"][\"articles\"][:5]\n return input_json","source_hash":"3f09a5fbb7ea28a5623babe922123749f0b531906a3b84b5bd8beac8609893a6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.newsapi.everything.convert","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.newsapi.everything.convert#L5-L10","kind":"function","name":"convert","path":"real_agents/adapters/data_model/plugin/newsapi/everything.py","language":"python","start_line":5,"end_line":10,"context_start_line":1,"context_end_line":10,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"][\"articles\"] = input_json[\"out\"][\"articles\"][:5]\n return input_json","source_hash":"3f09a5fbb7ea28a5623babe922123749f0b531906a3b84b5bd8beac8609893a6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.biztoc.search_news","uri":"program://OpenAgents/module/real_agents.adapters.data_model.plugin.biztoc.search_news#L1-L18","kind":"module","name":"real_agents.adapters.data_model.plugin.biztoc.search_news","path":"real_agents/adapters/data_model/plugin/biztoc/search_news.py","language":"python","start_line":1,"end_line":18,"context_start_line":1,"context_end_line":18,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"] = input_json[\"out\"][:5]\n extracted_keys = [\n \"body\",\n \"title\",\n \"created\",\n \"url\",\n \"tags\",\n ]\n input_json[\"out\"] = [{k: r[k] for k in extracted_keys if k in r} for r in input_json[\"out\"]]\n return input_json","source_hash":"da872220f52400906a3c0cc980e5ed52f36612fb5770ce7b8b58a7f558f1cde7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.biztoc.search_news.convert","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.biztoc.search_news.convert#L5-L18","kind":"function","name":"convert","path":"real_agents/adapters/data_model/plugin/biztoc/search_news.py","language":"python","start_line":5,"end_line":18,"context_start_line":1,"context_end_line":18,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"] = input_json[\"out\"][:5]\n extracted_keys = [\n \"body\",\n \"title\",\n \"created\",\n \"url\",\n \"tags\",\n ]\n input_json[\"out\"] = [{k: r[k] for k in extracted_keys if k in r} for r in input_json[\"out\"]]\n return input_json","source_hash":"da872220f52400906a3c0cc980e5ed52f36612fb5770ce7b8b58a7f558f1cde7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.wanted_job_search.search_global","uri":"program://OpenAgents/module/real_agents.adapters.data_model.plugin.wanted_job_search.search_global#L1-L17","kind":"module","name":"real_agents.adapters.data_model.plugin.wanted_job_search.search_global","path":"real_agents/adapters/data_model/plugin/wanted_job_search/search_global.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"] = input_json[\"out\"][:5]\n\n for i, job in enumerate(input_json[\"out\"]):\n cleaned_job_item = input_json[\"out\"][i]\n del cleaned_job_item[\"id\"]\n del cleaned_job_item[\"created\"]\n input_json[\"out\"][i] = cleaned_job_item\n\n return input_json","source_hash":"54d58347abb813f550bfee90ff54fce50a4e1335bd8e161a9d1a91071b6fb3b1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.plugin.wanted_job_search.search_global.convert","uri":"program://OpenAgents/function/real_agents.adapters.data_model.plugin.wanted_job_search.search_global.convert#L5-L17","kind":"function","name":"convert","path":"real_agents/adapters/data_model/plugin/wanted_job_search/search_global.py","language":"python","start_line":5,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"] = input_json[\"out\"][:5]\n\n for i, job in enumerate(input_json[\"out\"]):\n cleaned_job_item = input_json[\"out\"][i]\n del cleaned_job_item[\"id\"]\n del cleaned_job_item[\"created\"]\n input_json[\"out\"][i] = cleaned_job_item\n\n return input_json","source_hash":"54d58347abb813f550bfee90ff54fce50a4e1335bd8e161a9d1a91071b6fb3b1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates","uri":"program://OpenAgents/module/real_agents.adapters.data_model.templates.skg_templates.table_templates#L1-L174","kind":"module","name":"real_agents.adapters.data_model.templates.skg_templates.table_templates","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":1,"end_line":174,"context_start_line":1,"context_end_line":174,"code":"import subprocess\nimport sys\nfrom copy import deepcopy\nfrom typing import Any, Dict, Union\n\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport tiktoken\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(\n table_data: Union[pd.DataFrame, Dict[str, Any]], table_name: str = \"table\", visible_rows_num: int = 3\n) -> Dict[str, str]:\n \"\"\"\n Convert table data to string representations in different formats.\n\n :param table_data: A dictionary with \"cols\" (list of strings) and \"rows\"\n (list of lists of strings) as keys.\n :param table_name: The name of the table.\n :param visible_rows_num: The number of rows to be displayed in the representation.\n :return: A dictionary with the string table representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"tabulate\", \"prettytable\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages\n install_required_packages()\n from prettytable import PrettyTable\n\n # Handle situation when the table_data is already a dataframe, FIXME: this is a hack\n new_table_data = {}\n if isinstance(table_data, pd.DataFrame):\n new_table_data[\"cols\"] = table_data.columns\n new_table_data[\"rows\"] = table_data.values.tolist()\n table_data = new_table_data\n\n # Type check for table_data\n if not isinstance(table_data, dict) or \"cols\" not in table_data or \"rows\" not in table_data:\n raise TypeError(\"table_data must be a dictionary with 'cols' and 'rows' as keys.\")\n\n table_data_for_observable = deepcopy(table_data)\n if len(table_data_for_observable[\"rows\"]) > visible_rows_num:\n table_data_for_observable[\"rows\"] = table_data_for_observable[\"rows\"][:visible_rows_num]\n table_data_for_observable[\"rows\"].append([\"...\"] * len(table_data_for_observable[\"cols\"]))\n\n # Create dataframe from table_data\n df = pd.DataFrame(table_data_for_observable[\"rows\"], columns=table_data_for_observable[\"cols\"])\n\n # Generate tables in different formats\n markdown_table = df.to_markdown(index=False)\n html_table = df.to_html(index=False)\n latex_table = df.to_latex(index=False)\n csv_table = df.to_csv(index=False)\n tsv_table = df.to_csv(index=False, sep=\"\\t\")\n rest_table = df.to_string(index=False)\n\n def bbcode_mode_table(data_frame: pd.DataFrame) -> str:\n bbcode_table = \"[table]\\n\"\n for row in data_frame.itertuples(index=False):\n bbcode_table += \"[tr]\\n\"\n for value in row:\n bbcode_table += f\"[td]{value}[/td]\\n\"\n bbcode_table += \"[/tr]\\n\"\n bbcode_table += \"[/table]\"\n return bbcode_table\n\n def mediawiki_mode_table(data_frame: pd.DataFrame) -> str:\n mediawiki_table = '{| class=\"wikitable\"\\n|-\\n'\n for col in data_frame.columns:\n mediawiki_table += f\"! {col}\\n\"\n for row in data_frame.itertuples(index=False):\n mediawiki_table += \"|-\\n\"\n for value in row:\n mediawiki_table += f\"| {value}\\n\"\n mediawiki_table += \"|}\"\n return mediawiki_table\n\n def org_mode_table(data_frame: pd.DataFrame) -> str:\n org_table = (\n \"| \"\n + \" | \".join(data_frame.columns)\n + \" |\\n|-\"\n + \" | -\".join([\"-\" * len(col) for col in data_frame.columns])\n + \" |\\n\"\n )\n for row in data_frame.itertuples(index=False):\n org_table += \"| \" + \" | \".join([str(value) for value in row]) + \" |\\n\"\n return org_table\n\n bbcode_table = bbcode_mode_table(df)\n mediawiki_table = mediawiki_mode_table(df)\n org_table = org_mode_table(df)\n\n pretty_table = PrettyTable()\n pretty_table.field_names = table_data[\"cols\"]\n for row in table_data[\"rows\"]:\n pretty_table.add_row(row)\n pretty_table = str(pretty_table)\n\n # New function to generate SQL table\n def sql_mode_table(data_frame: pd.DataFrame, _table_name: str) -> str:\n sql_table_str = f\"CREATE TABLE {table_name}(\\n\"\n\n for col in data_frame.columns:\n sql_table_str += f\"{col} text,\\n\"\n\n # Remove the last comma and add the primary key constraint\n sql_table_str = sql_table_str[:-2] + f\",\\nPRIMARY KEY ({data_frame.columns[0]})\\n);\"\n\n sql_table_str += \"\\n/*\\n{} example rows:\\n\".format(len(data_frame))\n for i, _row in data_frame.iterrows():\n _row = \"\\t\".join([str(_cell) for _cell in _row.to_list()])\n sql_table_str += f\"{_row}\\n\"\n sql_table_str += \"*/\"\n\n return sql_table_str\n\n sql_table = sql_mode_table(df, table_name)\n\n # Return the representation in different formats as a dictionary\n return {\n \"Markdown\": markdown_table,\n \"HTML\": html_table,\n \"LaTeX\": latex_table,\n \"CSV\": csv_table,\n \"TSV\": tsv_table,\n \"reStructuredText\": rest_table,\n \"BBCode\": bbcode_table,\n \"MediaWiki\": mediawiki_table,\n \"Org mode\": org_table,\n \"PrettyTable\": pretty_table,\n \"SQL\": sql_table,\n }\n\n\ndef serialize_df(\n table_data: pd.DataFrame,\n table_name: str,\n table_path: str,\n serialize_method: str = \"tsv\",\n num_visible_rows: int = 3,\n max_tokens: int = 1000,\n data_dir_splitter: str = \"backend/data/\",\n) -> str:\n \"\"\"Convert dataframe to a string representation.\"\"\"\n if serialize_method == \"tsv\":\n # Here it means ignore the \"path/to/the/data/ max_tokens:\n string = enc.decode(enc_tokens[:max_tokens])\n elif serialize_method == \"database\":\n engine = create_engine(\"sqlite:///:memory:\")\n table_data.to_sql(table_name, engine)\n db = SQLDatabase(engine)\n # TODO: Now access the internal variable\n setattr(db, \"_sample_rows_in_table_info\", num_visible_rows)\n string = db.get_table_info()\n else:\n raise ValueError(\"Unknown serialization method.\")\n return string","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates.convert","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.table_templates.convert#L13-L139","kind":"function","name":"convert","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":13,"end_line":139,"context_start_line":1,"context_end_line":159,"code":"import subprocess\nimport sys\nfrom copy import deepcopy\nfrom typing import Any, Dict, Union\n\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport tiktoken\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(\n table_data: Union[pd.DataFrame, Dict[str, Any]], table_name: str = \"table\", visible_rows_num: int = 3\n) -> Dict[str, str]:\n \"\"\"\n Convert table data to string representations in different formats.\n\n :param table_data: A dictionary with \"cols\" (list of strings) and \"rows\"\n (list of lists of strings) as keys.\n :param table_name: The name of the table.\n :param visible_rows_num: The number of rows to be displayed in the representation.\n :return: A dictionary with the string table representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"tabulate\", \"prettytable\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages\n install_required_packages()\n from prettytable import PrettyTable\n\n # Handle situation when the table_data is already a dataframe, FIXME: this is a hack\n new_table_data = {}\n if isinstance(table_data, pd.DataFrame):\n new_table_data[\"cols\"] = table_data.columns\n new_table_data[\"rows\"] = table_data.values.tolist()\n table_data = new_table_data\n\n # Type check for table_data\n if not isinstance(table_data, dict) or \"cols\" not in table_data or \"rows\" not in table_data:\n raise TypeError(\"table_data must be a dictionary with 'cols' and 'rows' as keys.\")\n\n table_data_for_observable = deepcopy(table_data)\n if len(table_data_for_observable[\"rows\"]) > visible_rows_num:\n table_data_for_observable[\"rows\"] = table_data_for_observable[\"rows\"][:visible_rows_num]\n table_data_for_observable[\"rows\"].append([\"...\"] * len(table_data_for_observable[\"cols\"]))\n\n # Create dataframe from table_data\n df = pd.DataFrame(table_data_for_observable[\"rows\"], columns=table_data_for_observable[\"cols\"])\n\n # Generate tables in different formats\n markdown_table = df.to_markdown(index=False)\n html_table = df.to_html(index=False)\n latex_table = df.to_latex(index=False)\n csv_table = df.to_csv(index=False)\n tsv_table = df.to_csv(index=False, sep=\"\\t\")\n rest_table = df.to_string(index=False)\n\n def bbcode_mode_table(data_frame: pd.DataFrame) -> str:\n bbcode_table = \"[table]\\n\"\n for row in data_frame.itertuples(index=False):\n bbcode_table += \"[tr]\\n\"\n for value in row:\n bbcode_table += f\"[td]{value}[/td]\\n\"\n bbcode_table += \"[/tr]\\n\"\n bbcode_table += \"[/table]\"\n return bbcode_table\n\n def mediawiki_mode_table(data_frame: pd.DataFrame) -> str:\n mediawiki_table = '{| class=\"wikitable\"\\n|-\\n'\n for col in data_frame.columns:\n mediawiki_table += f\"! {col}\\n\"\n for row in data_frame.itertuples(index=False):\n mediawiki_table += \"|-\\n\"\n for value in row:\n mediawiki_table += f\"| {value}\\n\"\n mediawiki_table += \"|}\"\n return mediawiki_table\n\n def org_mode_table(data_frame: pd.DataFrame) -> str:\n org_table = (\n \"| \"\n + \" | \".join(data_frame.columns)\n + \" |\\n|-\"\n + \" | -\".join([\"-\" * len(col) for col in data_frame.columns])\n + \" |\\n\"\n )\n for row in data_frame.itertuples(index=False):\n org_table += \"| \" + \" | \".join([str(value) for value in row]) + \" |\\n\"\n return org_table\n\n bbcode_table = bbcode_mode_table(df)\n mediawiki_table = mediawiki_mode_table(df)\n org_table = org_mode_table(df)\n\n pretty_table = PrettyTable()\n pretty_table.field_names = table_data[\"cols\"]\n for row in table_data[\"rows\"]:\n pretty_table.add_row(row)\n pretty_table = str(pretty_table)\n\n # New function to generate SQL table\n def sql_mode_table(data_frame: pd.DataFrame, _table_name: str) -> str:\n sql_table_str = f\"CREATE TABLE {table_name}(\\n\"\n\n for col in data_frame.columns:\n sql_table_str += f\"{col} text,\\n\"\n\n # Remove the last comma and add the primary key constraint\n sql_table_str = sql_table_str[:-2] + f\",\\nPRIMARY KEY ({data_frame.columns[0]})\\n);\"\n\n sql_table_str += \"\\n/*\\n{} example rows:\\n\".format(len(data_frame))\n for i, _row in data_frame.iterrows():\n _row = \"\\t\".join([str(_cell) for _cell in _row.to_list()])\n sql_table_str += f\"{_row}\\n\"\n sql_table_str += \"*/\"\n\n return sql_table_str\n\n sql_table = sql_mode_table(df, table_name)\n\n # Return the representation in different formats as a dictionary\n return {\n \"Markdown\": markdown_table,\n \"HTML\": html_table,\n \"LaTeX\": latex_table,\n \"CSV\": csv_table,\n \"TSV\": tsv_table,\n \"reStructuredText\": rest_table,\n \"BBCode\": bbcode_table,\n \"MediaWiki\": mediawiki_table,\n \"Org mode\": org_table,\n \"PrettyTable\": pretty_table,\n \"SQL\": sql_table,\n }\n\n\ndef serialize_df(\n table_data: pd.DataFrame,\n table_name: str,\n table_path: str,\n serialize_method: str = \"tsv\",\n num_visible_rows: int = 3,\n max_tokens: int = 1000,\n data_dir_splitter: str = \"backend/data/\",\n) -> str:\n \"\"\"Convert dataframe to a string representation.\"\"\"\n if serialize_method == \"tsv\":\n # Here it means ignore the \"path/to/the/data/ str:\n \"\"\"Convert dataframe to a string representation.\"\"\"\n if serialize_method == \"tsv\":\n # Here it means ignore the \"path/to/the/data/ max_tokens:\n string = enc.decode(enc_tokens[:max_tokens])\n elif serialize_method == \"database\":\n engine = create_engine(\"sqlite:///:memory:\")\n table_data.to_sql(table_name, engine)\n db = SQLDatabase(engine)\n # TODO: Now access the internal variable\n setattr(db, \"_sample_rows_in_table_info\", num_visible_rows)\n string = db.get_table_info()\n else:\n raise ValueError(\"Unknown serialization method.\")\n return string","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates.install_required_packages","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.table_templates.install_required_packages#L26-L30","kind":"function","name":"install_required_packages","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":26,"end_line":30,"context_start_line":6,"context_end_line":50,"code":"import pandas as pd\nfrom sqlalchemy import create_engine\nimport tiktoken\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(\n table_data: Union[pd.DataFrame, Dict[str, Any]], table_name: str = \"table\", visible_rows_num: int = 3\n) -> Dict[str, str]:\n \"\"\"\n Convert table data to string representations in different formats.\n\n :param table_data: A dictionary with \"cols\" (list of strings) and \"rows\"\n (list of lists of strings) as keys.\n :param table_name: The name of the table.\n :param visible_rows_num: The number of rows to be displayed in the representation.\n :return: A dictionary with the string table representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"tabulate\", \"prettytable\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages\n install_required_packages()\n from prettytable import PrettyTable\n\n # Handle situation when the table_data is already a dataframe, FIXME: this is a hack\n new_table_data = {}\n if isinstance(table_data, pd.DataFrame):\n new_table_data[\"cols\"] = table_data.columns\n new_table_data[\"rows\"] = table_data.values.tolist()\n table_data = new_table_data\n\n # Type check for table_data\n if not isinstance(table_data, dict) or \"cols\" not in table_data or \"rows\" not in table_data:\n raise TypeError(\"table_data must be a dictionary with 'cols' and 'rows' as keys.\")\n\n table_data_for_observable = deepcopy(table_data)\n if len(table_data_for_observable[\"rows\"]) > visible_rows_num:\n table_data_for_observable[\"rows\"] = table_data_for_observable[\"rows\"][:visible_rows_num]\n table_data_for_observable[\"rows\"].append([\"...\"] * len(table_data_for_observable[\"cols\"]))","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates.bbcode_mode_table","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.table_templates.bbcode_mode_table#L63-L71","kind":"function","name":"bbcode_mode_table","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":63,"end_line":71,"context_start_line":43,"context_end_line":91,"code":" # Type check for table_data\n if not isinstance(table_data, dict) or \"cols\" not in table_data or \"rows\" not in table_data:\n raise TypeError(\"table_data must be a dictionary with 'cols' and 'rows' as keys.\")\n\n table_data_for_observable = deepcopy(table_data)\n if len(table_data_for_observable[\"rows\"]) > visible_rows_num:\n table_data_for_observable[\"rows\"] = table_data_for_observable[\"rows\"][:visible_rows_num]\n table_data_for_observable[\"rows\"].append([\"...\"] * len(table_data_for_observable[\"cols\"]))\n\n # Create dataframe from table_data\n df = pd.DataFrame(table_data_for_observable[\"rows\"], columns=table_data_for_observable[\"cols\"])\n\n # Generate tables in different formats\n markdown_table = df.to_markdown(index=False)\n html_table = df.to_html(index=False)\n latex_table = df.to_latex(index=False)\n csv_table = df.to_csv(index=False)\n tsv_table = df.to_csv(index=False, sep=\"\\t\")\n rest_table = df.to_string(index=False)\n\n def bbcode_mode_table(data_frame: pd.DataFrame) -> str:\n bbcode_table = \"[table]\\n\"\n for row in data_frame.itertuples(index=False):\n bbcode_table += \"[tr]\\n\"\n for value in row:\n bbcode_table += f\"[td]{value}[/td]\\n\"\n bbcode_table += \"[/tr]\\n\"\n bbcode_table += \"[/table]\"\n return bbcode_table\n\n def mediawiki_mode_table(data_frame: pd.DataFrame) -> str:\n mediawiki_table = '{| class=\"wikitable\"\\n|-\\n'\n for col in data_frame.columns:\n mediawiki_table += f\"! {col}\\n\"\n for row in data_frame.itertuples(index=False):\n mediawiki_table += \"|-\\n\"\n for value in row:\n mediawiki_table += f\"| {value}\\n\"\n mediawiki_table += \"|}\"\n return mediawiki_table\n\n def org_mode_table(data_frame: pd.DataFrame) -> str:\n org_table = (\n \"| \"\n + \" | \".join(data_frame.columns)\n + \" |\\n|-\"\n + \" | -\".join([\"-\" * len(col) for col in data_frame.columns])\n + \" |\\n\"\n )","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates.mediawiki_mode_table","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.table_templates.mediawiki_mode_table#L73-L82","kind":"function","name":"mediawiki_mode_table","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":73,"end_line":82,"context_start_line":53,"context_end_line":102,"code":" df = pd.DataFrame(table_data_for_observable[\"rows\"], columns=table_data_for_observable[\"cols\"])\n\n # Generate tables in different formats\n markdown_table = df.to_markdown(index=False)\n html_table = df.to_html(index=False)\n latex_table = df.to_latex(index=False)\n csv_table = df.to_csv(index=False)\n tsv_table = df.to_csv(index=False, sep=\"\\t\")\n rest_table = df.to_string(index=False)\n\n def bbcode_mode_table(data_frame: pd.DataFrame) -> str:\n bbcode_table = \"[table]\\n\"\n for row in data_frame.itertuples(index=False):\n bbcode_table += \"[tr]\\n\"\n for value in row:\n bbcode_table += f\"[td]{value}[/td]\\n\"\n bbcode_table += \"[/tr]\\n\"\n bbcode_table += \"[/table]\"\n return bbcode_table\n\n def mediawiki_mode_table(data_frame: pd.DataFrame) -> str:\n mediawiki_table = '{| class=\"wikitable\"\\n|-\\n'\n for col in data_frame.columns:\n mediawiki_table += f\"! {col}\\n\"\n for row in data_frame.itertuples(index=False):\n mediawiki_table += \"|-\\n\"\n for value in row:\n mediawiki_table += f\"| {value}\\n\"\n mediawiki_table += \"|}\"\n return mediawiki_table\n\n def org_mode_table(data_frame: pd.DataFrame) -> str:\n org_table = (\n \"| \"\n + \" | \".join(data_frame.columns)\n + \" |\\n|-\"\n + \" | -\".join([\"-\" * len(col) for col in data_frame.columns])\n + \" |\\n\"\n )\n for row in data_frame.itertuples(index=False):\n org_table += \"| \" + \" | \".join([str(value) for value in row]) + \" |\\n\"\n return org_table\n\n bbcode_table = bbcode_mode_table(df)\n mediawiki_table = mediawiki_mode_table(df)\n org_table = org_mode_table(df)\n\n pretty_table = PrettyTable()\n pretty_table.field_names = table_data[\"cols\"]\n for row in table_data[\"rows\"]:","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates.org_mode_table","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.table_templates.org_mode_table#L84-L94","kind":"function","name":"org_mode_table","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":84,"end_line":94,"context_start_line":64,"context_end_line":114,"code":" bbcode_table = \"[table]\\n\"\n for row in data_frame.itertuples(index=False):\n bbcode_table += \"[tr]\\n\"\n for value in row:\n bbcode_table += f\"[td]{value}[/td]\\n\"\n bbcode_table += \"[/tr]\\n\"\n bbcode_table += \"[/table]\"\n return bbcode_table\n\n def mediawiki_mode_table(data_frame: pd.DataFrame) -> str:\n mediawiki_table = '{| class=\"wikitable\"\\n|-\\n'\n for col in data_frame.columns:\n mediawiki_table += f\"! {col}\\n\"\n for row in data_frame.itertuples(index=False):\n mediawiki_table += \"|-\\n\"\n for value in row:\n mediawiki_table += f\"| {value}\\n\"\n mediawiki_table += \"|}\"\n return mediawiki_table\n\n def org_mode_table(data_frame: pd.DataFrame) -> str:\n org_table = (\n \"| \"\n + \" | \".join(data_frame.columns)\n + \" |\\n|-\"\n + \" | -\".join([\"-\" * len(col) for col in data_frame.columns])\n + \" |\\n\"\n )\n for row in data_frame.itertuples(index=False):\n org_table += \"| \" + \" | \".join([str(value) for value in row]) + \" |\\n\"\n return org_table\n\n bbcode_table = bbcode_mode_table(df)\n mediawiki_table = mediawiki_mode_table(df)\n org_table = org_mode_table(df)\n\n pretty_table = PrettyTable()\n pretty_table.field_names = table_data[\"cols\"]\n for row in table_data[\"rows\"]:\n pretty_table.add_row(row)\n pretty_table = str(pretty_table)\n\n # New function to generate SQL table\n def sql_mode_table(data_frame: pd.DataFrame, _table_name: str) -> str:\n sql_table_str = f\"CREATE TABLE {table_name}(\\n\"\n\n for col in data_frame.columns:\n sql_table_str += f\"{col} text,\\n\"\n\n # Remove the last comma and add the primary key constraint\n sql_table_str = sql_table_str[:-2] + f\",\\nPRIMARY KEY ({data_frame.columns[0]})\\n);\"","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.table_templates.sql_mode_table","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.table_templates.sql_mode_table#L107-L122","kind":"function","name":"sql_mode_table","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":107,"end_line":122,"context_start_line":87,"context_end_line":142,"code":" + \" | \".join(data_frame.columns)\n + \" |\\n|-\"\n + \" | -\".join([\"-\" * len(col) for col in data_frame.columns])\n + \" |\\n\"\n )\n for row in data_frame.itertuples(index=False):\n org_table += \"| \" + \" | \".join([str(value) for value in row]) + \" |\\n\"\n return org_table\n\n bbcode_table = bbcode_mode_table(df)\n mediawiki_table = mediawiki_mode_table(df)\n org_table = org_mode_table(df)\n\n pretty_table = PrettyTable()\n pretty_table.field_names = table_data[\"cols\"]\n for row in table_data[\"rows\"]:\n pretty_table.add_row(row)\n pretty_table = str(pretty_table)\n\n # New function to generate SQL table\n def sql_mode_table(data_frame: pd.DataFrame, _table_name: str) -> str:\n sql_table_str = f\"CREATE TABLE {table_name}(\\n\"\n\n for col in data_frame.columns:\n sql_table_str += f\"{col} text,\\n\"\n\n # Remove the last comma and add the primary key constraint\n sql_table_str = sql_table_str[:-2] + f\",\\nPRIMARY KEY ({data_frame.columns[0]})\\n);\"\n\n sql_table_str += \"\\n/*\\n{} example rows:\\n\".format(len(data_frame))\n for i, _row in data_frame.iterrows():\n _row = \"\\t\".join([str(_cell) for _cell in _row.to_list()])\n sql_table_str += f\"{_row}\\n\"\n sql_table_str += \"*/\"\n\n return sql_table_str\n\n sql_table = sql_mode_table(df, table_name)\n\n # Return the representation in different formats as a dictionary\n return {\n \"Markdown\": markdown_table,\n \"HTML\": html_table,\n \"LaTeX\": latex_table,\n \"CSV\": csv_table,\n \"TSV\": tsv_table,\n \"reStructuredText\": rest_table,\n \"BBCode\": bbcode_table,\n \"MediaWiki\": mediawiki_table,\n \"Org mode\": org_table,\n \"PrettyTable\": pretty_table,\n \"SQL\": sql_table,\n }\n\n\ndef serialize_df(","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.database_templates","uri":"program://OpenAgents/module/real_agents.adapters.data_model.templates.skg_templates.database_templates#L1-L74","kind":"module","name":"real_agents.adapters.data_model.templates.skg_templates.database_templates","path":"real_agents/adapters/data_model/templates/skg_templates/database_templates.py","language":"python","start_line":1,"end_line":74,"context_start_line":1,"context_end_line":74,"code":"import sqlite3\nfrom typing import Dict, Union\n\nimport pandas as pd\nimport tiktoken\n\n\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import (\n convert as convert_table,\n)\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(db_input: Union[str, Dict[str, pd.DataFrame]], visible_rows_num: int = 3) -> Dict[str, str]:\n \"\"\"\n Convert database data to string representations in different formats.\n\n :param db_input: the path to the sqlite database file, or a pd.DataFrame.\n :param visible_rows_num: the number of rows to be displayed in each table.\n :return: A dictionary with the string database representations in different formats.\n \"\"\"\n if isinstance(db_input, str):\n conn = sqlite3.connect(db_input)\n cursor = conn.cursor()\n cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n table_names = [name[0] for name in cursor.fetchall()]\n dfs = {table_name: pd.read_sql_query(f\"SELECT * FROM {table_name}\", conn) for table_name in table_names}\n elif isinstance(db_input, dict) and all(isinstance(df, pd.DataFrame) for df in db_input.values()):\n dfs = db_input\n else:\n raise ValueError(\"db_input should be either a SQLite database file path or a dictionary of pandas DataFrames\")\n\n representations = {\n \"Markdown\": \"\",\n \"HTML\": \"\",\n \"LaTeX\": \"\",\n \"CSV\": \"\",\n \"TSV\": \"\",\n \"reStructuredText\": \"\",\n \"BBCode\": \"\",\n \"MediaWiki\": \"\",\n \"Org mode\": \"\",\n \"PrettyTable\": \"\",\n \"SQL\": \"\",\n }\n\n for table_name, df in dfs.items():\n table_data = {\"cols\": df.columns.tolist(), \"rows\": df.values.tolist()}\n table_representations = convert_table(table_data, table_name, visible_rows_num)\n for _format, table_representation in table_representations.items():\n representations[_format] += table_representation + \"\\n\\n\"\n\n return representations\n\n\ndef serialize_db(\n db: SQLDatabase,\n serialize_method: str = \"database\",\n num_visible_rows: int = 3,\n max_tokens: int = 1000,\n) -> str:\n \"\"\"Convert database engine to a string representation.\"\"\"\n if serialize_method == \"database\":\n # TODO: Now access the internal variable\n setattr(db, \"_sample_rows_in_table_info\", num_visible_rows)\n string = db.get_table_info()\n # Truncate the string if it is too long\n enc = tiktoken.get_encoding(\"cl100k_base\")\n enc_tokens = enc.encode(string)\n if len(enc_tokens) > max_tokens:\n string = enc.decode(enc_tokens[:max_tokens])\n else:\n raise ValueError(\"Unknown serialization method.\")\n return string","source_hash":"5341e373080d2974e900e97bad0610dcf75ea450b7542cc75b4b2a927032871a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.database_templates.convert","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.database_templates.convert#L14-L53","kind":"function","name":"convert","path":"real_agents/adapters/data_model/templates/skg_templates/database_templates.py","language":"python","start_line":14,"end_line":53,"context_start_line":1,"context_end_line":73,"code":"import sqlite3\nfrom typing import Dict, Union\n\nimport pandas as pd\nimport tiktoken\n\n\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import (\n convert as convert_table,\n)\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(db_input: Union[str, Dict[str, pd.DataFrame]], visible_rows_num: int = 3) -> Dict[str, str]:\n \"\"\"\n Convert database data to string representations in different formats.\n\n :param db_input: the path to the sqlite database file, or a pd.DataFrame.\n :param visible_rows_num: the number of rows to be displayed in each table.\n :return: A dictionary with the string database representations in different formats.\n \"\"\"\n if isinstance(db_input, str):\n conn = sqlite3.connect(db_input)\n cursor = conn.cursor()\n cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n table_names = [name[0] for name in cursor.fetchall()]\n dfs = {table_name: pd.read_sql_query(f\"SELECT * FROM {table_name}\", conn) for table_name in table_names}\n elif isinstance(db_input, dict) and all(isinstance(df, pd.DataFrame) for df in db_input.values()):\n dfs = db_input\n else:\n raise ValueError(\"db_input should be either a SQLite database file path or a dictionary of pandas DataFrames\")\n\n representations = {\n \"Markdown\": \"\",\n \"HTML\": \"\",\n \"LaTeX\": \"\",\n \"CSV\": \"\",\n \"TSV\": \"\",\n \"reStructuredText\": \"\",\n \"BBCode\": \"\",\n \"MediaWiki\": \"\",\n \"Org mode\": \"\",\n \"PrettyTable\": \"\",\n \"SQL\": \"\",\n }\n\n for table_name, df in dfs.items():\n table_data = {\"cols\": df.columns.tolist(), \"rows\": df.values.tolist()}\n table_representations = convert_table(table_data, table_name, visible_rows_num)\n for _format, table_representation in table_representations.items():\n representations[_format] += table_representation + \"\\n\\n\"\n\n return representations\n\n\ndef serialize_db(\n db: SQLDatabase,\n serialize_method: str = \"database\",\n num_visible_rows: int = 3,\n max_tokens: int = 1000,\n) -> str:\n \"\"\"Convert database engine to a string representation.\"\"\"\n if serialize_method == \"database\":\n # TODO: Now access the internal variable\n setattr(db, \"_sample_rows_in_table_info\", num_visible_rows)\n string = db.get_table_info()\n # Truncate the string if it is too long\n enc = tiktoken.get_encoding(\"cl100k_base\")\n enc_tokens = enc.encode(string)\n if len(enc_tokens) > max_tokens:\n string = enc.decode(enc_tokens[:max_tokens])\n else:\n raise ValueError(\"Unknown serialization method.\")","source_hash":"5341e373080d2974e900e97bad0610dcf75ea450b7542cc75b4b2a927032871a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.database_templates.serialize_db","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.database_templates.serialize_db#L56-L74","kind":"function","name":"serialize_db","path":"real_agents/adapters/data_model/templates/skg_templates/database_templates.py","language":"python","start_line":56,"end_line":74,"context_start_line":36,"context_end_line":74,"code":" \"LaTeX\": \"\",\n \"CSV\": \"\",\n \"TSV\": \"\",\n \"reStructuredText\": \"\",\n \"BBCode\": \"\",\n \"MediaWiki\": \"\",\n \"Org mode\": \"\",\n \"PrettyTable\": \"\",\n \"SQL\": \"\",\n }\n\n for table_name, df in dfs.items():\n table_data = {\"cols\": df.columns.tolist(), \"rows\": df.values.tolist()}\n table_representations = convert_table(table_data, table_name, visible_rows_num)\n for _format, table_representation in table_representations.items():\n representations[_format] += table_representation + \"\\n\\n\"\n\n return representations\n\n\ndef serialize_db(\n db: SQLDatabase,\n serialize_method: str = \"database\",\n num_visible_rows: int = 3,\n max_tokens: int = 1000,\n) -> str:\n \"\"\"Convert database engine to a string representation.\"\"\"\n if serialize_method == \"database\":\n # TODO: Now access the internal variable\n setattr(db, \"_sample_rows_in_table_info\", num_visible_rows)\n string = db.get_table_info()\n # Truncate the string if it is too long\n enc = tiktoken.get_encoding(\"cl100k_base\")\n enc_tokens = enc.encode(string)\n if len(enc_tokens) > max_tokens:\n string = enc.decode(enc_tokens[:max_tokens])\n else:\n raise ValueError(\"Unknown serialization method.\")\n return string","source_hash":"5341e373080d2974e900e97bad0610dcf75ea450b7542cc75b4b2a927032871a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates","uri":"program://OpenAgents/module/real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates#L1-L41","kind":"module","name":"real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates","path":"real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","language":"python","start_line":1,"end_line":41,"context_start_line":1,"context_end_line":41,"code":"import subprocess\nimport sys\nfrom typing import Dict, List, Tuple\n\n\ndef convert(kg_input: List[Tuple], name_space: str = \"\") -> Dict[str, str]:\n \"\"\"\n Convert knowledge graph data to string representations in different formats.\n\n :param kg_input: the list of knowledge graph triples.\n :param name_space: of the knowledge graph.\n :return: A dictionary with the string knowledge graph representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"rdflib\", \"rdflib-jsonld\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages\n install_required_packages()\n from rdflib import Graph, Namespace, URIRef\n\n g = Graph()\n\n # Define a namespace for the knowledge graph\n kg_ns = Namespace(name_space)\n g.bind(\"kg\", kg_ns)\n\n # Add the triples to the graph\n for s, p, o in kg_input:\n subject = URIRef(kg_ns[s])\n predicate = URIRef(kg_ns[p])\n object = URIRef(kg_ns[o])\n g.add((subject, predicate, object))\n\n # Serialize the graph into the desired format\n representations = {_format: g.serialize(format=_format) for _format in [\"json-ld\", \"turtle\", \"n3\", \"nt\"]}\n\n return representations","source_hash":"5d9e3d58bbe95d3d47840d56ea85f0a6194cc4c57f432ad1cf1b942d057bb9ea","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates.convert","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates.convert#L6-L41","kind":"function","name":"convert","path":"real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","language":"python","start_line":6,"end_line":41,"context_start_line":1,"context_end_line":41,"code":"import subprocess\nimport sys\nfrom typing import Dict, List, Tuple\n\n\ndef convert(kg_input: List[Tuple], name_space: str = \"\") -> Dict[str, str]:\n \"\"\"\n Convert knowledge graph data to string representations in different formats.\n\n :param kg_input: the list of knowledge graph triples.\n :param name_space: of the knowledge graph.\n :return: A dictionary with the string knowledge graph representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"rdflib\", \"rdflib-jsonld\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages\n install_required_packages()\n from rdflib import Graph, Namespace, URIRef\n\n g = Graph()\n\n # Define a namespace for the knowledge graph\n kg_ns = Namespace(name_space)\n g.bind(\"kg\", kg_ns)\n\n # Add the triples to the graph\n for s, p, o in kg_input:\n subject = URIRef(kg_ns[s])\n predicate = URIRef(kg_ns[p])\n object = URIRef(kg_ns[o])\n g.add((subject, predicate, object))\n\n # Serialize the graph into the desired format\n representations = {_format: g.serialize(format=_format) for _format in [\"json-ld\", \"turtle\", \"n3\", \"nt\"]}\n\n return representations","source_hash":"5d9e3d58bbe95d3d47840d56ea85f0a6194cc4c57f432ad1cf1b942d057bb9ea","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates.install_required_packages","uri":"program://OpenAgents/function/real_agents.adapters.data_model.templates.skg_templates.knowledge_graph_templates.install_required_packages#L15-L19","kind":"function","name":"install_required_packages","path":"real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","language":"python","start_line":15,"end_line":19,"context_start_line":1,"context_end_line":39,"code":"import subprocess\nimport sys\nfrom typing import Dict, List, Tuple\n\n\ndef convert(kg_input: List[Tuple], name_space: str = \"\") -> Dict[str, str]:\n \"\"\"\n Convert knowledge graph data to string representations in different formats.\n\n :param kg_input: the list of knowledge graph triples.\n :param name_space: of the knowledge graph.\n :return: A dictionary with the string knowledge graph representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"rdflib\", \"rdflib-jsonld\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages\n install_required_packages()\n from rdflib import Graph, Namespace, URIRef\n\n g = Graph()\n\n # Define a namespace for the knowledge graph\n kg_ns = Namespace(name_space)\n g.bind(\"kg\", kg_ns)\n\n # Add the triples to the graph\n for s, p, o in kg_input:\n subject = URIRef(kg_ns[s])\n predicate = URIRef(kg_ns[p])\n object = URIRef(kg_ns[o])\n g.add((subject, predicate, object))\n\n # Serialize the graph into the desired format\n representations = {_format: g.serialize(format=_format) for _format in [\"json-ld\", \"turtle\", \"n3\", \"nt\"]}","source_hash":"5d9e3d58bbe95d3d47840d56ea85f0a6194cc4c57f432ad1cf1b942d057bb9ea","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.base","uri":"program://OpenAgents/module/real_agents.adapters.executors.base#L1-L10","kind":"module","name":"real_agents.adapters.executors.base","path":"real_agents/adapters/executors/base.py","language":"python","start_line":1,"end_line":10,"context_start_line":1,"context_end_line":10,"code":"from abc import ABC, abstractmethod\nfrom typing import Any, Dict, Optional\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass BaseExecutor(ABC):\n @abstractmethod\n def run(self, user_intent: str, grounding_source: Optional[SQLDatabase]) -> Dict[str, Any]:\n \"\"\"Run the executor.\"\"\"","source_hash":"9c6a48a6c4d5038792b1387868571b2a4188032eef384fde4b9283e1455985b9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.base.BaseExecutor","uri":"program://OpenAgents/class/real_agents.adapters.executors.base.BaseExecutor#L7-L10","kind":"class","name":"BaseExecutor","path":"real_agents/adapters/executors/base.py","language":"python","start_line":7,"end_line":10,"context_start_line":1,"context_end_line":10,"code":"from abc import ABC, abstractmethod\nfrom typing import Any, Dict, Optional\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass BaseExecutor(ABC):\n @abstractmethod\n def run(self, user_intent: str, grounding_source: Optional[SQLDatabase]) -> Dict[str, Any]:\n \"\"\"Run the executor.\"\"\"","source_hash":"9c6a48a6c4d5038792b1387868571b2a4188032eef384fde4b9283e1455985b9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.base.run","uri":"program://OpenAgents/function/real_agents.adapters.executors.base.run#L9-L10","kind":"function","name":"run","path":"real_agents/adapters/executors/base.py","language":"python","start_line":9,"end_line":10,"context_start_line":1,"context_end_line":10,"code":"from abc import ABC, abstractmethod\nfrom typing import Any, Dict, Optional\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass BaseExecutor(ABC):\n @abstractmethod\n def run(self, user_intent: str, grounding_source: Optional[SQLDatabase]) -> Dict[str, Any]:\n \"\"\"Run the executor.\"\"\"","source_hash":"9c6a48a6c4d5038792b1387868571b2a4188032eef384fde4b9283e1455985b9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.chat_executor","uri":"program://OpenAgents/module/real_agents.adapters.executors.chat_executor#L1-L60","kind":"module","name":"real_agents.adapters.executors.chat_executor","path":"real_agents/adapters/executors/chat_executor.py","language":"python","start_line":1,"end_line":60,"context_start_line":1,"context_end_line":60,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.prompts import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\nfrom langchain.chains import ConversationChain\n\nfrom real_agents.adapters.executors.base import BaseExecutor\nfrom real_agents.adapters.memory import ConversationBufferMemory\n\n\nclass ChatExecutor(BaseExecutor):\n \"\"\"Chat Executor.\"\"\"\n\n _DEFAULT_TEMPLATE = \"The following is a friendly conversation between a human and an AI. \\\n The AI is talkative and provides lots of specific details from its context. \\\n If the AI does not know the answer to a question, it truthfully says it does not know.\"\n output_key: str = \"result\"\n\n def __init__(self) -> None:\n \"\"\"Initialize the executor\"\"\"\n self.memory = ConversationBufferMemory(return_messages=True)\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on.\n llm: Language model to use.\n verbose: Whether to print the logging.\n\n Returns:\n Result of string.\n \"\"\"\n prompt = ChatPromptTemplate.from_messages(\n [\n SystemMessagePromptTemplate.from_template(self._DEFAULT_TEMPLATE),\n MessagesPlaceholder(variable_name=\"history\"),\n HumanMessagePromptTemplate.from_template(\"{input}\"),\n ]\n )\n method = ConversationChain(\n llm=llm,\n prompt=prompt,\n verbose=verbose,\n memory=self.memory,\n )\n result = method.predict(input=user_intent)\n output = {self.output_key: result}\n return output","source_hash":"32ba2516f69e7e7ed64264232f16f9d5c18d0874870d1f7fc06f5cf90024e51a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.chat_executor.ChatExecutor","uri":"program://OpenAgents/class/real_agents.adapters.executors.chat_executor.ChatExecutor#L16-L60","kind":"class","name":"ChatExecutor","path":"real_agents/adapters/executors/chat_executor.py","language":"python","start_line":16,"end_line":60,"context_start_line":1,"context_end_line":60,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.prompts import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\nfrom langchain.chains import ConversationChain\n\nfrom real_agents.adapters.executors.base import BaseExecutor\nfrom real_agents.adapters.memory import ConversationBufferMemory\n\n\nclass ChatExecutor(BaseExecutor):\n \"\"\"Chat Executor.\"\"\"\n\n _DEFAULT_TEMPLATE = \"The following is a friendly conversation between a human and an AI. \\\n The AI is talkative and provides lots of specific details from its context. \\\n If the AI does not know the answer to a question, it truthfully says it does not know.\"\n output_key: str = \"result\"\n\n def __init__(self) -> None:\n \"\"\"Initialize the executor\"\"\"\n self.memory = ConversationBufferMemory(return_messages=True)\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on.\n llm: Language model to use.\n verbose: Whether to print the logging.\n\n Returns:\n Result of string.\n \"\"\"\n prompt = ChatPromptTemplate.from_messages(\n [\n SystemMessagePromptTemplate.from_template(self._DEFAULT_TEMPLATE),\n MessagesPlaceholder(variable_name=\"history\"),\n HumanMessagePromptTemplate.from_template(\"{input}\"),\n ]\n )\n method = ConversationChain(\n llm=llm,\n prompt=prompt,\n verbose=verbose,\n memory=self.memory,\n )\n result = method.predict(input=user_intent)\n output = {self.output_key: result}\n return output","source_hash":"32ba2516f69e7e7ed64264232f16f9d5c18d0874870d1f7fc06f5cf90024e51a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.chat_executor.__init__","uri":"program://OpenAgents/function/real_agents.adapters.executors.chat_executor.__init__#L24-L26","kind":"function","name":"__init__","path":"real_agents/adapters/executors/chat_executor.py","language":"python","start_line":24,"end_line":26,"context_start_line":4,"context_end_line":46,"code":"from langchain.prompts import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\nfrom langchain.chains import ConversationChain\n\nfrom real_agents.adapters.executors.base import BaseExecutor\nfrom real_agents.adapters.memory import ConversationBufferMemory\n\n\nclass ChatExecutor(BaseExecutor):\n \"\"\"Chat Executor.\"\"\"\n\n _DEFAULT_TEMPLATE = \"The following is a friendly conversation between a human and an AI. \\\n The AI is talkative and provides lots of specific details from its context. \\\n If the AI does not know the answer to a question, it truthfully says it does not know.\"\n output_key: str = \"result\"\n\n def __init__(self) -> None:\n \"\"\"Initialize the executor\"\"\"\n self.memory = ConversationBufferMemory(return_messages=True)\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on.\n llm: Language model to use.\n verbose: Whether to print the logging.\n\n Returns:\n Result of string.\n \"\"\"\n prompt = ChatPromptTemplate.from_messages(\n [","source_hash":"32ba2516f69e7e7ed64264232f16f9d5c18d0874870d1f7fc06f5cf90024e51a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.chat_executor.run","uri":"program://OpenAgents/function/real_agents.adapters.executors.chat_executor.run#L28-L60","kind":"function","name":"run","path":"real_agents/adapters/executors/chat_executor.py","language":"python","start_line":28,"end_line":60,"context_start_line":8,"context_end_line":60,"code":" SystemMessagePromptTemplate,\n)\nfrom langchain.chains import ConversationChain\n\nfrom real_agents.adapters.executors.base import BaseExecutor\nfrom real_agents.adapters.memory import ConversationBufferMemory\n\n\nclass ChatExecutor(BaseExecutor):\n \"\"\"Chat Executor.\"\"\"\n\n _DEFAULT_TEMPLATE = \"The following is a friendly conversation between a human and an AI. \\\n The AI is talkative and provides lots of specific details from its context. \\\n If the AI does not know the answer to a question, it truthfully says it does not know.\"\n output_key: str = \"result\"\n\n def __init__(self) -> None:\n \"\"\"Initialize the executor\"\"\"\n self.memory = ConversationBufferMemory(return_messages=True)\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n verbose: bool = True,\n ) -> Dict[str, Any]:\n \"\"\"Run the executor.\n\n Args:\n user_intent: User intent to execute.\n grounding_source: Grounding source to execute the program on.\n llm: Language model to use.\n verbose: Whether to print the logging.\n\n Returns:\n Result of string.\n \"\"\"\n prompt = ChatPromptTemplate.from_messages(\n [\n SystemMessagePromptTemplate.from_template(self._DEFAULT_TEMPLATE),\n MessagesPlaceholder(variable_name=\"history\"),\n HumanMessagePromptTemplate.from_template(\"{input}\"),\n ]\n )\n method = ConversationChain(\n llm=llm,\n prompt=prompt,\n verbose=verbose,\n memory=self.memory,\n )\n result = method.predict(input=user_intent)\n output = {self.output_key: result}\n return output","source_hash":"32ba2516f69e7e7ed64264232f16f9d5c18d0874870d1f7fc06f5cf90024e51a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.chat_memory","uri":"program://OpenAgents/module/real_agents.adapters.executors.question_suggestion.chat_memory#L1-L15","kind":"module","name":"real_agents.adapters.executors.question_suggestion.chat_memory","path":"real_agents/adapters/executors/question_suggestion/chat_memory.py","language":"python","start_line":1,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY\n\n\nclass QuestionSuggestionChainChatMemory(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainChatMemory:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY)\n return cls(llm_chain=llm_chain)","source_hash":"d30cd4754b287bb9573920c6929414cf41112828cc5fe6c6d6785a486aabfa80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.chat_memory.QuestionSuggestionChainChatMemory","uri":"program://OpenAgents/class/real_agents.adapters.executors.question_suggestion.chat_memory.QuestionSuggestionChainChatMemory#L10-L15","kind":"class","name":"QuestionSuggestionChainChatMemory","path":"real_agents/adapters/executors/question_suggestion/chat_memory.py","language":"python","start_line":10,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY\n\n\nclass QuestionSuggestionChainChatMemory(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainChatMemory:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY)\n return cls(llm_chain=llm_chain)","source_hash":"d30cd4754b287bb9573920c6929414cf41112828cc5fe6c6d6785a486aabfa80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.chat_memory.from_prompt","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.chat_memory.from_prompt#L12-L15","kind":"function","name":"from_prompt","path":"real_agents/adapters/executors/question_suggestion/chat_memory.py","language":"python","start_line":12,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY\n\n\nclass QuestionSuggestionChainChatMemory(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainChatMemory:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY)\n return cls(llm_chain=llm_chain)","source_hash":"d30cd4754b287bb9573920c6929414cf41112828cc5fe6c6d6785a486aabfa80","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base","uri":"program://OpenAgents/module/real_agents.adapters.executors.question_suggestion.base#L1-L66","kind":"module","name":"real_agents.adapters.executors.question_suggestion.base","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":1,"end_line":66,"context_start_line":1,"context_end_line":66,"code":"from __future__ import annotations\n\nfrom typing import Dict, List, Optional\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_BASE\n\n\nclass QuestionSuggestionChainBase(Chain, BaseModel):\n \"\"\"Question Suggestion by Language Models.\"\"\"\n\n llm_chain: LLMChain\n output_key: str = \"questions\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, List[str]]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n questions = self.llm_chain.predict(**inputs)\n _run_manager.on_text(questions, color=\"green\", end=\"\\n\", verbose=False)\n return {self.output_keys[0]: self.extract_questions(questions)}\n\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainBase:\n \"\"\"Load from base prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_BASE)\n return cls(llm_chain=llm_chain)","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base.QuestionSuggestionChainBase","uri":"program://OpenAgents/class/real_agents.adapters.executors.question_suggestion.base.QuestionSuggestionChainBase#L14-L66","kind":"class","name":"QuestionSuggestionChainBase","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":14,"end_line":66,"context_start_line":1,"context_end_line":66,"code":"from __future__ import annotations\n\nfrom typing import Dict, List, Optional\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_BASE\n\n\nclass QuestionSuggestionChainBase(Chain, BaseModel):\n \"\"\"Question Suggestion by Language Models.\"\"\"\n\n llm_chain: LLMChain\n output_key: str = \"questions\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, List[str]]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n questions = self.llm_chain.predict(**inputs)\n _run_manager.on_text(questions, color=\"green\", end=\"\\n\", verbose=False)\n return {self.output_keys[0]: self.extract_questions(questions)}\n\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainBase:\n \"\"\"Load from base prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_BASE)\n return cls(llm_chain=llm_chain)","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base.Config","uri":"program://OpenAgents/class/real_agents.adapters.executors.question_suggestion.base.Config#L20-L24","kind":"class","name":"Config","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":20,"end_line":24,"context_start_line":1,"context_end_line":44,"code":"from __future__ import annotations\n\nfrom typing import Dict, List, Optional\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_BASE\n\n\nclass QuestionSuggestionChainBase(Chain, BaseModel):\n \"\"\"Question Suggestion by Language Models.\"\"\"\n\n llm_chain: LLMChain\n output_key: str = \"questions\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base.input_keys","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.base.input_keys#L27-L32","kind":"function","name":"input_keys","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":27,"end_line":32,"context_start_line":7,"context_end_line":52,"code":"from langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_BASE\n\n\nclass QuestionSuggestionChainBase(Chain, BaseModel):\n \"\"\"Question Suggestion by Language Models.\"\"\"\n\n llm_chain: LLMChain\n output_key: str = \"questions\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base.output_keys","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.base.output_keys#L35-L40","kind":"function","name":"output_keys","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":35,"end_line":40,"context_start_line":15,"context_end_line":60,"code":" \"\"\"Question Suggestion by Language Models.\"\"\"\n\n llm_chain: LLMChain\n output_key: str = \"questions\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, List[str]]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n questions = self.llm_chain.predict(**inputs)\n _run_manager.on_text(questions, color=\"green\", end=\"\\n\", verbose=False)\n return {self.output_keys[0]: self.extract_questions(questions)}","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base.extract_questions","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.base.extract_questions#L42-L50","kind":"function","name":"extract_questions","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":42,"end_line":50,"context_start_line":22,"context_end_line":66,"code":"\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, List[str]]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n questions = self.llm_chain.predict(**inputs)\n _run_manager.on_text(questions, color=\"green\", end=\"\\n\", verbose=False)\n return {self.output_keys[0]: self.extract_questions(questions)}\n\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainBase:\n \"\"\"Load from base prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_BASE)\n return cls(llm_chain=llm_chain)","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base._call","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.base._call#L52-L60","kind":"function","name":"_call","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":52,"end_line":60,"context_start_line":32,"context_end_line":66,"code":" return self.llm_chain.prompt.input_variables\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n return [self.output_key]\n\n def extract_questions(self, s: str) -> List[str]:\n components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, List[str]]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n questions = self.llm_chain.predict(**inputs)\n _run_manager.on_text(questions, color=\"green\", end=\"\\n\", verbose=False)\n return {self.output_keys[0]: self.extract_questions(questions)}\n\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainBase:\n \"\"\"Load from base prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_BASE)\n return cls(llm_chain=llm_chain)","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.base.from_prompt","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.base.from_prompt#L63-L66","kind":"function","name":"from_prompt","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":63,"end_line":66,"context_start_line":43,"context_end_line":66,"code":" components = s.split(\"\\n\")\n questions = []\n count = 1\n for c in components:\n if c.startswith(f\"{count}\"):\n questions.append(c.replace(f\"{count}.\", \"\").replace(f\"{count}\", \"\").strip())\n count += 1\n return questions\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, List[str]]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n questions = self.llm_chain.predict(**inputs)\n _run_manager.on_text(questions, color=\"green\", end=\"\\n\", verbose=False)\n return {self.output_keys[0]: self.extract_questions(questions)}\n\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainBase:\n \"\"\"Load from base prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_BASE)\n return cls(llm_chain=llm_chain)","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.user_profile","uri":"program://OpenAgents/module/real_agents.adapters.executors.question_suggestion.user_profile#L1-L15","kind":"module","name":"real_agents.adapters.executors.question_suggestion.user_profile","path":"real_agents/adapters/executors/question_suggestion/user_profile.py","language":"python","start_line":1,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_USER_PROFILE\n\n\nclass QuestionSuggestionChainUserProfile(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainUserProfile:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_USER_PROFILE)\n return cls(llm_chain=llm_chain)","source_hash":"309949ef2d126a8a35edd88f80ef37e7f640d8a05a202c7fa0fe6f63960ed9b6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.user_profile.QuestionSuggestionChainUserProfile","uri":"program://OpenAgents/class/real_agents.adapters.executors.question_suggestion.user_profile.QuestionSuggestionChainUserProfile#L10-L15","kind":"class","name":"QuestionSuggestionChainUserProfile","path":"real_agents/adapters/executors/question_suggestion/user_profile.py","language":"python","start_line":10,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_USER_PROFILE\n\n\nclass QuestionSuggestionChainUserProfile(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainUserProfile:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_USER_PROFILE)\n return cls(llm_chain=llm_chain)","source_hash":"309949ef2d126a8a35edd88f80ef37e7f640d8a05a202c7fa0fe6f63960ed9b6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.user_profile.from_prompt","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.user_profile.from_prompt#L12-L15","kind":"function","name":"from_prompt","path":"real_agents/adapters/executors/question_suggestion/user_profile.py","language":"python","start_line":12,"end_line":15,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_USER_PROFILE\n\n\nclass QuestionSuggestionChainUserProfile(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainUserProfile:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_USER_PROFILE)\n return cls(llm_chain=llm_chain)","source_hash":"309949ef2d126a8a35edd88f80ef37e7f640d8a05a202c7fa0fe6f63960ed9b6","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.prompts","uri":"program://OpenAgents/module/real_agents.adapters.executors.question_suggestion.prompts#L1-L54","kind":"module","name":"real_agents.adapters.executors.question_suggestion.prompts","path":"real_agents/adapters/executors/question_suggestion/prompts.py","language":"python","start_line":1,"end_line":54,"context_start_line":1,"context_end_line":54,"code":"from langchain import PromptTemplate\n\ntemplate_base = (\n \"{input_string}\\nPlease provide {num_questions} natural language questions related to the above contents, \"\n \"but very different from each other. These questions should be diverse, challenging, \"\n \"and targeted towards different perspectives. \"\n \"You should ask these questions like you would ask a human, \"\n \"but strictly follow the style of your role-playing character.\\n\"\n \"Do not explicitly mention the provided contents; \"\n \"instead use natural language descriptions for them. \"\n \"The final result should be a numbered list.\".strip() + \"\\n\"\n)\n\nQUESTION_SUGGESTION_PROMPT_BASE = PromptTemplate(\n input_variables=[\"input_string\", \"num_questions\"], template=template_base\n)\n\ntemplate_user_profile = (\n \"{input_string}\\n--------------------\\n\"\n \"{user_description}\\n\"\n \"From now on, you should speak in a style that fully conforms to the given role. \\n\"\n \"Please provide {num_questions} natural language questions related to the above database, \"\n \"but very different from each other. These questions should be diverse, challenging, \"\n \"and targeted towards different database tables and columns as well as query types. \"\n \"You should ask these questions like you would ask a human, \"\n \"but strictly follow the style of your role-playing character.\\n\"\n \"Do not explicitly mention column or table names in the database; \"\n \"instead use natural language descriptions for them. \"\n \"The final result should be a numbered list.\".strip() + \"\\n\"\n)\n\nQUESTION_SUGGESTION_PROMPT_USER_PROFILE = PromptTemplate(\n input_variables=[\"input_string\", \"user_description\", \"num_questions\"], template=template_user_profile\n)\n\ntemplate_chat_memory = (\n \"{input_string}\\n--------------------\\n\"\n \"Here is the conversation between Human and AI.\\n\"\n \"{chat_memory}\\n\"\n \"--------------------\\n\"\n \"Please provide {num_questions} natural language questions related to the above contents, \"\n \"but very different from each other. These questions should be diverse, challenging, \"\n \"and targeted towards different perspectives.\\n\"\n \"Keep each questions shorter than 15 words.\\n\"\n \"You should ask these questions like you would ask a human, \"\n \"but strictly follow the style of your role-playing character.\\n\"\n \"Do not explicitly mention the provided contents; \"\n \"instead use natural language descriptions for them. \"\n \"The final result should be a numbered list.\".strip() + \"\\n\"\n)\n\nQUESTION_SUGGESTION_PROMPT_CHAT_MEMORY = PromptTemplate(\n input_variables=[\"input_string\", \"chat_memory\", \"num_questions\"], template=template_chat_memory\n)","source_hash":"e41d54786895dc71f9d40174dd0f5ba1338660e6554811a5ff970ce8090da2df","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.question_suggestion_executor","uri":"program://OpenAgents/module/real_agents.adapters.executors.question_suggestion.question_suggestion_executor#L1-L45","kind":"module","name":"real_agents.adapters.executors.question_suggestion.question_suggestion_executor","path":"real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","language":"python","start_line":1,"end_line":45,"context_start_line":1,"context_end_line":45,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import AIMessage, HumanMessage\n\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.adapters.executors.question_suggestion.chat_memory import QuestionSuggestionChainChatMemory\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.user_profile import QuestionSuggestionChainUserProfile\n\n\nclass QuestionSuggestionExecutor:\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n num_questions: int = 3,\n mode: str = \"\",\n user_profile: str = \"\",\n chat_memory: ConversationReActBufferMemory = ConversationReActBufferMemory(),\n ) -> Dict[str, Any]:\n if mode == \"base\":\n method = QuestionSuggestionChainBase.from_prompt(llm)\n inputs = {\"input_string\": user_intent, \"num_questions\": num_questions}\n elif mode == \"user_profile\":\n method = QuestionSuggestionChainUserProfile.from_prompt(llm)\n with open(user_profile) as f:\n inputs = {\"input_string\": user_intent, \"num_questions\": num_questions, \"user_description\": f.read()}\n elif mode == \"chat_memory\":\n method = QuestionSuggestionChainChatMemory.from_prompt(llm)\n raw_history = chat_memory.load_memory_variables({})[\"chat_history\"]\n refine_history = []\n for msg in raw_history[-4:]:\n if isinstance(msg, HumanMessage):\n refine_history.append(f\"Human: {msg.content}\")\n elif isinstance(msg, AIMessage):\n refine_history.append(f\"AI: {msg.content}\")\n inputs = {\n \"input_string\": user_intent,\n \"num_questions\": num_questions,\n \"chat_memory\": \"\\n\".join(refine_history),\n }\n else:\n raise ValueError(f\"Mode {mode} is not supported\")\n return method(inputs)","source_hash":"4ab05b3161254739021aa7a1ad92a2290bbdea3addf67f61a571713b6d427982","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.question_suggestion_executor.QuestionSuggestionExecutor","uri":"program://OpenAgents/class/real_agents.adapters.executors.question_suggestion.question_suggestion_executor.QuestionSuggestionExecutor#L12-L45","kind":"class","name":"QuestionSuggestionExecutor","path":"real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","language":"python","start_line":12,"end_line":45,"context_start_line":1,"context_end_line":45,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import AIMessage, HumanMessage\n\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.adapters.executors.question_suggestion.chat_memory import QuestionSuggestionChainChatMemory\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.user_profile import QuestionSuggestionChainUserProfile\n\n\nclass QuestionSuggestionExecutor:\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n num_questions: int = 3,\n mode: str = \"\",\n user_profile: str = \"\",\n chat_memory: ConversationReActBufferMemory = ConversationReActBufferMemory(),\n ) -> Dict[str, Any]:\n if mode == \"base\":\n method = QuestionSuggestionChainBase.from_prompt(llm)\n inputs = {\"input_string\": user_intent, \"num_questions\": num_questions}\n elif mode == \"user_profile\":\n method = QuestionSuggestionChainUserProfile.from_prompt(llm)\n with open(user_profile) as f:\n inputs = {\"input_string\": user_intent, \"num_questions\": num_questions, \"user_description\": f.read()}\n elif mode == \"chat_memory\":\n method = QuestionSuggestionChainChatMemory.from_prompt(llm)\n raw_history = chat_memory.load_memory_variables({})[\"chat_history\"]\n refine_history = []\n for msg in raw_history[-4:]:\n if isinstance(msg, HumanMessage):\n refine_history.append(f\"Human: {msg.content}\")\n elif isinstance(msg, AIMessage):\n refine_history.append(f\"AI: {msg.content}\")\n inputs = {\n \"input_string\": user_intent,\n \"num_questions\": num_questions,\n \"chat_memory\": \"\\n\".join(refine_history),\n }\n else:\n raise ValueError(f\"Mode {mode} is not supported\")\n return method(inputs)","source_hash":"4ab05b3161254739021aa7a1ad92a2290bbdea3addf67f61a571713b6d427982","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.executors.question_suggestion.question_suggestion_executor.run","uri":"program://OpenAgents/function/real_agents.adapters.executors.question_suggestion.question_suggestion_executor.run#L13-L45","kind":"function","name":"run","path":"real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","language":"python","start_line":13,"end_line":45,"context_start_line":1,"context_end_line":45,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import AIMessage, HumanMessage\n\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.adapters.executors.question_suggestion.chat_memory import QuestionSuggestionChainChatMemory\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.user_profile import QuestionSuggestionChainUserProfile\n\n\nclass QuestionSuggestionExecutor:\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n num_questions: int = 3,\n mode: str = \"\",\n user_profile: str = \"\",\n chat_memory: ConversationReActBufferMemory = ConversationReActBufferMemory(),\n ) -> Dict[str, Any]:\n if mode == \"base\":\n method = QuestionSuggestionChainBase.from_prompt(llm)\n inputs = {\"input_string\": user_intent, \"num_questions\": num_questions}\n elif mode == \"user_profile\":\n method = QuestionSuggestionChainUserProfile.from_prompt(llm)\n with open(user_profile) as f:\n inputs = {\"input_string\": user_intent, \"num_questions\": num_questions, \"user_description\": f.read()}\n elif mode == \"chat_memory\":\n method = QuestionSuggestionChainChatMemory.from_prompt(llm)\n raw_history = chat_memory.load_memory_variables({})[\"chat_history\"]\n refine_history = []\n for msg in raw_history[-4:]:\n if isinstance(msg, HumanMessage):\n refine_history.append(f\"Human: {msg.content}\")\n elif isinstance(msg, AIMessage):\n refine_history.append(f\"AI: {msg.content}\")\n inputs = {\n \"input_string\": user_intent,\n \"num_questions\": num_questions,\n \"chat_memory\": \"\\n\".join(refine_history),\n }\n else:\n raise ValueError(f\"Mode {mode} is not supported\")\n return method(inputs)","source_hash":"4ab05b3161254739021aa7a1ad92a2290bbdea3addf67f61a571713b6d427982","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent","uri":"program://OpenAgents/module/real_agents.adapters.agent_helpers.agent#L1-L659","kind":"module","name":"real_agents.adapters.agent_helpers.agent","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":1,"end_line":659,"context_start_line":1,"context_end_line":659,"code":"\"\"\"Chain that takes in an input and produces an action and action input.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom abc import abstractmethod\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union\nimport yaml\nfrom pydantic import BaseModel, root_validator\n\nfrom langchain.agents.agent_types import AgentType\nfrom langchain.agents.tools import InvalidTool\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.base import BaseCallbackManager\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForToolRun,\n CallbackManagerForChainRun,\n CallbackManagerForToolRun,\n Callbacks,\n)\nfrom langchain.chains.base import Chain\nfrom langchain.input import get_color_mapping\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.few_shot import FewShotPromptTemplate\nfrom langchain.prompts.prompt import PromptTemplate\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n BaseMessage,\n BaseOutputParser,\n OutputParserException,\n)\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.llm import LLMChain\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass BaseSingleActionAgent(BaseModel):\n \"\"\"Base Agent class.\"\"\"\n\n @property\n def return_values(self) -> List[str]:\n \"\"\"Return values of the agent.\"\"\"\n return [\"output\"]\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return None\n\n @abstractmethod\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n\n @abstractmethod\n async def aplan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n\n @property\n @abstractmethod\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n\n def return_stopped_response(\n self,\n early_stopping_method: str,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n else:\n raise ValueError(f\"Got unsupported early_stopping_method `{early_stopping_method}`\")\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n **kwargs: Any,\n ) -> BaseSingleActionAgent:\n raise NotImplementedError\n\n @property\n def _agent_type(self) -> str:\n \"\"\"Return Identifier of agent type.\"\"\"\n raise NotImplementedError\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n _type = self._agent_type\n if isinstance(_type, AgentType):\n _dict[\"_type\"] = str(_type.value)\n else:\n _dict[\"_type\"] = _type\n return _dict\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the agent.\n\n Args:\n file_path: Path to file to save the agent to.\n\n Example:\n .. code-block:: python\n\n # If working with agent executor\n agent.agent.save(file_path=\"path/agent.yaml\")\n \"\"\"\n # Convert file to Path object.\n if isinstance(file_path, str):\n save_path = Path(file_path)\n else:\n save_path = file_path\n\n directory_path = save_path.parent\n directory_path.mkdir(parents=True, exist_ok=True)\n\n # Fetch dictionary to save\n agent_dict = self.dict()\n\n if save_path.suffix == \".json\":\n with open(file_path, \"w\") as f:\n json.dump(agent_dict, f, indent=4)\n elif save_path.suffix == \".yaml\":\n with open(file_path, \"w\") as f:\n yaml.dump(agent_dict, f, default_flow_style=False)\n else:\n raise ValueError(f\"{save_path} must be json or yaml\")\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {}\n\n\nclass AgentOutputParser(BaseOutputParser):\n @abstractmethod\n def parse(self, text: str) -> Union[AgentAction, AgentFinish]:\n \"\"\"Parse text into agent action/finish.\"\"\"\n\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data() if isinstance(full_observation, DataModel) else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n return thoughts\n\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs)\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n async def aplan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n def get_full_inputs(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) -> Dict[str, Any]:\n \"\"\"Create the full inputs for the LLMChain from intermediate steps.\"\"\"\n thoughts = self._construct_scratchpad(intermediate_steps)\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n return full_inputs\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return list(set(self.llm_chain.input_keys) - {\"agent_scratchpad\"})\n\n @root_validator()\n def validate_prompt(cls, values: Dict) -> Dict:\n \"\"\"Validate that prompt matches format.\"\"\"\n prompt = values[\"llm_chain\"].prompt\n if \"agent_scratchpad\" not in prompt.input_variables:\n logger.warning(\n \"`agent_scratchpad` should be a variable in prompt.input_variables.\"\n \" Did not find it, so adding it at the end.\"\n )\n prompt.input_variables.append(\"agent_scratchpad\")\n if isinstance(prompt, PromptTemplate):\n prompt.template += \"\\n{agent_scratchpad}\"\n elif isinstance(prompt, FewShotPromptTemplate):\n prompt.suffix += \"\\n{agent_scratchpad}\"\n else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n llm_chain = LLMChain(\n llm=llm,\n prompt=cls.create_prompt(tools),\n callback_manager=callback_manager,\n )\n tool_names = [tool.name for tool in tools]\n _output_parser = output_parser or cls._get_default_output_parser()\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )\n\n def return_stopped_response(\n self,\n early_stopping_method: str,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n elif early_stopping_method == \"generate\":\n # Generate does one final forward pass\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data()\n if isinstance(full_observation, DataModel)\n else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n # Adding to the previous steps, we now tell the LLM to make a final pred\n thoughts += \"\\n\\nI now need to return a final answer based on the previous steps:\"\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n full_output = self.llm_chain.predict(**full_inputs)\n # We try to extract a final answer\n parsed_output = self.output_parser.parse(full_output)\n if isinstance(parsed_output, AgentFinish):\n # If we can extract, we send the correct stuff\n return parsed_output\n else:\n # If we can extract, but the tool is not the final tool,\n # we just return the full output\n return AgentFinish({\"output\": full_output}, full_output)\n else:\n raise ValueError(\n \"early_stopping_method should be one of `force` or `generate`, \" f\"got {early_stopping_method}\"\n )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass AgentExecutor(Chain):\n \"\"\"Consists of an agent using tools.\"\"\"\n\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n\n @classmethod\n def from_agent_and_tools(\n cls,\n agent: BaseSingleActionAgent,\n tools: Sequence[BaseTool],\n **kwargs: Any,\n ) -> AgentExecutor:\n \"\"\"Create from agent and tools.\"\"\"\n return cls(agent=agent, tools=tools, **kwargs)\n\n @root_validator()\n def validate_tools(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n agent = values[\"agent\"]\n tools = values[\"tools\"]\n allowed_tools = agent.get_allowed_tools()\n if allowed_tools is not None:\n if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:\n run_manager.on_agent_finish(output, color=\"green\", verbose=self.verbose)\n final_output = output.return_values\n if self.return_intermediate_steps:\n final_output[\"intermediate_steps\"] = intermediate_steps\n return final_output\n\n def _take_next_step(\n self,\n name_to_tool_map: Dict[str, BaseTool],\n color_mapping: Dict[str, str],\n inputs: Dict[str, str],\n intermediate_steps: List[Tuple[AgentAction, str]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n \"\"\"Take a single step in the thought-action-observation loop.\n\n Override this to take control of how the agent makes and acts on choices.\n \"\"\"\n try:\n # Call the LLM to see what to do.\n output = self.agent.plan(\n intermediate_steps,\n callbacks=run_manager.get_child() if run_manager else None,\n **inputs,\n )\n except OutputParserException as e:\n if isinstance(self.handle_parsing_errors, bool):\n raise_error = not self.handle_parsing_errors\n else:\n raise_error = False\n if raise_error:\n raise e\n text = str(e)\n if isinstance(self.handle_parsing_errors, bool):\n observation = \"Invalid or incomplete response\"\n elif isinstance(self.handle_parsing_errors, str):\n observation = self.handle_parsing_errors\n elif callable(self.handle_parsing_errors):\n observation = self.handle_parsing_errors(e)\n else:\n raise ValueError(\"Got unexpected type of `handle_parsing_errors`\")\n output = AgentAction(\"_Exception\", observation, text)\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n observation = ExceptionTool().run(\n output.tool_input,\n verbose=self.verbose,\n color=None,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n return [(output, observation)]\n # If the tool chosen is the finishing tool, then we end and return.\n if isinstance(output, AgentFinish):\n return output\n actions: List[AgentAction]\n if isinstance(output, AgentAction):\n actions = [output]\n else:\n actions = output\n result = []\n for agent_action in actions:\n if run_manager:\n run_manager.on_agent_action(agent_action, color=\"green\")\n # Otherwise we lookup the tool\n if agent_action.tool in name_to_tool_m\n# ... truncated ...","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":true} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.BaseSingleActionAgent","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.agent.BaseSingleActionAgent#L43-L172","kind":"class","name":"BaseSingleActionAgent","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":43,"end_line":172,"context_start_line":23,"context_end_line":192,"code":"from langchain.chains.base import Chain\nfrom langchain.input import get_color_mapping\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.few_shot import FewShotPromptTemplate\nfrom langchain.prompts.prompt import PromptTemplate\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n BaseMessage,\n BaseOutputParser,\n OutputParserException,\n)\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.llm import LLMChain\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass BaseSingleActionAgent(BaseModel):\n \"\"\"Base Agent class.\"\"\"\n\n @property\n def return_values(self) -> List[str]:\n \"\"\"Return values of the agent.\"\"\"\n return [\"output\"]\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return None\n\n @abstractmethod\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n\n @abstractmethod\n async def aplan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n\n @property\n @abstractmethod\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n\n def return_stopped_response(\n self,\n early_stopping_method: str,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n else:\n raise ValueError(f\"Got unsupported early_stopping_method `{early_stopping_method}`\")\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n **kwargs: Any,\n ) -> BaseSingleActionAgent:\n raise NotImplementedError\n\n @property\n def _agent_type(self) -> str:\n \"\"\"Return Identifier of agent type.\"\"\"\n raise NotImplementedError\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n _type = self._agent_type\n if isinstance(_type, AgentType):\n _dict[\"_type\"] = str(_type.value)\n else:\n _dict[\"_type\"] = _type\n return _dict\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the agent.\n\n Args:\n file_path: Path to file to save the agent to.\n\n Example:\n .. code-block:: python\n\n # If working with agent executor\n agent.agent.save(file_path=\"path/agent.yaml\")\n \"\"\"\n # Convert file to Path object.\n if isinstance(file_path, str):\n save_path = Path(file_path)\n else:\n save_path = file_path\n\n directory_path = save_path.parent\n directory_path.mkdir(parents=True, exist_ok=True)\n\n # Fetch dictionary to save\n agent_dict = self.dict()\n\n if save_path.suffix == \".json\":\n with open(file_path, \"w\") as f:\n json.dump(agent_dict, f, indent=4)\n elif save_path.suffix == \".yaml\":\n with open(file_path, \"w\") as f:\n yaml.dump(agent_dict, f, default_flow_style=False)\n else:\n raise ValueError(f\"{save_path} must be json or yaml\")\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {}\n\n\nclass AgentOutputParser(BaseOutputParser):\n @abstractmethod\n def parse(self, text: str) -> Union[AgentAction, AgentFinish]:\n \"\"\"Parse text into agent action/finish.\"\"\"\n\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.AgentOutputParser","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.agent.AgentOutputParser#L175-L178","kind":"class","name":"AgentOutputParser","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":175,"end_line":178,"context_start_line":155,"context_end_line":198,"code":"\n directory_path = save_path.parent\n directory_path.mkdir(parents=True, exist_ok=True)\n\n # Fetch dictionary to save\n agent_dict = self.dict()\n\n if save_path.suffix == \".json\":\n with open(file_path, \"w\") as f:\n json.dump(agent_dict, f, indent=4)\n elif save_path.suffix == \".yaml\":\n with open(file_path, \"w\") as f:\n yaml.dump(agent_dict, f, default_flow_style=False)\n else:\n raise ValueError(f\"{save_path} must be json or yaml\")\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {}\n\n\nclass AgentOutputParser(BaseOutputParser):\n @abstractmethod\n def parse(self, text: str) -> Union[AgentAction, AgentFinish]:\n \"\"\"Parse text into agent action/finish.\"\"\"\n\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.Agent","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.agent.Agent#L181-L400","kind":"class","name":"Agent","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":181,"end_line":400,"context_start_line":161,"context_end_line":420,"code":"\n if save_path.suffix == \".json\":\n with open(file_path, \"w\") as f:\n json.dump(agent_dict, f, indent=4)\n elif save_path.suffix == \".yaml\":\n with open(file_path, \"w\") as f:\n yaml.dump(agent_dict, f, default_flow_style=False)\n else:\n raise ValueError(f\"{save_path} must be json or yaml\")\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {}\n\n\nclass AgentOutputParser(BaseOutputParser):\n @abstractmethod\n def parse(self, text: str) -> Union[AgentAction, AgentFinish]:\n \"\"\"Parse text into agent action/finish.\"\"\"\n\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data() if isinstance(full_observation, DataModel) else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n return thoughts\n\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs)\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n async def aplan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n def get_full_inputs(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) -> Dict[str, Any]:\n \"\"\"Create the full inputs for the LLMChain from intermediate steps.\"\"\"\n thoughts = self._construct_scratchpad(intermediate_steps)\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n return full_inputs\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return list(set(self.llm_chain.input_keys) - {\"agent_scratchpad\"})\n\n @root_validator()\n def validate_prompt(cls, values: Dict) -> Dict:\n \"\"\"Validate that prompt matches format.\"\"\"\n prompt = values[\"llm_chain\"].prompt\n if \"agent_scratchpad\" not in prompt.input_variables:\n logger.warning(\n \"`agent_scratchpad` should be a variable in prompt.input_variables.\"\n \" Did not find it, so adding it at the end.\"\n )\n prompt.input_variables.append(\"agent_scratchpad\")\n if isinstance(prompt, PromptTemplate):\n prompt.template += \"\\n{agent_scratchpad}\"\n elif isinstance(prompt, FewShotPromptTemplate):\n prompt.suffix += \"\\n{agent_scratchpad}\"\n else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n llm_chain = LLMChain(\n llm=llm,\n prompt=cls.create_prompt(tools),\n callback_manager=callback_manager,\n )\n tool_names = [tool.name for tool in tools]\n _output_parser = output_parser or cls._get_default_output_parser()\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )\n\n def return_stopped_response(\n self,\n early_stopping_method: str,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n elif early_stopping_method == \"generate\":\n # Generate does one final forward pass\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data()\n if isinstance(full_observation, DataModel)\n else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n # Adding to the previous steps, we now tell the LLM to make a final pred\n thoughts += \"\\n\\nI now need to return a final answer based on the previous steps:\"\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n full_output = self.llm_chain.predict(**full_inputs)\n # We try to extract a final answer\n parsed_output = self.output_parser.parse(full_output)\n if isinstance(parsed_output, AgentFinish):\n # If we can extract, we send the correct stuff\n return parsed_output\n else:\n # If we can extract, but the tool is not the final tool,\n # we just return the full output\n return AgentFinish({\"output\": full_output}, full_output)\n else:\n raise ValueError(\n \"early_stopping_method should be one of `force` or `generate`, \" f\"got {early_stopping_method}\"\n )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.ExceptionTool","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.agent.ExceptionTool#L403-L419","kind":"class","name":"ExceptionTool","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":403,"end_line":419,"context_start_line":383,"context_end_line":439,"code":" parsed_output = self.output_parser.parse(full_output)\n if isinstance(parsed_output, AgentFinish):\n # If we can extract, we send the correct stuff\n return parsed_output\n else:\n # If we can extract, but the tool is not the final tool,\n # we just return the full output\n return AgentFinish({\"output\": full_output}, full_output)\n else:\n raise ValueError(\n \"early_stopping_method should be one of `force` or `generate`, \" f\"got {early_stopping_method}\"\n )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass AgentExecutor(Chain):\n \"\"\"Consists of an agent using tools.\"\"\"\n\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n\n @classmethod\n def from_agent_and_tools(\n cls,\n agent: BaseSingleActionAgent,\n tools: Sequence[BaseTool],\n **kwargs: Any,\n ) -> AgentExecutor:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.AgentExecutor","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.agent.AgentExecutor#L422-L659","kind":"class","name":"AgentExecutor","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":422,"end_line":659,"context_start_line":402,"context_end_line":659,"code":"\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass AgentExecutor(Chain):\n \"\"\"Consists of an agent using tools.\"\"\"\n\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n\n @classmethod\n def from_agent_and_tools(\n cls,\n agent: BaseSingleActionAgent,\n tools: Sequence[BaseTool],\n **kwargs: Any,\n ) -> AgentExecutor:\n \"\"\"Create from agent and tools.\"\"\"\n return cls(agent=agent, tools=tools, **kwargs)\n\n @root_validator()\n def validate_tools(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n agent = values[\"agent\"]\n tools = values[\"tools\"]\n allowed_tools = agent.get_allowed_tools()\n if allowed_tools is not None:\n if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:\n run_manager.on_agent_finish(output, color=\"green\", verbose=self.verbose)\n final_output = output.return_values\n if self.return_intermediate_steps:\n final_output[\"intermediate_steps\"] = intermediate_steps\n return final_output\n\n def _take_next_step(\n self,\n name_to_tool_map: Dict[str, BaseTool],\n color_mapping: Dict[str, str],\n inputs: Dict[str, str],\n intermediate_steps: List[Tuple[AgentAction, str]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n \"\"\"Take a single step in the thought-action-observation loop.\n\n Override this to take control of how the agent makes and acts on choices.\n \"\"\"\n try:\n # Call the LLM to see what to do.\n output = self.agent.plan(\n intermediate_steps,\n callbacks=run_manager.get_child() if run_manager else None,\n **inputs,\n )\n except OutputParserException as e:\n if isinstance(self.handle_parsing_errors, bool):\n raise_error = not self.handle_parsing_errors\n else:\n raise_error = False\n if raise_error:\n raise e\n text = str(e)\n if isinstance(self.handle_parsing_errors, bool):\n observation = \"Invalid or incomplete response\"\n elif isinstance(self.handle_parsing_errors, str):\n observation = self.handle_parsing_errors\n elif callable(self.handle_parsing_errors):\n observation = self.handle_parsing_errors(e)\n else:\n raise ValueError(\"Got unexpected type of `handle_parsing_errors`\")\n output = AgentAction(\"_Exception\", observation, text)\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n observation = ExceptionTool().run(\n output.tool_input,\n verbose=self.verbose,\n color=None,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n return [(output, observation)]\n # If the tool chosen is the finishing tool, then we end and return.\n if isinstance(output, AgentFinish):\n return output\n actions: List[AgentAction]\n if isinstance(output, AgentAction):\n actions = [output]\n else:\n actions = output\n result = []\n for agent_action in actions:\n if run_manager:\n run_manager.on_agent_action(agent_action, color=\"green\")\n # Otherwise we lookup the tool\n if agent_action.tool in name_to_tool_map:\n tool = name_to_tool_map[agent_action.tool]\n return_direct = tool.return_direct\n color = color_mapping[agent_action.tool]\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n if return_direct:\n tool_run_kwargs[\"llm_prefix\"] = \"\"\n # We then call the tool on the tool input to get an observation\n observation = tool.run(\n agent_action.tool_input,\n verbose=self.verbose,\n color=color,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n else:\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n observation = InvalidTool().run(\n agent_action.tool,\n verbose=self.verbose,\n color=None,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n result.append((agent_action, observation))\n return result\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n \"\"\"Run text through and get agent response.\"\"\"\n # Construct a mapping of tool name to tool for easy lookup\n name_to_tool_map = {tool.name: tool for tool in self.tools}\n # We construct a mapping from each tool to a color, used for logging.\n color_mapping = get_color_mapping([tool.name for tool in self.tools], excluded_colors=[\"green\"])\n intermediate_steps: List[Tuple[AgentAction, str]] = []\n # Let's start tracking the number of iterations and time elapsed\n iterations = 0\n time_elapsed = 0.0\n start_time = time.time()\n # We now enter the agent loop (until it returns something).\n while self._should_continue(iterations, time_elapsed):\n next_step_output = self._take_next_step(\n name_to_tool_map,\n color_mapping,\n inputs,\n intermediate_steps,\n run_manager=run_manager,\n )\n if isinstance(next_step_output, AgentFinish):\n return self._return(next_step_output, intermediate_steps, run_manager=run_manager)\n\n intermediate_steps.extend(next_step_output)\n if len(next_step_output) == 1:\n next_step_action = next_step_output[0]\n # See if tool should return directly\n tool_return = self._get_tool_return(next_step_action)\n if tool_return is not None:\n return self._return(tool_return, intermediate_steps, run_manager=run_manager)\n iterations += 1\n time_elapsed = time.time() - start_time\n output = self.agent.return_stopped_response(self.early_stopping_method, intermediate_steps, **inputs)\n return self._return(output, intermediate_steps, run_manager=run_manager)\n\n def _get_tool_return(self, next_step_output: Tuple[AgentAction, Union[str, DataModel]]) -> Optional[AgentFinish]:\n \"\"\"Check if the tool is a returning tool.\"\"\"\n agent_action, full_observation = next_step_output\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.memory.style\n )\n name_to_tool_map = {tool.name: tool for tool in self.tools}\n # Invalid tools won't be in the map, so we return False.\n if agent_action.tool in name_to_tool_map:\n if name_to_tool_map[agent_action.tool].return_direct:\n return AgentFinish(\n {self.agent.return_values[0]: observation},\n \"\",\n )\n return None","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.return_values","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.return_values#L203-L204","kind":"function","name":"return_values","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":203,"end_line":204,"context_start_line":183,"context_end_line":224,"code":"\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.get_allowed_tools","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.get_allowed_tools#L199-L200","kind":"function","name":"get_allowed_tools","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":199,"end_line":200,"context_start_line":179,"context_end_line":220,"code":"\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.plan","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.plan#L230-L250","kind":"function","name":"plan","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":230,"end_line":250,"context_start_line":210,"context_end_line":270,"code":" @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data() if isinstance(full_observation, DataModel) else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n return thoughts\n\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs)\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n async def aplan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.aplan","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.aplan#L252-L271","kind":"function","name":"aplan","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":252,"end_line":271,"context_start_line":232,"context_end_line":291,"code":" intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs)\n full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n async def aplan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n def get_full_inputs(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) -> Dict[str, Any]:\n \"\"\"Create the full inputs for the LLMChain from intermediate steps.\"\"\"\n thoughts = self._construct_scratchpad(intermediate_steps)\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n return full_inputs\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return list(set(self.llm_chain.input_keys) - {\"agent_scratchpad\"})\n\n @root_validator()\n def validate_prompt(cls, values: Dict) -> Dict:\n \"\"\"Validate that prompt matches format.\"\"\"\n prompt = values[\"llm_chain\"].prompt","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.input_keys","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.input_keys#L475-L480","kind":"function","name":"input_keys","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":475,"end_line":480,"context_start_line":455,"context_end_line":500,"code":" return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.return_stopped_response","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.return_stopped_response#L356-L394","kind":"function","name":"return_stopped_response","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":356,"end_line":394,"context_start_line":336,"context_end_line":414,"code":" callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n llm_chain = LLMChain(\n llm=llm,\n prompt=cls.create_prompt(tools),\n callback_manager=callback_manager,\n )\n tool_names = [tool.name for tool in tools]\n _output_parser = output_parser or cls._get_default_output_parser()\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )\n\n def return_stopped_response(\n self,\n early_stopping_method: str,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n elif early_stopping_method == \"generate\":\n # Generate does one final forward pass\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data()\n if isinstance(full_observation, DataModel)\n else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n # Adding to the previous steps, we now tell the LLM to make a final pred\n thoughts += \"\\n\\nI now need to return a final answer based on the previous steps:\"\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n full_output = self.llm_chain.predict(**full_inputs)\n # We try to extract a final answer\n parsed_output = self.output_parser.parse(full_output)\n if isinstance(parsed_output, AgentFinish):\n # If we can extract, we send the correct stuff\n return parsed_output\n else:\n # If we can extract, but the tool is not the final tool,\n # we just return the full output\n return AgentFinish({\"output\": full_output}, full_output)\n else:\n raise ValueError(\n \"early_stopping_method should be one of `force` or `generate`, \" f\"got {early_stopping_method}\"\n )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.from_llm_and_tools","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.from_llm_and_tools#L332-L354","kind":"function","name":"from_llm_and_tools","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":332,"end_line":354,"context_start_line":312,"context_end_line":374,"code":" @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n llm_chain = LLMChain(\n llm=llm,\n prompt=cls.create_prompt(tools),\n callback_manager=callback_manager,\n )\n tool_names = [tool.name for tool in tools]\n _output_parser = output_parser or cls._get_default_output_parser()\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )\n\n def return_stopped_response(\n self,\n early_stopping_method: str,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n elif early_stopping_method == \"generate\":\n # Generate does one final forward pass\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data()\n if isinstance(full_observation, DataModel)\n else full_observation","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._agent_type","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._agent_type#L124-L126","kind":"function","name":"_agent_type","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":124,"end_line":126,"context_start_line":104,"context_end_line":146,"code":" **kwargs: Any,\n ) -> AgentFinish:\n \"\"\"Return response when agent has been stopped due to max iterations.\"\"\"\n if early_stopping_method == \"force\":\n # `force` just returns a constant string\n return AgentFinish({\"output\": \"Agent stopped due to iteration limit or time limit.\"}, \"\")\n else:\n raise ValueError(f\"Got unsupported early_stopping_method `{early_stopping_method}`\")\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n **kwargs: Any,\n ) -> BaseSingleActionAgent:\n raise NotImplementedError\n\n @property\n def _agent_type(self) -> str:\n \"\"\"Return Identifier of agent type.\"\"\"\n raise NotImplementedError\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n _type = self._agent_type\n if isinstance(_type, AgentType):\n _dict[\"_type\"] = str(_type.value)\n else:\n _dict[\"_type\"] = _type\n return _dict\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the agent.\n\n Args:\n file_path: Path to file to save the agent to.\n\n Example:\n .. code-block:: python\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.dict","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.dict#L193-L197","kind":"function","name":"dict","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":193,"end_line":197,"context_start_line":173,"context_end_line":217,"code":"\n\nclass AgentOutputParser(BaseOutputParser):\n @abstractmethod\n def parse(self, text: str) -> Union[AgentAction, AgentFinish]:\n \"\"\"Parse text into agent action/finish.\"\"\"\n\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.save","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.save#L462-L468","kind":"function","name":"save","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":462,"end_line":468,"context_start_line":442,"context_end_line":488,"code":"\n @root_validator()\n def validate_tools(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n agent = values[\"agent\"]\n tools = values[\"tools\"]\n allowed_tools = agent.get_allowed_tools()\n if allowed_tools is not None:\n if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.tool_run_logging_kwargs","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.tool_run_logging_kwargs#L396-L400","kind":"function","name":"tool_run_logging_kwargs","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":396,"end_line":400,"context_start_line":376,"context_end_line":420,"code":" thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n # Adding to the previous steps, we now tell the LLM to make a final pred\n thoughts += \"\\n\\nI now need to return a final answer based on the previous steps:\"\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n full_output = self.llm_chain.predict(**full_inputs)\n # We try to extract a final answer\n parsed_output = self.output_parser.parse(full_output)\n if isinstance(parsed_output, AgentFinish):\n # If we can extract, we send the correct stuff\n return parsed_output\n else:\n # If we can extract, but the tool is not the final tool,\n # we just return the full output\n return AgentFinish({\"output\": full_output}, full_output)\n else:\n raise ValueError(\n \"early_stopping_method should be one of `force` or `generate`, \" f\"got {early_stopping_method}\"\n )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.parse","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.parse#L177-L178","kind":"function","name":"parse","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":177,"end_line":178,"context_start_line":157,"context_end_line":198,"code":" directory_path.mkdir(parents=True, exist_ok=True)\n\n # Fetch dictionary to save\n agent_dict = self.dict()\n\n if save_path.suffix == \".json\":\n with open(file_path, \"w\") as f:\n json.dump(agent_dict, f, indent=4)\n elif save_path.suffix == \".yaml\":\n with open(file_path, \"w\") as f:\n yaml.dump(agent_dict, f, default_flow_style=False)\n else:\n raise ValueError(f\"{save_path} must be json or yaml\")\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {}\n\n\nclass AgentOutputParser(BaseOutputParser):\n @abstractmethod\n def parse(self, text: str) -> Union[AgentAction, AgentFinish]:\n \"\"\"Parse text into agent action/finish.\"\"\"\n\n\nclass Agent(BaseSingleActionAgent):\n \"\"\"Class responsible for calling the language model and deciding the action.\n\n This is driven by an LLMChain. The prompt in the LLMChain MUST include\n a variable called \"agent_scratchpad\" where the agent can put its\n intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._fix_text","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._fix_text#L206-L208","kind":"function","name":"_fix_text","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":206,"end_line":208,"context_start_line":186,"context_end_line":228,"code":" intermediary work.\n \"\"\"\n\n llm_chain: LLMChain\n output_parser: AgentOutputParser\n allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data() if isinstance(full_observation, DataModel) else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n return thoughts","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._stop","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._stop#L211-L215","kind":"function","name":"_stop","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":211,"end_line":215,"context_start_line":191,"context_end_line":235,"code":" allowed_tools: Optional[List[str]] = None\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of agent.\"\"\"\n _dict = super().dict()\n del _dict[\"output_parser\"]\n return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data() if isinstance(full_observation, DataModel) else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n return thoughts\n\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._construct_scratchpad","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._construct_scratchpad#L217-L228","kind":"function","name":"_construct_scratchpad","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":217,"end_line":228,"context_start_line":197,"context_end_line":248,"code":" return _dict\n\n def get_allowed_tools(self) -> Optional[List[str]]:\n return self.allowed_tools\n\n @property\n def return_values(self) -> List[str]:\n return [\"output\"]\n\n def _fix_text(self, text: str) -> str:\n \"\"\"Fix the text.\"\"\"\n raise ValueError(\"fix_text not implemented for this agent.\")\n\n @property\n def _stop(self) -> List[str]:\n return [\n f\"\\n{self.observation_prefix.rstrip()}\",\n f\"\\n\\t{self.observation_prefix.rstrip()}\",\n ]\n\n def _construct_scratchpad(\n self, intermediate_steps: List[Tuple[AgentAction, str]]\n ) -> Union[str, List[BaseMessage]]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts = \"\"\n for action, full_observation in intermediate_steps:\n thoughts += action.log\n observation = (\n full_observation.get_llm_side_data() if isinstance(full_observation, DataModel) else full_observation\n )\n thoughts += f\"\\n{self.observation_prefix}{observation}\\n{self.llm_prefix}\"\n return thoughts\n\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs)","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.get_full_inputs","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.get_full_inputs#L273-L278","kind":"function","name":"get_full_inputs","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":273,"end_line":278,"context_start_line":253,"context_end_line":298,"code":" self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n callbacks: Callbacks to run.\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n def get_full_inputs(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) -> Dict[str, Any]:\n \"\"\"Create the full inputs for the LLMChain from intermediate steps.\"\"\"\n thoughts = self._construct_scratchpad(intermediate_steps)\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n return full_inputs\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return list(set(self.llm_chain.input_keys) - {\"agent_scratchpad\"})\n\n @root_validator()\n def validate_prompt(cls, values: Dict) -> Dict:\n \"\"\"Validate that prompt matches format.\"\"\"\n prompt = values[\"llm_chain\"].prompt\n if \"agent_scratchpad\" not in prompt.input_variables:\n logger.warning(\n \"`agent_scratchpad` should be a variable in prompt.input_variables.\"\n \" Did not find it, so adding it at the end.\"\n )\n prompt.input_variables.append(\"agent_scratchpad\")\n if isinstance(prompt, PromptTemplate):","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.validate_prompt","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.validate_prompt#L289-L304","kind":"function","name":"validate_prompt","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":289,"end_line":304,"context_start_line":269,"context_end_line":324,"code":" full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)\n return self.output_parser.parse(full_output)\n\n def get_full_inputs(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) -> Dict[str, Any]:\n \"\"\"Create the full inputs for the LLMChain from intermediate steps.\"\"\"\n thoughts = self._construct_scratchpad(intermediate_steps)\n new_inputs = {\"agent_scratchpad\": thoughts, \"stop\": self._stop}\n full_inputs = {**kwargs, **new_inputs}\n return full_inputs\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return list(set(self.llm_chain.input_keys) - {\"agent_scratchpad\"})\n\n @root_validator()\n def validate_prompt(cls, values: Dict) -> Dict:\n \"\"\"Validate that prompt matches format.\"\"\"\n prompt = values[\"llm_chain\"].prompt\n if \"agent_scratchpad\" not in prompt.input_variables:\n logger.warning(\n \"`agent_scratchpad` should be a variable in prompt.input_variables.\"\n \" Did not find it, so adding it at the end.\"\n )\n prompt.input_variables.append(\"agent_scratchpad\")\n if isinstance(prompt, PromptTemplate):\n prompt.template += \"\\n{agent_scratchpad}\"\n elif isinstance(prompt, FewShotPromptTemplate):\n prompt.suffix += \"\\n{agent_scratchpad}\"\n else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.observation_prefix","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.observation_prefix#L308-L309","kind":"function","name":"observation_prefix","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":308,"end_line":309,"context_start_line":288,"context_end_line":329,"code":" @root_validator()\n def validate_prompt(cls, values: Dict) -> Dict:\n \"\"\"Validate that prompt matches format.\"\"\"\n prompt = values[\"llm_chain\"].prompt\n if \"agent_scratchpad\" not in prompt.input_variables:\n logger.warning(\n \"`agent_scratchpad` should be a variable in prompt.input_variables.\"\n \" Did not find it, so adding it at the end.\"\n )\n prompt.input_variables.append(\"agent_scratchpad\")\n if isinstance(prompt, PromptTemplate):\n prompt.template += \"\\n{agent_scratchpad}\"\n elif isinstance(prompt, FewShotPromptTemplate):\n prompt.suffix += \"\\n{agent_scratchpad}\"\n else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.llm_prefix","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.llm_prefix#L313-L314","kind":"function","name":"llm_prefix","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":313,"end_line":314,"context_start_line":293,"context_end_line":334,"code":" logger.warning(\n \"`agent_scratchpad` should be a variable in prompt.input_variables.\"\n \" Did not find it, so adding it at the end.\"\n )\n prompt.input_variables.append(\"agent_scratchpad\")\n if isinstance(prompt, PromptTemplate):\n prompt.template += \"\\n{agent_scratchpad}\"\n elif isinstance(prompt, FewShotPromptTemplate):\n prompt.suffix += \"\\n{agent_scratchpad}\"\n else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.create_prompt","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.create_prompt#L318-L319","kind":"function","name":"create_prompt","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":318,"end_line":319,"context_start_line":298,"context_end_line":339,"code":" if isinstance(prompt, PromptTemplate):\n prompt.template += \"\\n{agent_scratchpad}\"\n elif isinstance(prompt, FewShotPromptTemplate):\n prompt.suffix += \"\\n{agent_scratchpad}\"\n else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._validate_tools","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._validate_tools#L322-L324","kind":"function","name":"_validate_tools","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":322,"end_line":324,"context_start_line":302,"context_end_line":344,"code":" else:\n raise ValueError(f\"Got unexpected prompt type {type(prompt)}\")\n return values\n\n @property\n @abstractmethod\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n llm_chain = LLMChain(\n llm=llm,\n prompt=cls.create_prompt(tools),","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._get_default_output_parser","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._get_default_output_parser#L328-L329","kind":"function","name":"_get_default_output_parser","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":328,"end_line":329,"context_start_line":308,"context_end_line":349,"code":" def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n\n @property\n @abstractmethod\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the LLM call with.\"\"\"\n\n @classmethod\n @abstractmethod\n def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:\n \"\"\"Create a prompt for this class.\"\"\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n \"\"\"Validate that appropriate tools are passed in.\"\"\"\n pass\n\n @classmethod\n @abstractmethod\n def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:\n \"\"\"Get default output parser for this class.\"\"\"\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callback_manager: Optional[BaseCallbackManager] = None,\n output_parser: Optional[AgentOutputParser] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n llm_chain = LLMChain(\n llm=llm,\n prompt=cls.create_prompt(tools),\n callback_manager=callback_manager,\n )\n tool_names = [tool.name for tool in tools]\n _output_parser = output_parser or cls._get_default_output_parser()\n return cls(","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._run","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._run#L407-L412","kind":"function","name":"_run","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":407,"end_line":412,"context_start_line":387,"context_end_line":432,"code":" else:\n # If we can extract, but the tool is not the final tool,\n # we just return the full output\n return AgentFinish({\"output\": full_output}, full_output)\n else:\n raise ValueError(\n \"early_stopping_method should be one of `force` or `generate`, \" f\"got {early_stopping_method}\"\n )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass AgentExecutor(Chain):\n \"\"\"Consists of an agent using tools.\"\"\"\n\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._arun","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._arun#L414-L419","kind":"function","name":"_arun","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":414,"end_line":419,"context_start_line":394,"context_end_line":439,"code":" )\n\n def tool_run_logging_kwargs(self) -> Dict:\n return {\n \"llm_prefix\": self.llm_prefix,\n \"observation_prefix\": self.observation_prefix,\n }\n\n\nclass ExceptionTool(BaseTool):\n name = \"_Exception\"\n description = \"Exception tool\"\n\n def _run(\n self,\n query: str,\n run_manager: Optional[CallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass AgentExecutor(Chain):\n \"\"\"Consists of an agent using tools.\"\"\"\n\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n\n @classmethod\n def from_agent_and_tools(\n cls,\n agent: BaseSingleActionAgent,\n tools: Sequence[BaseTool],\n **kwargs: Any,\n ) -> AgentExecutor:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.from_agent_and_tools","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.from_agent_and_tools#L434-L441","kind":"function","name":"from_agent_and_tools","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":434,"end_line":441,"context_start_line":414,"context_end_line":461,"code":" async def _arun(\n self,\n query: str,\n run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n ) -> str:\n return query\n\n\nclass AgentExecutor(Chain):\n \"\"\"Consists of an agent using tools.\"\"\"\n\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n\n @classmethod\n def from_agent_and_tools(\n cls,\n agent: BaseSingleActionAgent,\n tools: Sequence[BaseTool],\n **kwargs: Any,\n ) -> AgentExecutor:\n \"\"\"Create from agent and tools.\"\"\"\n return cls(agent=agent, tools=tools, **kwargs)\n\n @root_validator()\n def validate_tools(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n agent = values[\"agent\"]\n tools = values[\"tools\"]\n allowed_tools = agent.get_allowed_tools()\n if allowed_tools is not None:\n if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.validate_tools","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.validate_tools#L444-L455","kind":"function","name":"validate_tools","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":444,"end_line":455,"context_start_line":424,"context_end_line":475,"code":"\n agent: BaseSingleActionAgent\n tools: Sequence[BaseTool]\n return_intermediate_steps: bool = False\n max_iterations: Optional[int] = 5\n max_execution_time: Optional[float] = None\n early_stopping_method: str = \"force\"\n handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False\n\n @classmethod\n def from_agent_and_tools(\n cls,\n agent: BaseSingleActionAgent,\n tools: Sequence[BaseTool],\n **kwargs: Any,\n ) -> AgentExecutor:\n \"\"\"Create from agent and tools.\"\"\"\n return cls(agent=agent, tools=tools, **kwargs)\n\n @root_validator()\n def validate_tools(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n agent = values[\"agent\"]\n tools = values[\"tools\"]\n allowed_tools = agent.get_allowed_tools()\n if allowed_tools is not None:\n if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.validate_return_direct_tool","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.validate_return_direct_tool#L458-L460","kind":"function","name":"validate_return_direct_tool","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":458,"end_line":460,"context_start_line":438,"context_end_line":480,"code":" **kwargs: Any,\n ) -> AgentExecutor:\n \"\"\"Create from agent and tools.\"\"\"\n return cls(agent=agent, tools=tools, **kwargs)\n\n @root_validator()\n def validate_tools(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n agent = values[\"agent\"]\n tools = values[\"tools\"]\n allowed_tools = agent.get_allowed_tools()\n if allowed_tools is not None:\n if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.save_agent","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.save_agent#L470-L472","kind":"function","name":"save_agent","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":470,"end_line":472,"context_start_line":450,"context_end_line":492,"code":" if set(allowed_tools) != set([tool.name for tool in tools]):\n raise ValueError(\n f\"Allowed tools ({allowed_tools}) different than \"\n f\"provided tools ({[tool.name for tool in tools]})\"\n )\n return values\n\n @root_validator()\n def validate_return_direct_tool(cls, values: Dict) -> Dict:\n \"\"\"Validate that tools are compatible with agent.\"\"\"\n return values\n\n def save(self, file_path: Union[Path, str]) -> None:\n \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.output_keys","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.output_keys#L483-L491","kind":"function","name":"output_keys","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":483,"end_line":491,"context_start_line":463,"context_end_line":511,"code":" \"\"\"Raise error - saving not supported for Agent Executors.\"\"\"\n raise ValueError(\n \"Saving not supported for agent executors. \"\n \"If you are trying to save the agent, please use the \"\n \"`.save_agent(...)`\"\n )\n\n def save_agent(self, file_path: Union[Path, str]) -> None:\n \"\"\"Save the underlying agent.\"\"\"\n return self.agent.save(file_path)\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent.lookup_tool","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent.lookup_tool#L493-L495","kind":"function","name":"lookup_tool","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":493,"end_line":495,"context_start_line":473,"context_end_line":515,"code":"\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the input keys.\n\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:\n run_manager.on_agent_finish(output, color=\"green\", verbose=self.verbose)\n final_output = output.return_values\n if self.return_intermediate_steps:\n final_output[\"intermediate_steps\"] = intermediate_steps","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._should_continue","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._should_continue#L497-L503","kind":"function","name":"_should_continue","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":497,"end_line":503,"context_start_line":477,"context_end_line":523,"code":"\n :meta private:\n \"\"\"\n return self.agent.input_keys\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the singular output key.\n\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:\n run_manager.on_agent_finish(output, color=\"green\", verbose=self.verbose)\n final_output = output.return_values\n if self.return_intermediate_steps:\n final_output[\"intermediate_steps\"] = intermediate_steps\n return final_output\n\n def _take_next_step(\n self,\n name_to_tool_map: Dict[str, BaseTool],\n color_mapping: Dict[str, str],\n inputs: Dict[str, str],\n intermediate_steps: List[Tuple[AgentAction, str]],","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._return","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._return#L505-L516","kind":"function","name":"_return","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":505,"end_line":516,"context_start_line":485,"context_end_line":536,"code":"\n :meta private:\n \"\"\"\n if self.return_intermediate_steps:\n return self.agent.return_values + [\"intermediate_steps\"]\n else:\n return self.agent.return_values\n\n def lookup_tool(self, name: str) -> BaseTool:\n \"\"\"Lookup tool by name.\"\"\"\n return {tool.name: tool for tool in self.tools}[name]\n\n def _should_continue(self, iterations: int, time_elapsed: float) -> bool:\n if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:\n run_manager.on_agent_finish(output, color=\"green\", verbose=self.verbose)\n final_output = output.return_values\n if self.return_intermediate_steps:\n final_output[\"intermediate_steps\"] = intermediate_steps\n return final_output\n\n def _take_next_step(\n self,\n name_to_tool_map: Dict[str, BaseTool],\n color_mapping: Dict[str, str],\n inputs: Dict[str, str],\n intermediate_steps: List[Tuple[AgentAction, str]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n \"\"\"Take a single step in the thought-action-observation loop.\n\n Override this to take control of how the agent makes and acts on choices.\n \"\"\"\n try:\n # Call the LLM to see what to do.\n output = self.agent.plan(\n intermediate_steps,\n callbacks=run_manager.get_child() if run_manager else None,\n **inputs,\n )","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._take_next_step","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._take_next_step#L518-L601","kind":"function","name":"_take_next_step","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":518,"end_line":601,"context_start_line":498,"context_end_line":621,"code":" if self.max_iterations is not None and iterations >= self.max_iterations:\n return False\n if self.max_execution_time is not None and time_elapsed >= self.max_execution_time:\n return False\n\n return True\n\n def _return(\n self,\n output: AgentFinish,\n intermediate_steps: list,\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n if run_manager:\n run_manager.on_agent_finish(output, color=\"green\", verbose=self.verbose)\n final_output = output.return_values\n if self.return_intermediate_steps:\n final_output[\"intermediate_steps\"] = intermediate_steps\n return final_output\n\n def _take_next_step(\n self,\n name_to_tool_map: Dict[str, BaseTool],\n color_mapping: Dict[str, str],\n inputs: Dict[str, str],\n intermediate_steps: List[Tuple[AgentAction, str]],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n \"\"\"Take a single step in the thought-action-observation loop.\n\n Override this to take control of how the agent makes and acts on choices.\n \"\"\"\n try:\n # Call the LLM to see what to do.\n output = self.agent.plan(\n intermediate_steps,\n callbacks=run_manager.get_child() if run_manager else None,\n **inputs,\n )\n except OutputParserException as e:\n if isinstance(self.handle_parsing_errors, bool):\n raise_error = not self.handle_parsing_errors\n else:\n raise_error = False\n if raise_error:\n raise e\n text = str(e)\n if isinstance(self.handle_parsing_errors, bool):\n observation = \"Invalid or incomplete response\"\n elif isinstance(self.handle_parsing_errors, str):\n observation = self.handle_parsing_errors\n elif callable(self.handle_parsing_errors):\n observation = self.handle_parsing_errors(e)\n else:\n raise ValueError(\"Got unexpected type of `handle_parsing_errors`\")\n output = AgentAction(\"_Exception\", observation, text)\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n observation = ExceptionTool().run(\n output.tool_input,\n verbose=self.verbose,\n color=None,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n return [(output, observation)]\n # If the tool chosen is the finishing tool, then we end and return.\n if isinstance(output, AgentFinish):\n return output\n actions: List[AgentAction]\n if isinstance(output, AgentAction):\n actions = [output]\n else:\n actions = output\n result = []\n for agent_action in actions:\n if run_manager:\n run_manager.on_agent_action(agent_action, color=\"green\")\n # Otherwise we lookup the tool\n if agent_action.tool in name_to_tool_map:\n tool = name_to_tool_map[agent_action.tool]\n return_direct = tool.return_direct\n color = color_mapping[agent_action.tool]\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n if return_direct:\n tool_run_kwargs[\"llm_prefix\"] = \"\"\n # We then call the tool on the tool input to get an observation\n observation = tool.run(\n agent_action.tool_input,\n verbose=self.verbose,\n color=color,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n else:\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n observation = InvalidTool().run(\n agent_action.tool,\n verbose=self.verbose,\n color=None,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n result.append((agent_action, observation))\n return result\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n \"\"\"Run text through and get agent response.\"\"\"\n # Construct a mapping of tool name to tool for easy lookup\n name_to_tool_map = {tool.name: tool for tool in self.tools}\n # We construct a mapping from each tool to a color, used for logging.\n color_mapping = get_color_mapping([tool.name for tool in self.tools], excluded_colors=[\"green\"])\n intermediate_steps: List[Tuple[AgentAction, str]] = []\n # Let's start tracking the number of iterations and time elapsed\n iterations = 0\n time_elapsed = 0.0\n start_time = time.time()\n # We now enter the agent loop (until it returns something).\n while self._should_continue(iterations, time_elapsed):\n next_step_output = self._take_next_step(\n name_to_tool_map,","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._call","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._call#L603-L640","kind":"function","name":"_call","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":603,"end_line":640,"context_start_line":583,"context_end_line":659,"code":" # We then call the tool on the tool input to get an observation\n observation = tool.run(\n agent_action.tool_input,\n verbose=self.verbose,\n color=color,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n else:\n tool_run_kwargs = self.agent.tool_run_logging_kwargs()\n observation = InvalidTool().run(\n agent_action.tool,\n verbose=self.verbose,\n color=None,\n callbacks=run_manager.get_child() if run_manager else None,\n **tool_run_kwargs,\n )\n result.append((agent_action, observation))\n return result\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n \"\"\"Run text through and get agent response.\"\"\"\n # Construct a mapping of tool name to tool for easy lookup\n name_to_tool_map = {tool.name: tool for tool in self.tools}\n # We construct a mapping from each tool to a color, used for logging.\n color_mapping = get_color_mapping([tool.name for tool in self.tools], excluded_colors=[\"green\"])\n intermediate_steps: List[Tuple[AgentAction, str]] = []\n # Let's start tracking the number of iterations and time elapsed\n iterations = 0\n time_elapsed = 0.0\n start_time = time.time()\n # We now enter the agent loop (until it returns something).\n while self._should_continue(iterations, time_elapsed):\n next_step_output = self._take_next_step(\n name_to_tool_map,\n color_mapping,\n inputs,\n intermediate_steps,\n run_manager=run_manager,\n )\n if isinstance(next_step_output, AgentFinish):\n return self._return(next_step_output, intermediate_steps, run_manager=run_manager)\n\n intermediate_steps.extend(next_step_output)\n if len(next_step_output) == 1:\n next_step_action = next_step_output[0]\n # See if tool should return directly\n tool_return = self._get_tool_return(next_step_action)\n if tool_return is not None:\n return self._return(tool_return, intermediate_steps, run_manager=run_manager)\n iterations += 1\n time_elapsed = time.time() - start_time\n output = self.agent.return_stopped_response(self.early_stopping_method, intermediate_steps, **inputs)\n return self._return(output, intermediate_steps, run_manager=run_manager)\n\n def _get_tool_return(self, next_step_output: Tuple[AgentAction, Union[str, DataModel]]) -> Optional[AgentFinish]:\n \"\"\"Check if the tool is a returning tool.\"\"\"\n agent_action, full_observation = next_step_output\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.memory.style\n )\n name_to_tool_map = {tool.name: tool for tool in self.tools}\n # Invalid tools won't be in the map, so we return False.\n if agent_action.tool in name_to_tool_map:\n if name_to_tool_map[agent_action.tool].return_direct:\n return AgentFinish(\n {self.agent.return_values[0]: observation},\n \"\",\n )\n return None","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.agent._get_tool_return","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.agent._get_tool_return#L642-L659","kind":"function","name":"_get_tool_return","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":642,"end_line":659,"context_start_line":622,"context_end_line":659,"code":" color_mapping,\n inputs,\n intermediate_steps,\n run_manager=run_manager,\n )\n if isinstance(next_step_output, AgentFinish):\n return self._return(next_step_output, intermediate_steps, run_manager=run_manager)\n\n intermediate_steps.extend(next_step_output)\n if len(next_step_output) == 1:\n next_step_action = next_step_output[0]\n # See if tool should return directly\n tool_return = self._get_tool_return(next_step_action)\n if tool_return is not None:\n return self._return(tool_return, intermediate_steps, run_manager=run_manager)\n iterations += 1\n time_elapsed = time.time() - start_time\n output = self.agent.return_stopped_response(self.early_stopping_method, intermediate_steps, **inputs)\n return self._return(output, intermediate_steps, run_manager=run_manager)\n\n def _get_tool_return(self, next_step_output: Tuple[AgentAction, Union[str, DataModel]]) -> Optional[AgentFinish]:\n \"\"\"Check if the tool is a returning tool.\"\"\"\n agent_action, full_observation = next_step_output\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n observation = MessageDataModel.extract_tool_response_for_llm(\n llm_raw_observation, tool_style=self.memory.style\n )\n name_to_tool_map = {tool.name: tool for tool in self.tools}\n # Invalid tools won't be in the map, so we return False.\n if agent_action.tool in name_to_tool_map:\n if name_to_tool_map[agent_action.tool].return_direct:\n return AgentFinish(\n {self.agent.return_values[0]: observation},\n \"\",\n )\n return None","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser","uri":"program://OpenAgents/module/real_agents.adapters.agent_helpers.output_parser#L1-L75","kind":"module","name":"real_agents.adapters.agent_helpers.output_parser","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":1,"end_line":75,"context_start_line":1,"context_end_line":75,"code":"from __future__ import annotations\n\nfrom typing import Optional, Union\nfrom pydantic import Extra\n\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n)\nfrom real_agents.adapters.agent_helpers.agent import AgentOutputParser\nfrom real_agents.adapters.schema import AgentTransition\n\n\nclass ConversationOutputParser(AgentOutputParser):\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:\n from real_agents.data_agent.copilot_prompt import FORMAT_INSTRUCTIONS as COPILOT_FORMAT_INSTRUCTIONS\n from real_agents.plugins_agent.plugin_prompt import FORMAT_INSTRUCTIONS as PLUGINS_FORMAT_INSTRUCTIONS\n from real_agents.web_agent.webot_prompt import FORMAT_INSTRUCTIONS as WEBOT_FORMAT_INSTRUCTIONS\n\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:\n if \"```\" in json_string:\n return json_string.split(\"```\")[0]\n else:\n return None\n\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n return match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\")\n\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n try:\n _action = _extract_value(cleaned_output, \"action\")\n _action_input = _extract_value(cleaned_output, \"action_input\")\n if _action == \"Final Answer\":\n return AgentFinish({\"output\": _action_input}, cleaned_output)\n\n # Transition sentence should only be used not final answer.\n _explanation = _extract_explanation(cleaned_output)\n return AgentAction(_action, _action_input, cleaned_output)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n action = lines[1].strip()\n import textwrap\n\n action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return AgentAction(action, action_input, cleaned_output)\n\n return AgentFinish({\"output\": cleaned_output}, cleaned_output)\n\n @property\n def _type(self) -> str:\n return \"conversational_chat\"","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser.ConversationOutputParser","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.output_parser.ConversationOutputParser#L14-L75","kind":"class","name":"ConversationOutputParser","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":14,"end_line":75,"context_start_line":1,"context_end_line":75,"code":"from __future__ import annotations\n\nfrom typing import Optional, Union\nfrom pydantic import Extra\n\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n)\nfrom real_agents.adapters.agent_helpers.agent import AgentOutputParser\nfrom real_agents.adapters.schema import AgentTransition\n\n\nclass ConversationOutputParser(AgentOutputParser):\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:\n from real_agents.data_agent.copilot_prompt import FORMAT_INSTRUCTIONS as COPILOT_FORMAT_INSTRUCTIONS\n from real_agents.plugins_agent.plugin_prompt import FORMAT_INSTRUCTIONS as PLUGINS_FORMAT_INSTRUCTIONS\n from real_agents.web_agent.webot_prompt import FORMAT_INSTRUCTIONS as WEBOT_FORMAT_INSTRUCTIONS\n\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:\n if \"```\" in json_string:\n return json_string.split(\"```\")[0]\n else:\n return None\n\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n return match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\")\n\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n try:\n _action = _extract_value(cleaned_output, \"action\")\n _action_input = _extract_value(cleaned_output, \"action_input\")\n if _action == \"Final Answer\":\n return AgentFinish({\"output\": _action_input}, cleaned_output)\n\n # Transition sentence should only be used not final answer.\n _explanation = _extract_explanation(cleaned_output)\n return AgentAction(_action, _action_input, cleaned_output)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n action = lines[1].strip()\n import textwrap\n\n action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return AgentAction(action, action_input, cleaned_output)\n\n return AgentFinish({\"output\": cleaned_output}, cleaned_output)\n\n @property\n def _type(self) -> str:\n return \"conversational_chat\"","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser.Config","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.output_parser.Config#L15-L19","kind":"class","name":"Config","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":15,"end_line":19,"context_start_line":1,"context_end_line":39,"code":"from __future__ import annotations\n\nfrom typing import Optional, Union\nfrom pydantic import Extra\n\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n)\nfrom real_agents.adapters.agent_helpers.agent import AgentOutputParser\nfrom real_agents.adapters.schema import AgentTransition\n\n\nclass ConversationOutputParser(AgentOutputParser):\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:\n from real_agents.data_agent.copilot_prompt import FORMAT_INSTRUCTIONS as COPILOT_FORMAT_INSTRUCTIONS\n from real_agents.plugins_agent.plugin_prompt import FORMAT_INSTRUCTIONS as PLUGINS_FORMAT_INSTRUCTIONS\n from real_agents.web_agent.webot_prompt import FORMAT_INSTRUCTIONS as WEBOT_FORMAT_INSTRUCTIONS\n\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser.get_format_instructions","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.output_parser.get_format_instructions#L21-L33","kind":"function","name":"get_format_instructions","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":21,"end_line":33,"context_start_line":1,"context_end_line":53,"code":"from __future__ import annotations\n\nfrom typing import Optional, Union\nfrom pydantic import Extra\n\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n)\nfrom real_agents.adapters.agent_helpers.agent import AgentOutputParser\nfrom real_agents.adapters.schema import AgentTransition\n\n\nclass ConversationOutputParser(AgentOutputParser):\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:\n from real_agents.data_agent.copilot_prompt import FORMAT_INSTRUCTIONS as COPILOT_FORMAT_INSTRUCTIONS\n from real_agents.plugins_agent.plugin_prompt import FORMAT_INSTRUCTIONS as PLUGINS_FORMAT_INSTRUCTIONS\n from real_agents.web_agent.webot_prompt import FORMAT_INSTRUCTIONS as WEBOT_FORMAT_INSTRUCTIONS\n\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:\n if \"```\" in json_string:\n return json_string.split(\"```\")[0]\n else:\n return None\n\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n return match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\")\n\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n try:","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser.parse","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.output_parser.parse#L35-L71","kind":"function","name":"parse","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":35,"end_line":71,"context_start_line":15,"context_end_line":75,"code":" class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:\n from real_agents.data_agent.copilot_prompt import FORMAT_INSTRUCTIONS as COPILOT_FORMAT_INSTRUCTIONS\n from real_agents.plugins_agent.plugin_prompt import FORMAT_INSTRUCTIONS as PLUGINS_FORMAT_INSTRUCTIONS\n from real_agents.web_agent.webot_prompt import FORMAT_INSTRUCTIONS as WEBOT_FORMAT_INSTRUCTIONS\n\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:\n if \"```\" in json_string:\n return json_string.split(\"```\")[0]\n else:\n return None\n\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n return match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\")\n\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n try:\n _action = _extract_value(cleaned_output, \"action\")\n _action_input = _extract_value(cleaned_output, \"action_input\")\n if _action == \"Final Answer\":\n return AgentFinish({\"output\": _action_input}, cleaned_output)\n\n # Transition sentence should only be used not final answer.\n _explanation = _extract_explanation(cleaned_output)\n return AgentAction(_action, _action_input, cleaned_output)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n action = lines[1].strip()\n import textwrap\n\n action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return AgentAction(action, action_input, cleaned_output)\n\n return AgentFinish({\"output\": cleaned_output}, cleaned_output)\n\n @property\n def _type(self) -> str:\n return \"conversational_chat\"","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser._type","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.output_parser._type#L74-L75","kind":"function","name":"_type","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":74,"end_line":75,"context_start_line":54,"context_end_line":75,"code":" _action = _extract_value(cleaned_output, \"action\")\n _action_input = _extract_value(cleaned_output, \"action_input\")\n if _action == \"Final Answer\":\n return AgentFinish({\"output\": _action_input}, cleaned_output)\n\n # Transition sentence should only be used not final answer.\n _explanation = _extract_explanation(cleaned_output)\n return AgentAction(_action, _action_input, cleaned_output)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n action = lines[1].strip()\n import textwrap\n\n action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return AgentAction(action, action_input, cleaned_output)\n\n return AgentFinish({\"output\": cleaned_output}, cleaned_output)\n\n @property\n def _type(self) -> str:\n return \"conversational_chat\"","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser._extract_explanation","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.output_parser._extract_explanation#L39-L43","kind":"function","name":"_extract_explanation","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":39,"end_line":43,"context_start_line":19,"context_end_line":63,"code":" arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:\n from real_agents.data_agent.copilot_prompt import FORMAT_INSTRUCTIONS as COPILOT_FORMAT_INSTRUCTIONS\n from real_agents.plugins_agent.plugin_prompt import FORMAT_INSTRUCTIONS as PLUGINS_FORMAT_INSTRUCTIONS\n from real_agents.web_agent.webot_prompt import FORMAT_INSTRUCTIONS as WEBOT_FORMAT_INSTRUCTIONS\n\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:\n if \"```\" in json_string:\n return json_string.split(\"```\")[0]\n else:\n return None\n\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n return match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\")\n\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n try:\n _action = _extract_value(cleaned_output, \"action\")\n _action_input = _extract_value(cleaned_output, \"action_input\")\n if _action == \"Final Answer\":\n return AgentFinish({\"output\": _action_input}, cleaned_output)\n\n # Transition sentence should only be used not final answer.\n _explanation = _extract_explanation(cleaned_output)\n return AgentAction(_action, _action_input, cleaned_output)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.output_parser._extract_value","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.output_parser._extract_value#L45-L51","kind":"function","name":"_extract_value","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":45,"end_line":51,"context_start_line":25,"context_end_line":71,"code":"\n if app_name == \"copilot\":\n return COPILOT_FORMAT_INSTRUCTIONS\n elif app_name == \"webot\":\n return WEBOT_FORMAT_INSTRUCTIONS\n elif app_name == \"plugins\":\n return PLUGINS_FORMAT_INSTRUCTIONS\n else:\n raise ValueError(f\"Unknown app_name {app_name}\")\n\n def parse(self, text: str) -> Union[AgentTransition, AgentAction, AgentFinish]:\n cleaned_output = text.strip()\n import re\n\n def _extract_explanation(json_string: str) -> Optional[str]:\n if \"```\" in json_string:\n return json_string.split(\"```\")[0]\n else:\n return None\n\n def _extract_value(json_string: str, key: str) -> str:\n pattern = re.compile(rf'\"?{key}\"?\\s*:\\s*(\"((?:[^\"\\\\]|\\\\.)*)\"|(\\b[^,\\s]*\\b))', re.MULTILINE)\n match = pattern.search(json_string)\n if match:\n return match.group(1).replace('\\\\\"', '\"').replace(\"\\\\\\\\\", \"\\\\\").strip('\"').strip(\"'\")\n\n raise ValueError(f\"Could not find {key} in {json_string}\")\n\n try:\n _action = _extract_value(cleaned_output, \"action\")\n _action_input = _extract_value(cleaned_output, \"action_input\")\n if _action == \"Final Answer\":\n return AgentFinish({\"output\": _action_input}, cleaned_output)\n\n # Transition sentence should only be used not final answer.\n _explanation = _extract_explanation(cleaned_output)\n return AgentAction(_action, _action_input, cleaned_output)\n except Exception:\n if cleaned_output.startswith(\"Action:\"):\n lines = cleaned_output.splitlines()\n action = lines[1].strip()\n import textwrap\n\n action_input = textwrap.dedent(\"\\n\".join(lines[2:])).strip()\n return AgentAction(action, action_input, cleaned_output)\n\n return AgentFinish({\"output\": cleaned_output}, cleaned_output)","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools","uri":"program://OpenAgents/module/real_agents.adapters.agent_helpers.tools#L1-L179","kind":"module","name":"real_agents.adapters.agent_helpers.tools","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":1,"end_line":179,"context_start_line":1,"context_end_line":179,"code":"\"\"\"Interface for tools.\"\"\"\nfrom inspect import signature\nfrom typing import Any, Awaitable, Callable, Dict, Optional, Type, Union\nfrom pydantic import BaseModel, validate_arguments\n\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.data_model import DataModel\nfrom real_agents.adapters.callbacks.manager import (\n CallbackManager,\n Callbacks,\n)\n\n\nclass Tool(BaseTool):\n \"\"\"Tool that takes in function or coroutine directly.\"\"\"\n\n description: str = \"\"\n func: Callable[..., str]\n \"\"\"The function to run when the tool is called.\"\"\"\n coroutine: Optional[Callable[..., Awaitable[str]]] = None\n \"\"\"The asynchronous version of the function.\"\"\"\n\n @property\n def args(self) -> dict:\n if self.args_schema is not None:\n return self.args_schema.schema()[\"properties\"]\n else:\n inferred_model = validate_arguments(self.func).model # type: ignore\n schema = inferred_model.schema()[\"properties\"]\n valid_keys = signature(self.func).parameters\n return {k: schema[k] for k in valid_keys}\n\n def _run(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool.\"\"\"\n return self.func(*args, **kwargs)\n\n async def _arun(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n if self.coroutine:\n return await self.coroutine(*args, **kwargs)\n raise NotImplementedError(\"Tool does not support async\")\n\n # TODO: this is for backwards compatibility, remove in future\n def __init__(\n self, name: str, func: Callable[[str], Union[Dict[Any, Any], DataModel]], description: str, **kwargs: Any\n ) -> None:\n \"\"\"Initialize tool.\"\"\"\n super(Tool, self).__init__(name=name, func=func, description=description, **kwargs)\n\n def run(\n self,\n tool_input: Union[str, Dict],\n verbose: Optional[bool] = None,\n start_color: Optional[str] = \"green\",\n color: Optional[str] = \"green\",\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run the tool.\"\"\"\n parsed_input = self._parse_input(tool_input)\n if not self.verbose and verbose is not None:\n verbose_ = verbose\n else:\n verbose_ = self.verbose\n\n # todo: fix this place\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, verbose=verbose_)\n # TODO: maybe also pass through run_manager is _run supports kwargs\n new_arg_supported = signature(self._run).parameters.get(\"run_manager\")\n run_manager = callback_manager.on_tool_start(\n {\"name\": self.name, \"description\": self.description},\n tool_input if isinstance(tool_input, str) else str(tool_input),\n color=start_color,\n **kwargs,\n )\n try:\n tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)\n observation = (\n self._run(*tool_args, run_manager=run_manager, **tool_kwargs)\n if new_arg_supported\n else self._run(*tool_args, **tool_kwargs)\n )\n except (Exception, KeyboardInterrupt) as e:\n run_manager.on_tool_error(e)\n raise e\n\n run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,\n args_schema: Optional[Type[BaseModel]] = None,\n infer_schema: bool = True,\n) -> Callable:\n \"\"\"Make tools out of functions, can be used with or without arguments.\n\n Args:\n *args: The arguments to the tool.\n return_direct: Whether to return directly from the tool rather\n than continuing the agent loop.\n args_schema: optional argument schema for user to specify\n infer_schema: Whether to infer the schema of the arguments from\n the function's signature. This also makes the resultant tool\n accept a dictionary input to its `run()` function.\n\n Requires:\n - Function must be of type (str) -> str\n - Function must have a docstring\n\n Examples:\n .. code-block:: python\n\n @tool\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n\n @tool(\"search\", return_direct=True)\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n \"\"\"\n\n def _make_with_name(tool_name: str) -> Callable:\n def _make_tool(func: Callable) -> Tool:\n assert func.__doc__, \"Function must have a docstring\"\n # Description example:\n # search_api(query: str) - Searches the API for the query.\n description = f\"{tool_name}{signature(func)} - {func.__doc__.strip()}\"\n _args_schema = args_schema\n if _args_schema is None and infer_schema:\n _args_schema = validate_arguments(func).model # type: ignore\n tool_ = Tool(\n name=tool_name,\n func=func,\n args_schema=_args_schema,\n description=description,\n return_direct=return_direct,\n )\n return tool_\n\n return _make_tool\n\n if len(args) == 1 and isinstance(args[0], str):\n # if the argument is a string, then we use the string as the tool name\n # Example usage: @tool(\"search\", return_direct=True)\n return _make_with_name(args[0])\n elif len(args) == 1 and callable(args[0]):\n # if the argument is a function, then we use the function name as the tool name\n # Example usage: @tool\n return _make_with_name(args[0].__name__)(args[0])\n elif len(args) == 0:\n # if there are no arguments, then we use the function name as the tool name\n # Example usage: @tool(return_direct=True)\n def _partial(func: Callable[[str], str]) -> BaseTool:\n return _make_with_name(func.__name__)(func)\n\n return _partial\n else:\n raise ValueError(\"Too many arguments for tool decorator\")","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools.Tool","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.tools.Tool#L15-L90","kind":"class","name":"Tool","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":15,"end_line":90,"context_start_line":1,"context_end_line":110,"code":"\"\"\"Interface for tools.\"\"\"\nfrom inspect import signature\nfrom typing import Any, Awaitable, Callable, Dict, Optional, Type, Union\nfrom pydantic import BaseModel, validate_arguments\n\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.data_model import DataModel\nfrom real_agents.adapters.callbacks.manager import (\n CallbackManager,\n Callbacks,\n)\n\n\nclass Tool(BaseTool):\n \"\"\"Tool that takes in function or coroutine directly.\"\"\"\n\n description: str = \"\"\n func: Callable[..., str]\n \"\"\"The function to run when the tool is called.\"\"\"\n coroutine: Optional[Callable[..., Awaitable[str]]] = None\n \"\"\"The asynchronous version of the function.\"\"\"\n\n @property\n def args(self) -> dict:\n if self.args_schema is not None:\n return self.args_schema.schema()[\"properties\"]\n else:\n inferred_model = validate_arguments(self.func).model # type: ignore\n schema = inferred_model.schema()[\"properties\"]\n valid_keys = signature(self.func).parameters\n return {k: schema[k] for k in valid_keys}\n\n def _run(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool.\"\"\"\n return self.func(*args, **kwargs)\n\n async def _arun(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n if self.coroutine:\n return await self.coroutine(*args, **kwargs)\n raise NotImplementedError(\"Tool does not support async\")\n\n # TODO: this is for backwards compatibility, remove in future\n def __init__(\n self, name: str, func: Callable[[str], Union[Dict[Any, Any], DataModel]], description: str, **kwargs: Any\n ) -> None:\n \"\"\"Initialize tool.\"\"\"\n super(Tool, self).__init__(name=name, func=func, description=description, **kwargs)\n\n def run(\n self,\n tool_input: Union[str, Dict],\n verbose: Optional[bool] = None,\n start_color: Optional[str] = \"green\",\n color: Optional[str] = \"green\",\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run the tool.\"\"\"\n parsed_input = self._parse_input(tool_input)\n if not self.verbose and verbose is not None:\n verbose_ = verbose\n else:\n verbose_ = self.verbose\n\n # todo: fix this place\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, verbose=verbose_)\n # TODO: maybe also pass through run_manager is _run supports kwargs\n new_arg_supported = signature(self._run).parameters.get(\"run_manager\")\n run_manager = callback_manager.on_tool_start(\n {\"name\": self.name, \"description\": self.description},\n tool_input if isinstance(tool_input, str) else str(tool_input),\n color=start_color,\n **kwargs,\n )\n try:\n tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)\n observation = (\n self._run(*tool_args, run_manager=run_manager, **tool_kwargs)\n if new_arg_supported\n else self._run(*tool_args, **tool_kwargs)\n )\n except (Exception, KeyboardInterrupt) as e:\n run_manager.on_tool_error(e)\n raise e\n\n run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools.InvalidTool","uri":"program://OpenAgents/class/real_agents.adapters.agent_helpers.tools.InvalidTool#L93-L105","kind":"class","name":"InvalidTool","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":93,"end_line":105,"context_start_line":73,"context_end_line":125,"code":" tool_input if isinstance(tool_input, str) else str(tool_input),\n color=start_color,\n **kwargs,\n )\n try:\n tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)\n observation = (\n self._run(*tool_args, run_manager=run_manager, **tool_kwargs)\n if new_arg_supported\n else self._run(*tool_args, **tool_kwargs)\n )\n except (Exception, KeyboardInterrupt) as e:\n run_manager.on_tool_error(e)\n raise e\n\n run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,\n args_schema: Optional[Type[BaseModel]] = None,\n infer_schema: bool = True,\n) -> Callable:\n \"\"\"Make tools out of functions, can be used with or without arguments.\n\n Args:\n *args: The arguments to the tool.\n return_direct: Whether to return directly from the tool rather\n than continuing the agent loop.\n args_schema: optional argument schema for user to specify\n infer_schema: Whether to infer the schema of the arguments from\n the function's signature. This also makes the resultant tool\n accept a dictionary input to its `run()` function.\n\n Requires:","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools.tool","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools.tool#L108-L179","kind":"function","name":"tool","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":108,"end_line":179,"context_start_line":88,"context_end_line":179,"code":" run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,\n args_schema: Optional[Type[BaseModel]] = None,\n infer_schema: bool = True,\n) -> Callable:\n \"\"\"Make tools out of functions, can be used with or without arguments.\n\n Args:\n *args: The arguments to the tool.\n return_direct: Whether to return directly from the tool rather\n than continuing the agent loop.\n args_schema: optional argument schema for user to specify\n infer_schema: Whether to infer the schema of the arguments from\n the function's signature. This also makes the resultant tool\n accept a dictionary input to its `run()` function.\n\n Requires:\n - Function must be of type (str) -> str\n - Function must have a docstring\n\n Examples:\n .. code-block:: python\n\n @tool\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n\n @tool(\"search\", return_direct=True)\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n \"\"\"\n\n def _make_with_name(tool_name: str) -> Callable:\n def _make_tool(func: Callable) -> Tool:\n assert func.__doc__, \"Function must have a docstring\"\n # Description example:\n # search_api(query: str) - Searches the API for the query.\n description = f\"{tool_name}{signature(func)} - {func.__doc__.strip()}\"\n _args_schema = args_schema\n if _args_schema is None and infer_schema:\n _args_schema = validate_arguments(func).model # type: ignore\n tool_ = Tool(\n name=tool_name,\n func=func,\n args_schema=_args_schema,\n description=description,\n return_direct=return_direct,\n )\n return tool_\n\n return _make_tool\n\n if len(args) == 1 and isinstance(args[0], str):\n # if the argument is a string, then we use the string as the tool name\n # Example usage: @tool(\"search\", return_direct=True)\n return _make_with_name(args[0])\n elif len(args) == 1 and callable(args[0]):\n # if the argument is a function, then we use the function name as the tool name\n # Example usage: @tool\n return _make_with_name(args[0].__name__)(args[0])\n elif len(args) == 0:\n # if there are no arguments, then we use the function name as the tool name\n # Example usage: @tool(return_direct=True)\n def _partial(func: Callable[[str], str]) -> BaseTool:\n return _make_with_name(func.__name__)(func)\n\n return _partial\n else:\n raise ValueError(\"Too many arguments for tool decorator\")","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools.args","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools.args#L25-L32","kind":"function","name":"args","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":25,"end_line":32,"context_start_line":5,"context_end_line":52,"code":"\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.data_model import DataModel\nfrom real_agents.adapters.callbacks.manager import (\n CallbackManager,\n Callbacks,\n)\n\n\nclass Tool(BaseTool):\n \"\"\"Tool that takes in function or coroutine directly.\"\"\"\n\n description: str = \"\"\n func: Callable[..., str]\n \"\"\"The function to run when the tool is called.\"\"\"\n coroutine: Optional[Callable[..., Awaitable[str]]] = None\n \"\"\"The asynchronous version of the function.\"\"\"\n\n @property\n def args(self) -> dict:\n if self.args_schema is not None:\n return self.args_schema.schema()[\"properties\"]\n else:\n inferred_model = validate_arguments(self.func).model # type: ignore\n schema = inferred_model.schema()[\"properties\"]\n valid_keys = signature(self.func).parameters\n return {k: schema[k] for k in valid_keys}\n\n def _run(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool.\"\"\"\n return self.func(*args, **kwargs)\n\n async def _arun(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n if self.coroutine:\n return await self.coroutine(*args, **kwargs)\n raise NotImplementedError(\"Tool does not support async\")\n\n # TODO: this is for backwards compatibility, remove in future\n def __init__(\n self, name: str, func: Callable[[str], Union[Dict[Any, Any], DataModel]], description: str, **kwargs: Any\n ) -> None:\n \"\"\"Initialize tool.\"\"\"\n super(Tool, self).__init__(name=name, func=func, description=description, **kwargs)\n\n def run(\n self,","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools._run","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools._run#L99-L101","kind":"function","name":"_run","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":99,"end_line":101,"context_start_line":79,"context_end_line":121,"code":" observation = (\n self._run(*tool_args, run_manager=run_manager, **tool_kwargs)\n if new_arg_supported\n else self._run(*tool_args, **tool_kwargs)\n )\n except (Exception, KeyboardInterrupt) as e:\n run_manager.on_tool_error(e)\n raise e\n\n run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,\n args_schema: Optional[Type[BaseModel]] = None,\n infer_schema: bool = True,\n) -> Callable:\n \"\"\"Make tools out of functions, can be used with or without arguments.\n\n Args:\n *args: The arguments to the tool.\n return_direct: Whether to return directly from the tool rather\n than continuing the agent loop.\n args_schema: optional argument schema for user to specify\n infer_schema: Whether to infer the schema of the arguments from","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools._arun","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools._arun#L103-L105","kind":"function","name":"_arun","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":103,"end_line":105,"context_start_line":83,"context_end_line":125,"code":" )\n except (Exception, KeyboardInterrupt) as e:\n run_manager.on_tool_error(e)\n raise e\n\n run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,\n args_schema: Optional[Type[BaseModel]] = None,\n infer_schema: bool = True,\n) -> Callable:\n \"\"\"Make tools out of functions, can be used with or without arguments.\n\n Args:\n *args: The arguments to the tool.\n return_direct: Whether to return directly from the tool rather\n than continuing the agent loop.\n args_schema: optional argument schema for user to specify\n infer_schema: Whether to infer the schema of the arguments from\n the function's signature. This also makes the resultant tool\n accept a dictionary input to its `run()` function.\n\n Requires:","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools.__init__","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools.__init__#L45-L49","kind":"function","name":"__init__","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":45,"end_line":49,"context_start_line":25,"context_end_line":69,"code":" def args(self) -> dict:\n if self.args_schema is not None:\n return self.args_schema.schema()[\"properties\"]\n else:\n inferred_model = validate_arguments(self.func).model # type: ignore\n schema = inferred_model.schema()[\"properties\"]\n valid_keys = signature(self.func).parameters\n return {k: schema[k] for k in valid_keys}\n\n def _run(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool.\"\"\"\n return self.func(*args, **kwargs)\n\n async def _arun(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n if self.coroutine:\n return await self.coroutine(*args, **kwargs)\n raise NotImplementedError(\"Tool does not support async\")\n\n # TODO: this is for backwards compatibility, remove in future\n def __init__(\n self, name: str, func: Callable[[str], Union[Dict[Any, Any], DataModel]], description: str, **kwargs: Any\n ) -> None:\n \"\"\"Initialize tool.\"\"\"\n super(Tool, self).__init__(name=name, func=func, description=description, **kwargs)\n\n def run(\n self,\n tool_input: Union[str, Dict],\n verbose: Optional[bool] = None,\n start_color: Optional[str] = \"green\",\n color: Optional[str] = \"green\",\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run the tool.\"\"\"\n parsed_input = self._parse_input(tool_input)\n if not self.verbose and verbose is not None:\n verbose_ = verbose\n else:\n verbose_ = self.verbose\n\n # todo: fix this place\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, verbose=verbose_)\n # TODO: maybe also pass through run_manager is _run supports kwargs","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools.run","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools.run#L51-L90","kind":"function","name":"run","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":51,"end_line":90,"context_start_line":31,"context_end_line":110,"code":" valid_keys = signature(self.func).parameters\n return {k: schema[k] for k in valid_keys}\n\n def _run(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool.\"\"\"\n return self.func(*args, **kwargs)\n\n async def _arun(self, *args: Any, **kwargs: Any) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n if self.coroutine:\n return await self.coroutine(*args, **kwargs)\n raise NotImplementedError(\"Tool does not support async\")\n\n # TODO: this is for backwards compatibility, remove in future\n def __init__(\n self, name: str, func: Callable[[str], Union[Dict[Any, Any], DataModel]], description: str, **kwargs: Any\n ) -> None:\n \"\"\"Initialize tool.\"\"\"\n super(Tool, self).__init__(name=name, func=func, description=description, **kwargs)\n\n def run(\n self,\n tool_input: Union[str, Dict],\n verbose: Optional[bool] = None,\n start_color: Optional[str] = \"green\",\n color: Optional[str] = \"green\",\n callbacks: Callbacks = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run the tool.\"\"\"\n parsed_input = self._parse_input(tool_input)\n if not self.verbose and verbose is not None:\n verbose_ = verbose\n else:\n verbose_ = self.verbose\n\n # todo: fix this place\n callback_manager = CallbackManager.configure(callbacks, self.callbacks, verbose=verbose_)\n # TODO: maybe also pass through run_manager is _run supports kwargs\n new_arg_supported = signature(self._run).parameters.get(\"run_manager\")\n run_manager = callback_manager.on_tool_start(\n {\"name\": self.name, \"description\": self.description},\n tool_input if isinstance(tool_input, str) else str(tool_input),\n color=start_color,\n **kwargs,\n )\n try:\n tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)\n observation = (\n self._run(*tool_args, run_manager=run_manager, **tool_kwargs)\n if new_arg_supported\n else self._run(*tool_args, **tool_kwargs)\n )\n except (Exception, KeyboardInterrupt) as e:\n run_manager.on_tool_error(e)\n raise e\n\n run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)\n\n return observation\n\n\nclass InvalidTool(BaseTool):\n \"\"\"Tool that is run when invalid tool name is encountered by agent.\"\"\"\n\n name = \"invalid_tool\"\n description = \"Called when tool name is invalid.\"\n\n def _run(self, tool_name: str) -> str:\n \"\"\"Use the tool.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n async def _arun(self, tool_name: str) -> str:\n \"\"\"Use the tool asynchronously.\"\"\"\n return f\"{tool_name} is not a valid tool, try another one.\"\n\n\ndef tool(\n *args: Union[str, Callable],\n return_direct: bool = False,","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools._make_with_name","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools._make_with_name#L143-L161","kind":"function","name":"_make_with_name","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":143,"end_line":161,"context_start_line":123,"context_end_line":179,"code":" accept a dictionary input to its `run()` function.\n\n Requires:\n - Function must be of type (str) -> str\n - Function must have a docstring\n\n Examples:\n .. code-block:: python\n\n @tool\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n\n @tool(\"search\", return_direct=True)\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n \"\"\"\n\n def _make_with_name(tool_name: str) -> Callable:\n def _make_tool(func: Callable) -> Tool:\n assert func.__doc__, \"Function must have a docstring\"\n # Description example:\n # search_api(query: str) - Searches the API for the query.\n description = f\"{tool_name}{signature(func)} - {func.__doc__.strip()}\"\n _args_schema = args_schema\n if _args_schema is None and infer_schema:\n _args_schema = validate_arguments(func).model # type: ignore\n tool_ = Tool(\n name=tool_name,\n func=func,\n args_schema=_args_schema,\n description=description,\n return_direct=return_direct,\n )\n return tool_\n\n return _make_tool\n\n if len(args) == 1 and isinstance(args[0], str):\n # if the argument is a string, then we use the string as the tool name\n # Example usage: @tool(\"search\", return_direct=True)\n return _make_with_name(args[0])\n elif len(args) == 1 and callable(args[0]):\n # if the argument is a function, then we use the function name as the tool name\n # Example usage: @tool\n return _make_with_name(args[0].__name__)(args[0])\n elif len(args) == 0:\n # if there are no arguments, then we use the function name as the tool name\n # Example usage: @tool(return_direct=True)\n def _partial(func: Callable[[str], str]) -> BaseTool:\n return _make_with_name(func.__name__)(func)\n\n return _partial\n else:\n raise ValueError(\"Too many arguments for tool decorator\")","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools._make_tool","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools._make_tool#L144-L159","kind":"function","name":"_make_tool","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":144,"end_line":159,"context_start_line":124,"context_end_line":179,"code":"\n Requires:\n - Function must be of type (str) -> str\n - Function must have a docstring\n\n Examples:\n .. code-block:: python\n\n @tool\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n\n @tool(\"search\", return_direct=True)\n def search_api(query: str) -> str:\n # Searches the API for the query.\n return\n \"\"\"\n\n def _make_with_name(tool_name: str) -> Callable:\n def _make_tool(func: Callable) -> Tool:\n assert func.__doc__, \"Function must have a docstring\"\n # Description example:\n # search_api(query: str) - Searches the API for the query.\n description = f\"{tool_name}{signature(func)} - {func.__doc__.strip()}\"\n _args_schema = args_schema\n if _args_schema is None and infer_schema:\n _args_schema = validate_arguments(func).model # type: ignore\n tool_ = Tool(\n name=tool_name,\n func=func,\n args_schema=_args_schema,\n description=description,\n return_direct=return_direct,\n )\n return tool_\n\n return _make_tool\n\n if len(args) == 1 and isinstance(args[0], str):\n # if the argument is a string, then we use the string as the tool name\n # Example usage: @tool(\"search\", return_direct=True)\n return _make_with_name(args[0])\n elif len(args) == 1 and callable(args[0]):\n # if the argument is a function, then we use the function name as the tool name\n # Example usage: @tool\n return _make_with_name(args[0].__name__)(args[0])\n elif len(args) == 0:\n # if there are no arguments, then we use the function name as the tool name\n # Example usage: @tool(return_direct=True)\n def _partial(func: Callable[[str], str]) -> BaseTool:\n return _make_with_name(func.__name__)(func)\n\n return _partial\n else:\n raise ValueError(\"Too many arguments for tool decorator\")","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.adapters.agent_helpers.tools._partial","uri":"program://OpenAgents/function/real_agents.adapters.agent_helpers.tools._partial#L174-L175","kind":"function","name":"_partial","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":174,"end_line":175,"context_start_line":154,"context_end_line":179,"code":" func=func,\n args_schema=_args_schema,\n description=description,\n return_direct=return_direct,\n )\n return tool_\n\n return _make_tool\n\n if len(args) == 1 and isinstance(args[0], str):\n # if the argument is a string, then we use the string as the tool name\n # Example usage: @tool(\"search\", return_direct=True)\n return _make_with_name(args[0])\n elif len(args) == 1 and callable(args[0]):\n # if the argument is a function, then we use the function name as the tool name\n # Example usage: @tool\n return _make_with_name(args[0].__name__)(args[0])\n elif len(args) == 0:\n # if there are no arguments, then we use the function name as the tool name\n # Example usage: @tool(return_direct=True)\n def _partial(func: Callable[[str], str]) -> BaseTool:\n return _make_with_name(func.__name__)(func)\n\n return _partial\n else:\n raise ValueError(\"Too many arguments for tool decorator\")","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin_prompt","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugin_prompt#L1-L84","kind":"module","name":"real_agents.plugins_agent.plugin_prompt","path":"real_agents/plugins_agent/plugin_prompt.py","language":"python","start_line":1,"end_line":84,"context_start_line":1,"context_end_line":84,"code":"# flake8: noqa\nimport datetime\n\nPREFIX = (\n \"\"\"You are XLang Plugins Agent, a friendly and intuitive assistant developed by the XLang Team to guide you through every aspects of your work and your daily life. XLang Agent is always at your fingertips through our interactive chat system.\n\nYou can aware of what plugins you have, and use the plugins properly in right order to finish what user wants.\n\nToday is\n\"\"\".strip() + \" \"\n + datetime.datetime.now().strftime(\"%Y-%m-%d\")\n + \"\"\", and you should adapt the input to fit into the date, for example, seasonal information, or today's date as coordinate, etc.\n\nTo make your response informative, always speak includes the following information in MARKDOWN format when responding a message, that is:\n1. Natural language explanation, that make explain the API output in a human readable way;\n2. Organized information such as bullet points or MARKDOWN tables, followed by the links to the items (that in the API output), news etc. if API output contains the information;\n3. The links should in MARKDOWN format and have value in it. If reference information is provided in the API output, like links to the items, news etc. Your explanation MUST provide the links on each items and links can be clicked on when API output contains the information. The links better attach on some natural language explanation through MARKDOWN syntax, for example, - [Renewable Energy - Center for Climate and Energy Solutions](https://www.c2es.org/content/renewable-energy/);\n4. If there are image we would like to display, please use MARKDOWN syntax to display it, for example, ![image](https://www.c2es.org/content/renewable-energy/);\n5. Try to speak more and show all the information you got in a organized way, that will make you a better assistant, especially when you are giving the final answer.\n\nPLUGINS\n------\nThe plugins you can use are:\n\"\"\".strip() + \"\\n\"\n)\n\nFORMAT_INSTRUCTIONS = \"\"\"RESPONSE FORMAT INSTRUCTIONS\n----------------------------\n\nWhen you use tools or generate final answer, please output a response in one of two formats:\n**Option 1: Explain and Use plugin**\nIf the response involves using a plugin, you can start with a natural language explanation[Optional], plus exactly one plugin calling[MUST], and ends with no more words. The plugin calling format should be a markdown code snippet with the following JSON schema:\n\n```json\n{{{{\n \"action\": string wrapped with \\\"\\\", // The action to take. Must be one in the list [{tool_names}]\n \"action_input\": string wrapped with \\\"\\\" // Query to be input to the action plugin. Pass as much information as possible to the plugin from the history of the conversation.\n}}}}\n```\nNEVER EVER EVER make up a plugin not in [{tool_names}]\nYou MUST pass as much information as possible to the plugin from the history of the conversation. It could be natural language or structured language like jsonl, csv, etc. BUT MUST in a single line.\n(Please note that ONLY ONE plugin should be used per response.)\n\n**Option #2: **\nIf you want to respond directly to the human without using a plugin, provide a plain natural language response. However, if you initially generated a natural language response and then decide to use a plugin, make sure to include the plugin action and input after the initial response.\n\nBegin.\n\"\"\"\n\nSUFFIX = \"{input}\"\n\nTEMPLATE_TOOL_RESPONSE = \"\"\"PLUGINS RESPONSE:\n---------------------\n{observation}\n\nTHOUGHT\n--------------------\n\nOkay, So what's next? Are the plugins' response enough to answer human's initial query? Please follow these instructions:\n\n1. Evaluate plugin Response [Mandatory]: Carefully evaluate the plugin's response and determine if it sufficiently addresses the human's query. Consider the content and implications of the plugin's response.\n\n2. Consider Additional plugin Use [Optional 2 or 3]: If the plugin response does not fully address the query or if an error occurred during execution, you may proceed with additional plugin usage. However, exercise caution and limit the number of iterations to a maximum of 5. You can start with a natural language explanation[Optional], plus exactly one plugin calling[MUST]. Follow this format for additional plugin usage:\n\n```json\n{{{{\n \"action\": string wrapped with \\\"\\\", // The action to take. Must be one of [{tool_names}]\n \"action_input\": string wrapped with \\\"\\\" // Query to be input to the action plugin. Pass as much information as possible to the plugin from the history of the conversation.\n}}}}\n```\n(Please note that ONLY ONE plugin should be used per response.)\n\n3. Deliver Comprehensive Answer [Optional 2 or 3]: If the plugin response sufficiently addresses the query, deliver a comprehensive answer to the human. Focus solely on the content and implications of the plugin's response. MUST NOT include explanations of the plugin's functions.\n\nNote. you must do 1; For 2 and 3, You must choose one from them.\n\nBegin.\n\"\"\"\n\n# models like anthropic claude-v1 or claude-2 can only return valid completion with human message as the last message, so we append the fake AI message at the end.\nfake_continue_prompt = {\n \"claude-2\": \"you can start to think and respond to me using the above formats. No Apology. Just respond with format in Option 2(use tool) or Option 3(direct text response), no other words.\\n\\nBegin.\",\n \"claude-v1\": \"you can start to think and respond to me using the above formats. No Apology. Just respond with format in Option 2(use tool) or Option 3(direct text response), no other words.\\n\\nBegin.\",\n}","source_hash":"91df179697c5736562a29082a4d8874e8a87ba66582f1c446f3800db6c7da8b1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugin#L1-L208","kind":"module","name":"real_agents.plugins_agent.plugin","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":1,"end_line":208,"context_start_line":1,"context_end_line":208,"code":"\"\"\"An agent designed to hold a conversation in addition to using tools. (Specially designed for plugins model)\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra, Field\nfrom typing_extensions import override\n\nfrom langchain.agents.agent import AgentOutputParser\nfrom langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains import LLMChain\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n AIMessage,\n BaseMessage,\n BaseOutputParser,\n HumanMessage\n)\nfrom langchain.callbacks.manager import (\n Callbacks\n)\nfrom langchain.tools.base import BaseTool\nfrom langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.plugins_agent.plugin_prompt import (\n PREFIX,\n SUFFIX,\n TEMPLATE_TOOL_RESPONSE,\n fake_continue_prompt\n)\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalPluginChatAgent(ConversationalChatAgent):\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n tool_strings = tool_strings.replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"plugins\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs,\n max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.ConversationalPluginChatAgent","uri":"program://OpenAgents/class/real_agents.plugins_agent.plugin.ConversationalPluginChatAgent#L44-L208","kind":"class","name":"ConversationalPluginChatAgent","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":44,"end_line":208,"context_start_line":24,"context_end_line":208,"code":"from langchain.prompts import (\n BasePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.plugins_agent.plugin_prompt import (\n PREFIX,\n SUFFIX,\n TEMPLATE_TOOL_RESPONSE,\n fake_continue_prompt\n)\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalPluginChatAgent(ConversationalChatAgent):\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n tool_strings = tool_strings.replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"plugins\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs,\n max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.Config","uri":"program://OpenAgents/class/real_agents.plugins_agent.plugin.Config#L54-L58","kind":"class","name":"Config","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":54,"end_line":58,"context_start_line":34,"context_end_line":78,"code":"from real_agents.plugins_agent.plugin_prompt import (\n PREFIX,\n SUFFIX,\n TEMPLATE_TOOL_RESPONSE,\n fake_continue_prompt\n)\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalPluginChatAgent(ConversationalChatAgent):\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin._get_default_output_parser","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin._get_default_output_parser#L61-L64","kind":"function","name":"_get_default_output_parser","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":61,"end_line":64,"context_start_line":41,"context_end_line":84,"code":"from real_agents.data_agent.copilot import ConversationalChatAgent\n\n\nclass ConversationalPluginChatAgent(ConversationalChatAgent):\n \"\"\"An agent designed to hold a conversation in addition to using plugin tool.\"\"\"\n\n output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin._agent_type","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin._agent_type#L67-L68","kind":"function","name":"_agent_type","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":67,"end_line":68,"context_start_line":47,"context_end_line":88,"code":" output_parser: ConversationOutputParser = Field(\n default_factory=ConversationOutputParser()\n )\n\n template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.observation_prefix","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin.observation_prefix#L71-L73","kind":"function","name":"observation_prefix","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":71,"end_line":73,"context_start_line":51,"context_end_line":93,"code":" template_tool_response: str = TEMPLATE_TOOL_RESPONSE\n continue_model: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.llm_prefix","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin.llm_prefix#L76-L78","kind":"function","name":"llm_prefix","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":76,"end_line":78,"context_start_line":56,"context_end_line":98,"code":"\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n @classmethod\n def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n tool_strings = tool_strings.replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin._validate_tools","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin._validate_tools#L81-L83","kind":"function","name":"_validate_tools","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":81,"end_line":83,"context_start_line":61,"context_end_line":103,"code":" def _get_default_output_parser(\n cls, **kwargs: Any\n ) -> ConversationOutputParser:\n return ConversationOutputParser()\n\n @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n tool_strings = tool_strings.replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"plugins\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.create_prompt","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin.create_prompt#L86-L116","kind":"function","name":"create_prompt","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":86,"end_line":116,"context_start_line":66,"context_end_line":136,"code":" @property\n def _agent_type(self) -> str:\n raise NotImplementedError\n\n @property\n def observation_prefix(self) -> str:\n \"\"\"Prefix to append the observation with.\"\"\"\n return \"Observation: \"\n\n @property\n def llm_prefix(self) -> str:\n \"\"\"Prefix to append the llm call with.\"\"\"\n return \"Thought:\"\n\n @classmethod\n def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:\n super()._validate_tools(tools)\n validate_tools_single_input(cls.__name__, tools)\n\n @classmethod\n def create_prompt(\n cls,\n tools: Sequence[BaseTool],\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n output_parser: Optional[BaseOutputParser] = None,\n ) -> BasePromptTemplate:\n tool_strings = \"\\n\".join([f\"Name: {tool.name}\\nDescription: {tool.description}\" for tool in tools])\n tool_strings = tool_strings.replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n tool_names = \", \".join([tool.name for tool in tools])\n _output_parser = output_parser or cls._get_default_output_parser()\n\n format_instructions = _output_parser.get_format_instructions(\"plugins\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin._construct_scratchpad","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin._construct_scratchpad#L118-L143","kind":"function","name":"_construct_scratchpad","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":118,"end_line":143,"context_start_line":98,"context_end_line":163,"code":"\n format_instructions = _output_parser.get_format_instructions(\"plugins\")\n format_instructions = format_instructions.format(tool_names=tool_names)\n\n # system message\n system_message = system_message + f\"{tool_strings}\\n\\n{format_instructions}\"\n\n # human input\n final_prompt = human_message\n\n if input_variables is None:\n input_variables = [\"input\", \"chat_history\", \"agent_scratchpad\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_message),\n MessagesPlaceholder(variable_name=\"chat_history\"),\n HumanMessagePromptTemplate.from_template(final_prompt),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def _construct_scratchpad(self, intermediate_steps: List[Tuple[AgentAction, str]]) -> List[BaseMessage]:\n \"\"\"Construct the scratchpad that lets the agent continue its thought process.\"\"\"\n thoughts: List[BaseMessage] = []\n # Try to only use AI message for scratchpad\n content = []\n for idx, (action, full_observation) in enumerate(intermediate_steps):\n content.append(MessageDataModel.extract_action_for_llm(action.log))\n\n observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.plan","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin.plan#L146-L173","kind":"function","name":"plan","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":146,"end_line":173,"context_start_line":126,"context_end_line":193,"code":" observation = full_observation\n if isinstance(full_observation, DataModel):\n llm_raw_observation = full_observation.get_llm_side_data()\n\n observation = MessageDataModel.extract_tool_response_for_llm(llm_raw_observation, tool_style=\"plugin\")\n tool_response = self.template_tool_response.format(\n observation=str(observation), tool_names=self.allowed_tools\n )\n if idx == len(intermediate_steps) - 1:\n content.append(tool_response)\n else:\n content.append(observation)\n\n content_str = \"\\n\".join(content)\n thoughts.append(AIMessage(content=content_str))\n if self.continue_model is not None and len(intermediate_steps) != 0:\n thoughts.append(HumanMessage(content=fake_continue_prompt[self.continue_model]))\n return thoughts\n\n @override\n def plan(\n self,\n intermediate_steps: List[Tuple[AgentAction, str]],\n **kwargs: Any,\n ) -> Union[AgentAction, AgentFinish]:\n \"\"\"Given input, decided what to do.\n\n Args:\n intermediate_steps: Steps the LLM has taken to date,\n along with observations\n **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs,\n max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugin.from_llm_and_tools","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugin.from_llm_and_tools#L176-L208","kind":"function","name":"from_llm_and_tools","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":176,"end_line":208,"context_start_line":156,"context_end_line":208,"code":" **kwargs: User inputs.\n\n Returns:\n Action specifying what tool to use.\n \"\"\"\n full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)\n system_prompt = self.llm_chain.prompt.messages[0].format().content\n system_prompt_tokens = MessageDataModel._count_tokens(\n system_prompt\n )\n max_tokens = 8000\n max_gen_tokens = 1000\n # FIXME: need more accurate token limit calculation\n full_inputs = MessageDataModel.truncate_chat_history(full_inputs,\n max_token=max_tokens - system_prompt_tokens - max_gen_tokens)\n full_output = self.llm_chain.predict(**full_inputs)\n\n return self.output_parser.parse(full_output)\n\n @classmethod\n def from_llm_and_tools(\n cls,\n llm: BaseLanguageModel,\n tools: Sequence[BaseTool],\n callbacks: Callbacks = None,\n output_parser: Optional[AgentOutputParser] = None,\n system_message: str = PREFIX,\n human_message: str = SUFFIX,\n input_variables: Optional[List[str]] = None,\n **kwargs: Any,\n ) -> Agent:\n \"\"\"Construct an agent from an LLM and tools.\"\"\"\n cls._validate_tools(tools)\n\n _output_parser = output_parser or cls._get_default_output_parser()\n prompt = cls.create_prompt(\n tools,\n system_message=system_message,\n human_message=human_message,\n input_variables=input_variables,\n output_parser=_output_parser,\n )\n llm_chain = LLMChain(\n llm=llm,\n prompt=prompt,\n )\n tool_names = [tool.name for tool in tools]\n return cls(\n llm_chain=llm_chain,\n allowed_tools=tool_names,\n output_parser=_output_parser,\n **kwargs,\n )","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.plugin_names","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.plugin_names#L1-L28","kind":"module","name":"real_agents.plugins_agent.plugins.plugin_names","path":"real_agents/plugins_agent/plugins/plugin_names.py","language":"python","start_line":1,"end_line":28,"context_start_line":1,"context_end_line":28,"code":"\"\"\"The .py file to control the plugin we use in the chatbot\"\"\"\nimport os\nfrom enum import Enum\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass PluginName(str, Enum):\n \"\"\"\n Enum class for plugin names\n each name is a plugin name 🔌 , each value is the folder name 📁 of the plugin\n \"\"\"\n KLARNA = \"klarna\"\n ZAPIER = \"zapier\"\n COURSERA = \"Coursera\"\n JOBSEARCH = \"jobsearch\"\n SHOW_ME = \"show_me\"\n SPEAK = \"speak\"\n CREATE_QR_CODE = \"create_qr_code\"\n MAPS = \"maps\"\n ASKYOURPDF = \"askyourpdf\"\n OUTSCHOOL = \"Outschool\"\n NBA_STATS = \"nba_stats\"\n WOLFRAM = \"wolfram\"\n WEB_SCRAPER = \"web_scraper\"\n DREAMINTERPRETER = \"DreamInterpreter\"\n BIZTOC = \"biztoc\"\n XWEATHER = \"XWeather\"","source_hash":"e534a5981fc840bae9714838f671aeac7ca69ba60ab660ccebcd3174eea50f75","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.plugin_names.PluginName","uri":"program://OpenAgents/class/real_agents.plugins_agent.plugins.plugin_names.PluginName#L8-L28","kind":"class","name":"PluginName","path":"real_agents/plugins_agent/plugins/plugin_names.py","language":"python","start_line":8,"end_line":28,"context_start_line":1,"context_end_line":28,"code":"\"\"\"The .py file to control the plugin we use in the chatbot\"\"\"\nimport os\nfrom enum import Enum\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass PluginName(str, Enum):\n \"\"\"\n Enum class for plugin names\n each name is a plugin name 🔌 , each value is the folder name 📁 of the plugin\n \"\"\"\n KLARNA = \"klarna\"\n ZAPIER = \"zapier\"\n COURSERA = \"Coursera\"\n JOBSEARCH = \"jobsearch\"\n SHOW_ME = \"show_me\"\n SPEAK = \"speak\"\n CREATE_QR_CODE = \"create_qr_code\"\n MAPS = \"maps\"\n ASKYOURPDF = \"askyourpdf\"\n OUTSCHOOL = \"Outschool\"\n NBA_STATS = \"nba_stats\"\n WOLFRAM = \"wolfram\"\n WEB_SCRAPER = \"web_scraper\"\n DREAMINTERPRETER = \"DreamInterpreter\"\n BIZTOC = \"biztoc\"\n XWEATHER = \"XWeather\"","source_hash":"e534a5981fc840bae9714838f671aeac7ca69ba60ab660ccebcd3174eea50f75","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.utils","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.utils#L1-L112","kind":"module","name":"real_agents.plugins_agent.plugins.utils","path":"real_agents/plugins_agent/plugins/utils.py","language":"python","start_line":1,"end_line":112,"context_start_line":1,"context_end_line":112,"code":"\"\"\"Utils for plugins (loading and more to add)\"\"\"\nimport importlib\nimport json\nimport os\nimport sys\nfrom collections import defaultdict\nfrom typing import Any\nimport yaml\nfrom tqdm import tqdm\n\nfrom real_agents.adapters.data_model import APIYamlModel, SpecModel\nfrom real_agents.plugins_agent.plugins.plugin_names import PluginName\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\nAI_PLUGIN_FILE = \"ai-plugin.json\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nPATH_FOLDER = \"paths\"\n\n\ndef _load_module(name: str, file_path: str) -> Any:\n spec = importlib.util.spec_from_file_location(name, file_path)\n ret = importlib.util.module_from_spec(spec)\n sys.modules[name] = ret\n spec.loader.exec_module(ret)\n return ret\n\n\ndef load_plugin_elements_by_name(plugin_name: str):\n # Check if the plugin name is valid\n assert plugin_name in PluginName.__members__, \"Unknown plugin name {}.\".format(plugin_name)\n plugin_dir_name = PluginName[plugin_name].value\n\n # Load in the plugin meta info\n plugin_file_path = os.path.join(CURRENT_PATH, plugin_dir_name)\n data_model_file_path = os.path.join(CURRENT_PATH, \"..\", \"data_model\", \"plugin\", plugin_dir_name)\n\n meta_info_path = os.path.join(plugin_file_path, AI_PLUGIN_FILE)\n assert os.path.exists(meta_info_path), f\"Missing file {meta_info_path} that contains meta info for {plugin_name}\"\n with open(meta_info_path, \"r\") as f:\n meta_info = json.load(f)\n\n # Load all supported endpoints\n tmp = _load_module(plugin_dir_name, os.path.join(plugin_file_path, \"paths\", \"__init__.py\"))\n assert hasattr(tmp, \"path_dict\"), f\"Missing variable path_dict in __init__.py\"\n\n # Load in the plugin spec\n yaml_path = os.path.join(plugin_file_path, PLUGIN_SPEC_FILE)\n assert os.path.exists(yaml_path), f\"Missing file: {yaml_path}\"\n\n # fixme: Ugly here, change the whole logic of SpecModel and APIYamlModel to refactor this\n # Load yaml from yaml_path\n openapi_yaml_json = APIYamlModel.from_yaml(yaml_path).to_json()\n if sorted(list(openapi_yaml_json[\"paths\"].keys())) != sorted(list(tmp.path_dict.values())):\n print(f\"{yaml_path} and {plugin_dir_name}/paths/__init__.py do not match. Load the later.\")\n # Create a new yaml file with only the endpoints in path_dict\n openapi_yaml_json[\"paths\"] = {\n path: openapi_yaml_json[\"paths\"][path] for path in tmp.path_dict.values()\n }\n new_yaml_file_path = os.path.join(plugin_file_path, \"tmp\", \"openapi.yaml\")\n os.makedirs(os.path.dirname(new_yaml_file_path), exist_ok=True)\n with open(new_yaml_file_path, \"w\") as f:\n # Save in the new yaml file, in yaml format\n yaml.safe_dump(openapi_yaml_json, f, sort_keys=False)\n yaml_path = new_yaml_file_path\n\n spec_model = SpecModel(yaml_path)\n description = spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n\n filename2endpoint = tmp.path_dict\n endpoint2caller = {}\n endpoint2output_model = defaultdict(lambda x: x)\n for fn, ep in filename2endpoint.items():\n # load api callers for different endpoints\n tmp = _load_module(f\"{plugin_dir_name}:{fn}:caller\", os.path.join(plugin_file_path, \"paths\", fn + \".py\"))\n assert hasattr(tmp, \"call_api\"), f\"Missing function call_api in {fn}.py\"\n endpoint2caller[ep] = tmp.call_api\n\n # load output data model for different endpoints\n data_model_path = os.path.join(data_model_file_path, fn + \".py\")\n if not os.path.exists(data_model_path):\n output_model = lambda x: x\n else:\n tmp = _load_module(f\"{plugin_dir_name}:{fn}:output_model\", data_model_path)\n assert hasattr(tmp, \"convert\"), f\"Missing function convert in {fn}.py\"\n output_model = tmp.convert\n endpoint2output_model[ep] = output_model\n\n need_auth = meta_info[\"manifest\"][\"auth\"][\"type\"] not in [None, \"None\", \"none\", \"Null\", \"null\"]\n\n return {\n \"name\": plugin_name,\n \"description\": description,\n \"meta_info\": meta_info,\n \"spec_model\": spec_model,\n \"endpoint2caller\": endpoint2caller,\n \"endpoint2output_model\": endpoint2output_model,\n \"need_auth\": need_auth,\n }\n\n\ndef load_all_plugins_elements():\n all_plugins_elements = {}\n\n for plugin_name in tqdm(PluginName.__members__):\n try:\n all_plugins_elements[plugin_name] = load_plugin_elements_by_name(plugin_name)\n except Exception as e:\n print(f\"Error when loading plugin {plugin_name}: {e}\")\n\n return all_plugins_elements","source_hash":"718cbc3c87bdf85e7b089c964d08e319be8ec467c9fe5e1a97a16211de323524","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.utils._load_module","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.utils._load_module#L21-L26","kind":"function","name":"_load_module","path":"real_agents/plugins_agent/plugins/utils.py","language":"python","start_line":21,"end_line":26,"context_start_line":1,"context_end_line":46,"code":"\"\"\"Utils for plugins (loading and more to add)\"\"\"\nimport importlib\nimport json\nimport os\nimport sys\nfrom collections import defaultdict\nfrom typing import Any\nimport yaml\nfrom tqdm import tqdm\n\nfrom real_agents.adapters.data_model import APIYamlModel, SpecModel\nfrom real_agents.plugins_agent.plugins.plugin_names import PluginName\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\nAI_PLUGIN_FILE = \"ai-plugin.json\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nPATH_FOLDER = \"paths\"\n\n\ndef _load_module(name: str, file_path: str) -> Any:\n spec = importlib.util.spec_from_file_location(name, file_path)\n ret = importlib.util.module_from_spec(spec)\n sys.modules[name] = ret\n spec.loader.exec_module(ret)\n return ret\n\n\ndef load_plugin_elements_by_name(plugin_name: str):\n # Check if the plugin name is valid\n assert plugin_name in PluginName.__members__, \"Unknown plugin name {}.\".format(plugin_name)\n plugin_dir_name = PluginName[plugin_name].value\n\n # Load in the plugin meta info\n plugin_file_path = os.path.join(CURRENT_PATH, plugin_dir_name)\n data_model_file_path = os.path.join(CURRENT_PATH, \"..\", \"data_model\", \"plugin\", plugin_dir_name)\n\n meta_info_path = os.path.join(plugin_file_path, AI_PLUGIN_FILE)\n assert os.path.exists(meta_info_path), f\"Missing file {meta_info_path} that contains meta info for {plugin_name}\"\n with open(meta_info_path, \"r\") as f:\n meta_info = json.load(f)\n\n # Load all supported endpoints\n tmp = _load_module(plugin_dir_name, os.path.join(plugin_file_path, \"paths\", \"__init__.py\"))\n assert hasattr(tmp, \"path_dict\"), f\"Missing variable path_dict in __init__.py\"\n","source_hash":"718cbc3c87bdf85e7b089c964d08e319be8ec467c9fe5e1a97a16211de323524","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.utils.load_plugin_elements_by_name","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.utils.load_plugin_elements_by_name#L29-L100","kind":"function","name":"load_plugin_elements_by_name","path":"real_agents/plugins_agent/plugins/utils.py","language":"python","start_line":29,"end_line":100,"context_start_line":9,"context_end_line":112,"code":"from tqdm import tqdm\n\nfrom real_agents.adapters.data_model import APIYamlModel, SpecModel\nfrom real_agents.plugins_agent.plugins.plugin_names import PluginName\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\nAI_PLUGIN_FILE = \"ai-plugin.json\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nPATH_FOLDER = \"paths\"\n\n\ndef _load_module(name: str, file_path: str) -> Any:\n spec = importlib.util.spec_from_file_location(name, file_path)\n ret = importlib.util.module_from_spec(spec)\n sys.modules[name] = ret\n spec.loader.exec_module(ret)\n return ret\n\n\ndef load_plugin_elements_by_name(plugin_name: str):\n # Check if the plugin name is valid\n assert plugin_name in PluginName.__members__, \"Unknown plugin name {}.\".format(plugin_name)\n plugin_dir_name = PluginName[plugin_name].value\n\n # Load in the plugin meta info\n plugin_file_path = os.path.join(CURRENT_PATH, plugin_dir_name)\n data_model_file_path = os.path.join(CURRENT_PATH, \"..\", \"data_model\", \"plugin\", plugin_dir_name)\n\n meta_info_path = os.path.join(plugin_file_path, AI_PLUGIN_FILE)\n assert os.path.exists(meta_info_path), f\"Missing file {meta_info_path} that contains meta info for {plugin_name}\"\n with open(meta_info_path, \"r\") as f:\n meta_info = json.load(f)\n\n # Load all supported endpoints\n tmp = _load_module(plugin_dir_name, os.path.join(plugin_file_path, \"paths\", \"__init__.py\"))\n assert hasattr(tmp, \"path_dict\"), f\"Missing variable path_dict in __init__.py\"\n\n # Load in the plugin spec\n yaml_path = os.path.join(plugin_file_path, PLUGIN_SPEC_FILE)\n assert os.path.exists(yaml_path), f\"Missing file: {yaml_path}\"\n\n # fixme: Ugly here, change the whole logic of SpecModel and APIYamlModel to refactor this\n # Load yaml from yaml_path\n openapi_yaml_json = APIYamlModel.from_yaml(yaml_path).to_json()\n if sorted(list(openapi_yaml_json[\"paths\"].keys())) != sorted(list(tmp.path_dict.values())):\n print(f\"{yaml_path} and {plugin_dir_name}/paths/__init__.py do not match. Load the later.\")\n # Create a new yaml file with only the endpoints in path_dict\n openapi_yaml_json[\"paths\"] = {\n path: openapi_yaml_json[\"paths\"][path] for path in tmp.path_dict.values()\n }\n new_yaml_file_path = os.path.join(plugin_file_path, \"tmp\", \"openapi.yaml\")\n os.makedirs(os.path.dirname(new_yaml_file_path), exist_ok=True)\n with open(new_yaml_file_path, \"w\") as f:\n # Save in the new yaml file, in yaml format\n yaml.safe_dump(openapi_yaml_json, f, sort_keys=False)\n yaml_path = new_yaml_file_path\n\n spec_model = SpecModel(yaml_path)\n description = spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n\n filename2endpoint = tmp.path_dict\n endpoint2caller = {}\n endpoint2output_model = defaultdict(lambda x: x)\n for fn, ep in filename2endpoint.items():\n # load api callers for different endpoints\n tmp = _load_module(f\"{plugin_dir_name}:{fn}:caller\", os.path.join(plugin_file_path, \"paths\", fn + \".py\"))\n assert hasattr(tmp, \"call_api\"), f\"Missing function call_api in {fn}.py\"\n endpoint2caller[ep] = tmp.call_api\n\n # load output data model for different endpoints\n data_model_path = os.path.join(data_model_file_path, fn + \".py\")\n if not os.path.exists(data_model_path):\n output_model = lambda x: x\n else:\n tmp = _load_module(f\"{plugin_dir_name}:{fn}:output_model\", data_model_path)\n assert hasattr(tmp, \"convert\"), f\"Missing function convert in {fn}.py\"\n output_model = tmp.convert\n endpoint2output_model[ep] = output_model\n\n need_auth = meta_info[\"manifest\"][\"auth\"][\"type\"] not in [None, \"None\", \"none\", \"Null\", \"null\"]\n\n return {\n \"name\": plugin_name,\n \"description\": description,\n \"meta_info\": meta_info,\n \"spec_model\": spec_model,\n \"endpoint2caller\": endpoint2caller,\n \"endpoint2output_model\": endpoint2output_model,\n \"need_auth\": need_auth,\n }\n\n\ndef load_all_plugins_elements():\n all_plugins_elements = {}\n\n for plugin_name in tqdm(PluginName.__members__):\n try:\n all_plugins_elements[plugin_name] = load_plugin_elements_by_name(plugin_name)\n except Exception as e:\n print(f\"Error when loading plugin {plugin_name}: {e}\")\n\n return all_plugins_elements","source_hash":"718cbc3c87bdf85e7b089c964d08e319be8ec467c9fe5e1a97a16211de323524","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.utils.load_all_plugins_elements","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.utils.load_all_plugins_elements#L103-L112","kind":"function","name":"load_all_plugins_elements","path":"real_agents/plugins_agent/plugins/utils.py","language":"python","start_line":103,"end_line":112,"context_start_line":83,"context_end_line":112,"code":" output_model = lambda x: x\n else:\n tmp = _load_module(f\"{plugin_dir_name}:{fn}:output_model\", data_model_path)\n assert hasattr(tmp, \"convert\"), f\"Missing function convert in {fn}.py\"\n output_model = tmp.convert\n endpoint2output_model[ep] = output_model\n\n need_auth = meta_info[\"manifest\"][\"auth\"][\"type\"] not in [None, \"None\", \"none\", \"Null\", \"null\"]\n\n return {\n \"name\": plugin_name,\n \"description\": description,\n \"meta_info\": meta_info,\n \"spec_model\": spec_model,\n \"endpoint2caller\": endpoint2caller,\n \"endpoint2output_model\": endpoint2output_model,\n \"need_auth\": need_auth,\n }\n\n\ndef load_all_plugins_elements():\n all_plugins_elements = {}\n\n for plugin_name in tqdm(PluginName.__members__):\n try:\n all_plugins_elements[plugin_name] = load_plugin_elements_by_name(plugin_name)\n except Exception as e:\n print(f\"Error when loading plugin {plugin_name}: {e}\")\n\n return all_plugins_elements","source_hash":"718cbc3c87bdf85e7b089c964d08e319be8ec467c9fe5e1a97a16211de323524","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.tool_selector#L1-L207","kind":"module","name":"real_agents.plugins_agent.plugins.tool_selector","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":1,"end_line":207,"context_start_line":1,"context_end_line":207,"code":"\"\"\"Implementation of Tool Selector that automate the selection of tools for the question (or sub-question).\"\"\"\nimport os\nimport pickle\n\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom tqdm import tqdm\nfrom typing import Any\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom langchain.embeddings.huggingface import HuggingFaceInstructEmbeddings\n\nDEFAULT_TOOL_INSTRUCTION = \"Represent the tool description for retrieval:\"\nDEFAULT_QUERY_INSTRUCTION = \"Represent the question for retrieving tools that can be used to solve the question:\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\nEMBEDDING_CACHE_PATH = os.path.join(CURRENT_PATH, \"..\", \"..\", \"..\", \"backend\", \"static\", \"tool_embeddings\")\nif not os.path.exists(EMBEDDING_CACHE_PATH):\n os.makedirs(EMBEDDING_CACHE_PATH)\n\n\nclass ToolSelector:\n \"\"\"\n This class is used to select the appropriate tool list for the question.\n \"\"\"\n\n # add valid mode here if needed\n valid_modes = [\"embedding\"]\n\n \"\"\"\n Example:\n .. code-block:: python\n mode_args = {\"embedding\": HuggingFaceInstructEmbeddings, \"model_name\": \"hkunlp/instructor-large\", \"embed_instruction\": \"Represent the tool description for retrieval:\", \"query_instruction\": \"Represent the question for retrieving tools that can be used to solve the question:\"}\n tool_selector = ToolSelector(mode=\"embedding\", mode_args=mode_args)\n model_name = \"hkunlp/instructor-large\"\n model_kwargs = {'device': 'cpu'}\n hf = HuggingFaceInstructEmbeddings(\n model_name=model_name, model_kwargs=model_kwargs\n )\n \"\"\"\n user_id: str = None\n chat_id: str = None\n\n def __init__(self, tools_list: list = [], mode: str = \"embedding\", mode_args=None, api_key_pool: Any = None):\n \"\"\"\n Initialize the tool selector.\n \"\"\"\n if mode_args is None:\n mode_args = {}\n if mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{mode}'. Valid modes are {self.valid_modes}\")\n self.tool_paths = [\n plugin_file_path\n for plugin_file_path in os.listdir(CURRENT_PATH)\n if \".py\" not in plugin_file_path\n and plugin_file_path != \"_scripts\"\n and plugin_file_path != \"__pycache__\"\n and plugin_file_path != \"README.md\"\n and plugin_file_path != \"descriptions.json\"\n ]\n self.tool_list = tools_list\n self.mode = mode\n self.api_key_pool = api_key_pool\n if mode == \"embedding\":\n self._init_embedding(mode_args)\n else:\n raise ValueError(f\"Unhandled mode '{mode}'.\")\n\n def _init_embedding(self, mode_args: dict):\n embedding = mode_args.get(\"embedding\", HuggingFaceInstructEmbeddings)\n if embedding == HuggingFaceInstructEmbeddings:\n model_name = mode_args.get(\"model_name\", \"hkunlp/instructor-large\")\n embed_instruction = mode_args.get(\"embed_instruction\", DEFAULT_TOOL_INSTRUCTION)\n query_instruction = mode_args.get(\"query_instruction\", DEFAULT_QUERY_INSTRUCTION)\n self.embedding = HuggingFaceInstructEmbeddings(\n model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction\n )\n\n def get_tool_descriptions(self) -> list:\n \"\"\"\n Get the tool descriptions.\n \"\"\"\n descriptions = []\n tool_paths = self.tool_paths\n yaml_paths = [os.path.join(CURRENT_PATH, tool_name, PLUGIN_SPEC_FILE) for tool_name in tool_paths]\n for yaml_path, plugin_file_path in tqdm(zip(yaml_paths, tool_paths), total=len(yaml_paths)):\n if os.path.isdir(os.path.join(CURRENT_PATH, plugin_file_path)):\n retrieved = False\n try:\n spec_model = SpecModel(yaml_path)\n retrieved = True\n except:\n print(\"Error loading yaml\", yaml_path)\n if not retrieved:\n description = \"No description.\"\n else:\n description = (\n spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n )\n descriptions.append(description)\n return descriptions\n\n def get_api_key_from_tool_name(self, tool_name: str) -> str:\n \"\"\"\n Get the API key from the tool name.\n \"\"\"\n user_id = self.user_id\n api_key_info = self.api_key_pool.get_pool_info_with_id(user_id, default_value=[])\n if len([i for i in api_key_info if i[\"tool_name\"] == tool_name]) != 0:\n api_key = [i for i in api_key_info if i[\"tool_name\"] == tool_name][0][\"api_key\"]\n else:\n api_key = None\n return api_key\n\n def check_plugin_valid(self, tool_path: str) -> bool:\n \"\"\"\n Check if the plugin is valid. Return false if this plugin requires an API key but the user has not provided one or plugin not found.\n \"\"\"\n plugins = self.tool_list\n # check if plugin exists and get the plugin if it exists\n if len([i for i in plugins if i[\"name\"].lower() == tool_path.lower()]) != 0:\n plugin = [i for i in plugins if i[\"name\"].lower() == tool_path.lower()][0]\n else:\n plugin = None\n print(f\"Plugin {tool_path} not found.\")\n\n # check if plugin requires an API key and if the user has provided one\n if plugin is not None:\n if plugin[\"require_api_key\"] and self.get_api_key_from_tool_name(tool_path) == None:\n return False\n else:\n return True\n else:\n return False\n\n def load_query_from_message_list(self, message_list: list[dict[str, str]], user_intent: str) -> str:\n \"\"\"\n Load the query from the message list.\n \"\"\"\n\n \"\"\"\n Example:\n message_list = [{'message_type': 'human_message', 'message_content': 'buy nike shoes', 'message_id': 362, 'parent_message_id': -1}, {'message_type': 'ai_message', 'message_content': '', 'message_id': 363, 'parent_message_id': 362}]\n \"\"\"\n # concatenate all history messages into one single query\n # The message_list is the history message list so we need to concatenate user intent(current message) to the end of the message list\n query = \"\"\n for message in message_list:\n # only concatenate human messages since we only need to retrieve tools based on user intent and the ai_message can be long sometimes which will influence the embedding\n if \"message_content\" in message.keys() and \"message_type\" in message.keys() and message[\n \"message_type\"] == 'human_message':\n query += (message[\"message_content\"] + \" \")\n else:\n continue\n query += user_intent\n return query\n\n def select_tools(self, query: str = \"\", top_k: int = 8):\n \"\"\"\n Select the top k tools based on the similarity between the query and the tool description.\n \"\"\"\n if query == \"\":\n raise ValueError(\"Query cannot be empty.\")\n if self.mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{self.mode}'. Valid modes are {self.valid_modes}\")\n\n if self.mode == \"embedding\":\n return self._select_tools_embedding(query, top_k)\n else:\n raise ValueError(f\"Unhandled mode '{self.mode}'.\")\n\n def _select_tools_embedding(self, query: str, top_k: int) -> list[str]:\n embedding = self.embedding\n # check if the embedding is InstructorEmbeddings\n if isinstance(self.embedding, HuggingFaceInstructEmbeddings):\n tool_embeddings = []\n for name, description in zip(self.tool_paths, self.get_tool_descriptions()):\n # Define file path for the cached embedding\n tool_embedding_file = EMBEDDING_CACHE_PATH + \"/\" + name + \".pkl\"\n # Check if tool embedding is already cached\n if os.path.isfile(tool_embedding_file):\n with open(tool_embedding_file, \"rb\") as f:\n tool_embedding = pickle.load(f)\n # no cached embedding, compute and cache it\n else:\n tool_embedding = embedding.embed_documents([description])\n with open(tool_embedding_file, \"wb\") as f:\n pickle.dump(tool_embedding, f)\n tool_embeddings.extend(tool_embedding)\n\n query_embeddings = [embedding.embed_query(query)]\n\n similarities = cosine_similarity(query_embeddings, tool_embeddings)\n\n # eliminate invalid plugins\n for idx, tool_path in enumerate(self.tool_paths):\n if not self.check_plugin_valid(tool_path):\n similarities[0][idx] = -1\n\n # get indices of top k similarities\n top_k_indices = np.argsort(similarities.flatten())[-top_k:]\n\n top_k_indices = top_k_indices.tolist()\n\n # return upper case tool names since tool id is the upper case of its name\n return [tool_name.upper() for idx, tool_name in enumerate(self.tool_paths) if idx in top_k_indices]","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.ToolSelector","uri":"program://OpenAgents/class/real_agents.plugins_agent.plugins.tool_selector.ToolSelector#L22-L207","kind":"class","name":"ToolSelector","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":22,"end_line":207,"context_start_line":2,"context_end_line":207,"code":"import os\nimport pickle\n\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom tqdm import tqdm\nfrom typing import Any\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom langchain.embeddings.huggingface import HuggingFaceInstructEmbeddings\n\nDEFAULT_TOOL_INSTRUCTION = \"Represent the tool description for retrieval:\"\nDEFAULT_QUERY_INSTRUCTION = \"Represent the question for retrieving tools that can be used to solve the question:\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\nEMBEDDING_CACHE_PATH = os.path.join(CURRENT_PATH, \"..\", \"..\", \"..\", \"backend\", \"static\", \"tool_embeddings\")\nif not os.path.exists(EMBEDDING_CACHE_PATH):\n os.makedirs(EMBEDDING_CACHE_PATH)\n\n\nclass ToolSelector:\n \"\"\"\n This class is used to select the appropriate tool list for the question.\n \"\"\"\n\n # add valid mode here if needed\n valid_modes = [\"embedding\"]\n\n \"\"\"\n Example:\n .. code-block:: python\n mode_args = {\"embedding\": HuggingFaceInstructEmbeddings, \"model_name\": \"hkunlp/instructor-large\", \"embed_instruction\": \"Represent the tool description for retrieval:\", \"query_instruction\": \"Represent the question for retrieving tools that can be used to solve the question:\"}\n tool_selector = ToolSelector(mode=\"embedding\", mode_args=mode_args)\n model_name = \"hkunlp/instructor-large\"\n model_kwargs = {'device': 'cpu'}\n hf = HuggingFaceInstructEmbeddings(\n model_name=model_name, model_kwargs=model_kwargs\n )\n \"\"\"\n user_id: str = None\n chat_id: str = None\n\n def __init__(self, tools_list: list = [], mode: str = \"embedding\", mode_args=None, api_key_pool: Any = None):\n \"\"\"\n Initialize the tool selector.\n \"\"\"\n if mode_args is None:\n mode_args = {}\n if mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{mode}'. Valid modes are {self.valid_modes}\")\n self.tool_paths = [\n plugin_file_path\n for plugin_file_path in os.listdir(CURRENT_PATH)\n if \".py\" not in plugin_file_path\n and plugin_file_path != \"_scripts\"\n and plugin_file_path != \"__pycache__\"\n and plugin_file_path != \"README.md\"\n and plugin_file_path != \"descriptions.json\"\n ]\n self.tool_list = tools_list\n self.mode = mode\n self.api_key_pool = api_key_pool\n if mode == \"embedding\":\n self._init_embedding(mode_args)\n else:\n raise ValueError(f\"Unhandled mode '{mode}'.\")\n\n def _init_embedding(self, mode_args: dict):\n embedding = mode_args.get(\"embedding\", HuggingFaceInstructEmbeddings)\n if embedding == HuggingFaceInstructEmbeddings:\n model_name = mode_args.get(\"model_name\", \"hkunlp/instructor-large\")\n embed_instruction = mode_args.get(\"embed_instruction\", DEFAULT_TOOL_INSTRUCTION)\n query_instruction = mode_args.get(\"query_instruction\", DEFAULT_QUERY_INSTRUCTION)\n self.embedding = HuggingFaceInstructEmbeddings(\n model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction\n )\n\n def get_tool_descriptions(self) -> list:\n \"\"\"\n Get the tool descriptions.\n \"\"\"\n descriptions = []\n tool_paths = self.tool_paths\n yaml_paths = [os.path.join(CURRENT_PATH, tool_name, PLUGIN_SPEC_FILE) for tool_name in tool_paths]\n for yaml_path, plugin_file_path in tqdm(zip(yaml_paths, tool_paths), total=len(yaml_paths)):\n if os.path.isdir(os.path.join(CURRENT_PATH, plugin_file_path)):\n retrieved = False\n try:\n spec_model = SpecModel(yaml_path)\n retrieved = True\n except:\n print(\"Error loading yaml\", yaml_path)\n if not retrieved:\n description = \"No description.\"\n else:\n description = (\n spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n )\n descriptions.append(description)\n return descriptions\n\n def get_api_key_from_tool_name(self, tool_name: str) -> str:\n \"\"\"\n Get the API key from the tool name.\n \"\"\"\n user_id = self.user_id\n api_key_info = self.api_key_pool.get_pool_info_with_id(user_id, default_value=[])\n if len([i for i in api_key_info if i[\"tool_name\"] == tool_name]) != 0:\n api_key = [i for i in api_key_info if i[\"tool_name\"] == tool_name][0][\"api_key\"]\n else:\n api_key = None\n return api_key\n\n def check_plugin_valid(self, tool_path: str) -> bool:\n \"\"\"\n Check if the plugin is valid. Return false if this plugin requires an API key but the user has not provided one or plugin not found.\n \"\"\"\n plugins = self.tool_list\n # check if plugin exists and get the plugin if it exists\n if len([i for i in plugins if i[\"name\"].lower() == tool_path.lower()]) != 0:\n plugin = [i for i in plugins if i[\"name\"].lower() == tool_path.lower()][0]\n else:\n plugin = None\n print(f\"Plugin {tool_path} not found.\")\n\n # check if plugin requires an API key and if the user has provided one\n if plugin is not None:\n if plugin[\"require_api_key\"] and self.get_api_key_from_tool_name(tool_path) == None:\n return False\n else:\n return True\n else:\n return False\n\n def load_query_from_message_list(self, message_list: list[dict[str, str]], user_intent: str) -> str:\n \"\"\"\n Load the query from the message list.\n \"\"\"\n\n \"\"\"\n Example:\n message_list = [{'message_type': 'human_message', 'message_content': 'buy nike shoes', 'message_id': 362, 'parent_message_id': -1}, {'message_type': 'ai_message', 'message_content': '', 'message_id': 363, 'parent_message_id': 362}]\n \"\"\"\n # concatenate all history messages into one single query\n # The message_list is the history message list so we need to concatenate user intent(current message) to the end of the message list\n query = \"\"\n for message in message_list:\n # only concatenate human messages since we only need to retrieve tools based on user intent and the ai_message can be long sometimes which will influence the embedding\n if \"message_content\" in message.keys() and \"message_type\" in message.keys() and message[\n \"message_type\"] == 'human_message':\n query += (message[\"message_content\"] + \" \")\n else:\n continue\n query += user_intent\n return query\n\n def select_tools(self, query: str = \"\", top_k: int = 8):\n \"\"\"\n Select the top k tools based on the similarity between the query and the tool description.\n \"\"\"\n if query == \"\":\n raise ValueError(\"Query cannot be empty.\")\n if self.mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{self.mode}'. Valid modes are {self.valid_modes}\")\n\n if self.mode == \"embedding\":\n return self._select_tools_embedding(query, top_k)\n else:\n raise ValueError(f\"Unhandled mode '{self.mode}'.\")\n\n def _select_tools_embedding(self, query: str, top_k: int) -> list[str]:\n embedding = self.embedding\n # check if the embedding is InstructorEmbeddings\n if isinstance(self.embedding, HuggingFaceInstructEmbeddings):\n tool_embeddings = []\n for name, description in zip(self.tool_paths, self.get_tool_descriptions()):\n # Define file path for the cached embedding\n tool_embedding_file = EMBEDDING_CACHE_PATH + \"/\" + name + \".pkl\"\n # Check if tool embedding is already cached\n if os.path.isfile(tool_embedding_file):\n with open(tool_embedding_file, \"rb\") as f:\n tool_embedding = pickle.load(f)\n # no cached embedding, compute and cache it\n else:\n tool_embedding = embedding.embed_documents([description])\n with open(tool_embedding_file, \"wb\") as f:\n pickle.dump(tool_embedding, f)\n tool_embeddings.extend(tool_embedding)\n\n query_embeddings = [embedding.embed_query(query)]\n\n similarities = cosine_similarity(query_embeddings, tool_embeddings)\n\n # eliminate invalid plugins\n for idx, tool_path in enumerate(self.tool_paths):\n if not self.check_plugin_valid(tool_path):\n similarities[0][idx] = -1\n\n # get indices of top k similarities\n top_k_indices = np.argsort(similarities.flatten())[-top_k:]\n\n top_k_indices = top_k_indices.tolist()\n\n # return upper case tool names since tool id is the upper case of its name\n return [tool_name.upper() for idx, tool_name in enumerate(self.tool_paths) if idx in top_k_indices]","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.__init__","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector.__init__#L44-L67","kind":"function","name":"__init__","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":44,"end_line":67,"context_start_line":24,"context_end_line":87,"code":" This class is used to select the appropriate tool list for the question.\n \"\"\"\n\n # add valid mode here if needed\n valid_modes = [\"embedding\"]\n\n \"\"\"\n Example:\n .. code-block:: python\n mode_args = {\"embedding\": HuggingFaceInstructEmbeddings, \"model_name\": \"hkunlp/instructor-large\", \"embed_instruction\": \"Represent the tool description for retrieval:\", \"query_instruction\": \"Represent the question for retrieving tools that can be used to solve the question:\"}\n tool_selector = ToolSelector(mode=\"embedding\", mode_args=mode_args)\n model_name = \"hkunlp/instructor-large\"\n model_kwargs = {'device': 'cpu'}\n hf = HuggingFaceInstructEmbeddings(\n model_name=model_name, model_kwargs=model_kwargs\n )\n \"\"\"\n user_id: str = None\n chat_id: str = None\n\n def __init__(self, tools_list: list = [], mode: str = \"embedding\", mode_args=None, api_key_pool: Any = None):\n \"\"\"\n Initialize the tool selector.\n \"\"\"\n if mode_args is None:\n mode_args = {}\n if mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{mode}'. Valid modes are {self.valid_modes}\")\n self.tool_paths = [\n plugin_file_path\n for plugin_file_path in os.listdir(CURRENT_PATH)\n if \".py\" not in plugin_file_path\n and plugin_file_path != \"_scripts\"\n and plugin_file_path != \"__pycache__\"\n and plugin_file_path != \"README.md\"\n and plugin_file_path != \"descriptions.json\"\n ]\n self.tool_list = tools_list\n self.mode = mode\n self.api_key_pool = api_key_pool\n if mode == \"embedding\":\n self._init_embedding(mode_args)\n else:\n raise ValueError(f\"Unhandled mode '{mode}'.\")\n\n def _init_embedding(self, mode_args: dict):\n embedding = mode_args.get(\"embedding\", HuggingFaceInstructEmbeddings)\n if embedding == HuggingFaceInstructEmbeddings:\n model_name = mode_args.get(\"model_name\", \"hkunlp/instructor-large\")\n embed_instruction = mode_args.get(\"embed_instruction\", DEFAULT_TOOL_INSTRUCTION)\n query_instruction = mode_args.get(\"query_instruction\", DEFAULT_QUERY_INSTRUCTION)\n self.embedding = HuggingFaceInstructEmbeddings(\n model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction\n )\n\n def get_tool_descriptions(self) -> list:\n \"\"\"\n Get the tool descriptions.\n \"\"\"\n descriptions = []\n tool_paths = self.tool_paths\n yaml_paths = [os.path.join(CURRENT_PATH, tool_name, PLUGIN_SPEC_FILE) for tool_name in tool_paths]\n for yaml_path, plugin_file_path in tqdm(zip(yaml_paths, tool_paths), total=len(yaml_paths)):\n if os.path.isdir(os.path.join(CURRENT_PATH, plugin_file_path)):","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector._init_embedding","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector._init_embedding#L69-L77","kind":"function","name":"_init_embedding","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":69,"end_line":77,"context_start_line":49,"context_end_line":97,"code":" mode_args = {}\n if mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{mode}'. Valid modes are {self.valid_modes}\")\n self.tool_paths = [\n plugin_file_path\n for plugin_file_path in os.listdir(CURRENT_PATH)\n if \".py\" not in plugin_file_path\n and plugin_file_path != \"_scripts\"\n and plugin_file_path != \"__pycache__\"\n and plugin_file_path != \"README.md\"\n and plugin_file_path != \"descriptions.json\"\n ]\n self.tool_list = tools_list\n self.mode = mode\n self.api_key_pool = api_key_pool\n if mode == \"embedding\":\n self._init_embedding(mode_args)\n else:\n raise ValueError(f\"Unhandled mode '{mode}'.\")\n\n def _init_embedding(self, mode_args: dict):\n embedding = mode_args.get(\"embedding\", HuggingFaceInstructEmbeddings)\n if embedding == HuggingFaceInstructEmbeddings:\n model_name = mode_args.get(\"model_name\", \"hkunlp/instructor-large\")\n embed_instruction = mode_args.get(\"embed_instruction\", DEFAULT_TOOL_INSTRUCTION)\n query_instruction = mode_args.get(\"query_instruction\", DEFAULT_QUERY_INSTRUCTION)\n self.embedding = HuggingFaceInstructEmbeddings(\n model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction\n )\n\n def get_tool_descriptions(self) -> list:\n \"\"\"\n Get the tool descriptions.\n \"\"\"\n descriptions = []\n tool_paths = self.tool_paths\n yaml_paths = [os.path.join(CURRENT_PATH, tool_name, PLUGIN_SPEC_FILE) for tool_name in tool_paths]\n for yaml_path, plugin_file_path in tqdm(zip(yaml_paths, tool_paths), total=len(yaml_paths)):\n if os.path.isdir(os.path.join(CURRENT_PATH, plugin_file_path)):\n retrieved = False\n try:\n spec_model = SpecModel(yaml_path)\n retrieved = True\n except:\n print(\"Error loading yaml\", yaml_path)\n if not retrieved:\n description = \"No description.\"\n else:\n description = (","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.get_tool_descriptions","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector.get_tool_descriptions#L79-L102","kind":"function","name":"get_tool_descriptions","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":79,"end_line":102,"context_start_line":59,"context_end_line":122,"code":" and plugin_file_path != \"descriptions.json\"\n ]\n self.tool_list = tools_list\n self.mode = mode\n self.api_key_pool = api_key_pool\n if mode == \"embedding\":\n self._init_embedding(mode_args)\n else:\n raise ValueError(f\"Unhandled mode '{mode}'.\")\n\n def _init_embedding(self, mode_args: dict):\n embedding = mode_args.get(\"embedding\", HuggingFaceInstructEmbeddings)\n if embedding == HuggingFaceInstructEmbeddings:\n model_name = mode_args.get(\"model_name\", \"hkunlp/instructor-large\")\n embed_instruction = mode_args.get(\"embed_instruction\", DEFAULT_TOOL_INSTRUCTION)\n query_instruction = mode_args.get(\"query_instruction\", DEFAULT_QUERY_INSTRUCTION)\n self.embedding = HuggingFaceInstructEmbeddings(\n model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction\n )\n\n def get_tool_descriptions(self) -> list:\n \"\"\"\n Get the tool descriptions.\n \"\"\"\n descriptions = []\n tool_paths = self.tool_paths\n yaml_paths = [os.path.join(CURRENT_PATH, tool_name, PLUGIN_SPEC_FILE) for tool_name in tool_paths]\n for yaml_path, plugin_file_path in tqdm(zip(yaml_paths, tool_paths), total=len(yaml_paths)):\n if os.path.isdir(os.path.join(CURRENT_PATH, plugin_file_path)):\n retrieved = False\n try:\n spec_model = SpecModel(yaml_path)\n retrieved = True\n except:\n print(\"Error loading yaml\", yaml_path)\n if not retrieved:\n description = \"No description.\"\n else:\n description = (\n spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n )\n descriptions.append(description)\n return descriptions\n\n def get_api_key_from_tool_name(self, tool_name: str) -> str:\n \"\"\"\n Get the API key from the tool name.\n \"\"\"\n user_id = self.user_id\n api_key_info = self.api_key_pool.get_pool_info_with_id(user_id, default_value=[])\n if len([i for i in api_key_info if i[\"tool_name\"] == tool_name]) != 0:\n api_key = [i for i in api_key_info if i[\"tool_name\"] == tool_name][0][\"api_key\"]\n else:\n api_key = None\n return api_key\n\n def check_plugin_valid(self, tool_path: str) -> bool:\n \"\"\"\n Check if the plugin is valid. Return false if this plugin requires an API key but the user has not provided one or plugin not found.\n \"\"\"\n plugins = self.tool_list\n # check if plugin exists and get the plugin if it exists\n if len([i for i in plugins if i[\"name\"].lower() == tool_path.lower()]) != 0:","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.get_api_key_from_tool_name","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector.get_api_key_from_tool_name#L104-L114","kind":"function","name":"get_api_key_from_tool_name","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":104,"end_line":114,"context_start_line":84,"context_end_line":134,"code":" tool_paths = self.tool_paths\n yaml_paths = [os.path.join(CURRENT_PATH, tool_name, PLUGIN_SPEC_FILE) for tool_name in tool_paths]\n for yaml_path, plugin_file_path in tqdm(zip(yaml_paths, tool_paths), total=len(yaml_paths)):\n if os.path.isdir(os.path.join(CURRENT_PATH, plugin_file_path)):\n retrieved = False\n try:\n spec_model = SpecModel(yaml_path)\n retrieved = True\n except:\n print(\"Error loading yaml\", yaml_path)\n if not retrieved:\n description = \"No description.\"\n else:\n description = (\n spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n )\n descriptions.append(description)\n return descriptions\n\n def get_api_key_from_tool_name(self, tool_name: str) -> str:\n \"\"\"\n Get the API key from the tool name.\n \"\"\"\n user_id = self.user_id\n api_key_info = self.api_key_pool.get_pool_info_with_id(user_id, default_value=[])\n if len([i for i in api_key_info if i[\"tool_name\"] == tool_name]) != 0:\n api_key = [i for i in api_key_info if i[\"tool_name\"] == tool_name][0][\"api_key\"]\n else:\n api_key = None\n return api_key\n\n def check_plugin_valid(self, tool_path: str) -> bool:\n \"\"\"\n Check if the plugin is valid. Return false if this plugin requires an API key but the user has not provided one or plugin not found.\n \"\"\"\n plugins = self.tool_list\n # check if plugin exists and get the plugin if it exists\n if len([i for i in plugins if i[\"name\"].lower() == tool_path.lower()]) != 0:\n plugin = [i for i in plugins if i[\"name\"].lower() == tool_path.lower()][0]\n else:\n plugin = None\n print(f\"Plugin {tool_path} not found.\")\n\n # check if plugin requires an API key and if the user has provided one\n if plugin is not None:\n if plugin[\"require_api_key\"] and self.get_api_key_from_tool_name(tool_path) == None:\n return False\n else:\n return True\n else:","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.check_plugin_valid","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector.check_plugin_valid#L116-L135","kind":"function","name":"check_plugin_valid","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":116,"end_line":135,"context_start_line":96,"context_end_line":155,"code":" else:\n description = (\n spec_model.full_spec[\"info\"][\"description\"] if \"description\" in spec_model.full_spec[\n \"info\"] else \"No description.\"\n )\n descriptions.append(description)\n return descriptions\n\n def get_api_key_from_tool_name(self, tool_name: str) -> str:\n \"\"\"\n Get the API key from the tool name.\n \"\"\"\n user_id = self.user_id\n api_key_info = self.api_key_pool.get_pool_info_with_id(user_id, default_value=[])\n if len([i for i in api_key_info if i[\"tool_name\"] == tool_name]) != 0:\n api_key = [i for i in api_key_info if i[\"tool_name\"] == tool_name][0][\"api_key\"]\n else:\n api_key = None\n return api_key\n\n def check_plugin_valid(self, tool_path: str) -> bool:\n \"\"\"\n Check if the plugin is valid. Return false if this plugin requires an API key but the user has not provided one or plugin not found.\n \"\"\"\n plugins = self.tool_list\n # check if plugin exists and get the plugin if it exists\n if len([i for i in plugins if i[\"name\"].lower() == tool_path.lower()]) != 0:\n plugin = [i for i in plugins if i[\"name\"].lower() == tool_path.lower()][0]\n else:\n plugin = None\n print(f\"Plugin {tool_path} not found.\")\n\n # check if plugin requires an API key and if the user has provided one\n if plugin is not None:\n if plugin[\"require_api_key\"] and self.get_api_key_from_tool_name(tool_path) == None:\n return False\n else:\n return True\n else:\n return False\n\n def load_query_from_message_list(self, message_list: list[dict[str, str]], user_intent: str) -> str:\n \"\"\"\n Load the query from the message list.\n \"\"\"\n\n \"\"\"\n Example:\n message_list = [{'message_type': 'human_message', 'message_content': 'buy nike shoes', 'message_id': 362, 'parent_message_id': -1}, {'message_type': 'ai_message', 'message_content': '', 'message_id': 363, 'parent_message_id': 362}]\n \"\"\"\n # concatenate all history messages into one single query\n # The message_list is the history message list so we need to concatenate user intent(current message) to the end of the message list\n query = \"\"\n for message in message_list:\n # only concatenate human messages since we only need to retrieve tools based on user intent and the ai_message can be long sometimes which will influence the embedding\n if \"message_content\" in message.keys() and \"message_type\" in message.keys() and message[\n \"message_type\"] == 'human_message':\n query += (message[\"message_content\"] + \" \")\n else:\n continue","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.load_query_from_message_list","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector.load_query_from_message_list#L137-L157","kind":"function","name":"load_query_from_message_list","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":137,"end_line":157,"context_start_line":117,"context_end_line":177,"code":" \"\"\"\n Check if the plugin is valid. Return false if this plugin requires an API key but the user has not provided one or plugin not found.\n \"\"\"\n plugins = self.tool_list\n # check if plugin exists and get the plugin if it exists\n if len([i for i in plugins if i[\"name\"].lower() == tool_path.lower()]) != 0:\n plugin = [i for i in plugins if i[\"name\"].lower() == tool_path.lower()][0]\n else:\n plugin = None\n print(f\"Plugin {tool_path} not found.\")\n\n # check if plugin requires an API key and if the user has provided one\n if plugin is not None:\n if plugin[\"require_api_key\"] and self.get_api_key_from_tool_name(tool_path) == None:\n return False\n else:\n return True\n else:\n return False\n\n def load_query_from_message_list(self, message_list: list[dict[str, str]], user_intent: str) -> str:\n \"\"\"\n Load the query from the message list.\n \"\"\"\n\n \"\"\"\n Example:\n message_list = [{'message_type': 'human_message', 'message_content': 'buy nike shoes', 'message_id': 362, 'parent_message_id': -1}, {'message_type': 'ai_message', 'message_content': '', 'message_id': 363, 'parent_message_id': 362}]\n \"\"\"\n # concatenate all history messages into one single query\n # The message_list is the history message list so we need to concatenate user intent(current message) to the end of the message list\n query = \"\"\n for message in message_list:\n # only concatenate human messages since we only need to retrieve tools based on user intent and the ai_message can be long sometimes which will influence the embedding\n if \"message_content\" in message.keys() and \"message_type\" in message.keys() and message[\n \"message_type\"] == 'human_message':\n query += (message[\"message_content\"] + \" \")\n else:\n continue\n query += user_intent\n return query\n\n def select_tools(self, query: str = \"\", top_k: int = 8):\n \"\"\"\n Select the top k tools based on the similarity between the query and the tool description.\n \"\"\"\n if query == \"\":\n raise ValueError(\"Query cannot be empty.\")\n if self.mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{self.mode}'. Valid modes are {self.valid_modes}\")\n\n if self.mode == \"embedding\":\n return self._select_tools_embedding(query, top_k)\n else:\n raise ValueError(f\"Unhandled mode '{self.mode}'.\")\n\n def _select_tools_embedding(self, query: str, top_k: int) -> list[str]:\n embedding = self.embedding\n # check if the embedding is InstructorEmbeddings\n if isinstance(self.embedding, HuggingFaceInstructEmbeddings):\n tool_embeddings = []","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector.select_tools","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector.select_tools#L159-L171","kind":"function","name":"select_tools","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":159,"end_line":171,"context_start_line":139,"context_end_line":191,"code":" Load the query from the message list.\n \"\"\"\n\n \"\"\"\n Example:\n message_list = [{'message_type': 'human_message', 'message_content': 'buy nike shoes', 'message_id': 362, 'parent_message_id': -1}, {'message_type': 'ai_message', 'message_content': '', 'message_id': 363, 'parent_message_id': 362}]\n \"\"\"\n # concatenate all history messages into one single query\n # The message_list is the history message list so we need to concatenate user intent(current message) to the end of the message list\n query = \"\"\n for message in message_list:\n # only concatenate human messages since we only need to retrieve tools based on user intent and the ai_message can be long sometimes which will influence the embedding\n if \"message_content\" in message.keys() and \"message_type\" in message.keys() and message[\n \"message_type\"] == 'human_message':\n query += (message[\"message_content\"] + \" \")\n else:\n continue\n query += user_intent\n return query\n\n def select_tools(self, query: str = \"\", top_k: int = 8):\n \"\"\"\n Select the top k tools based on the similarity between the query and the tool description.\n \"\"\"\n if query == \"\":\n raise ValueError(\"Query cannot be empty.\")\n if self.mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{self.mode}'. Valid modes are {self.valid_modes}\")\n\n if self.mode == \"embedding\":\n return self._select_tools_embedding(query, top_k)\n else:\n raise ValueError(f\"Unhandled mode '{self.mode}'.\")\n\n def _select_tools_embedding(self, query: str, top_k: int) -> list[str]:\n embedding = self.embedding\n # check if the embedding is InstructorEmbeddings\n if isinstance(self.embedding, HuggingFaceInstructEmbeddings):\n tool_embeddings = []\n for name, description in zip(self.tool_paths, self.get_tool_descriptions()):\n # Define file path for the cached embedding\n tool_embedding_file = EMBEDDING_CACHE_PATH + \"/\" + name + \".pkl\"\n # Check if tool embedding is already cached\n if os.path.isfile(tool_embedding_file):\n with open(tool_embedding_file, \"rb\") as f:\n tool_embedding = pickle.load(f)\n # no cached embedding, compute and cache it\n else:\n tool_embedding = embedding.embed_documents([description])\n with open(tool_embedding_file, \"wb\") as f:\n pickle.dump(tool_embedding, f)\n tool_embeddings.extend(tool_embedding)\n","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.tool_selector._select_tools_embedding","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.tool_selector._select_tools_embedding#L173-L207","kind":"function","name":"_select_tools_embedding","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":173,"end_line":207,"context_start_line":153,"context_end_line":207,"code":" query += (message[\"message_content\"] + \" \")\n else:\n continue\n query += user_intent\n return query\n\n def select_tools(self, query: str = \"\", top_k: int = 8):\n \"\"\"\n Select the top k tools based on the similarity between the query and the tool description.\n \"\"\"\n if query == \"\":\n raise ValueError(\"Query cannot be empty.\")\n if self.mode not in self.valid_modes:\n raise ValueError(f\"Invalid mode '{self.mode}'. Valid modes are {self.valid_modes}\")\n\n if self.mode == \"embedding\":\n return self._select_tools_embedding(query, top_k)\n else:\n raise ValueError(f\"Unhandled mode '{self.mode}'.\")\n\n def _select_tools_embedding(self, query: str, top_k: int) -> list[str]:\n embedding = self.embedding\n # check if the embedding is InstructorEmbeddings\n if isinstance(self.embedding, HuggingFaceInstructEmbeddings):\n tool_embeddings = []\n for name, description in zip(self.tool_paths, self.get_tool_descriptions()):\n # Define file path for the cached embedding\n tool_embedding_file = EMBEDDING_CACHE_PATH + \"/\" + name + \".pkl\"\n # Check if tool embedding is already cached\n if os.path.isfile(tool_embedding_file):\n with open(tool_embedding_file, \"rb\") as f:\n tool_embedding = pickle.load(f)\n # no cached embedding, compute and cache it\n else:\n tool_embedding = embedding.embed_documents([description])\n with open(tool_embedding_file, \"wb\") as f:\n pickle.dump(tool_embedding, f)\n tool_embeddings.extend(tool_embedding)\n\n query_embeddings = [embedding.embed_query(query)]\n\n similarities = cosine_similarity(query_embeddings, tool_embeddings)\n\n # eliminate invalid plugins\n for idx, tool_path in enumerate(self.tool_paths):\n if not self.check_plugin_valid(tool_path):\n similarities[0][idx] = -1\n\n # get indices of top k similarities\n top_k_indices = np.argsort(similarities.flatten())[-top_k:]\n\n top_k_indices = top_k_indices.tolist()\n\n # return upper case tool names since tool id is the upper case of its name\n return [tool_name.upper() for idx, tool_name in enumerate(self.tool_paths) if idx in top_k_indices]","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.klarna.paths.products","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.klarna.paths.products#L1-L23","kind":"module","name":"real_agents.plugins_agent.plugins.klarna.paths.products","path":"real_agents/plugins_agent/plugins/klarna/paths/products.py","language":"python","start_line":1,"end_line":23,"context_start_line":1,"context_end_line":23,"code":"\"\"\"Search for products by keyword, price range, and size.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://www.klarna.com/us/shopping/public/openai/v0/products\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Accept\": \"application/json\"}\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"q\": \"nike shoes\",\n# \"size\": 10,\n# \"min_price\": 50,\n# \"max_price\": 100\n# }","source_hash":"ea6bb7aa683e46462cecc3e2edde025fd72fcbb9e686f37b933e49d8825ecb31","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.klarna.paths.products.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.klarna.paths.products.call_api#L9-L16","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/klarna/paths/products.py","language":"python","start_line":9,"end_line":16,"context_start_line":1,"context_end_line":23,"code":"\"\"\"Search for products by keyword, price range, and size.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://www.klarna.com/us/shopping/public/openai/v0/products\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Accept\": \"application/json\"}\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"q\": \"nike shoes\",\n# \"size\": 10,\n# \"min_price\": 50,\n# \"max_price\": 100\n# }","source_hash":"ea6bb7aa683e46462cecc3e2edde025fd72fcbb9e686f37b933e49d8825ecb31","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.Coursera.paths.search","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.Coursera.paths.search#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.Coursera.paths.search","path":"real_agents/plugins_agent/plugins/Coursera/paths/search.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Search Coursera API for courses matching a query.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://www.coursera.org/api/rest/v1/search\", json=input_json)\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"7b630acf4b7c89ae7f7bd7cd9413445840534eb35c7da07bc2747c7bf191a371","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.Coursera.paths.search.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.Coursera.paths.search.call_api#L7-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/Coursera/paths/search.py","language":"python","start_line":7,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Search Coursera API for courses matching a query.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://www.coursera.org/api/rest/v1/search\", json=input_json)\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"7b630acf4b7c89ae7f7bd7cd9413445840534eb35c7da07bc2747c7bf191a371","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.nba_stats.paths.basketball_stats","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.nba_stats.paths.basketball_stats#L1-L13","kind":"module","name":"real_agents.plugins_agent.plugins.nba_stats.paths.basketball_stats","path":"real_agents/plugins_agent/plugins/nba_stats/paths/basketball_stats.py","language":"python","start_line":1,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"NBA stats API path.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://nba-gpt-prod.onrender.com/basketball_stats\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"b81243f5258e0e039c069529f0278205e79ade5bd4189bf97ea7802e25e9477a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.nba_stats.paths.basketball_stats.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.nba_stats.paths.basketball_stats.call_api#L7-L13","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/nba_stats/paths/basketball_stats.py","language":"python","start_line":7,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"NBA stats API path.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://nba-gpt-prod.onrender.com/basketball_stats\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"b81243f5258e0e039c069529f0278205e79ade5bd4189bf97ea7802e25e9477a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.askyourpdf.paths.download_pdf","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.askyourpdf.paths.download_pdf#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.askyourpdf.paths.download_pdf","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/download_pdf.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Download PDF from AskYourPDF API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://plugin.askyourpdf.com/api/download_pdf\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"46c3316702bb92057472ca05230eaa0ad9603e8e21233302d5aee257a0fb6fb9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.askyourpdf.paths.download_pdf.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.askyourpdf.paths.download_pdf.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/download_pdf.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Download PDF from AskYourPDF API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://plugin.askyourpdf.com/api/download_pdf\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"46c3316702bb92057472ca05230eaa0ad9603e8e21233302d5aee257a0fb6fb9","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.askyourpdf.paths.perform_query","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.askyourpdf.paths.perform_query#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.askyourpdf.paths.perform_query","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/perform_query.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Perform query to AskYourPDF API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://plugin.askyourpdf.com/query\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"85b14f55580aa32960a81b00108b04e00f37b53842d92bf053a75eb13266e487","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.askyourpdf.paths.perform_query.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.askyourpdf.paths.perform_query.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/perform_query.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Perform query to AskYourPDF API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://plugin.askyourpdf.com/query\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"85b14f55580aa32960a81b00108b04e00f37b53842d92bf053a75eb13266e487","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.wolfram.paths.llm_api","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.wolfram.paths.llm_api#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.wolfram.paths.llm_api","path":"real_agents/plugins_agent/plugins/wolfram/paths/llm_api.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"LLM API for Wolfram Alpha\"\"\"\nimport requests\n\n\ndef call_api(input_json, api_key):\n input_json[\"appid\"] = api_key\n response = requests.get(\"https://www.wolframalpha.com/api/v1/llm-api\", params=input_json)\n\n if response.status_code == 200:\n return response.content.decode(\"utf-8\")\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"a361623fa8b8727ae5d124b2b41efca3770d4cffb607c35abf59f61e005f603e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.wolfram.paths.llm_api.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.wolfram.paths.llm_api.call_api#L5-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/wolfram/paths/llm_api.py","language":"python","start_line":5,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"LLM API for Wolfram Alpha\"\"\"\nimport requests\n\n\ndef call_api(input_json, api_key):\n input_json[\"appid\"] = api_key\n response = requests.get(\"https://www.wolframalpha.com/api/v1/llm-api\", params=input_json)\n\n if response.status_code == 200:\n return response.content.decode(\"utf-8\")\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"a361623fa8b8727ae5d124b2b41efca3770d4cffb607c35abf59f61e005f603e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.wolfram.paths.cloud_plugin","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.wolfram.paths.cloud_plugin#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.wolfram.paths.cloud_plugin","path":"real_agents/plugins_agent/plugins/wolfram/paths/cloud_plugin.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Cloud plugin for Wolfram Alpha API\"\"\"\nimport requests\n\n\ndef call_api(input_json, api_key):\n input_json[\"appid\"] = api_key\n response = requests.get(\"https://www.wolframalpha.com/api/v1/cloud-plugin\", params=input_json)\n\n if response.status_code == 200:\n return response.content.decode(\"utf-8\")\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"2e6a8a9c25e8ed7cf0349f4278e6ad6471d9c449897eb9aff331dd89c2d75904","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.wolfram.paths.cloud_plugin.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.wolfram.paths.cloud_plugin.call_api#L5-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/wolfram/paths/cloud_plugin.py","language":"python","start_line":5,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Cloud plugin for Wolfram Alpha API\"\"\"\nimport requests\n\n\ndef call_api(input_json, api_key):\n input_json[\"appid\"] = api_key\n response = requests.get(\"https://www.wolframalpha.com/api/v1/cloud-plugin\", params=input_json)\n\n if response.status_code == 200:\n return response.content.decode(\"utf-8\")\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"2e6a8a9c25e8ed7cf0349f4278e6ad6471d9c449897eb9aff331dd89c2d75904","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.show_me.paths.render_diagram","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.show_me.paths.render_diagram#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.show_me.paths.render_diagram","path":"real_agents/plugins_agent/plugins/show_me/paths/render_diagram.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Render diagram path for Show Me plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/render/\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"2245b15fd30b105697d483558e6f59a396e78783bee0774dffc7935b49871bdc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.show_me.paths.render_diagram.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.show_me.paths.render_diagram.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/show_me/paths/render_diagram.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Render diagram path for Show Me plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/render/\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"2245b15fd30b105697d483558e6f59a396e78783bee0774dffc7935b49871bdc","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.show_me.paths.show_carousel","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.show_me.paths.show_carousel#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.show_me.paths.show_carousel","path":"real_agents/plugins_agent/plugins/show_me/paths/show_carousel.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Show Carousel path for Show Me plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/show-carousel\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"94a06975dbb473d3689749138fc9e29fc419ab92927e054ac92f7ef6c58c1627","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.show_me.paths.show_carousel.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.show_me.paths.show_carousel.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/show_me/paths/show_carousel.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Show Carousel path for Show Me plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/show-carousel\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"94a06975dbb473d3689749138fc9e29fc419ab92927e054ac92f7ef6c58c1627","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.show_me.paths.diagram_guidelines","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.show_me.paths.diagram_guidelines#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.show_me.paths.diagram_guidelines","path":"real_agents/plugins_agent/plugins/show_me/paths/diagram_guidelines.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Guidelines for diagramming\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/diagram-guidelines\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"00e8cff7cafa9c40e4493e9bbd27f162bd0cde920125500fddac6cdefeff8995","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.show_me.paths.diagram_guidelines.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.show_me.paths.diagram_guidelines.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/show_me/paths/diagram_guidelines.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Guidelines for diagramming\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/diagram-guidelines\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"00e8cff7cafa9c40e4493e9bbd27f162bd0cde920125500fddac6cdefeff8995","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.XWeather.paths.get_radar","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.XWeather.paths.get_radar#L1-L13","kind":"module","name":"real_agents.plugins_agent.plugins.XWeather.paths.get_radar","path":"real_agents/plugins_agent/plugins/XWeather/paths/get_radar.py","language":"python","start_line":1,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Radars API path.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n location = input_json['location']\n response = requests.get(f\"https://openai-plugin.xweather.com/radar/{location}\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4cbe4b545793e9f8668ad8df1762ed0d2b2f12880d927a284efa138e29b35344","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.XWeather.paths.get_radar.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.XWeather.paths.get_radar.call_api#L6-L13","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/XWeather/paths/get_radar.py","language":"python","start_line":6,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Radars API path.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n location = input_json['location']\n response = requests.get(f\"https://openai-plugin.xweather.com/radar/{location}\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4cbe4b545793e9f8668ad8df1762ed0d2b2f12880d927a284efa138e29b35344","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.XWeather.paths.weather_summary","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.XWeather.paths.weather_summary#L1-L13","kind":"module","name":"real_agents.plugins_agent.plugins.XWeather.paths.weather_summary","path":"real_agents/plugins_agent/plugins/XWeather/paths/weather_summary.py","language":"python","start_line":1,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Weather summary path for XWeather plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://openai-plugin.xweather.com/weather/summary/{}\".format(input_json['location'])\n response = requests.get(url)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bd534023f21a6cc46b1e407a3a9506256e1e8c3d33fc90690ed26c5a209e3e32","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.XWeather.paths.weather_summary.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.XWeather.paths.weather_summary.call_api#L6-L13","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/XWeather/paths/weather_summary.py","language":"python","start_line":6,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Weather summary path for XWeather plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://openai-plugin.xweather.com/weather/summary/{}\".format(input_json['location'])\n response = requests.get(url)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bd534023f21a6cc46b1e407a3a9506256e1e8c3d33fc90690ed26c5a209e3e32","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.XWeather.paths.weather_forecast","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.XWeather.paths.weather_forecast#L1-L14","kind":"module","name":"real_agents.plugins_agent.plugins.XWeather.paths.weather_forecast","path":"real_agents/plugins_agent/plugins/XWeather/paths/weather_forecast.py","language":"python","start_line":1,"end_line":14,"context_start_line":1,"context_end_line":14,"code":"\"\"\"Weather Forecast API Path.\"\"\"\nfrom typing import Dict, Any\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n location = input_json[\"location\"]\n url = f\"https://openai-plugin.xweather.com/weather/forecast/{location}\"\n response = requests.get(url)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"25b87494b7ba2a6a116e03987ec2b7f9c4d2ca112e11001b588ab7310ff8b44a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.XWeather.paths.weather_forecast.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.XWeather.paths.weather_forecast.call_api#L6-L14","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/XWeather/paths/weather_forecast.py","language":"python","start_line":6,"end_line":14,"context_start_line":1,"context_end_line":14,"code":"\"\"\"Weather Forecast API Path.\"\"\"\nfrom typing import Dict, Any\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n location = input_json[\"location\"]\n url = f\"https://openai-plugin.xweather.com/weather/forecast/{location}\"\n response = requests.get(url)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"25b87494b7ba2a6a116e03987ec2b7f9c4d2ca112e11001b588ab7310ff8b44a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.biztoc.paths.search_news","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.biztoc.paths.search_news#L1-L13","kind":"module","name":"real_agents.plugins_agent.plugins.biztoc.paths.search_news","path":"real_agents/plugins_agent/plugins/biztoc/paths/search_news.py","language":"python","start_line":1,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Search news from Biztoc API.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://ai.biztoc.com/ai/news\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"334841dd12ea6cd3307f732fa38bd6373d77b2a7bf205dc5f9d3c62704332e5c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.biztoc.paths.search_news.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.biztoc.paths.search_news.call_api#L7-L13","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/biztoc/paths/search_news.py","language":"python","start_line":7,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Search news from Biztoc API.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://ai.biztoc.com/ai/news\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"334841dd12ea6cd3307f732fa38bd6373d77b2a7bf205dc5f9d3c62704332e5c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.web_scraper.paths.scraper","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.web_scraper.paths.scraper#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.web_scraper.paths.scraper","path":"real_agents/plugins_agent/plugins/web_scraper/paths/scraper.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Scrape data from a website using the Scraper API.\"\"\"\nimport requests\nfrom typing import Any, Dict\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://scraper.gafo.tech/scrape\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"25f69297e08c968dcb59abfcf80bddbce48755b7129068381d49766ec20d0b7f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.web_scraper.paths.scraper.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.web_scraper.paths.scraper.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/web_scraper/paths/scraper.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Scrape data from a website using the Scraper API.\"\"\"\nimport requests\nfrom typing import Any, Dict\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://scraper.gafo.tech/scrape\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"25f69297e08c968dcb59abfcf80bddbce48755b7129068381d49766ec20d0b7f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.jobsearch.paths.jobs","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.jobsearch.paths.jobs#L1-L16","kind":"module","name":"real_agents.plugins_agent.plugins.jobsearch.paths.jobs","path":"real_agents/plugins_agent/plugins/jobsearch/paths/jobs.py","language":"python","start_line":1,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Jobsearch API jobs path.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://jobsearch.vencio.de/jobs\"\n headers = {\n \"Content-Type\": \"application/json\"\n }\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"d0676f7f7f00c242be9d60bd4e5e1df922d93d9562f440ba5c38de8f431cc6a0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.jobsearch.paths.jobs.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.jobsearch.paths.jobs.call_api#L6-L16","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/jobsearch/paths/jobs.py","language":"python","start_line":6,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Jobsearch API jobs path.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://jobsearch.vencio.de/jobs\"\n headers = {\n \"Content-Type\": \"application/json\"\n }\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"d0676f7f7f00c242be9d60bd4e5e1df922d93d9562f440ba5c38de8f431cc6a0","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.maps.paths.generate_map","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.maps.paths.generate_map#L1-L14","kind":"module","name":"real_agents.plugins_agent.plugins.maps.paths.generate_map","path":"real_agents/plugins_agent/plugins/maps/paths/generate_map.py","language":"python","start_line":1,"end_line":14,"context_start_line":1,"context_end_line":14,"code":"\"\"\"Maps plugin for generating maps from latlng coordinates.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n query_param = input_json[\"latlng\"]\n response = requests.get(f\"https://maps.smoothplugins.com/?latlng={query_param}\")\n\n if response.status_code == 200:\n return {\"result\": response.content.decode()}\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4979327a1d23ac44c2d3e5f5902f0273f7854a07ab469455955ca9ee8e60e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.maps.paths.generate_map.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.maps.paths.generate_map.call_api#L7-L14","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/maps/paths/generate_map.py","language":"python","start_line":7,"end_line":14,"context_start_line":1,"context_end_line":14,"code":"\"\"\"Maps plugin for generating maps from latlng coordinates.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n query_param = input_json[\"latlng\"]\n response = requests.get(f\"https://maps.smoothplugins.com/?latlng={query_param}\")\n\n if response.status_code == 200:\n return {\"result\": response.content.decode()}\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4979327a1d23ac44c2d3e5f5902f0273f7854a07ab469455955ca9ee8e60e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.speak.paths.explain_phrase","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.speak.paths.explain_phrase#L1-L24","kind":"module","name":"real_agents.plugins_agent.plugins.speak.paths.explain_phrase","path":"real_agents/plugins_agent/plugins/speak/paths/explain_phrase.py","language":"python","start_line":1,"end_line":24,"context_start_line":1,"context_end_line":24,"code":"\"\"\"Explains a foreign phrase in the context of a full query.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/explain-phrase\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"foreign_phrase\": \"no mames\",\n# \"learning_language\": \"Spanish\",\n# \"native_language\": \"English\",\n# \"additional_context\": \"Informal conversation\",\n# \"full_query\": \"What does no mames mean in English?\"\n# }","source_hash":"0aa193e2f4582a52f653b9f80f182c671be5610da262cc9285f38ce59a97398c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.speak.paths.explain_phrase.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.speak.paths.explain_phrase.call_api#L9-L16","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/speak/paths/explain_phrase.py","language":"python","start_line":9,"end_line":16,"context_start_line":1,"context_end_line":24,"code":"\"\"\"Explains a foreign phrase in the context of a full query.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/explain-phrase\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"foreign_phrase\": \"no mames\",\n# \"learning_language\": \"Spanish\",\n# \"native_language\": \"English\",\n# \"additional_context\": \"Informal conversation\",\n# \"full_query\": \"What does no mames mean in English?\"\n# }","source_hash":"0aa193e2f4582a52f653b9f80f182c671be5610da262cc9285f38ce59a97398c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.speak.paths.translate","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.speak.paths.translate#L1-L24","kind":"module","name":"real_agents.plugins_agent.plugins.speak.paths.translate","path":"real_agents/plugins_agent/plugins/speak/paths/translate.py","language":"python","start_line":1,"end_line":24,"context_start_line":1,"context_end_line":24,"code":"\"\"\"Translate a phrase from one language to another.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/translate\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"phrase_to_translate\": \"Hello, how are you?\",\n# \"learning_language\": \"Spanish\",\n# \"native_language\": \"English\",\n# \"additional_context\": \"Casual conversation\",\n# \"full_query\": \"How do I say hello, how are you in Spanish?\"\n# }","source_hash":"a027cc9ed769781310acf189ecfb65e5a62550337e4764d5d7f83cdf30ed9fce","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.speak.paths.translate.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.speak.paths.translate.call_api#L9-L16","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/speak/paths/translate.py","language":"python","start_line":9,"end_line":16,"context_start_line":1,"context_end_line":24,"code":"\"\"\"Translate a phrase from one language to another.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/translate\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"phrase_to_translate\": \"Hello, how are you?\",\n# \"learning_language\": \"Spanish\",\n# \"native_language\": \"English\",\n# \"additional_context\": \"Casual conversation\",\n# \"full_query\": \"How do I say hello, how are you in Spanish?\"\n# }","source_hash":"a027cc9ed769781310acf189ecfb65e5a62550337e4764d5d7f83cdf30ed9fce","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.speak.paths.explain_task","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.speak.paths.explain_task#L1-L24","kind":"module","name":"real_agents.plugins_agent.plugins.speak.paths.explain_task","path":"real_agents/plugins_agent/plugins/speak/paths/explain_task.py","language":"python","start_line":1,"end_line":24,"context_start_line":1,"context_end_line":24,"code":"\"\"\"Explain the task to the user.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/explain-task\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"task_description\": \"ask for directions,\n# \"learning_language\": \"French\",\n# \"native_language\": \"English\",\n# \"additional_context\": \"In a city\",\n# \"full_query\": \"How do I ask for directions in French?\"\n# }","source_hash":"d7794f92e04fd54e60fd6ce949ea300b4dc55401f04e982b7282c1109f1e3d18","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.speak.paths.explain_task.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.speak.paths.explain_task.call_api#L9-L16","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/speak/paths/explain_task.py","language":"python","start_line":9,"end_line":16,"context_start_line":1,"context_end_line":24,"code":"\"\"\"Explain the task to the user.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/explain-task\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"task_description\": \"ask for directions,\n# \"learning_language\": \"French\",\n# \"native_language\": \"English\",\n# \"additional_context\": \"In a city\",\n# \"full_query\": \"How do I ask for directions in French?\"\n# }","source_hash":"d7794f92e04fd54e60fd6ce949ea300b4dc55401f04e982b7282c1109f1e3d18","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.Outschool.paths.search_teachers","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.Outschool.paths.search_teachers#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.Outschool.paths.search_teachers","path":"real_agents/plugins_agent/plugins/Outschool/paths/search_teachers.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Search for teachers on Outschool.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://chatgpt-plugin.outschool.com/api/teachers\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4a3dc66cfd59adecb4d2e1b7d6ac01b92fa5cc39f19c731ff72757e835b1a64a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.Outschool.paths.search_teachers.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.Outschool.paths.search_teachers.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/Outschool/paths/search_teachers.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Search for teachers on Outschool.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://chatgpt-plugin.outschool.com/api/teachers\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4a3dc66cfd59adecb4d2e1b7d6ac01b92fa5cc39f19c731ff72757e835b1a64a","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.Outschool.paths.get_classes","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.Outschool.paths.get_classes#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.Outschool.paths.get_classes","path":"real_agents/plugins_agent/plugins/Outschool/paths/get_classes.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Get classes from Outschool API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://chatgpt-plugin.outschool.com/api/classes\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"dccddafedb33e6cd847f43b31ed26b5e633d4e04b203ef7975c35b9f9ac79b8e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.Outschool.paths.get_classes.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.Outschool.paths.get_classes.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/Outschool/paths/get_classes.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Get classes from Outschool API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://chatgpt-plugin.outschool.com/api/classes\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"dccddafedb33e6cd847f43b31ed26b5e633d4e04b203ef7975c35b9f9ac79b8e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.DreamInterpreter.paths.data","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.DreamInterpreter.paths.data#L1-L13","kind":"module","name":"real_agents.plugins_agent.plugins.DreamInterpreter.paths.data","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/data.py","language":"python","start_line":1,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Data path for DreamInterpreter plugin.\"\"\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://dreamplugin.bgnetmobile.com/api/data\", json=input_json)\n\n if response.status_code == 200:\n return response.content\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4145ab9eeb3faa08bfc243f4686606e840f23f82c533951bf1494d1b316adf18","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.DreamInterpreter.paths.data.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.DreamInterpreter.paths.data.call_api#L7-L13","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/data.py","language":"python","start_line":7,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Data path for DreamInterpreter plugin.\"\"\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://dreamplugin.bgnetmobile.com/api/data\", json=input_json)\n\n if response.status_code == 200:\n return response.content\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4145ab9eeb3faa08bfc243f4686606e840f23f82c533951bf1494d1b316adf18","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.DreamInterpreter.paths.dream_interpreter","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.DreamInterpreter.paths.dream_interpreter#L1-L12","kind":"module","name":"real_agents.plugins_agent.plugins.DreamInterpreter.paths.dream_interpreter","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/dream_interpreter.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Dream Interpreter plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n dream_text = input_json[\"DreamText\"]\n response = requests.get(f\"https://dreamplugin.bgnetmobile.com/getDream/{dream_text}\")\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"320e469d83f64c26397e4df79fcdce472c14779d719395affd974a477eee191f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.DreamInterpreter.paths.dream_interpreter.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.DreamInterpreter.paths.dream_interpreter.call_api#L6-L12","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/dream_interpreter.py","language":"python","start_line":6,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Dream Interpreter plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n dream_text = input_json[\"DreamText\"]\n response = requests.get(f\"https://dreamplugin.bgnetmobile.com/getDream/{dream_text}\")\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"320e469d83f64c26397e4df79fcdce472c14779d719395affd974a477eee191f","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.personnel","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.zapier.personnel#L1-L78","kind":"module","name":"real_agents.plugins_agent.plugins.zapier.personnel","path":"real_agents/plugins_agent/plugins/zapier/personnel.py","language":"python","start_line":1,"end_line":78,"context_start_line":1,"context_end_line":78,"code":"\"\"\"The Zapier plugin personnel openapi.yaml handling, since it is special case, we need to handle it separately\"\"\"\nimport os\nimport requests\n\nFILE_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\n# You need to manage your actions first in https://nla.zapier.com/providers/\n\n# Reload the openapi\ndef reload_openapi(api_key, openapi_json):\n # Original data\n headers = {\"X-API-Key\": api_key, }\n # Call read the openapi\n url = \"https://nla.zapier.com/api/v1/exposed/\"\n\n data = None\n while True:\n try:\n response = requests.get(url, headers=headers)\n data = response.json()\n break\n except Exception as e:\n print(e)\n # if an error occurs, continue to retry\n import time\n time.sleep(5)\n continue\n\n try:\n data = data['results']\n except Exception as e:\n print(e)\n return openapi_json, {}\n\n new_paths = {}\n for item in data:\n new_paths['/api/v1/exposed/{}/execute/'.format(item[\"id\"])] = {\n 'post': { # assuming POST method for all operations\n 'operationId': item['operation_id'],\n 'description': item['description'],\n 'parameters': [\n {'name': k, 'in': 'query', 'required': True, 'schema': {'type': v}}\n for k, v in item['params'].items()\n ],\n \"security\": {\n \"SessionAuth\": [],\n \"AccessPointApiKeyHeader\": [],\n \"AccessPointApiKeyQuery\": [],\n \"AccessPointOAuth\": []\n }\n }\n }\n\n openapi_json['paths'] = openapi_json['paths'] | new_paths\n\n return openapi_json, new_paths\n\n\n# Reload the endpoints\ndef reload_endpoints(new_paths):\n new_endpoint2caller = {}\n for new_path in new_paths:\n # create the call function\n def call_api(input_json, api_key):\n import requests\n headers = {\"X-API-Key\": api_key}\n url = \"https://nla.zapier.com\" + new_path\n response = requests.post(url, headers=headers, json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return response.text\n\n new_endpoint2caller[new_path] = call_api\n\n return new_endpoint2caller","source_hash":"19453d3a371326bf33bb5253a96c8a562ced11ef47150120967797d189dca242","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.personnel.reload_openapi","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.personnel.reload_openapi#L11-L57","kind":"function","name":"reload_openapi","path":"real_agents/plugins_agent/plugins/zapier/personnel.py","language":"python","start_line":11,"end_line":57,"context_start_line":1,"context_end_line":77,"code":"\"\"\"The Zapier plugin personnel openapi.yaml handling, since it is special case, we need to handle it separately\"\"\"\nimport os\nimport requests\n\nFILE_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\n# You need to manage your actions first in https://nla.zapier.com/providers/\n\n# Reload the openapi\ndef reload_openapi(api_key, openapi_json):\n # Original data\n headers = {\"X-API-Key\": api_key, }\n # Call read the openapi\n url = \"https://nla.zapier.com/api/v1/exposed/\"\n\n data = None\n while True:\n try:\n response = requests.get(url, headers=headers)\n data = response.json()\n break\n except Exception as e:\n print(e)\n # if an error occurs, continue to retry\n import time\n time.sleep(5)\n continue\n\n try:\n data = data['results']\n except Exception as e:\n print(e)\n return openapi_json, {}\n\n new_paths = {}\n for item in data:\n new_paths['/api/v1/exposed/{}/execute/'.format(item[\"id\"])] = {\n 'post': { # assuming POST method for all operations\n 'operationId': item['operation_id'],\n 'description': item['description'],\n 'parameters': [\n {'name': k, 'in': 'query', 'required': True, 'schema': {'type': v}}\n for k, v in item['params'].items()\n ],\n \"security\": {\n \"SessionAuth\": [],\n \"AccessPointApiKeyHeader\": [],\n \"AccessPointApiKeyQuery\": [],\n \"AccessPointOAuth\": []\n }\n }\n }\n\n openapi_json['paths'] = openapi_json['paths'] | new_paths\n\n return openapi_json, new_paths\n\n\n# Reload the endpoints\ndef reload_endpoints(new_paths):\n new_endpoint2caller = {}\n for new_path in new_paths:\n # create the call function\n def call_api(input_json, api_key):\n import requests\n headers = {\"X-API-Key\": api_key}\n url = \"https://nla.zapier.com\" + new_path\n response = requests.post(url, headers=headers, json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return response.text\n\n new_endpoint2caller[new_path] = call_api\n","source_hash":"19453d3a371326bf33bb5253a96c8a562ced11ef47150120967797d189dca242","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.personnel.reload_endpoints","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.personnel.reload_endpoints#L61-L78","kind":"function","name":"reload_endpoints","path":"real_agents/plugins_agent/plugins/zapier/personnel.py","language":"python","start_line":61,"end_line":78,"context_start_line":41,"context_end_line":78,"code":" 'description': item['description'],\n 'parameters': [\n {'name': k, 'in': 'query', 'required': True, 'schema': {'type': v}}\n for k, v in item['params'].items()\n ],\n \"security\": {\n \"SessionAuth\": [],\n \"AccessPointApiKeyHeader\": [],\n \"AccessPointApiKeyQuery\": [],\n \"AccessPointOAuth\": []\n }\n }\n }\n\n openapi_json['paths'] = openapi_json['paths'] | new_paths\n\n return openapi_json, new_paths\n\n\n# Reload the endpoints\ndef reload_endpoints(new_paths):\n new_endpoint2caller = {}\n for new_path in new_paths:\n # create the call function\n def call_api(input_json, api_key):\n import requests\n headers = {\"X-API-Key\": api_key}\n url = \"https://nla.zapier.com\" + new_path\n response = requests.post(url, headers=headers, json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return response.text\n\n new_endpoint2caller[new_path] = call_api\n\n return new_endpoint2caller","source_hash":"19453d3a371326bf33bb5253a96c8a562ced11ef47150120967797d189dca242","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.personnel.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.personnel.call_api#L65-L74","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/zapier/personnel.py","language":"python","start_line":65,"end_line":74,"context_start_line":45,"context_end_line":78,"code":" ],\n \"security\": {\n \"SessionAuth\": [],\n \"AccessPointApiKeyHeader\": [],\n \"AccessPointApiKeyQuery\": [],\n \"AccessPointOAuth\": []\n }\n }\n }\n\n openapi_json['paths'] = openapi_json['paths'] | new_paths\n\n return openapi_json, new_paths\n\n\n# Reload the endpoints\ndef reload_endpoints(new_paths):\n new_endpoint2caller = {}\n for new_path in new_paths:\n # create the call function\n def call_api(input_json, api_key):\n import requests\n headers = {\"X-API-Key\": api_key}\n url = \"https://nla.zapier.com\" + new_path\n response = requests.post(url, headers=headers, json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return response.text\n\n new_endpoint2caller[new_path] = call_api\n\n return new_endpoint2caller","source_hash":"19453d3a371326bf33bb5253a96c8a562ced11ef47150120967797d189dca242","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.exposed","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.zapier.paths.exposed#L1-L17","kind":"module","name":"real_agents.plugins_agent.plugins.zapier.paths.exposed","path":"real_agents/plugins_agent/plugins/zapier/paths/exposed.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Exposed paths for the Zapier plugin.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/exposed/\"\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"934c56342bd51cec80340176673ee4868ae25d24846367bc40aee37175e3b390","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.exposed.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.paths.exposed.call_api#L7-L17","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/zapier/paths/exposed.py","language":"python","start_line":7,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Exposed paths for the Zapier plugin.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/exposed/\"\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"934c56342bd51cec80340176673ee4868ae25d24846367bc40aee37175e3b390","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.configuration_link","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.zapier.paths.configuration_link#L1-L17","kind":"module","name":"real_agents.plugins_agent.plugins.zapier.paths.configuration_link","path":"real_agents/plugins_agent/plugins/zapier/paths/configuration_link.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Configuration Link Path.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/configuration-link/\"\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"6898f8dce698eaf5a059d4b1379cca11fb3712da1e2088a6dde4beb6f702b05c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.configuration_link.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.paths.configuration_link.call_api#L7-L17","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/zapier/paths/configuration_link.py","language":"python","start_line":7,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Configuration Link Path.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/configuration-link/\"\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"6898f8dce698eaf5a059d4b1379cca11fb3712da1e2088a6dde4beb6f702b05c","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.preview_a_zap","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.zapier.paths.preview_a_zap#L1-L18","kind":"module","name":"real_agents.plugins_agent.plugins.zapier.paths.preview_a_zap","path":"real_agents/plugins_agent/plugins/zapier/paths/preview_a_zap.py","language":"python","start_line":1,"end_line":18,"context_start_line":1,"context_end_line":18,"code":"\"\"\"Preview a Zapier Zap.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n \"Content-Type\": \"application/json\"\n }\n url = \"https://nla.zapier.com/api/v1/preview-a-zap/\"\n response = requests.post(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bbff868c4711c486bb0248e4a3c14345e07a0c09def8e5f26119e2a0768c0c3d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.preview_a_zap.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.paths.preview_a_zap.call_api#L7-L18","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/zapier/paths/preview_a_zap.py","language":"python","start_line":7,"end_line":18,"context_start_line":1,"context_end_line":18,"code":"\"\"\"Preview a Zapier Zap.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n \"Content-Type\": \"application/json\"\n }\n url = \"https://nla.zapier.com/api/v1/preview-a-zap/\"\n response = requests.post(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bbff868c4711c486bb0248e4a3c14345e07a0c09def8e5f26119e2a0768c0c3d","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.execution_log","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.zapier.paths.execution_log#L1-L17","kind":"module","name":"real_agents.plugins_agent.plugins.zapier.paths.execution_log","path":"real_agents/plugins_agent/plugins/zapier/paths/execution_log.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Execution log path for Zapier plugin.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/\" + input_json['execution-log']\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bdfe51174c63eacd443955c8ed76308dd5254cf0892e432e6bd12ebb175024a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.execution_log.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.paths.execution_log.call_api#L7-L17","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/zapier/paths/execution_log.py","language":"python","start_line":7,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Execution log path for Zapier plugin.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/\" + input_json['execution-log']\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bdfe51174c63eacd443955c8ed76308dd5254cf0892e432e6bd12ebb175024a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.search_actions","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.zapier.paths.search_actions#L1-L17","kind":"module","name":"real_agents.plugins_agent.plugins.zapier.paths.search_actions","path":"real_agents/plugins_agent/plugins/zapier/paths/search_actions.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Search Actions\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/search/actions/\"\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"04a7d703852560b97735097cd73ccb97b5319eaaaa261e7002946dc133776db7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.zapier.paths.search_actions.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.zapier.paths.search_actions.call_api#L7-L17","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/zapier/paths/search_actions.py","language":"python","start_line":7,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Search Actions\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/search/actions/\"\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"04a7d703852560b97735097cd73ccb97b5319eaaaa261e7002946dc133776db7","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.create_qr_code.paths.create_qr","uri":"program://OpenAgents/module/real_agents.plugins_agent.plugins.create_qr_code.paths.create_qr#L1-L13","kind":"module","name":"real_agents.plugins_agent.plugins.create_qr_code.paths.create_qr","path":"real_agents/plugins_agent/plugins/create_qr_code/paths/create_qr.py","language":"python","start_line":1,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Create QR Code API.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://create-qr-code.modelxy.com/create-qr-code\"\n response = requests.get(url, params=input_json)\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"c2bb5f1a83c2828dcbae2c0adf52764441326b4544d160d22b911d0472b11506","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.plugins.create_qr_code.paths.create_qr.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.plugins.create_qr_code.paths.create_qr.call_api#L7-L13","kind":"function","name":"call_api","path":"real_agents/plugins_agent/plugins/create_qr_code/paths/create_qr.py","language":"python","start_line":7,"end_line":13,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Create QR Code API.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://create-qr-code.modelxy.com/create-qr-code\"\n response = requests.get(url, params=input_json)\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"c2bb5f1a83c2828dcbae2c0adf52764441326b4544d160d22b911d0472b11506","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor","uri":"program://OpenAgents/module/real_agents.plugins_agent.executors.plugin_executor#L1-L77","kind":"module","name":"real_agents.plugins_agent.executors.plugin_executor","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":1,"end_line":77,"context_start_line":1,"context_end_line":77,"code":"\"\"\"Executor that manage the plugins calling\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Callable, Dict, Union\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom real_agents.plugins_agent import APICallingChain\nfrom real_agents.plugins_agent.plugins.utils import load_plugin_elements_by_name\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\nclass PluginExecutor(BaseModel):\n \"\"\"Executor to call plugins that handle the spec showing, endpoint calling and output modeling.\"\"\"\n name: str\n description: str\n spec_model: SpecModel\n meta_info: Dict[str, Any]\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n\n api_key: str = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def full_description(self, add_extra_description_from_plugin=True):\n description = (\n self.description + \"\\nOpenAPI information:\\n\" + indent_multiline_string(\n self.spec_model.prepare_spec())\n if add_extra_description_from_plugin\n else self.description\n )\n return description\n\n @classmethod\n def from_plugin_name(cls, plugin_name: str, ) -> PluginExecutor:\n plugin_info = load_plugin_elements_by_name(plugin_name)\n\n return cls(\n name=plugin_info[\"name\"],\n description=plugin_info[\"description\"],\n spec_model=plugin_info[\"spec_model\"],\n meta_info=plugin_info[\"meta_info\"],\n endpoint2caller=plugin_info[\"endpoint2caller\"],\n endpoint2output_model=plugin_info[\"endpoint2output_model\"],\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = APICallingChain.from_llm_and_plugin(\n llm,\n self.meta_info,\n self.spec_model,\n self.endpoint2caller,\n self.endpoint2output_model,\n self.api_key,\n )\n\n output = method(inputs)\n return output\n\n def load_personnel_info(self):\n new_endpoint2caller, new_endpoints2output_model = self.spec_model.load_personnel_info(\n api_key=self.api_key)\n self.endpoint2caller = self.endpoint2caller | new_endpoint2caller\n self.endpoint2output_model = self.endpoint2output_model | new_endpoints2output_model","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor.PluginExecutor","uri":"program://OpenAgents/class/real_agents.plugins_agent.executors.plugin_executor.PluginExecutor#L15-L77","kind":"class","name":"PluginExecutor","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":15,"end_line":77,"context_start_line":1,"context_end_line":77,"code":"\"\"\"Executor that manage the plugins calling\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Callable, Dict, Union\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom real_agents.plugins_agent import APICallingChain\nfrom real_agents.plugins_agent.plugins.utils import load_plugin_elements_by_name\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\nclass PluginExecutor(BaseModel):\n \"\"\"Executor to call plugins that handle the spec showing, endpoint calling and output modeling.\"\"\"\n name: str\n description: str\n spec_model: SpecModel\n meta_info: Dict[str, Any]\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n\n api_key: str = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def full_description(self, add_extra_description_from_plugin=True):\n description = (\n self.description + \"\\nOpenAPI information:\\n\" + indent_multiline_string(\n self.spec_model.prepare_spec())\n if add_extra_description_from_plugin\n else self.description\n )\n return description\n\n @classmethod\n def from_plugin_name(cls, plugin_name: str, ) -> PluginExecutor:\n plugin_info = load_plugin_elements_by_name(plugin_name)\n\n return cls(\n name=plugin_info[\"name\"],\n description=plugin_info[\"description\"],\n spec_model=plugin_info[\"spec_model\"],\n meta_info=plugin_info[\"meta_info\"],\n endpoint2caller=plugin_info[\"endpoint2caller\"],\n endpoint2output_model=plugin_info[\"endpoint2output_model\"],\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = APICallingChain.from_llm_and_plugin(\n llm,\n self.meta_info,\n self.spec_model,\n self.endpoint2caller,\n self.endpoint2output_model,\n self.api_key,\n )\n\n output = method(inputs)\n return output\n\n def load_personnel_info(self):\n new_endpoint2caller, new_endpoints2output_model = self.spec_model.load_personnel_info(\n api_key=self.api_key)\n self.endpoint2caller = self.endpoint2caller | new_endpoint2caller\n self.endpoint2output_model = self.endpoint2output_model | new_endpoints2output_model","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor.Config","uri":"program://OpenAgents/class/real_agents.plugins_agent.executors.plugin_executor.Config#L26-L30","kind":"class","name":"Config","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":26,"end_line":30,"context_start_line":6,"context_end_line":50,"code":"\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom real_agents.plugins_agent import APICallingChain\nfrom real_agents.plugins_agent.plugins.utils import load_plugin_elements_by_name\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\nclass PluginExecutor(BaseModel):\n \"\"\"Executor to call plugins that handle the spec showing, endpoint calling and output modeling.\"\"\"\n name: str\n description: str\n spec_model: SpecModel\n meta_info: Dict[str, Any]\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n\n api_key: str = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def full_description(self, add_extra_description_from_plugin=True):\n description = (\n self.description + \"\\nOpenAPI information:\\n\" + indent_multiline_string(\n self.spec_model.prepare_spec())\n if add_extra_description_from_plugin\n else self.description\n )\n return description\n\n @classmethod\n def from_plugin_name(cls, plugin_name: str, ) -> PluginExecutor:\n plugin_info = load_plugin_elements_by_name(plugin_name)\n\n return cls(\n name=plugin_info[\"name\"],\n description=plugin_info[\"description\"],\n spec_model=plugin_info[\"spec_model\"],\n meta_info=plugin_info[\"meta_info\"],","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor.full_description","uri":"program://OpenAgents/function/real_agents.plugins_agent.executors.plugin_executor.full_description#L33-L40","kind":"function","name":"full_description","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":33,"end_line":40,"context_start_line":13,"context_end_line":60,"code":"\n\nclass PluginExecutor(BaseModel):\n \"\"\"Executor to call plugins that handle the spec showing, endpoint calling and output modeling.\"\"\"\n name: str\n description: str\n spec_model: SpecModel\n meta_info: Dict[str, Any]\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n\n api_key: str = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def full_description(self, add_extra_description_from_plugin=True):\n description = (\n self.description + \"\\nOpenAPI information:\\n\" + indent_multiline_string(\n self.spec_model.prepare_spec())\n if add_extra_description_from_plugin\n else self.description\n )\n return description\n\n @classmethod\n def from_plugin_name(cls, plugin_name: str, ) -> PluginExecutor:\n plugin_info = load_plugin_elements_by_name(plugin_name)\n\n return cls(\n name=plugin_info[\"name\"],\n description=plugin_info[\"description\"],\n spec_model=plugin_info[\"spec_model\"],\n meta_info=plugin_info[\"meta_info\"],\n endpoint2caller=plugin_info[\"endpoint2caller\"],\n endpoint2output_model=plugin_info[\"endpoint2output_model\"],\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor.from_plugin_name","uri":"program://OpenAgents/function/real_agents.plugins_agent.executors.plugin_executor.from_plugin_name#L43-L53","kind":"function","name":"from_plugin_name","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":43,"end_line":53,"context_start_line":23,"context_end_line":73,"code":"\n api_key: str = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def full_description(self, add_extra_description_from_plugin=True):\n description = (\n self.description + \"\\nOpenAPI information:\\n\" + indent_multiline_string(\n self.spec_model.prepare_spec())\n if add_extra_description_from_plugin\n else self.description\n )\n return description\n\n @classmethod\n def from_plugin_name(cls, plugin_name: str, ) -> PluginExecutor:\n plugin_info = load_plugin_elements_by_name(plugin_name)\n\n return cls(\n name=plugin_info[\"name\"],\n description=plugin_info[\"description\"],\n spec_model=plugin_info[\"spec_model\"],\n meta_info=plugin_info[\"meta_info\"],\n endpoint2caller=plugin_info[\"endpoint2caller\"],\n endpoint2output_model=plugin_info[\"endpoint2output_model\"],\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = APICallingChain.from_llm_and_plugin(\n llm,\n self.meta_info,\n self.spec_model,\n self.endpoint2caller,\n self.endpoint2output_model,\n self.api_key,\n )\n\n output = method(inputs)\n return output\n\n def load_personnel_info(self):","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor.run","uri":"program://OpenAgents/function/real_agents.plugins_agent.executors.plugin_executor.run#L55-L71","kind":"function","name":"run","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":55,"end_line":71,"context_start_line":35,"context_end_line":77,"code":" self.description + \"\\nOpenAPI information:\\n\" + indent_multiline_string(\n self.spec_model.prepare_spec())\n if add_extra_description_from_plugin\n else self.description\n )\n return description\n\n @classmethod\n def from_plugin_name(cls, plugin_name: str, ) -> PluginExecutor:\n plugin_info = load_plugin_elements_by_name(plugin_name)\n\n return cls(\n name=plugin_info[\"name\"],\n description=plugin_info[\"description\"],\n spec_model=plugin_info[\"spec_model\"],\n meta_info=plugin_info[\"meta_info\"],\n endpoint2caller=plugin_info[\"endpoint2caller\"],\n endpoint2output_model=plugin_info[\"endpoint2output_model\"],\n )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = APICallingChain.from_llm_and_plugin(\n llm,\n self.meta_info,\n self.spec_model,\n self.endpoint2caller,\n self.endpoint2output_model,\n self.api_key,\n )\n\n output = method(inputs)\n return output\n\n def load_personnel_info(self):\n new_endpoint2caller, new_endpoints2output_model = self.spec_model.load_personnel_info(\n api_key=self.api_key)\n self.endpoint2caller = self.endpoint2caller | new_endpoint2caller\n self.endpoint2output_model = self.endpoint2output_model | new_endpoints2output_model","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.executors.plugin_executor.load_personnel_info","uri":"program://OpenAgents/function/real_agents.plugins_agent.executors.plugin_executor.load_personnel_info#L73-L77","kind":"function","name":"load_personnel_info","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":73,"end_line":77,"context_start_line":53,"context_end_line":77,"code":" )\n\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n ) -> Union[str, Dict[str, Any]]:\n inputs = {\"input_str\": user_intent}\n method = APICallingChain.from_llm_and_plugin(\n llm,\n self.meta_info,\n self.spec_model,\n self.endpoint2caller,\n self.endpoint2output_model,\n self.api_key,\n )\n\n output = method(inputs)\n return output\n\n def load_personnel_info(self):\n new_endpoint2caller, new_endpoints2output_model = self.spec_model.load_personnel_info(\n api_key=self.api_key)\n self.endpoint2caller = self.endpoint2caller | new_endpoint2caller\n self.endpoint2output_model = self.endpoint2output_model | new_endpoints2output_model","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base","uri":"program://OpenAgents/module/real_agents.plugins_agent.api_calling.base#L1-L322","kind":"module","name":"real_agents.plugins_agent.api_calling.base","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":1,"end_line":322,"context_start_line":1,"context_end_line":322,"code":"\"\"\"Implement API calling.\"\"\"\n\nfrom __future__ import annotations\n\nimport re\nimport traceback\nfrom typing import Any, Callable, Dict, List, Optional\nimport backoff\nimport json5\nfrom fuzzywuzzy import process\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains import LLMChain\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.plugins_agent.api_calling.custom_exceptions import ParsingError, \\\n APICallingError\nfrom real_agents.adapters.data_model import SpecModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.plugins_agent.api_calling.prompt import (\n RETRY_PROMPT,\n STOP_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\n\nclass APICallingChain(Chain, BaseModel):\n \"\"\"Chain for Calling API\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n llm_stop_chain: LLMChain\n\n meta_info: Dict[str, Any]\n spec_model: SpecModel\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n api_key: Optional[str] = None\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n retry_times = 1\n stop: str = \"\\n\\n\"\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property\n def need_auth(self):\n \"\"\"Whether the API call needs authentication.\"\"\"\n return not self.meta_info[\"manifest\"][\"auth\"][\"type\"] in [\n None,\n \"None\",\n \"none\",\n \"Null\",\n \"null\",\n ] # the value of type is not null in ai-plugin.json\n\n @backoff.on_exception(backoff.expo, Exception, max_tries=10, max_time=20)\n def call_api(self, endpoint, input_json):\n \"\"\"Call the API and return the output. Wrap the data in the output model.\"\"\"\n # Find the endpoint by fuzzy match, in case sometimes LLM generated a wrong endpoint\n if endpoint not in self.endpoint2caller:\n return \"Endpoint not found. Please try again.\"\n\n endpoint = process.extractOne(endpoint, list(self.endpoint2caller.keys()))[0]\n\n # Add fuzzy match for endpoint\n try:\n api_output = (\n self.endpoint2caller[endpoint](input_json, self.api_key)\n if self.need_auth\n else self.endpoint2caller[endpoint](input_json)\n )\n except Exception as e:\n raise APICallingError(f\"{e}\")\n\n compressed_output = self.endpoint2output_model[endpoint]({\"out\": api_output})[\n \"out\"]\n return compressed_output\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n endpoint = None\n input_json = None\n\n try:\n json_content = json5.loads(response)\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except:\n pattern = r\"```json\\n(.+?)\\n```\" if \"```json\" in response else r\"```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n try:\n json_content = json5.loads(match.group(1))\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except Exception as e:\n raise ParsingError(f\"{e}\")\n\n # When the endpoint is null, we use the default endpoint\n if endpoint is None or endpoint == \"null\" or endpoint == \"Null\" or endpoint == \"NULL\":\n endpoint = \"\\\\\"\n\n return {\"endpoint\": endpoint, \"input_json\": input_json}\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_stop_prompt(cls, system_prompt, stop_prompt) -> BasePromptTemplate:\n # Decide the stop when the LLM are getting the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\", \"api_output\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(stop_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def retry(self, input_str: str, trial_history: List[Dict],\n _run_manager: CallbackManagerForChainRun,\n vars_to_pass: Dict) -> None:\n response_content = (\n self.llm_basic_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str})\n if len(trial_history) == 0\n else self.llm_retry_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"trial_history\": trial_history}\n )\n )\n\n parsed_return = self.parse_response(response_content)\n\n _run_manager.on_text(parsed_return, indent=4, color=\"green\",\n verbose=self.verbose)\n\n endpoint, input_json = (\n parsed_return[\"endpoint\"],\n parsed_return[\"input_json\"],\n )\n\n vars_to_pass[\"endpoint\"] = endpoint\n vars_to_pass[\"input_json\"] = input_json\n\n api_output = self.call_api(endpoint, input_json)\n vars_to_pass[\"api_output\"] = api_output\n\n _run_manager.on_text(api_output, color=\"yellow\", verbose=self.verbose)\n\n should_stop = (\n self.llm_stop_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"api_output\": api_output})\n .lower()\n .strip()\n == \"yes\"\n )\n _run_manager.on_text(should_stop, color=\"yellow\", verbose=self.verbose)\n vars_to_pass[\"should_stop\"] = should_stop\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n\n trial_history = []\n count = 0\n vars_to_pass = {\"endpoint\": None, \"input_json\": None, \"api_output\": None,\n \"should_stop\": False}\n\n while count < self.retry_times:\n try:\n self.retry(input_str, trial_history, _run_manager, vars_to_pass)\n trial_history.append({\"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]})\n if vars_to_pass[\"should_stop\"]:\n break\n else:\n count += 1\n except ParsingError as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n except APICallingError as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n except Exception as e:\n # fixme: Handle the exception, make error message shorter\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\",\n verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n\n if count == self.retry_times:\n if \"errors\" in trial_history[-1]:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": trial_history[-1][\"errors\"]}\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"]} | (\n trial_history[-1]) # return the last trial history\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]}\n\n @classmethod\n def from_llm_and_plugin(\n cls,\n llm: BaseLanguageModel,\n meta_info: Dict[str, Any],\n spec_model: SpecModel,\n endpoint2caller: Dict[str, Callable],\n endpoint2output_model: Dict[str, Callable],\n api_key: str,\n ) -> APICallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n llm_stop_chain = LLMChain(\n llm=llm,\n prompt=cls.create_stop_prompt(\n system_prompt=SYSTEM_PROMPT,\n stop_prompt=STOP_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n llm_stop_chain=llm_stop_chain,\n meta_info=meta_info,\n spec_model=spec_model,\n endpoint2caller=endpoint2caller,\n endpoint2output_model=endpoint2output_model,\n api_key=api_key,\n )","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.APICallingChain","uri":"program://OpenAgents/class/real_agents.plugins_agent.api_calling.base.APICallingChain#L36-L322","kind":"class","name":"APICallingChain","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":36,"end_line":322,"context_start_line":16,"context_end_line":322,"code":"from langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.plugins_agent.api_calling.custom_exceptions import ParsingError, \\\n APICallingError\nfrom real_agents.adapters.data_model import SpecModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.plugins_agent.api_calling.prompt import (\n RETRY_PROMPT,\n STOP_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\n\nclass APICallingChain(Chain, BaseModel):\n \"\"\"Chain for Calling API\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n llm_stop_chain: LLMChain\n\n meta_info: Dict[str, Any]\n spec_model: SpecModel\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n api_key: Optional[str] = None\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n retry_times = 1\n stop: str = \"\\n\\n\"\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property\n def need_auth(self):\n \"\"\"Whether the API call needs authentication.\"\"\"\n return not self.meta_info[\"manifest\"][\"auth\"][\"type\"] in [\n None,\n \"None\",\n \"none\",\n \"Null\",\n \"null\",\n ] # the value of type is not null in ai-plugin.json\n\n @backoff.on_exception(backoff.expo, Exception, max_tries=10, max_time=20)\n def call_api(self, endpoint, input_json):\n \"\"\"Call the API and return the output. Wrap the data in the output model.\"\"\"\n # Find the endpoint by fuzzy match, in case sometimes LLM generated a wrong endpoint\n if endpoint not in self.endpoint2caller:\n return \"Endpoint not found. Please try again.\"\n\n endpoint = process.extractOne(endpoint, list(self.endpoint2caller.keys()))[0]\n\n # Add fuzzy match for endpoint\n try:\n api_output = (\n self.endpoint2caller[endpoint](input_json, self.api_key)\n if self.need_auth\n else self.endpoint2caller[endpoint](input_json)\n )\n except Exception as e:\n raise APICallingError(f\"{e}\")\n\n compressed_output = self.endpoint2output_model[endpoint]({\"out\": api_output})[\n \"out\"]\n return compressed_output\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n endpoint = None\n input_json = None\n\n try:\n json_content = json5.loads(response)\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except:\n pattern = r\"```json\\n(.+?)\\n```\" if \"```json\" in response else r\"```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n try:\n json_content = json5.loads(match.group(1))\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except Exception as e:\n raise ParsingError(f\"{e}\")\n\n # When the endpoint is null, we use the default endpoint\n if endpoint is None or endpoint == \"null\" or endpoint == \"Null\" or endpoint == \"NULL\":\n endpoint = \"\\\\\"\n\n return {\"endpoint\": endpoint, \"input_json\": input_json}\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_stop_prompt(cls, system_prompt, stop_prompt) -> BasePromptTemplate:\n # Decide the stop when the LLM are getting the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\", \"api_output\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(stop_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def retry(self, input_str: str, trial_history: List[Dict],\n _run_manager: CallbackManagerForChainRun,\n vars_to_pass: Dict) -> None:\n response_content = (\n self.llm_basic_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str})\n if len(trial_history) == 0\n else self.llm_retry_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"trial_history\": trial_history}\n )\n )\n\n parsed_return = self.parse_response(response_content)\n\n _run_manager.on_text(parsed_return, indent=4, color=\"green\",\n verbose=self.verbose)\n\n endpoint, input_json = (\n parsed_return[\"endpoint\"],\n parsed_return[\"input_json\"],\n )\n\n vars_to_pass[\"endpoint\"] = endpoint\n vars_to_pass[\"input_json\"] = input_json\n\n api_output = self.call_api(endpoint, input_json)\n vars_to_pass[\"api_output\"] = api_output\n\n _run_manager.on_text(api_output, color=\"yellow\", verbose=self.verbose)\n\n should_stop = (\n self.llm_stop_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"api_output\": api_output})\n .lower()\n .strip()\n == \"yes\"\n )\n _run_manager.on_text(should_stop, color=\"yellow\", verbose=self.verbose)\n vars_to_pass[\"should_stop\"] = should_stop\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n\n trial_history = []\n count = 0\n vars_to_pass = {\"endpoint\": None, \"input_json\": None, \"api_output\": None,\n \"should_stop\": False}\n\n while count < self.retry_times:\n try:\n self.retry(input_str, trial_history, _run_manager, vars_to_pass)\n trial_history.append({\"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]})\n if vars_to_pass[\"should_stop\"]:\n break\n else:\n count += 1\n except ParsingError as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n except APICallingError as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n except Exception as e:\n # fixme: Handle the exception, make error message shorter\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\",\n verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n\n if count == self.retry_times:\n if \"errors\" in trial_history[-1]:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": trial_history[-1][\"errors\"]}\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"]} | (\n trial_history[-1]) # return the last trial history\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]}\n\n @classmethod\n def from_llm_and_plugin(\n cls,\n llm: BaseLanguageModel,\n meta_info: Dict[str, Any],\n spec_model: SpecModel,\n endpoint2caller: Dict[str, Callable],\n endpoint2output_model: Dict[str, Callable],\n api_key: str,\n ) -> APICallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n llm_stop_chain = LLMChain(\n llm=llm,\n prompt=cls.create_stop_prompt(\n system_prompt=SYSTEM_PROMPT,\n stop_prompt=STOP_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n llm_stop_chain=llm_stop_chain,\n meta_info=meta_info,\n spec_model=spec_model,\n endpoint2caller=endpoint2caller,\n endpoint2output_model=endpoint2output_model,\n api_key=api_key,\n )","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.Config","uri":"program://OpenAgents/class/real_agents.plugins_agent.api_calling.base.Config#L57-L61","kind":"class","name":"Config","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":57,"end_line":61,"context_start_line":37,"context_end_line":81,"code":" \"\"\"Chain for Calling API\"\"\"\n\n llm_basic_chain: LLMChain\n llm_retry_chain: LLMChain\n llm_stop_chain: LLMChain\n\n meta_info: Dict[str, Any]\n spec_model: SpecModel\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n api_key: Optional[str] = None\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n retry_times = 1\n stop: str = \"\\n\\n\"\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.input_keys","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.input_keys#L64-L69","kind":"function","name":"input_keys","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":64,"end_line":69,"context_start_line":44,"context_end_line":89,"code":" spec_model: SpecModel\n endpoint2caller: Dict[str, Callable]\n endpoint2output_model: Dict[str, Callable]\n api_key: Optional[str] = None\n\n memory: Optional[ReadOnlySharedStringMemory] = None # fixme:\n retry_times = 1\n stop: str = \"\\n\\n\"\n verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.output_keys","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.output_keys#L72-L77","kind":"function","name":"output_keys","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":72,"end_line":77,"context_start_line":52,"context_end_line":97,"code":" verbose = True\n\n chat_id: Optional[str] = None\n user_id: Optional[str] = None\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property\n def need_auth(self):\n \"\"\"Whether the API call needs authentication.\"\"\"\n return not self.meta_info[\"manifest\"][\"auth\"][\"type\"] in [\n None,\n \"None\",\n \"none\",\n \"Null\",\n \"null\",","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.specs_str","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.specs_str#L80-L87","kind":"function","name":"specs_str","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":80,"end_line":87,"context_start_line":60,"context_end_line":107,"code":" extra = Extra.forbid\n arbitrary_types_allowed = True\n\n @property\n def input_keys(self) -> List[str]:\n \"\"\"Return the singular input key.\n\n :meta private:\n \"\"\"\n return [\"input_str\"]\n\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property\n def need_auth(self):\n \"\"\"Whether the API call needs authentication.\"\"\"\n return not self.meta_info[\"manifest\"][\"auth\"][\"type\"] in [\n None,\n \"None\",\n \"none\",\n \"Null\",\n \"null\",\n ] # the value of type is not null in ai-plugin.json\n\n @backoff.on_exception(backoff.expo, Exception, max_tries=10, max_time=20)\n def call_api(self, endpoint, input_json):\n \"\"\"Call the API and return the output. Wrap the data in the output model.\"\"\"\n # Find the endpoint by fuzzy match, in case sometimes LLM generated a wrong endpoint\n if endpoint not in self.endpoint2caller:\n return \"Endpoint not found. Please try again.\"\n\n endpoint = process.extractOne(endpoint, list(self.endpoint2caller.keys()))[0]","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.need_auth","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.need_auth#L90-L98","kind":"function","name":"need_auth","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":90,"end_line":98,"context_start_line":70,"context_end_line":118,"code":"\n @property\n def output_keys(self) -> List[str]:\n \"\"\"Return the output keys.\n\n :meta private:\n \"\"\"\n return [\"endpoint\", \"input_json\", \"api_output\"]\n\n @property\n def specs_str(self):\n \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property\n def need_auth(self):\n \"\"\"Whether the API call needs authentication.\"\"\"\n return not self.meta_info[\"manifest\"][\"auth\"][\"type\"] in [\n None,\n \"None\",\n \"none\",\n \"Null\",\n \"null\",\n ] # the value of type is not null in ai-plugin.json\n\n @backoff.on_exception(backoff.expo, Exception, max_tries=10, max_time=20)\n def call_api(self, endpoint, input_json):\n \"\"\"Call the API and return the output. Wrap the data in the output model.\"\"\"\n # Find the endpoint by fuzzy match, in case sometimes LLM generated a wrong endpoint\n if endpoint not in self.endpoint2caller:\n return \"Endpoint not found. Please try again.\"\n\n endpoint = process.extractOne(endpoint, list(self.endpoint2caller.keys()))[0]\n\n # Add fuzzy match for endpoint\n try:\n api_output = (\n self.endpoint2caller[endpoint](input_json, self.api_key)\n if self.need_auth\n else self.endpoint2caller[endpoint](input_json)\n )\n except Exception as e:\n raise APICallingError(f\"{e}\")\n","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.call_api","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.call_api#L101-L121","kind":"function","name":"call_api","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":101,"end_line":121,"context_start_line":81,"context_end_line":141,"code":" \"\"\"The str representation of spec.\"\"\"\n return \"\\n\".join(\n [\n f\"{i}.\\n{self.spec_model.prepare_spec_for_one_path(p, include_api_info=False)}\"\n for i, p in enumerate(self.spec_model.paths.keys())\n ]\n )\n\n @property\n def need_auth(self):\n \"\"\"Whether the API call needs authentication.\"\"\"\n return not self.meta_info[\"manifest\"][\"auth\"][\"type\"] in [\n None,\n \"None\",\n \"none\",\n \"Null\",\n \"null\",\n ] # the value of type is not null in ai-plugin.json\n\n @backoff.on_exception(backoff.expo, Exception, max_tries=10, max_time=20)\n def call_api(self, endpoint, input_json):\n \"\"\"Call the API and return the output. Wrap the data in the output model.\"\"\"\n # Find the endpoint by fuzzy match, in case sometimes LLM generated a wrong endpoint\n if endpoint not in self.endpoint2caller:\n return \"Endpoint not found. Please try again.\"\n\n endpoint = process.extractOne(endpoint, list(self.endpoint2caller.keys()))[0]\n\n # Add fuzzy match for endpoint\n try:\n api_output = (\n self.endpoint2caller[endpoint](input_json, self.api_key)\n if self.need_auth\n else self.endpoint2caller[endpoint](input_json)\n )\n except Exception as e:\n raise APICallingError(f\"{e}\")\n\n compressed_output = self.endpoint2output_model[endpoint]({\"out\": api_output})[\n \"out\"]\n return compressed_output\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n endpoint = None\n input_json = None\n\n try:\n json_content = json5.loads(response)\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except:\n pattern = r\"```json\\n(.+?)\\n```\" if \"```json\" in response else r\"```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n try:\n json_content = json5.loads(match.group(1))\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except Exception as e:","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.parse_response","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.parse_response#L123-L148","kind":"function","name":"parse_response","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":123,"end_line":148,"context_start_line":103,"context_end_line":168,"code":" # Find the endpoint by fuzzy match, in case sometimes LLM generated a wrong endpoint\n if endpoint not in self.endpoint2caller:\n return \"Endpoint not found. Please try again.\"\n\n endpoint = process.extractOne(endpoint, list(self.endpoint2caller.keys()))[0]\n\n # Add fuzzy match for endpoint\n try:\n api_output = (\n self.endpoint2caller[endpoint](input_json, self.api_key)\n if self.need_auth\n else self.endpoint2caller[endpoint](input_json)\n )\n except Exception as e:\n raise APICallingError(f\"{e}\")\n\n compressed_output = self.endpoint2output_model[endpoint]({\"out\": api_output})[\n \"out\"]\n return compressed_output\n\n def parse_response(self, response: str):\n \"\"\"Parse the endpoint and input_json\"\"\"\n endpoint = None\n input_json = None\n\n try:\n json_content = json5.loads(response)\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except:\n pattern = r\"```json\\n(.+?)\\n```\" if \"```json\" in response else r\"```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n try:\n json_content = json5.loads(match.group(1))\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except Exception as e:\n raise ParsingError(f\"{e}\")\n\n # When the endpoint is null, we use the default endpoint\n if endpoint is None or endpoint == \"null\" or endpoint == \"Null\" or endpoint == \"NULL\":\n endpoint = \"\\\\\"\n\n return {\"endpoint\": endpoint, \"input_json\": input_json}\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.create_basic_prompt","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.create_basic_prompt#L151-L159","kind":"function","name":"create_basic_prompt","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":151,"end_line":159,"context_start_line":131,"context_end_line":179,"code":" input_json = json_content[\"input_json\"]\n except:\n pattern = r\"```json\\n(.+?)\\n```\" if \"```json\" in response else r\"```\\n(.+?)\\n```\"\n match = re.search(pattern, response, re.DOTALL)\n\n if match:\n try:\n json_content = json5.loads(match.group(1))\n endpoint = json_content[\"endpoint\"]\n input_json = json_content[\"input_json\"]\n except Exception as e:\n raise ParsingError(f\"{e}\")\n\n # When the endpoint is null, we use the default endpoint\n if endpoint is None or endpoint == \"null\" or endpoint == \"Null\" or endpoint == \"NULL\":\n endpoint = \"\\\\\"\n\n return {\"endpoint\": endpoint, \"input_json\": input_json}\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_stop_prompt(cls, system_prompt, stop_prompt) -> BasePromptTemplate:\n # Decide the stop when the LLM are getting the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\", \"api_output\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(stop_prompt),\n ]","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.create_retry_prompt","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.create_retry_prompt#L162-L170","kind":"function","name":"create_retry_prompt","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":162,"end_line":170,"context_start_line":142,"context_end_line":190,"code":" raise ParsingError(f\"{e}\")\n\n # When the endpoint is null, we use the default endpoint\n if endpoint is None or endpoint == \"null\" or endpoint == \"Null\" or endpoint == \"NULL\":\n endpoint = \"\\\\\"\n\n return {\"endpoint\": endpoint, \"input_json\": input_json}\n\n @classmethod\n def create_basic_prompt(cls, system_prompt, user_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_stop_prompt(cls, system_prompt, stop_prompt) -> BasePromptTemplate:\n # Decide the stop when the LLM are getting the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\", \"api_output\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(stop_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def retry(self, input_str: str, trial_history: List[Dict],\n _run_manager: CallbackManagerForChainRun,\n vars_to_pass: Dict) -> None:\n response_content = (\n self.llm_basic_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str})\n if len(trial_history) == 0\n else self.llm_retry_chain.run(","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.create_stop_prompt","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.create_stop_prompt#L173-L181","kind":"function","name":"create_stop_prompt","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":173,"end_line":181,"context_start_line":153,"context_end_line":201,"code":" input_variables = [\"specs_str\", \"input_str\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(user_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_retry_prompt(cls, system_prompt, retry_prompt) -> BasePromptTemplate:\n # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_stop_prompt(cls, system_prompt, stop_prompt) -> BasePromptTemplate:\n # Decide the stop when the LLM are getting the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\", \"api_output\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(stop_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def retry(self, input_str: str, trial_history: List[Dict],\n _run_manager: CallbackManagerForChainRun,\n vars_to_pass: Dict) -> None:\n response_content = (\n self.llm_basic_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str})\n if len(trial_history) == 0\n else self.llm_retry_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"trial_history\": trial_history}\n )\n )\n\n parsed_return = self.parse_response(response_content)\n\n _run_manager.on_text(parsed_return, indent=4, color=\"green\",\n verbose=self.verbose)\n\n endpoint, input_json = (","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.retry","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.retry#L183-L223","kind":"function","name":"retry","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":183,"end_line":223,"context_start_line":163,"context_end_line":243,"code":" # Call the LLM to get the predicted end_point and input_json in retry\n input_variables = [\"specs_str\", \"input_str\", \"trial_history\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(retry_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n @classmethod\n def create_stop_prompt(cls, system_prompt, stop_prompt) -> BasePromptTemplate:\n # Decide the stop when the LLM are getting the predicted end_point and input_json\n input_variables = [\"specs_str\", \"input_str\", \"api_output\"]\n messages = [\n SystemMessagePromptTemplate.from_template(system_prompt),\n HumanMessagePromptTemplate.from_template(stop_prompt),\n ]\n\n return ChatPromptTemplate(input_variables=input_variables, messages=messages)\n\n def retry(self, input_str: str, trial_history: List[Dict],\n _run_manager: CallbackManagerForChainRun,\n vars_to_pass: Dict) -> None:\n response_content = (\n self.llm_basic_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str})\n if len(trial_history) == 0\n else self.llm_retry_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"trial_history\": trial_history}\n )\n )\n\n parsed_return = self.parse_response(response_content)\n\n _run_manager.on_text(parsed_return, indent=4, color=\"green\",\n verbose=self.verbose)\n\n endpoint, input_json = (\n parsed_return[\"endpoint\"],\n parsed_return[\"input_json\"],\n )\n\n vars_to_pass[\"endpoint\"] = endpoint\n vars_to_pass[\"input_json\"] = input_json\n\n api_output = self.call_api(endpoint, input_json)\n vars_to_pass[\"api_output\"] = api_output\n\n _run_manager.on_text(api_output, color=\"yellow\", verbose=self.verbose)\n\n should_stop = (\n self.llm_stop_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"api_output\": api_output})\n .lower()\n .strip()\n == \"yes\"\n )\n _run_manager.on_text(should_stop, color=\"yellow\", verbose=self.verbose)\n vars_to_pass[\"should_stop\"] = should_stop\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n\n trial_history = []\n count = 0\n vars_to_pass = {\"endpoint\": None, \"input_json\": None, \"api_output\": None,\n \"should_stop\": False}\n\n while count < self.retry_times:\n try:\n self.retry(input_str, trial_history, _run_manager, vars_to_pass)\n trial_history.append({\"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]})","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base._call","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base._call#L225-L278","kind":"function","name":"_call","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":225,"end_line":278,"context_start_line":205,"context_end_line":298,"code":"\n vars_to_pass[\"endpoint\"] = endpoint\n vars_to_pass[\"input_json\"] = input_json\n\n api_output = self.call_api(endpoint, input_json)\n vars_to_pass[\"api_output\"] = api_output\n\n _run_manager.on_text(api_output, color=\"yellow\", verbose=self.verbose)\n\n should_stop = (\n self.llm_stop_chain.run(\n **{\"specs_str\": self.specs_str, \"input_str\": input_str,\n \"api_output\": api_output})\n .lower()\n .strip()\n == \"yes\"\n )\n _run_manager.on_text(should_stop, color=\"yellow\", verbose=self.verbose)\n vars_to_pass[\"should_stop\"] = should_stop\n\n def _call(\n self,\n inputs: Dict[str, str],\n run_manager: Optional[CallbackManagerForChainRun] = None,\n ) -> Dict[str, Any]:\n _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()\n\n input_str = inputs[\"input_str\"]\n\n trial_history = []\n count = 0\n vars_to_pass = {\"endpoint\": None, \"input_json\": None, \"api_output\": None,\n \"should_stop\": False}\n\n while count < self.retry_times:\n try:\n self.retry(input_str, trial_history, _run_manager, vars_to_pass)\n trial_history.append({\"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]})\n if vars_to_pass[\"should_stop\"]:\n break\n else:\n count += 1\n except ParsingError as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n except APICallingError as e:\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n except Exception as e:\n # fixme: Handle the exception, make error message shorter\n _run_manager.on_text(str(e) + \"\\n\", color=\"red\", verbose=self.verbose)\n _run_manager.on_text(traceback.format_exc(), color=\"red\",\n verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n\n if count == self.retry_times:\n if \"errors\" in trial_history[-1]:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": trial_history[-1][\"errors\"]}\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"]} | (\n trial_history[-1]) # return the last trial history\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]}\n\n @classmethod\n def from_llm_and_plugin(\n cls,\n llm: BaseLanguageModel,\n meta_info: Dict[str, Any],\n spec_model: SpecModel,\n endpoint2caller: Dict[str, Callable],\n endpoint2output_model: Dict[str, Callable],\n api_key: str,\n ) -> APICallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.base.from_llm_and_plugin","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.base.from_llm_and_plugin#L281-L322","kind":"function","name":"from_llm_and_plugin","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":281,"end_line":322,"context_start_line":261,"context_end_line":322,"code":" _run_manager.on_text(traceback.format_exc(), color=\"red\",\n verbose=self.verbose)\n trial_history.append({\"errors\": str(e)})\n count += 1\n continue\n\n if count == self.retry_times:\n if \"errors\" in trial_history[-1]:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": trial_history[-1][\"errors\"]}\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"]} | (\n trial_history[-1]) # return the last trial history\n else:\n return {\"endpoint\": vars_to_pass[\"endpoint\"],\n \"input_json\": vars_to_pass[\"input_json\"],\n \"api_output\": vars_to_pass[\"api_output\"]}\n\n @classmethod\n def from_llm_and_plugin(\n cls,\n llm: BaseLanguageModel,\n meta_info: Dict[str, Any],\n spec_model: SpecModel,\n endpoint2caller: Dict[str, Callable],\n endpoint2output_model: Dict[str, Callable],\n api_key: str,\n ) -> APICallingChain:\n llm_basic_chain = LLMChain(\n llm=llm,\n prompt=cls.create_basic_prompt(\n system_prompt=SYSTEM_PROMPT,\n user_prompt=USER_PROMPT,\n ),\n )\n\n llm_retry_chain = LLMChain(\n llm=llm,\n prompt=cls.create_retry_prompt(\n system_prompt=SYSTEM_PROMPT,\n retry_prompt=RETRY_PROMPT,\n ),\n )\n llm_stop_chain = LLMChain(\n llm=llm,\n prompt=cls.create_stop_prompt(\n system_prompt=SYSTEM_PROMPT,\n stop_prompt=STOP_PROMPT,\n ),\n )\n\n return cls(\n llm_basic_chain=llm_basic_chain,\n llm_retry_chain=llm_retry_chain,\n llm_stop_chain=llm_stop_chain,\n meta_info=meta_info,\n spec_model=spec_model,\n endpoint2caller=endpoint2caller,\n endpoint2output_model=endpoint2output_model,\n api_key=api_key,\n )","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.prompt","uri":"program://OpenAgents/module/real_agents.plugins_agent.api_calling.prompt#L1-L102","kind":"module","name":"real_agents.plugins_agent.api_calling.prompt","path":"real_agents/plugins_agent/api_calling/prompt.py","language":"python","start_line":1,"end_line":102,"context_start_line":1,"context_end_line":102,"code":"SYSTEM_PROMPT = (\n \"\"\"You are acting like plugin system that understand user's needs and call APIs precisely for them.\"\"\".strip()\n + \"\\n\"\n)\n\nUSER_PROMPT = (\n \"\"\"\nHere are the endpoints specs:\n```\n{specs_str}\n```\nHere is the input string:\n```\n{input_str}\n```\nSelect the right endpoint called that can process the input string.\nYou need to wrap the input str into a json object, so that it could be fed into the function you selected. \nDuring wrapping, you should:\n1. modify the value of each key so that it satisfies the requirements in function specs.For example, if the type of the value should be a number, then you should modify it into a number;\n2. ignore the information that is not useful or not applicable to the function you selected.\nYou fill values into some slots in the input_json, and then call the API. If the API returns a valid output, then you succeed. Otherwise, you fail.\nReturn the function called and the json object in the following format:\n```\n{{\n \"endpoint\": \"xxx\",\n \"input_json\":{{\n \"xxx\": \"xxx\",\n \"xxx\": \"xxx\",\n ...\n }}\n}}\n```\n\"\"\".strip()\n + \"\\n\"\n)\n\nRETRY_PROMPT = (\n \"\"\"\nHere are the function specs:\n```\n{specs_str}\n```\nHere is the input string:\n```\n{input_str}\n```\nSelect the right function called that can process the input string.\nYou need to wrap the input str into a json object, so that it could be fed into the function you selected. During wrapping, you should:\n1. modify the value of each key so that it satisfies the requirements in function specs.For example, if the type of the value should be a number, then you should modify it into a number.\n2. ignore the information that is not useful or not applicable to the function you selected.\nReturn the function called and the json object in the following format:\n```\n{{\n \"endpoint\": \"xxx\",\n \"input_json\":{{\n \"xxx\": \"xxx\",\n \"xxx\": \"xxx\",\n ...\n }}\n}}\n```\nYou have tried to call function to process the input string but failed. The output do not have enough information to answer the tool input.\nHere is the history of your trials, each element in this list means a trial:\n```\n{trial_history}\n```\nYou should firstly analyze your trial history, find the value of the key \"errors\" in the output of each trial and check whether there are any errors\nThen you may consider changing the input_json or endpoint based on the error information in your trial history, function specs and the input string.\nReturn the function called and the json object in the following format:\n```\n{{\n \"endpoint\": \"xxx\",\n \"input_json\":{{\n \"xxx\": \"xxx\",\n \"xxx\": \"xxx\",\n ...\n }}\n}}\n```\n\"\"\".strip()\n + \"\\n\"\n)\n\nSTOP_PROMPT = (\n \"\"\"\nHere are the function specs:\n```\n{specs_str}\n```\nHere is the input string:\n```\n{input_str}\n```\nHere is the output that you get from calling the API:\n```\n{api_output}\n```\nYou need to decide whether the returned_block contains valid information or not. Some returned_block may not have enough information to answer the tool input, for example, the returned_block may be empty, or return a json that says some kind of answer.\nAnswer only by 'yes' or 'no'\n\"\"\".strip()\n + \"\\n\"\n)","source_hash":"a9f3cb2981535f746e141a21c65787e0d96f07c38256d5f36333c4bf74495b22","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.custom_exceptions","uri":"program://OpenAgents/module/real_agents.plugins_agent.api_calling.custom_exceptions#L1-L16","kind":"module","name":"real_agents.plugins_agent.api_calling.custom_exceptions","path":"real_agents/plugins_agent/api_calling/custom_exceptions.py","language":"python","start_line":1,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Customize exceptions for API calling.\"\"\"\n\n\nclass ParsingError(BaseException):\n \"\"\"Error occur when parsing.\"\"\"\n\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when parsing: {message}\")\n\n\nclass APICallingError(BaseException):\n \"\"\"Error occur when calling API.\"\"\"\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when calling API: {message}\")","source_hash":"608a58a8035b81b6d94ba47a58d85d9e90eaccfae6d6292aea3e249d8a370c7e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.custom_exceptions.ParsingError","uri":"program://OpenAgents/class/real_agents.plugins_agent.api_calling.custom_exceptions.ParsingError#L4-L9","kind":"class","name":"ParsingError","path":"real_agents/plugins_agent/api_calling/custom_exceptions.py","language":"python","start_line":4,"end_line":9,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Customize exceptions for API calling.\"\"\"\n\n\nclass ParsingError(BaseException):\n \"\"\"Error occur when parsing.\"\"\"\n\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when parsing: {message}\")\n\n\nclass APICallingError(BaseException):\n \"\"\"Error occur when calling API.\"\"\"\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when calling API: {message}\")","source_hash":"608a58a8035b81b6d94ba47a58d85d9e90eaccfae6d6292aea3e249d8a370c7e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.custom_exceptions.APICallingError","uri":"program://OpenAgents/class/real_agents.plugins_agent.api_calling.custom_exceptions.APICallingError#L12-L16","kind":"class","name":"APICallingError","path":"real_agents/plugins_agent/api_calling/custom_exceptions.py","language":"python","start_line":12,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Customize exceptions for API calling.\"\"\"\n\n\nclass ParsingError(BaseException):\n \"\"\"Error occur when parsing.\"\"\"\n\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when parsing: {message}\")\n\n\nclass APICallingError(BaseException):\n \"\"\"Error occur when calling API.\"\"\"\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when calling API: {message}\")","source_hash":"608a58a8035b81b6d94ba47a58d85d9e90eaccfae6d6292aea3e249d8a370c7e","truncated":false} {"repo_id":"OpenAgents","entity_id":"py:real_agents.plugins_agent.api_calling.custom_exceptions.__init__","uri":"program://OpenAgents/function/real_agents.plugins_agent.api_calling.custom_exceptions.__init__#L14-L16","kind":"function","name":"__init__","path":"real_agents/plugins_agent/api_calling/custom_exceptions.py","language":"python","start_line":14,"end_line":16,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Customize exceptions for API calling.\"\"\"\n\n\nclass ParsingError(BaseException):\n \"\"\"Error occur when parsing.\"\"\"\n\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when parsing: {message}\")\n\n\nclass APICallingError(BaseException):\n \"\"\"Error occur when calling API.\"\"\"\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when calling API: {message}\")","source_hash":"608a58a8035b81b6d94ba47a58d85d9e90eaccfae6d6292aea3e249d8a370c7e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/memory.py","uri":"program://OpenAgents/file/backend/memory.py","kind":"file","name":"backend/memory.py","path":"backend/memory.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict, List, Union\nfrom loguru import logger\nimport json\n\nfrom backend.app import app\nfrom backend.utils.running_time_storage import get_running_time_storage\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom real_agents.adapters.memory import BaseChatMemory\n\nHUMAN_MESSAGE_KEY = \"human_message\"\nAI_MESSAGE_KEY = \"ai_message\"\n\nLOCAL = \"local\"\nDATABASE = \"database\"\n\n\nclass UserMemoryManager:\n \"\"\"A class to manage the global memory including messages, grounding_sources,\n etc. on user level\"\"\"\n\n # api_key_pool:","source_hash":"0fe91e680fc8bd0ffeac8a17494614c19d09603adc560208460a1b2e945bf60c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/app.py","uri":"program://OpenAgents/file/backend/app.py","kind":"file","name":"backend/app.py","path":"backend/app.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"import os\n\nfrom flask import Flask\nfrom flask_cors import CORS\n\napp = Flask(__name__)\ncurrent_path = os.path.abspath(__file__)\napp.config[\"UPLOAD_FOLDER\"] = os.path.dirname(current_path) + \"/data\"\nos.makedirs(app.config[\"UPLOAD_FOLDER\"], exist_ok=True)\n# Execute code locally or remotely on docker\napp.config[\"CODE_EXECUTION_MODE\"] = os.getenv(\"CODE_EXECUTION_MODE\", \"local\")\nCORS(app)","source_hash":"74af841d92b325bc36ea5bd4fa5d27588e3e8b6c329275bdb773a9dd4ec680d4","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/schemas.py","uri":"program://OpenAgents/file/backend/schemas.py","kind":"file","name":"backend/schemas.py","path":"backend/schemas.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":19,"code":"APP_TYPES = [\"copilot\", \"plugins\", \"webot\"]\nTIME_STEP = 0.035\nTIME_OUT_MAP = {\"copilot\": 90, \"plugins\": 300, \"webot\": 600}\nSTREAM_BLOCK_TYPES = [\"image\", \"echarts\"]\nSTREAM_TOKEN_TYPES = [\"tool\", \"transition\", \"execution_result\", \"error\", \"kaggle_search\", \"kaggle_connect\", \"plain\"]\nEXECUTION_RESULT_MAX_TOKENS_MAP = {\"copilot\": 1000, \"plugins\": 2000, \"webot\": 20000}\n\nHEARTBEAT_INTERVAL = 10\n\n# define error code\nUNAUTH = 401\nUNFOUND = 404\nOVERLOAD = 503\nINTERNAL = 500\nUNSUPPORTED = 403\n\n# define models which need extra continue flag\nNEED_CONTINUE_MODEL = {\"claude-v1\", \"claude-2\"}\nDEFAULT_USER_ID = \"DefaultUser\"","source_hash":"1def68d2c8a3ad4f2d1427fd0c86bae0e432420f46727729c006913977de767c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/main.py","uri":"program://OpenAgents/file/backend/main.py","kind":"file","name":"backend/main.py","path":"backend/main.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nimport warnings\nimport threading\n\nfrom backend.app import app\nfrom backend.kernel_publisher import start_kernel_publisher\nfrom backend.utils.threading import ThreadManager\nfrom backend.utils.utils import VariableRegister, init_log\nfrom backend.memory import (\n ChatMemoryManager,\n MessageMemoryManager,\n UserMemoryManager,\n)\n\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\nlogger = init_log(\n error=os.path.join(\".logging\", \"error.log\"),\n runtime=os.path.join(\".logging\", \"runtime.log\"),\n serialize=os.path.join(\".logging\", \"serialize.log\"),\n trace=os.path.join(\".logging\", \"trace.log\"),","source_hash":"cb78f6504771eb49fa7fc33a1e4a191be4382072fd45140bb74025ccee3fc9fc","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/kernel_publisher.py","uri":"program://OpenAgents/file/backend/kernel_publisher.py","kind":"file","name":"backend/kernel_publisher.py","path":"backend/kernel_publisher.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import redis\nfrom typing import Any\n\nfrom backend.utils.utils import logger\nimport os\n\nr = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n\n\n# Set the queue and pending key\nQUEUE_RUNNING = \"kernel_running_queue\"\nQUEUE_PENDING = \"kernel_pending_queue\"\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"\n\nMAX_CONCURRENT_KERNELS = 300\n\n\ndef add_job_to_pending(job: Any) -> None:\n # always add the jobn to pending","source_hash":"62281100799e5b803d89ac83e5f8c1608c4dc9fb254d8c15d7323ef39f5624a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/__init__.py","uri":"program://OpenAgents/file/backend/__init__.py","kind":"file","name":"backend/__init__.py","path":"backend/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"# flake8: noqa\n# mypy: ignore-errors\n\nfrom backend.api import (\n chat_copilot,\n chat_plugin,\n chat_webot,\n conversation,\n file,\n recommend,\n tool,\n language_model,\n webot_actions,\n webot_instructions,\n data_tools,\n data_connector,\n)","source_hash":"24d154b033ffa5fe14a531b82c1cbebc1c6ce6ed2593f2a562425d8efd5c390c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/display_streaming.py","uri":"program://OpenAgents/file/backend/display_streaming.py","kind":"file","name":"backend/display_streaming.py","path":"backend/display_streaming.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Dict, Optional, List\nimport json\nimport base64\nimport re\nimport ast\n\nimport mo_sql_parsing\nfrom pydantic import BaseModel\n\nfrom real_agents.adapters.data_model import MessageDataModel, DataModel\n\n\ndef is_json(text: str) -> bool:\n try:\n json.loads(text)\n return True\n except json.JSONDecodeError:\n return False\n\n\ndef split_text_and_code(text: str) -> List:","source_hash":"3da2bf77f09cf0ea2c9e907cd3d17d9062f8b2b1afed14121e0fa2869b1fe1da","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/utils/running_time_storage.py","uri":"program://OpenAgents/file/backend/utils/running_time_storage.py","kind":"file","name":"backend/utils/running_time_storage.py","path":"backend/utils/running_time_storage.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":14,"code":"import redis\nfrom flask import g\nimport os\n\n\ndef get_running_time_storage():\n \"\"\"Connects to redis.\"\"\"\n if \"running_time_storage\" not in g:\n g.running_time_storage = redis.Redis(host=os.getenv(\"REDIS_SERVER\"), port=6379, decode_responses=True)\n # Set maxmemory to 200MB (value is in bytes)\n g.running_time_storage.config_set(\"maxmemory\", \"500000000\")\n # Set maxmemory policy to allkeys-lru (Least Recently Used)\n g.running_time_storage.config_set(\"maxmemory-policy\", \"allkeys-lru\")\n return g.running_time_storage","source_hash":"0350157e598fdbd5d0e3e1be5d47529c59915d4c4e69ad198a2b11aa996f0501","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/utils/user_conversation_storage.py","uri":"program://OpenAgents/file/backend/utils/user_conversation_storage.py","kind":"file","name":"backend/utils/user_conversation_storage.py","path":"backend/utils/user_conversation_storage.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":16,"code":"import pymongo\nfrom flask import g\nimport os\n\ndef get_user_conversation_storage():\n \"\"\"Connects to mongodb.\"\"\"\n if \"user_conversation_storage\" not in g:\n g.user_conversation_storage = pymongo.MongoClient(\"mongodb://{0}:27017/\".format(os.getenv(\"MONGO_SERVER\")))\n return g.user_conversation_storage[\"xlang\"]\n\n\ndef close_user_conversation_storage():\n \"\"\"Closes mongodb.\"\"\"\n user_conversation_storage = g.pop(\"user_conversation_storage\", None)\n if user_conversation_storage is not None:\n user_conversation_storage[\"xlang\"].close()","source_hash":"1bd093929108a82996140d5fa6f043df654d2bc6b6981e68648026ef57eb268c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/utils/streaming.py","uri":"program://OpenAgents/file/backend/utils/streaming.py","kind":"file","name":"backend/utils/streaming.py","path":"backend/utils/streaming.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import json\nimport re\nimport struct\nimport time\nfrom typing import Any, Dict, List, Optional, Literal\nimport multiprocess\nimport requests\nfrom bs4 import BeautifulSoup\n\nfrom backend.display_streaming import DisplayStream\nfrom backend.main import logger, message_pool, threading_pool\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.utils.utils import error_rendering\nfrom backend.memory import MessageMemoryManager\nfrom backend.schemas import (\n APP_TYPES,\n TIME_OUT_MAP,\n HEARTBEAT_INTERVAL,\n STREAM_BLOCK_TYPES,\n STREAM_TOKEN_TYPES,\n EXECUTION_RESULT_MAX_TOKENS_MAP,","source_hash":"e8f3f0fec47442647204d17f934e1ed6b9a8c4b6a54628a236ffda519cdec6ec","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/utils/utils.py","uri":"program://OpenAgents/file/backend/utils/utils.py","kind":"file","name":"backend/utils/utils.py","path":"backend/utils/utils.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nimport sys\nimport base64\nfrom pathlib import Path\nfrom typing import Any, Dict, Tuple, Union\n\nimport pandas as pd\nimport tiktoken\nfrom flask import Request\nfrom sqlalchemy import create_engine\nfrom PIL import Image\nfrom loguru import logger\n\nfrom real_agents.adapters.data_model import (\n DatabaseDataModel,\n DataModel,\n ImageDataModel,\n TableDataModel,\n KaggleDataModel,\n)\nfrom real_agents.data_agent import (","source_hash":"7e22247d7c2547a3bb9a1e012b486f0d181c110e3cb96c071ec5717a95cffffe","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/utils/threading.py","uri":"program://OpenAgents/file/backend/utils/threading.py","kind":"file","name":"backend/utils/threading.py","path":"backend/utils/threading.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Python program using\n# traces to kill threads\nfrom typing import Dict, Tuple, Optional\nfrom multiprocess import Process\n\n\nclass ThreadManager:\n \"\"\"Manager class of all user chat threads.\"\"\"\n\n def __init__(self) -> None:\n self.thread_pool: Dict[str, Process] = {}\n self.stop_pool: Dict[str, bool] = {}\n self.timeout_pool: Dict[str, bool] = {}\n self.run_error_pool: Dict[str, Optional[str]] = {}\n\n def register_thread(self, chat_id, thread: Process) -> None:\n self.thread_pool[chat_id] = thread\n self.stop_pool[chat_id] = False\n self.timeout_pool[chat_id] = False\n self.run_error_pool[chat_id] = None\n","source_hash":"bbbbc6e5b5b740c73c0f56b0137a2a55161eac3ecddc879cff55071a38550ede","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/utils/charts.py","uri":"program://OpenAgents/file/backend/utils/charts.py","kind":"file","name":"backend/utils/charts.py","path":"backend/utils/charts.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import json\n\n\ndef polish_echarts(echarts_str):\n \"\"\"Polishes the echarts output into prettier format.\"\"\"\n try:\n option = json.loads(echarts_str)\n\n # turn numeric axis into str\n category_flag = False\n for idx, series_data in enumerate(option[\"series\"]):\n if series_data[\"type\"] in [\"bar\", \"line\"]:\n category_flag = True\n break\n if category_flag:\n option[\"xAxis\"][0][\"data\"] = [str(_) for _ in option[\"xAxis\"][0][\"data\"]]\n for idx, series_data in enumerate(option[\"series\"]):\n try:\n option[\"series\"][idx][\"data\"] = [[str(_[0]), _[1]] for _ in series_data[\"data\"]]\n except:\n continue","source_hash":"91e6c4a6004302f90171eae2c027a1e0a3ff6a40eb67620d08c0e38b3caf6c0f","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/chat_webot.py","uri":"program://OpenAgents/file/backend/api/chat_webot.py","kind":"file","name":"backend/api/chat_webot.py","path":"backend/api/chat_webot.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from time import sleep\nimport copy\nimport redis\nimport json\nimport pickle\nimport traceback\nfrom flask import Response, request, stream_with_context\nfrom typing import Dict, Union\nimport os\n\nfrom langchain.schema import HumanMessage, SystemMessage\n\nfrom backend.api.language_model import get_llm\nfrom backend.main import app, message_id_register, message_pool, logger\nfrom backend.utils.streaming import single_round_chat_with_agent_streaming\nfrom backend.schemas import OVERLOAD, NEED_CONTINUE_MODEL\nfrom backend.schemas import DEFAULT_USER_ID\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks.agent_streaming import \\\n AgentStreamingStdOutCallbackHandler","source_hash":"b87766c817ebca23aa2766615d571171518fcd4773839201bd91ef73696c9edb","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/chat_plugin.py","uri":"program://OpenAgents/file/backend/api/chat_plugin.py","kind":"file","name":"backend/api/chat_plugin.py","path":"backend/api/chat_plugin.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import base64\nimport copy\nimport json\nimport os\nimport random\nimport traceback\nfrom typing import Dict, List, Union\n\nimport requests\nfrom flask import Response, request, stream_with_context\nfrom retrying import retry\n\nfrom backend.api.language_model import get_llm\nfrom backend.app import app\nfrom backend.main import message_id_register, message_pool, logger\nfrom backend.utils.streaming import single_round_chat_with_agent_streaming\nfrom backend.schemas import OVERLOAD, NEED_CONTINUE_MODEL, DEFAULT_USER_ID\nfrom backend.main import api_key_pool\nfrom real_agents.adapters.llm import BaseLanguageModel\nfrom real_agents.adapters.agent_helpers import AgentExecutor, Tool\nfrom real_agents.adapters.callbacks.agent_streaming import \\","source_hash":"e978353ac293a1fe8f487a2be607a5802d8fd65fde23d3b7da44dcf8f30c6fd9","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/conversation.py","uri":"program://OpenAgents/file/backend/api/conversation.py","kind":"file","name":"backend/api/conversation.py","path":"backend/api/conversation.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import struct\nimport json\nimport datetime\nfrom typing import Any, Generator\nfrom bson.objectid import ObjectId\nfrom flask import jsonify, request, Response\n\nfrom backend.app import app\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.main import threading_pool, logger\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.schemas import INTERNAL, UNFOUND\n\n\n@app.route(\"/api/conversations/get_conversation_list\", methods=[\"POST\"])\ndef get_conversation_list() -> Response:\n \"\"\"Gets the history conversations.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n conversations = []\n try:","source_hash":"675d2f4378f5e62135d6007780aba2de464f9bc7cd01e0e51e563f35884ff52b","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/data_connector.py","uri":"program://OpenAgents/file/backend/api/data_connector.py","kind":"file","name":"backend/api/data_connector.py","path":"backend/api/data_connector.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nfrom flask import request, Response\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n\nfrom backend.app import app\nfrom backend.utils.utils import create_personal_folder\nfrom backend.schemas import UNFOUND, INTERNAL, DEFAULT_USER_ID\n\napi = KaggleApi()\napi.authenticate()\n\n\n@app.route(\"/api/kaggle/download_dataset\", methods=[\"POST\"])\ndef kaggle_dataset_download() -> dict | Response:\n \"\"\"Use Kaggle-api to connect. \"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n url = request_json[\"url\"]\n if url.startswith(\"http\"):\n return {\"success\": False,\n \"message\": \"Please remove the http in your submitted URL.\"}","source_hash":"ce22f7578d02a5fdba3330b6a37a70536b751353f6f20dbff2a520c796dd7067","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/chat_copilot.py","uri":"program://OpenAgents/file/backend/api/chat_copilot.py","kind":"file","name":"backend/api/chat_copilot.py","path":"backend/api/chat_copilot.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import traceback\nfrom typing import Dict, List, Union\nfrom flask import Response, request, stream_with_context, Response\n\nfrom backend.api.file import _get_file_path_from_node\nfrom backend.api.language_model import get_llm\nfrom backend.app import app\nfrom backend.main import (\n grounding_source_pool,\n jupyter_kernel_pool,\n logger,\n message_id_register,\n message_pool,\n)\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.utils.utils import create_personal_folder\nfrom backend.utils.charts import polish_echarts\nfrom backend.utils.streaming import (\n single_round_chat_with_executor,\n single_round_chat_with_agent_streaming,\n)","source_hash":"0f8c7c6312238d9899d14cc2e2b090c6990d9b189cbb728d4a1900b4eaf05407","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/webot_actions.py","uri":"program://OpenAgents/file/backend/api/webot_actions.py","kind":"file","name":"backend/api/webot_actions.py","path":"backend/api/webot_actions.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from flask import request, jsonify, Response\n\nfrom backend.api.chat_webot import get_webot_from_redis, save_webot_to_redis\nfrom backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.language_model import get_llm\n\n\n@app.route(\"/api/webot/action\", methods=[\"POST\"])\ndef get_action() -> Response:\n \"\"\"Gets the next action to take for a given the current page HTML.\"\"\"\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n # Get request parameters\n request_json = request.get_json()\n processed_html = request_json[\"processed_html\"]\n llm = get_llm(\"gpt-4\")\n result = webot.run(processed_html, llm=llm)\n save_webot_to_redis(user_id=user_id, chat_id=chat_id, webot=webot)","source_hash":"390bd768c07e3791453ef84e5dd08122b3bbbe6c1b941db336ed6e5b08fb457d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/language_model.py","uri":"program://OpenAgents/file/backend/api/language_model.py","kind":"file","name":"backend/api/language_model.py","path":"backend/api/language_model.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\n\nfrom backend.app import app\nfrom real_agents.adapters.models import ChatOpenAI, ChatAnthropic\nfrom real_agents.adapters.llm import BaseLanguageModel\n\nLLAMA_DIR = \"PATH_TO_LLAMA_DIR\"\n\n\n@app.route(\"/api/llm_list\", methods=[\"POST\"])\ndef get_llm_list():\n \"\"\"Gets the whole llm list.\"\"\"\n return [\n {\"id\": llm, \"name\": llm} for llm in [\n \"gpt-3.5-turbo-16k\",\n \"gpt-4\",\n \"claude-v1\",\n \"claude-2\",\n \"lemur-chat\"\n ]\n ]","source_hash":"3f74dec87a124d3e466ae8340b95db7dd011dc44070dd12603a2db80b00ecf0d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/webot_instructions.py","uri":"program://OpenAgents/file/backend/api/webot_instructions.py","kind":"file","name":"backend/api/webot_instructions.py","path":"backend/api/webot_instructions.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from flask import request, jsonify, Response\n\nfrom backend.main import app\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.api.chat_webot import get_webot_from_redis, \\\n get_webot_status_from_redis, reset_webot_status\n\n\n@app.route(\"/api/webot/instructions\", methods=[\"POST\"])\ndef get_instruction() -> Response:\n request_json = request.get_json()\n user_id = request_json.pop(\"user_id\", DEFAULT_USER_ID)\n chat_id = request_json[\"chat_id\"]\n webot = get_webot_from_redis(user_id=user_id, chat_id=chat_id)\n return jsonify({\n \"chat_id\": chat_id,\n \"user_id\": user_id,\n \"instructions\": webot.instruction\n })\n\n","source_hash":"ce61740baa6cd8cc7b367c48fb54842362936ff624c8002a0cbad2c0afa33042","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/recommend.py","uri":"program://OpenAgents/file/backend/api/recommend.py","kind":"file","name":"backend/api/recommend.py","path":"backend/api/recommend.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Dict\nfrom flask import request, jsonify, Response\n\nfrom backend.main import message_pool\nfrom backend.app import app\nfrom backend.api.language_model import get_llm\nfrom backend.utils.utils import get_user_and_chat_id_from_request_json\nfrom real_agents.adapters.executors import QuestionSuggestionExecutor\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\n\n\n@app.route(\"/api/recommend\", methods=[\"POST\"])\ndef recommend() -> dict | Response:\n \"\"\"Recommends potential inputs for users. \"\"\"\n try:\n request_json = request.get_json()\n (user_id, chat_id) = get_user_and_chat_id_from_request_json(request_json)\n parent_message_id = int(request_json[\"parent_message_id\"])\n user_intent = request_json[\"user_intent\"]\n\n # Find the mainstat message list from leaf to root","source_hash":"263720024e29b26d363dcb715fe9664affdc43d5801020a87dbd0e34eb33c825","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/data_tools.py","uri":"program://OpenAgents/file/backend/api/data_tools.py","kind":"file","name":"backend/api/data_tools.py","path":"backend/api/data_tools.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import List\nfrom flask import jsonify\n\nfrom backend.app import app\n\nDATA_TOOLS = [\n {\n \"type\": \"language\",\n \"id\": \"1cea1f39-fe63-4b08-83d5-fa4c93db0c87\",\n \"name\": \"SQLQueryBuilder\",\n \"name_for_human\": \"SQL\",\n \"pretty_name_for_human\": \"SQL Query Generation\",\n \"icon\": \"\",\n \"description\": \"Using SQL as the programming language\",\n },\n {\n \"type\": \"language\",\n \"id\": \"0c135359-af7e-473b-8425-1393d2943b57\",\n \"name\": \"PythonCodeBuilder\",\n \"name_for_human\": \"Python\",\n \"pretty_name_for_human\": \"Python Code Generation\",","source_hash":"a4b856cdf01dded89c275e86bfc969e0d68c01d68c554697eed6594321131eb6","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/tool.py","uri":"program://OpenAgents/file/backend/api/tool.py","kind":"file","name":"backend/api/tool.py","path":"backend/api/tool.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from flask import request, jsonify, Response\n\nfrom backend.api.chat_plugin import plugins\nfrom backend.main import app, api_key_pool\nfrom backend.schemas import DEFAULT_USER_ID\n\n@app.route(\"/api/tool_list\", methods=[\"POST\"])\ndef get_tool_list() -> Response:\n \"\"\"parameters:\n {\n user_id: id of the user\n }\n return value:\n [{\n id: id of a plugin,\n name: name pf a plugin,\n description: description of the plugin,\n icon: icon of the plugin,\n require_api_key: whether the plugin requires api_key,\n api_key: the api key of the plugin, None if no api key\n }]","source_hash":"1e2273167cc833b32727d1fc339d71e241ecbc63f44c6c26c7d843fb6fe6e6d0","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:backend/api/file.py","uri":"program://OpenAgents/file/backend/api/file.py","kind":"file","name":"backend/api/file.py","path":"backend/api/file.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import json\nimport os\nimport shutil\nfrom typing import Dict, Any\nfrom flask import Response, jsonify, request, send_file\n\nfrom backend.app import app\nfrom backend.main import (\n grounding_source_pool,\n logger,\n message_id_register,\n message_pool,\n)\nfrom backend.schemas import DEFAULT_USER_ID\nfrom backend.utils.utils import create_personal_folder\nfrom backend.utils.user_conversation_storage import get_user_conversation_storage\nfrom backend.utils.utils import (\n allowed_file,\n get_data_model_cls,\n get_user_and_chat_id_from_request,\n get_user_and_chat_id_from_request_json,","source_hash":"9bb5f7e08de9a8552e99eefa36cf15729e4d4c563c22c5f7c321c6328d41f2e0","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/copilot_prompt.py","uri":"program://OpenAgents/file/real_agents/data_agent/copilot_prompt.py","kind":"file","name":"real_agents/data_agent/copilot_prompt.py","path":"real_agents/data_agent/copilot_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# flake8: noqa\n\nPREFIX = \"\"\"You are XLang Agent , a friendly and intuitive interface developed by the XLang Team to guide human through every stage of human data lifecycle. Whether human are loading, processing, or interpreting data, XLang Agent is always at human's fingertips through our interactive chat system.\n\nEmpowered by an array of innovative tools that can generate and execute code, XLang Agent delivers robust, reliable answers to human queries. Whenever possible, You employs these tools to give human rich insights, like dynamic code generation & execution and compelling visualizations. And You will always proactively and correctly using all tools to help with human.\n\nGet ready for a seamless and insightful journey with XLang Agent, the personal assistant for all things data!\n\nTOOLS\n------\nYou have direct access to following tools. \n\"\"\"\n\n\nFORMAT_INSTRUCTIONS = \"\"\"RESPONSE FORMAT INSTRUCTIONS\n----------------------------\n\nWhen you use tools or generate final answer, please output a response in one of two formats:\n**Option 1: Explain and Use Tool**\nIf the response involves using a tool, you can start with a natural language explanation[Optional], plus exactly one tool calling[MUST]. But **make sure no any words & answer appended after tool calling json**. The tool calling format should be a markdown code snippet with the following JSON schema:\n","source_hash":"5b25c668d0669d0127c5e65cf87b4fab85a6f09297c335c85c6fd51e2c4ef491","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/__init__.py","uri":"program://OpenAgents/file/real_agents/data_agent/__init__.py","kind":"file","name":"real_agents/data_agent/__init__.py","path":"real_agents/data_agent/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"from real_agents.data_agent.copilot import ConversationalChatAgent\nfrom real_agents.data_agent.evaluation.python_evaluator import PythonEvaluator\nfrom real_agents.data_agent.evaluation.sql_evaluator import SQLEvaluator\nfrom real_agents.data_agent.executors.code_generation_executor import CodeGenerationExecutor\nfrom real_agents.data_agent.executors.data_summary_executor import (\n DataSummaryExecutor,\n TableSummaryExecutor,\n ImageSummaryExecutor,\n)\nfrom real_agents.data_agent.executors.kaggle_data_loading_executor import KaggleDataLoadingExecutor\nfrom real_agents.data_agent.python.base import PythonChain\nfrom real_agents.data_agent.sql.base import SQLDatabaseChain\nfrom real_agents.adapters.schema import SQLDatabase","source_hash":"ec62675630f11ff0ba87f5b76cc680d9a811c63d5f1eb2327399e2a066e656b2","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/copilot.py","uri":"program://OpenAgents/file/real_agents/data_agent/copilot.py","kind":"file","name":"real_agents/data_agent/copilot.py","path":"real_agents/data_agent/copilot.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"An agent designed to hold a conversation in addition to using tools.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, List, Optional, Sequence, Tuple, Union\nfrom typing_extensions import override\nfrom pydantic import Field\n\nfrom langchain.agents.agent import AgentOutputParser\nfrom langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, Callbacks\nfrom langchain.chains import LLMChain\nfrom langchain.schema import AgentAction, AgentFinish, HumanMessage, AIMessage, BaseMessage, BaseOutputParser\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers.agent import Agent\nfrom real_agents.adapters.agent_helpers.output_parser import ConversationOutputParser\nfrom real_agents.data_agent.copilot_prompt import PREFIX, SUFFIX, TEMPLATE_TOOL_RESPONSE, fake_continue_prompt\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\nfrom langchain.prompts import (\n BasePromptTemplate,","source_hash":"25eaa33a16934f585e80aa52ae2f9a764978db835a07822e9944e88b69fe7a00","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/evaluation/python_evaluator.py","uri":"program://OpenAgents/file/real_agents/data_agent/evaluation/python_evaluator.py","kind":"file","name":"real_agents/data_agent/evaluation/python_evaluator.py","path":"real_agents/data_agent/evaluation/python_evaluator.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nfrom typing import Any, List, Optional, Tuple, Dict\nfrom pydantic import BaseModel\nimport requests\nimport time\nimport ast\n\nimport pandas as pd\nfrom io import StringIO\nimport redis\nfrom loguru import logger\n\nfrom IPython.core.interactiveshell import InteractiveShell\nfrom IPython.core.getipython import get_ipython\nfrom IPython.utils.capture import capture_output\n\n\n# subscribed channels\nSUBMIT_EVENT = \"job_submitted\"\nRUNNING_EVENT = \"job_started\"\nCOMPLETE_EVENT = \"job_completed\"","source_hash":"8115701b0aab09b633a2d1efc52c6d9f290c18b0eea62e401341aeda39a7628a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/evaluation/sql_evaluator.py","uri":"program://OpenAgents/file/real_agents/data_agent/evaluation/sql_evaluator.py","kind":"file","name":"real_agents/data_agent/evaluation/sql_evaluator.py","path":"real_agents/data_agent/evaluation/sql_evaluator.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import traceback\nfrom typing import Any, Dict, List\n\nfrom pydantic import root_validator\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass SQLEvaluator:\n \"\"\"\n Util class for SQL code evaluation.\n \"\"\"\n\n name = \"SQL Evaluator\"\n ERROR_PREFIX = \"[ERROR]: \"\n\n @root_validator(pre=True)\n def validate(cls, values: Dict) -> Any:\n \"\"\"validate requirements for evaluation\"\"\"\n try:\n import sqlite3 # noqa F401 E402","source_hash":"60a1492973ed959092ef08e38f5ef9f3fcfd1e54fb2e12695146e025a85b62bd","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/executors/kaggle_data_loading_executor.py","uri":"program://OpenAgents/file/real_agents/data_agent/executors/kaggle_data_loading_executor.py","kind":"file","name":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","path":"real_agents/data_agent/executors/kaggle_data_loading_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import json\nimport os\nimport re\nimport shutil\nimport uuid\nfrom typing import Any, Dict, List, Tuple\nimport requests\nfrom bs4 import BeautifulSoup\nfrom loguru import logger\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass KaggleDataLoadingExecutor:\n KAGGLE_TEMPLATE = \"\"\"\n\nDetermine whether the user input aims to (1) connect to a specific kaggle dataset that the user mentions its kaggle path","source_hash":"16a6f55c9aa717de5c7c6ab0cc4337d1818dc76488d5fb83936d2f584b6ede43","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/executors/code_generation_executor.py","uri":"program://OpenAgents/file/real_agents/data_agent/executors/code_generation_executor.py","kind":"file","name":"real_agents/data_agent/executors/code_generation_executor.py","path":"real_agents/data_agent/executors/code_generation_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict, List, Literal, Optional, Union\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.adapters.schema import SQLDatabase\nfrom real_agents.data_agent.python.base import PythonChain\nfrom real_agents.data_agent.sql.base import SQLDatabaseChain\n\n\nclass CodeGenerationExecutor:\n \"\"\"Code Generation Executor.\n\n Example:\n .. code-block:: python\n\n from real_agents.adapters.executors import CodeGenerationExecutor\n executor = CodeGenerationExecutor(programming_language=\"sql\")\n executor.run(\n user_intent=\"What is the name of the first employee?\",","source_hash":"b314aaeba498be047ab88b06eca616c66bb6f204989c85c8bb11211c63a0da5d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/executors/data_summary_executor.py","uri":"program://OpenAgents/file/real_agents/data_agent/executors/data_summary_executor.py","kind":"file","name":"real_agents/data_agent/executors/data_summary_executor.py","path":"real_agents/data_agent/executors/data_summary_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict, Tuple, Union\nfrom abc import ABC\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain import PromptTemplate\n\nfrom real_agents.adapters.callbacks.executor_streaming import ExecutorStreamingChainHandler\nfrom real_agents.adapters.data_model import DatabaseDataModel, TableDataModel, ImageDataModel\nfrom real_agents.adapters.llm import LLMChain\n\n\nclass DataSummaryExecutor(ABC):\n tool_name = \"DataProfiling\"\n\n def _intelligent_summary(self, grounding_source: ImageDataModel, num_insights: int, llm: BaseLanguageModel) -> str:\n \"\"\"Use LLM to generate data summary.\"\"\"\n pass\n\n\nclass TableSummaryExecutor(DataSummaryExecutor):\n SUMMARY_PROMPT_TEMPLATE = \"\"\"","source_hash":"c9295c373fdb09af9fde1fd890c5d5c3c631b1771ea8b863d9a85f6647d241a8","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/sql/base.py","uri":"program://OpenAgents/file/real_agents/data_agent/sql/base.py","kind":"file","name":"real_agents/data_agent/sql/base.py","path":"real_agents/data_agent/sql/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Chain for interacting with SQL Database.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional\nfrom pydantic import BaseModel, Extra, Field\nfrom loguru import logger\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain import BasePromptTemplate, FewShotPromptTemplate\n\nfrom real_agents.data_agent.evaluation.sql_evaluator import SQLEvaluator\nfrom real_agents.adapters.schema import SQLDatabase\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory\nfrom real_agents.data_agent.sql.prompt import (\n EXAMPLE_PROMPT,\n FEW_SHOT_INPUT_VARIABLES,\n FEW_SHOT_PREFIX,\n FEW_SHOT_SUFFIX,\n PROMPT,","source_hash":"d1c062ea5fbc74aef059565fe87cb883f268a1ceba0e37d5529b062ee082c0cb","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/sql/prompt.py","uri":"program://OpenAgents/file/real_agents/data_agent/sql/prompt.py","kind":"file","name":"real_agents/data_agent/sql/prompt.py","path":"real_agents/data_agent/sql/prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# flake8: noqa\nfrom langchain import PromptTemplate\n\n# Text-to-sql prompt\n_DEFAULT_TEMPLATE = \"\"\"Here are chat histories you may refer to, maybe empty.\n{chat_history}\n\nGiven an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\nPay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, remember to wrap the table names in double quotes.\nUse the following format:\nQuestion: \"Question here\"\nSQLQuery: \"SQL Query to run\"\nSQLResult: \"Result of the SQLQuery\"\nAnswer: \"Final answer here\"\nOnly use the tables listed below.\n{table_info}\nQuestion: {question}\"\"\"\nPROMPT = PromptTemplate(\n input_variables=[\"chat_history\", \"question\", \"table_info\", \"dialect\"],\n template=_DEFAULT_TEMPLATE,","source_hash":"7386a46be013ba5170bfc65af5fea5b13ba7c0e684bb8e4d45841d4781146cf7","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/python/base.py","uri":"program://OpenAgents/file/real_agents/data_agent/python/base.py","kind":"file","name":"real_agents/data_agent/python/base.py","path":"real_agents/data_agent/python/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Implements Python Code Generation. \"\"\"\nfrom __future__ import annotations\n\nimport re\nfrom typing import Any, Dict, List, Optional\n\nfrom bs4 import BeautifulSoup\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n)\nfrom langchain.schema import SystemMessage\nfrom loguru import logger\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.adapters.data_model import MessageDataModel\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory","source_hash":"91cc59a50596e6d52194423895674c9c3120583751e6be20e74d69b288ee476f","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/python/echarts_prompt.py","uri":"program://OpenAgents/file/real_agents/data_agent/python/echarts_prompt.py","kind":"file","name":"real_agents/data_agent/python/echarts_prompt.py","path":"real_agents/data_agent/python/echarts_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"ECHARTS_REF_CODE = \"\"\"Here are some examples of generating Py-Echarts Code based on the given table(s). Please generate new one based on the data and question human asks you, import the neccessary libraries and make sure the code is correct.\n\nIMPORTANT: You need to follow the coding style, and the type of the x, y axis. But also need to focus on the column name of the uploaded tables(if exists). Generally, PyEcharts does not accept numpy.int or numpy.float, etc. It only supports built-in data type like int, float, and str.\n\nGiven the following database:\ncompany_sales.xlsx\n year sales profit expenses employees\n0 2010 100 60 40 10\n1 2011 120 80 50 12\n2 2012 150 90 60 14\n3 2013 170 120 70 16\n[too long to show]\n\nQ: Could you help plot a bar chart with the year on the x-axis and the sales on the y-axis?\n\nimport pandas as pd\nfrom pyecharts.charts import Bar\nfrom pyecharts import options as opts\ndf = pd.read_excel('company_sales.xlsx')\nyears = [str(_) for _ in df['year'].tolist()]\nsales = [float(_) for _ in df['sales'].tolist()]","source_hash":"c3be57a5ba2c298c6726f10f732d54c04fe0cf5c850b50172a3b020412516dc0","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/python/system_prompt.py","uri":"program://OpenAgents/file/real_agents/data_agent/python/system_prompt.py","kind":"file","name":"real_agents/data_agent/python/system_prompt.py","path":"real_agents/data_agent/python/system_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"FUNCTION_ROLE_PLAY = \"\"\"def generate_continuous_elegant_python_code(history_dict: Dict[str, str], reference_code: str = \"\") -> str:\n \\\"\\\"\\\"\n This function generates elegant, coherent Python code based on a history of previously executed code and its corresponding results. The code is generated in response to human questions and is intended to continue from the last provided code snippet.\n\n The function takes two inputs: a `history_dict` and an optional `reference_code` string.\n\n The `history_dict` is a dictionary with the following keys:\n - 'history code': Contains the chat history of previously executed code snippets. It may be initially empty but will accumulate executed code over time.\n - 'human question': Contains the current question or instruction posed by the human user, which the generated code should respond to. Be aware that sometimes the 'human question' could contain code snippets, including instructions for loading data, which may need to be handled differently. It's not always appropriate to directly use the code in 'human question' without consideration.\n - 'data': Contains a list of data previews available for the task. It may include tables, images, and other data types.\n\n The `reference_code` string is optional and contains example codes, often related to a specific library or task, which can serve as a template for the code generation process. This parameter can be empty.\n\n IMPORTANT: Always refer to this history and the `reference_code` when generating new code in order to properly use existing variables and previously loaded resources, as well as to follow established coding patterns. DO NOT USE ECHARTS TO GENERATE CHARTS when reference code is empty.\n\n IMPORTANT: When `reference_code` is NOT EMPTY, the output MUST follow the style and use the libraries presented in the `reference_code` to accomplish the task.\n\n IMPORTANT: Avoid mere repetition of historical code. Always aim to generate novel and appropriate responses to the questions at hand.\n\n IMPORTANT: The 'data' key in the dictionary contains only random rows from a table. If a table has not been loaded before, load it from the correct path. You can assume it is in the current working directory. However, there's no need to load a table with every execution - only do this when necessary.\n","source_hash":"d4b999c9e7206b85b946c372c120a9ad5047d502c2da29b8e690a4b2207b8a63","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/data_agent/python/python_prompt.py","uri":"program://OpenAgents/file/real_agents/data_agent/python/python_prompt.py","kind":"file","name":"real_agents/data_agent/python/python_prompt.py","path":"real_agents/data_agent/python/python_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":20,"code":"USER_PROMPT = \"\"\"\nhistory_code = \\\"\\\"\\\"{history_code}\\\"\\\"\\\"\nhuman_question = \\\"\\\"\\\"{question}\n# DO NOT use function that will pop up a new window (e.g., PIL & Image.show() is NOT preferable, saving the PIL image is better)\n# However, feel free to use matplotlib.pyplot.show()\\\"\\\"\\\"\ndata = \\\"\\\"\\\"{data}\\\"\\\"\\\"\nreference_code = \\\"\\\"\\\"{reference_code}\\\"\\\"\\\"\n\nhistory_dict = {{\n \"history code\": history_code,\n \"human question\": human_question,\n \"data\": data,\n \"reference_code\": reference_code,\n}}\n\"\"\"\n\n\"\"\"\nfinal format:\nuser_prompt + reference_prompt + history_prompt\n\"\"\"","source_hash":"93ca116f2c1030468f8fb8c424f4b052ec0887963ec6ca789b80c71f9d34d4a6","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/__init__.py","uri":"program://OpenAgents/file/real_agents/web_agent/__init__.py","kind":"file","name":"real_agents/web_agent/__init__.py","path":"real_agents/web_agent/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":6,"code":"from real_agents.web_agent.webot import ConversationalWebotChatAgent\nfrom real_agents.web_agent.executors.webot_executor import WebotExecutor\nfrom real_agents.web_agent.executors.web_browsing_executor import WebBrowsingExecutor\nfrom real_agents.web_agent.web_browsing.base import WebotCallingChain\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.web_agent.web_browsing.react.base import ReActWebotChain","source_hash":"f68e758a48d740bc969611f090f0fe20aaaa51bc76373d1bd78a521030d57331","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/webot_prompt.py","uri":"program://OpenAgents/file/real_agents/web_agent/webot_prompt.py","kind":"file","name":"real_agents/web_agent/webot_prompt.py","path":"real_agents/web_agent/webot_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# flake8: noqa\nimport datetime\n\nPREFIX = (\n \"\"\"You are XLanG WeBot Agent, a friendly and intuitive assistant developed by the XLang Team to guide you through every aspects of your work and your daily life. XLanG Agent is always at your fingertips through our interactive chat system.\nHere are detailed instruction for you. Each time you generate response, you should think step by step to follow instructions below. You are a helpful assistant that is provided with a plugin called \"WeBot\" which is a web navigation agent tool and should leverage the power of it to help human to fulfill their needs, such as booking a hotel, buying a ticket, or searching for information, etc.\nHuman will ask you questions, and you can use WeBot to help them, they are assumed to know nothing about the WeBot.\n----------------------------\nHere are something you MUST remember:\n1. After receiving output from the WeBot, you should check \n 1.1 whether WeBot was interrupted, if so you should NEVER try again by yourself.\n 1.2 whether WeBot failed or had error(not because of interruption), if so you should tell the human the error.\n2. Today is\n\"\"\".strip() + \" \"\n + datetime.datetime.now().strftime(\"%Y-%m-%d\")\n + \"\"\", and you should adapt the input to fit into the date, for example, seasonal information, or today's date as coordinate, etc.\n\nNEVER EVER EVER use other plugins except WeBot.\nTRY YOUR BEST to break the question down into several parts and answer them one by one.\nTRY YOUR BEST to use the WeBot to help you answer the question, you don't need to mention that you will use which WeBot, just use it.\n","source_hash":"7bfa95c65875f0e0aea6d5f1d51b7f1a99e64f1c5ffa55d30c49d3171d2866a9","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/webot.py","uri":"program://OpenAgents/file/real_agents/web_agent/webot.py","kind":"file","name":"real_agents/web_agent/webot.py","path":"real_agents/web_agent/webot.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"An agent designed to hold a conversation in addition to using tools. (Specially designed for plugins model)\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra, Field\nfrom typing_extensions import override\n\nfrom langchain.agents.agent import AgentOutputParser\nfrom langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains import LLMChain\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n AIMessage,\n BaseMessage,\n BaseOutputParser,\n HumanMessage\n)\n\nfrom langchain.callbacks.manager import (","source_hash":"6259d6058efa9a4ef4ea97738a9a49abbb03b733aa7724d98e97034a19d1134d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/executors/web_browsing_executor.py","uri":"program://OpenAgents/file/real_agents/web_agent/executors/web_browsing_executor.py","kind":"file","name":"real_agents/web_agent/executors/web_browsing_executor.py","path":"real_agents/web_agent/executors/web_browsing_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nImplementation of the WebBrowsingExecutor.\nWebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n\"\"\"\nfrom typing import Any, Dict, List\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.web_agent.web_browsing.react.base import ReActWebotChain\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.adapters.data_model.html import HTMLDataModel\n\n\n# This executor is for the extension usage and not for the chat interface.\n# For the chat interface webot executor, refer xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebBrowsingExecutor:\n \"\"\"\n WebBrowsingExecutor takes start_url and instruction as input, iteratively perform the actions on the web, and return the result.\n \"\"\"\n\n def __init__(self, instruction: str, plan: str = \"\", mode: str = \"react\") -> None:","source_hash":"caac7aef86d2a0aa86216347eedfd03e1c7a539597a872ca014a1ffece056dbd","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/executors/webot_executor.py","uri":"program://OpenAgents/file/real_agents/web_agent/executors/webot_executor.py","kind":"file","name":"real_agents/web_agent/executors/webot_executor.py","path":"real_agents/web_agent/executors/webot_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nImplementation of the WebotExecutor class.\nWebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin\n\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, Union\n\nfrom langchain.base_language import BaseLanguageModel\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.web_agent.web_browsing.base import WebotCallingChain\n\n\n# This executor is for chat interface usage and not for the extension usage.\n# For the extension usage, refer to xlang/real_agents/web_agent/executors/web_browsing_executor.py\nclass WebotExecutor(BaseModel):\n \"\"\"\n WebotExecutor takes user's intent as input, return the start_url and instruction as the input for web browsing plugin (tool).\n \"\"\"\n name: str","source_hash":"eabd7c917f901b6e7c6b6bbdb373749139c0c240219d3e3bd7f517135c8b8672","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/base.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/base.py","kind":"file","name":"real_agents/web_agent/web_browsing/base.py","path":"real_agents/web_agent/web_browsing/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Implementation of the base webot calling chain.\"\"\"\nfrom __future__ import annotations\n\nimport re\nimport traceback\nfrom typing import Dict, List, Optional\n\nimport json5\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains import LLMChain\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\nfrom pydantic import BaseModel, Extra\n\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory","source_hash":"cb70fc6408d02120691af7ed45bbfe3f0fa494c02fd8e59a3574ef275ddfba1d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/prompt.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/prompt.py","kind":"file","name":"real_agents/web_agent/web_browsing/prompt.py","path":"real_agents/web_agent/web_browsing/prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"SYSTEM_PROMPT = (\n \"\"\"You are an expert at browsing website and you know a lot of website in case the user didn't explicitly mention the website they would like to search.\"\"\".strip()\n + \"\\n\"\n)\n\nUSER_PROMPT = (\n \"\"\"\nHere is the user's intent:\n```\n{input_str}\n```\nNow imagine you can use a tool called web agent to navigate on the web for you.\nyou should act as a user to tell the web agent instruction and the url to start\nthen it will take in the instruction and start at the url to navigate on the web.\nRemember:\n1. If you know the detailed url to take the action, you may set this as the start url. e.g. https://www.twitter.com/compose/tweet for writing a tweet on twitter\n2. If you don't know the detailed url, you may set the start url as the homepage of the website. e.g. https://www.imdb.com/ for movie related question\n3. If you are not sure whether the homepage will contain info that you need. Use https://www.google.com/ as the start url instead.\nHere is an example for your reference:\nthe user intent is to write a blog post on medium\nyou should out put like this:","source_hash":"ed35e4013ee78958ba89e7f744c7501f28e80b6196b3e40517968625f3864a30","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/schema.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/schema.py","kind":"file","name":"real_agents/web_agent/web_browsing/schema.py","path":"real_agents/web_agent/web_browsing/schema.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":15,"code":"# The schema (action space) for the web browsing task is defined here:\nACTIONS = [\n {\n \"name\": \"click\",\n \"description\": \"Clicks on an element\",\n \"args\": [{\"name\": \"elementId\", \"type\": \"number\"}],\n },\n {\n \"name\": \"setValue\",\n \"description\": \"Focuses on and sets the `value` of an input element.\",\n \"args\": [{\"name\": \"elementId\", \"type\": \"number\"}, {\"name\": \"value\", \"type\": \"string\"}],\n },\n {\"name\": \"finish\", \"description\": \"Indicates the task is finished\", \"args\": []},\n {\"name\": \"fail\", \"description\": \"Indicates that you are unable to complete the task\", \"args\": []},\n]","source_hash":"b6a0c2860b310b20bd5fc9d1079f8348f1fb926305443aedc023e031522e47fc","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/end2end/base.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/end2end/base.py","kind":"file","name":"real_agents/web_agent/web_browsing/end2end/base.py","path":"real_agents/web_agent/web_browsing/end2end/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Implementation for prompt based end2end web bots.\"\"\"\nfrom __future__ import annotations\n\nimport datetime\nimport re\nfrom typing import Any, Dict, List, Optional\nfrom loguru import logger\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n)\n\nfrom real_agents.adapters.memory import ReadOnlySharedStringMemory","source_hash":"abbbf6bc368803ce99e378555f9ca2e0b99ac600f898597187c51d7196ae68a3","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/end2end/prompt.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/end2end/prompt.py","kind":"file","name":"real_agents/web_agent/web_browsing/end2end/prompt.py","path":"real_agents/web_agent/web_browsing/end2end/prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"SYSTEM_PROMPT = (\n \"\"\"\nYou are a browser automation assistant.\n\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n{formattedActions}\n\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n\nclick(223)\n\nYou MUST always include the and open/close tags or else your response will be marked as invalid.\n\nRules you MUST follow:\n1. You must only take one step at a time. You cannot take multiple actions in a single response.\n2. You should not consider the action to present the result to the user. You only need to do available actions. If info in current page is enough for the user to solve the problem, you should finish.\n\"\"\".strip()\n + \"\\n\"","source_hash":"94ae720dc1423d5f5eb4c122ccf149e719b0e53c36f1ff09fdd5d0e2d6cdd9ba","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/react/base.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/react/base.py","kind":"file","name":"real_agents/web_agent/web_browsing/react/base.py","path":"real_agents/web_agent/web_browsing/react/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Implementation for prompt based react web bots.\"\"\"\nfrom __future__ import annotations\n\nimport datetime\nimport re\nfrom typing import Any, Dict, List, Optional, Tuple\nfrom loguru import logger\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.web_agent.web_browsing.end2end.base import WebotChain\nfrom real_agents.web_agent.web_browsing.react.prompt import (\n RETRY_PROMPT,\n SYSTEM_PROMPT,\n USER_PROMPT,\n)\n\nclass ReActWebotChain(WebotChain):\n \"\"\"Basic prompt based web bot that interact with websites.\"\"\"","source_hash":"2d4c87b062bbe3223b38ca0a3acab83d8f92db9b5047c3f145efda75768f1263","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/web_agent/web_browsing/react/prompt.py","uri":"program://OpenAgents/file/real_agents/web_agent/web_browsing/react/prompt.py","kind":"file","name":"real_agents/web_agent/web_browsing/react/prompt.py","path":"real_agents/web_agent/web_browsing/react/prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"SYSTEM_PROMPT = (\n \"\"\"\nYou are a browser automation assistant.\n\nYou will be given a user request and DOM of current webpage at a time, you need to take one action at a time and finally finish the task.\n\nThe last page you visited will be further fed into another model who is responsible for chatting with the user.\n\nYou MUST take one of the following actions. NEVER EVER EVER make up actions that do not exist:\n\n{formattedActions}\n\nYou will be be given a task to perform and the current state of the DOM. You will also be given previous actions that you have taken. You may retry a failed action up to one time.\n\nThis is an example of an action:\n\nI should click the add to cart button\nclick(223)\n\nYou MUST always include the and open/close tags or else your response will be marked as invalid.\n","source_hash":"c7f3a70226e6a0aef1b6f543f8d83c1f62a134fd8ccedbfc943bd6e893903bde","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/interactive_executor.py","uri":"program://OpenAgents/file/real_agents/adapters/interactive_executor.py","kind":"file","name":"real_agents/adapters/interactive_executor.py","path":"real_agents/adapters/interactive_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nfrom typing import Any, Optional, Sequence\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.agent_helpers import AgentExecutor\nfrom real_agents.data_agent.copilot import ConversationalChatAgent\nfrom real_agents.plugins_agent.plugin import ConversationalPluginChatAgent\nfrom real_agents.web_agent.webot import ConversationalWebotChatAgent\n\n\ndef initialize_agent(\n tools: Sequence[BaseTool],\n llm: BaseLanguageModel,\n continue_model: str = None,\n agent_kwargs: Optional[dict] = None,\n return_intermediate_steps: Optional[bool] = True,\n **kwargs: Any,\n) -> AgentExecutor:","source_hash":"4219fd5523ca138b0865dbfc217a0baf38a14375caa49a08322d2bd70117ae3c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/llm.py","uri":"program://OpenAgents/file/real_agents/adapters/llm.py","kind":"file","name":"real_agents/adapters/llm.py","path":"real_agents/adapters/llm.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Chain that just formats a prompt and calls an LLM.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import (\n AsyncCallbackManager,\n AsyncCallbackManagerForChainRun,\n CallbackManager,\n CallbackManagerForChainRun,\n Callbacks,\n)\nfrom langchain.chains.base import Chain\nfrom langchain.input import get_colored_text\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.prompt import PromptTemplate\nfrom langchain.schema import LLMResult, PromptValue\n\nfrom real_agents.adapters.data_model import DataModel","source_hash":"d88b7dd07ba96f392f8b97eed1a77179104e91e5693d49c70acfaa4697ad6e0b","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/schema.py","uri":"program://OpenAgents/file/real_agents/adapters/schema.py","kind":"file","name":"real_agents/adapters/schema.py","path":"real_agents/adapters/schema.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import NamedTuple\nfrom langchain import SQLDatabase\nfrom sqlalchemy import text\nfrom sqlalchemy.engine import Row\nfrom tabulate import tabulate\nfrom typing import List, Any\n\n\nclass AgentTransition(NamedTuple):\n \"\"\"Agent's transition to take.\"\"\"\n\n return_values: dict\n log: str\n\n\nEMPTY_RESULT_STR = \"NONE\" # to show NONE result in front-end.\n\n\nclass SQLDatabase(SQLDatabase):\n @staticmethod\n def _pretty_format(headers: Any, result: List[Row]) -> str:","source_hash":"8fcb728f7ffa2664eaf39b277e38caa2d1dca83f033ac5777f8b1c3ed74b5a2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/callbacks/streaming_stdout.py","uri":"program://OpenAgents/file/real_agents/adapters/callbacks/streaming_stdout.py","kind":"file","name":"real_agents/adapters/callbacks/streaming_stdout.py","path":"real_agents/adapters/callbacks/streaming_stdout.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.schema import AgentAction, AgentFinish, LLMResult\n\n\nclass StreamingStdOutCallbackHandler(BaseCallbackHandler):\n \"\"\"Callback handler for streaming. Only works with LLMs that support streaming.\"\"\"\n\n def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:\n \"\"\"Run when LLM starts running.\"\"\"\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n sys.stdout.write(token)\n sys.stdout.flush()\n\n def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n \"\"\"Run when LLM ends running.\"\"\"","source_hash":"691f6384373afab694b6144fc4c3847c8cfceacc974c7b102c39432ca0feca92","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/callbacks/executor_streaming.py","uri":"program://OpenAgents/file/real_agents/adapters/callbacks/executor_streaming.py","kind":"file","name":"real_agents/adapters/callbacks/executor_streaming.py","path":"real_agents/adapters/callbacks/executor_streaming.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"from typing import Any\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\n\nclass ExecutorStreamingChainHandler(StreamingStdOutCallbackHandler):\n is_end: bool = False\n _all = []\n\n @property\n def always_verbose(self) -> bool:\n \"\"\"Whether to call verbose callbacks even if verbose is False.\"\"\"\n return True\n\n def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n \"\"\"\"\"\"\n self._all.append(token)","source_hash":"d51017979877ca0bbcb9a00ef8a76511cf490b547657c04a0f672380e6b8cb2b","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/callbacks/base.py","uri":"program://OpenAgents/file/real_agents/adapters/callbacks/base.py","kind":"file","name":"real_agents/adapters/callbacks/base.py","path":"real_agents/adapters/callbacks/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Base callback handler that can be used to handle callbacks in langchain.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Dict, List, Optional, Union\nfrom uuid import UUID\n\nfrom langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult\n\n\nclass LLMManagerMixin:\n \"\"\"Mixin for LLM callbacks.\"\"\"\n\n def on_llm_new_token(\n self,\n token: str,\n *,\n run_id: UUID,\n parent_run_id: Optional[UUID] = None,\n **kwargs: Any,\n ) -> Any:\n \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"","source_hash":"ad3ac21af60922f598dd355f20b653f68b3a70f79ba96d4105bbb35c84049fad","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/callbacks/agent_streaming.py","uri":"program://OpenAgents/file/real_agents/adapters/callbacks/agent_streaming.py","kind":"file","name":"real_agents/adapters/callbacks/agent_streaming.py","path":"real_agents/adapters/callbacks/agent_streaming.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Callback Handler streams to stdout on new llm token.\"\"\"\nfrom typing import Any, Dict, List, Union\n\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n\nfrom real_agents.adapters.data_model import DataModel\n\n\nclass JSON_PDA:\n def __init__(self):\n self.stack = []\n self.state = \"start\"\n self.json = {}\n self.current_key = \"\"\n self.current_value = \"\"\n self.escape_next = False\n\n def transition(self, char):\n if self.escape_next:\n # Add the escaped character to the current key or value and return\n if self.state == \"open_key_quote\":","source_hash":"a5ef63a7351a9d89086bf8c79967c5013ba1e2d14f4f727ea1bd94ade6b58a94","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/callbacks/manager.py","uri":"program://OpenAgents/file/real_agents/adapters/callbacks/manager.py","kind":"file","name":"real_agents/adapters/callbacks/manager.py","path":"real_agents/adapters/callbacks/manager.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nimport asyncio\nimport functools\nimport logging\nimport os\nimport warnings\nfrom contextlib import contextmanager\nfrom contextvars import ContextVar\nfrom typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast\nfrom uuid import UUID, uuid4\n\nimport langchain\nfrom langchain.callbacks.base import (\n BaseCallbackHandler,\n BaseCallbackManager,\n ChainManagerMixin,\n LLMManagerMixin,\n RunManagerMixin,\n ToolManagerMixin,\n)","source_hash":"d4970d0dfcfe24d6ebb74a1c20c24bf4b04fa57c7264bf87f161983277e165b7","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/callbacks/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/callbacks/__init__.py","kind":"file","name":"real_agents/adapters/callbacks/__init__.py","path":"real_agents/adapters/callbacks/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":4,"code":"from real_agents.adapters.callbacks.agent_streaming import JSON_PDA, AgentStreamingStdOutCallbackHandler\nfrom real_agents.adapters.callbacks.base import BaseCallbackHandler, BaseCallbackManager, AsyncCallbackHandler\nfrom real_agents.adapters.callbacks.executor_streaming import ExecutorStreamingChainHandler\nfrom real_agents.adapters.callbacks.manager import CallbackManager, CallbackManagerForChainRun","source_hash":"c32ec0caccf5e10f4513a38d64a425b65667a944eedab885a4ce8bb1060860ea","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/models/base.py","uri":"program://OpenAgents/file/real_agents/adapters/models/base.py","kind":"file","name":"real_agents/adapters/models/base.py","path":"real_agents/adapters/models/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import asyncio\nimport inspect\nimport warnings\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Dict, List, Mapping, Optional, Sequence\n\nimport langchain\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.base import BaseCallbackManager\nfrom langchain.callbacks.manager import (\n AsyncCallbackManager,\n AsyncCallbackManagerForLLMRun,\n CallbackManager,\n CallbackManagerForLLMRun,\n Callbacks,\n)\nfrom langchain.schema import (\n BaseMessage,\n ChatGeneration,\n ChatResult,\n HumanMessage,","source_hash":"e93bbd9a407a6a5513002b4b87995408ad02068eb6a9badbdf4826321eda235a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/models/anthropic.py","uri":"program://OpenAgents/file/real_agents/adapters/models/anthropic.py","kind":"file","name":"real_agents/adapters/models/anthropic.py","path":"real_agents/adapters/models/anthropic.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict, List, Optional\nfrom pydantic import Extra\n\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForLLMRun,\n CallbackManagerForLLMRun,\n)\nfrom langchain.chat_models.base import BaseChatModel\nfrom langchain.llms.anthropic import _AnthropicCommon\nfrom langchain.schema import (\n AIMessage,\n BaseMessage,\n ChatGeneration,\n ChatMessage,\n ChatResult,\n HumanMessage,\n SystemMessage,\n)\n\n\nclass ChatAnthropic(BaseChatModel, _AnthropicCommon):","source_hash":"7cc1cc6c09960a3d85cde949b741b667f5859bd194793a817ca161e98d3e24da","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/models/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/models/__init__.py","kind":"file","name":"real_agents/adapters/models/__init__.py","path":"real_agents/adapters/models/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":16,"code":"from langchain.chat_models.google_palm import ChatGooglePalm\n\nfrom real_agents.adapters.models.anthropic import ChatAnthropic\nfrom real_agents.adapters.models.openai import ChatOpenAI\n\n__all__ = [\n \"ChatOpenAI\",\n \"ChatAnthropic\",\n \"ChatGooglePalm\",\n]\n\ntype_to_cls_dict = {\n \"chat_anthropic\": ChatAnthropic,\n \"chat_google_palm\": ChatGooglePalm,\n \"chat_openai\": ChatOpenAI,\n}","source_hash":"e983d9b84e1204aed1d9afb2bc5fcf6183ad92838c2780def6a789850d59b01e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/models/openai.py","uri":"program://OpenAgents/file/real_agents/adapters/models/openai.py","kind":"file","name":"real_agents/adapters/models/openai.py","path":"real_agents/adapters/models/openai.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"OpenAI chat wrapper.\"\"\"\nfrom __future__ import annotations\n\nimport logging\nimport sys\nfrom typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union\nfrom pydantic import Extra, Field, root_validator\nfrom tenacity import (\n before_sleep_log,\n retry,\n retry_if_exception_type,\n stop_after_attempt,\n wait_exponential,\n)\n\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForLLMRun,\n CallbackManagerForLLMRun,\n)\nfrom langchain.chat_models.base import BaseChatModel\nfrom langchain.schema import (","source_hash":"a46cea032f8f70e607732ec075e78a069d43c089ac99d04146451ad0407155f0","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/memory/read_only_string_memory.py","uri":"program://OpenAgents/file/real_agents/adapters/memory/read_only_string_memory.py","kind":"file","name":"real_agents/adapters/memory/read_only_string_memory.py","path":"real_agents/adapters/memory/read_only_string_memory.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict, List\n\nfrom langchain.schema import BaseMemory\n\n\nclass ReadOnlySharedStringMemory(BaseMemory):\n \"\"\"A memory wrapper that is read-only and cannot be changed.\"\"\"\n\n memory: BaseMemory\n\n @property\n def memory_variables(self) -> List[str]:\n \"\"\"Return memory variables.\"\"\"\n return self.memory.memory_variables\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n \"\"\"Load memory variables from memory.\"\"\"\n prev_memory_state = self.memory.return_messages\n self.memory.return_messages = False\n memory_string = self.memory.load_memory_variables(inputs)\n self.memory.return_messages = prev_memory_state","source_hash":"6e099bb72fbe01b4bd2f84367ac8fe67928527488aab4a88c4e9cc8785960f80","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/memory/buffer.py","uri":"program://OpenAgents/file/real_agents/adapters/memory/buffer.py","kind":"file","name":"real_agents/adapters/memory/buffer.py","path":"real_agents/adapters/memory/buffer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict, List, Optional, Tuple\nfrom pydantic import root_validator\n\nfrom langchain.memory.utils import get_prompt_input_key\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import BaseMessage, get_buffer_string\nfrom langchain.memory.chat_memory import BaseChatMemory, BaseMemory\n\nfrom real_agents.adapters.data_model import DataModel, MessageDataModel\n\n\nclass ConversationBufferMemory(BaseChatMemory):\n \"\"\"Buffer for storing conversation memory.\"\"\"\n\n human_prefix: str = \"Human\"\n ai_prefix: str = \"AI\"\n memory_key: str = \"history\" #: :meta private:\n\n @property\n def buffer(self) -> Any:\n \"\"\"String buffer of memory.\"\"\"","source_hash":"f6b4883e56461e1f5f31b7d71c74031a28492d674d45d36fc31656f9bc9c5b37","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/memory/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/memory/__init__.py","kind":"file","name":"real_agents/adapters/memory/__init__.py","path":"real_agents/adapters/memory/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Dict, List, Type\n\nfrom langchain.schema import BaseMemory\nfrom langchain.memory.chat_memory import BaseChatMemory\n\nfrom real_agents.adapters.memory.buffer import (\n ConversationBufferMemory,\n ConversationStringBufferMemory,\n)\nfrom real_agents.adapters.memory.read_only_string_memory import ReadOnlySharedStringMemory\nfrom real_agents.adapters.memory.buffer import ConversationReActBufferMemory\n\n\n\n__all__ = [\n \"ConversationBufferMemory\",\n \"ConversationReActBufferMemory\",\n \"ConversationStringBufferMemory\",\n \"BaseMemory\",\n \"BaseChatMemory\",\n \"ReadOnlySharedStringMemory\",","source_hash":"a386278191021ada37ae24ea0a08d0c2064df5dea4b839b05559085059896a15","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/message.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/message.py","kind":"file","name":"real_agents/adapters/data_model/message.py","path":"real_agents/adapters/data_model/message.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import re\nimport textwrap\nfrom typing import List, Dict, Any, Optional\nfrom langchain.schema import BaseMessage\nimport tiktoken\n\n# format of agent action\nACTION_FORMAT = \"\"\"```json\n{{\n \"action\": \"{_action}\",\n \"action_input\": \"{_action_input}\",\n}}\n```\"\"\"\n\n# format of tool call(code) & tool output(response)\nTOOL_FORMAT = {\n \"code\": \"\"\"\n{_intermediate_steps}\n\n\n","source_hash":"61a0dc74241d0bb3b43c6469a6468e1af2a17ac77b0ee725aafcbd31e763e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/base.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/base.py","kind":"file","name":"real_agents/adapters/data_model/base.py","path":"real_agents/adapters/data_model/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nimport uuid\nfrom typing import Any\n\nfrom pydantic import BaseModel\n\n\nclass DataModel(BaseModel):\n \"\"\"Base class for data models.\"\"\"\n\n id: str\n raw_data: Any\n raw_data_name: str\n raw_data_path: str\n llm_side_data: Any # could be string or potentially images for future needs\n human_side_data: Any\n\n def __hash__(self) -> int:\n return hash(self.id)\n","source_hash":"f4c04c63ca8f17e801d0edb1180d7023aebe52c7837fa02e6ca487204eb1a9dc","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/html.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/html.py","kind":"file","name":"real_agents/adapters/data_model/html.py","path":"real_agents/adapters/data_model/html.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import json\nfrom bs4 import BeautifulSoup\nfrom collections import defaultdict\nfrom typing import Any, Dict, List, Union\nfrom real_agents.adapters.data_model.base import DataModel\nimport requests\nimport re\nimport tiktoken\n\nJsonNode = Dict[str, Union[str, List[Any], int]]\nPossibleTemplate = Dict[str, Union[str, List[Any], int]]\nOptimizedTemplate = Dict[str, Union[str, List[Any], int, set]]\nPossibleTemplates = Dict[str, PossibleTemplate]\n\n\ndef find_potential_templates(node, possible_templates):\n \"\"\"Find all potential templates in the HTML tree.\"\"\"\n if node.name: # Element node\n attributes = {attr: node[attr] for attr in node.attrs}\n children = []\n for child in node.children:","source_hash":"c5c51bc4ee0a48fb6823fe89e4e4100f3882ceb7bea187892c8d7306058c3e2a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/database.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/database.py","kind":"file","name":"real_agents/adapters/data_model/database.py","path":"real_agents/adapters/data_model/database.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nimport os\nfrom typing import Any\n\nimport pandas as pd\nfrom sqlalchemy import create_engine, inspect\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.table import TableDataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass DatabaseDataModel(DataModel):\n \"\"\"A data model for database.\"\"\"\n\n @classmethod\n def from_table_data_model(cls, table_data_model: TableDataModel) -> DatabaseDataModel:\n os.makedirs(f\".db_cache/{table_data_model.id}\", exist_ok=True)\n db_path = os.path.join(","source_hash":"daf6587203ff2471ac41a4848392015a439dac584fb34badd044279f13623870","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/__init__.py","kind":"file","name":"real_agents/adapters/data_model/__init__.py","path":"real_agents/adapters/data_model/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.database import DatabaseDataModel\nfrom real_agents.adapters.data_model.image import ImageDataModel\nfrom real_agents.adapters.data_model.json import JsonDataModel\nfrom real_agents.adapters.data_model.message import MessageDataModel\nfrom real_agents.adapters.data_model.kaggle import KaggleDataModel\nfrom real_agents.adapters.data_model.plugin import APIYamlModel, SpecModel\nfrom real_agents.adapters.data_model.table import TableDataModel\n\n__all__ = [\n \"DataModel\",\n \"TableDataModel\",\n \"DatabaseDataModel\",\n \"ImageDataModel\",\n \"JsonDataModel\",\n \"KaggleDataModel\",\n \"APIYamlModel\",\n \"SpecModel\",\n \"MessageDataModel\",\n \"HTMLDataModel\",\n]","source_hash":"323856642617807226649d8759ed96e8cc520c1227c9c8f4c6b71c7a7823d7b3","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/utils.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/utils.py","kind":"file","name":"real_agents/adapters/data_model/utils.py","path":"real_agents/adapters/data_model/utils.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":2,"code":"def indent_multiline_string(multiline_string: str, indent: int = 1) -> str:\n return \"\\n\".join(\"\\t\" * indent + line for line in multiline_string.split(\"\\n\"))","source_hash":"d5702b5bcd10e6b7985d17b0e0a913e4e52ed5045ea93994c5ba748f7ea0a3f1","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/text.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/text.py","kind":"file","name":"real_agents/adapters/data_model/text.py","path":"real_agents/adapters/data_model/text.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":9,"code":"from real_agents.adapters.data_model.base import DataModel\n\n\nclass TextDataModel(DataModel):\n \"\"\"A data model for text, general purpose.\"\"\"\n\n def get_llm_side_data(self, max_chars: int = 5000) -> str:\n assert isinstance(self.raw_data, str)\n return self.raw_data[:max_chars]","source_hash":"54af962e1cd4a83217ea02a2068c3e45c3b1d9e54e6993bd5e8e4743e93015d1","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/table.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/table.py","kind":"file","name":"real_agents/adapters/data_model/table.py","path":"real_agents/adapters/data_model/table.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nimport json\nfrom typing import Any\n\nfrom pandas import DataFrame\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\n\n\nclass TableDataModel(DataModel):\n \"\"\"A data model for table.\"\"\"\n\n db_view: DataModel = None\n\n def set_db_view(self, db_data_model: DataModel) -> None:\n self.db_view = db_data_model\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:\n # Show the first few rows for observation.","source_hash":"1119eb474799340ba94789dc81259a6eb267ec7d7040b2fd897a30e3060ffda5","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/json.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/json.py","kind":"file","name":"real_agents/adapters/data_model/json.py","path":"real_agents/adapters/data_model/json.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import json\nfrom copy import deepcopy\nfrom typing import Dict, List\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass JsonDataModel(DataModel):\n \"\"\"A data model for json, general purpose.\"\"\"\n\n filter_keys: List[str] = []\n\n def get_llm_side_data(self, json_format: str = \"json\") -> str:\n if json_format == \"json\":\n assert isinstance(self.raw_data, Dict)\n llm_side_data = deepcopy(self.raw_data)\n for key, value in self.raw_data.items():\n if key in self.filter_keys:\n llm_side_data[key] = \"...\"\n continue\n","source_hash":"9637032777ecb623c69f596505ff900c488dd0ed03c88c959d3548e31842a256","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/kaggle.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/kaggle.py","kind":"file","name":"real_agents/adapters/data_model/kaggle.py","path":"real_agents/adapters/data_model/kaggle.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nfrom typing import Any, Dict\n\nimport pandas as pd\n\nfrom real_agents.adapters.data_model.base import DataModel\nfrom real_agents.adapters.data_model.templates.skg_templates.database_templates import serialize_db\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import serialize_df\nimport json\n\n\nclass KaggleDataModel(DataModel):\n \"\"\"A data model for KaggleDataModel.\n We only support the csv and sqlite format for now.\n raw_data is a Dict[str, TableDataModel]\n raw_data_path is List[str]\n raw_data_name is Dict[str, str]\n \"\"\"\n\n def get_llm_side_data(self, serialize_method: str = \"tsv\", num_visible_rows: int = 3) -> Any:","source_hash":"6832dc4b9f8a780eae9eeee72222498271da9973af41a6e894ff56b860b89050","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/image.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/image.py","kind":"file","name":"real_agents/adapters/data_model/image.py","path":"real_agents/adapters/data_model/image.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any\n\nfrom real_agents.adapters.data_model.base import DataModel\n\n\nclass ImageDataModel(DataModel):\n \"\"\"A data model for image.\"\"\"\n\n simple_filename = \"\"\n\n def get_raw_data(self) -> Any:\n return self.raw_data\n\n def get_llm_side_data(self) -> Any:\n if self.simple_filename == \"\":\n import os\n\n self.simple_filename = os.path.basename(self.raw_data_path)\n string = \"image: \" + self.simple_filename\n return string\n","source_hash":"1bacb2e976e325e2d0fa2e4d612593edb97d83fccabdcf0217bbfbc69e9eb302","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/plugin/spec.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/plugin/spec.py","kind":"file","name":"real_agents/adapters/data_model/plugin/spec.py","path":"real_agents/adapters/data_model/plugin/spec.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nfrom typing import Any, Dict\nimport importlib.util\nimport tiktoken\n\nfrom real_agents.adapters.data_model.plugin.base import APIYamlModel\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\ndef import_function_from_file(filepath, function_name):\n spec = importlib.util.spec_from_file_location(\"module.name\", filepath)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module)\n\n function = getattr(module, function_name)\n\n return function\n\n\ndef process_one_param(param_dict: Dict[str, Any]) -> str:\n name = param_dict.get(\"name\", None)","source_hash":"ec79a3ee66e6f691aeaae8aeb2a2b54b6ced5145c3d2aa190f827a981cd3f4cf","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/plugin/base.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/plugin/base.py","kind":"file","name":"real_agents/adapters/data_model/plugin/base.py","path":"real_agents/adapters/data_model/plugin/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nimport json\nimport os\nfrom typing import Any, Dict\n\nimport yaml\nfrom prance import ResolvingParser\nfrom pydantic import BaseModel\n\n# get the absolute path of the current file\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass APIYamlModel(BaseModel):\n info: Dict\n\n @classmethod\n def from_yaml(cls, yaml_path: str) -> APIYamlModel:\n return cls(info=APIYamlModel.yaml_to_json(yaml_path))\n","source_hash":"1b1c14af237cd28d9f0bf05d645d861f22cd8b63cebc301462928361fb300076","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/plugin/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/plugin/__init__.py","kind":"file","name":"real_agents/adapters/data_model/plugin/__init__.py","path":"real_agents/adapters/data_model/plugin/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"from real_agents.adapters.data_model.plugin.spec import APIYamlModel, SpecModel","source_hash":"47886e430e632a08d8821fec828918a054aabd68d0949a4a6ac1e2c153aff567","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/plugin/newsapi/everything.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/plugin/newsapi/everything.py","kind":"file","name":"real_agents/adapters/data_model/plugin/newsapi/everything.py","path":"real_agents/adapters/data_model/plugin/newsapi/everything.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":10,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"][\"articles\"] = input_json[\"out\"][\"articles\"][:5]\n return input_json","source_hash":"3f09a5fbb7ea28a5623babe922123749f0b531906a3b84b5bd8beac8609893a6","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/plugin/biztoc/search_news.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/plugin/biztoc/search_news.py","kind":"file","name":"real_agents/adapters/data_model/plugin/biztoc/search_news.py","path":"real_agents/adapters/data_model/plugin/biztoc/search_news.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":18,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"] = input_json[\"out\"][:5]\n extracted_keys = [\n \"body\",\n \"title\",\n \"created\",\n \"url\",\n \"tags\",\n ]\n input_json[\"out\"] = [{k: r[k] for k in extracted_keys if k in r} for r in input_json[\"out\"]]\n return input_json","source_hash":"da872220f52400906a3c0cc980e5ed52f36612fb5770ce7b8b58a7f558f1cde7","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/plugin/wanted_job_search/search_global.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/plugin/wanted_job_search/search_global.py","kind":"file","name":"real_agents/adapters/data_model/plugin/wanted_job_search/search_global.py","path":"real_agents/adapters/data_model/plugin/wanted_job_search/search_global.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"from copy import deepcopy\nfrom typing import Any, Dict\n\n\ndef convert(_input_json: Dict[str, Any]) -> Dict[str, Any]:\n input_json = deepcopy(_input_json)\n assert isinstance(input_json[\"out\"], list)\n\n input_json[\"out\"] = input_json[\"out\"][:5]\n\n for i, job in enumerate(input_json[\"out\"]):\n cleaned_job_item = input_json[\"out\"][i]\n del cleaned_job_item[\"id\"]\n del cleaned_job_item[\"created\"]\n input_json[\"out\"][i] = cleaned_job_item\n\n return input_json","source_hash":"54d58347abb813f550bfee90ff54fce50a4e1335bd8e161a9d1a91071b6fb3b1","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/templates/skg_templates/table_templates.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/templates/skg_templates/table_templates.py","kind":"file","name":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","path":"real_agents/adapters/data_model/templates/skg_templates/table_templates.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import subprocess\nimport sys\nfrom copy import deepcopy\nfrom typing import Any, Dict, Union\n\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport tiktoken\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(\n table_data: Union[pd.DataFrame, Dict[str, Any]], table_name: str = \"table\", visible_rows_num: int = 3\n) -> Dict[str, str]:\n \"\"\"\n Convert table data to string representations in different formats.\n\n :param table_data: A dictionary with \"cols\" (list of strings) and \"rows\"\n (list of lists of strings) as keys.\n :param table_name: The name of the table.","source_hash":"a99f5d3406d3f6f344298f5530fc91c57b70262d95b7245c6b4a7826260ba78d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/templates/skg_templates/database_templates.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/templates/skg_templates/database_templates.py","kind":"file","name":"real_agents/adapters/data_model/templates/skg_templates/database_templates.py","path":"real_agents/adapters/data_model/templates/skg_templates/database_templates.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import sqlite3\nfrom typing import Dict, Union\n\nimport pandas as pd\nimport tiktoken\n\n\nfrom real_agents.adapters.data_model.templates.skg_templates.table_templates import (\n convert as convert_table,\n)\nfrom real_agents.adapters.schema import SQLDatabase\n\n\ndef convert(db_input: Union[str, Dict[str, pd.DataFrame]], visible_rows_num: int = 3) -> Dict[str, str]:\n \"\"\"\n Convert database data to string representations in different formats.\n\n :param db_input: the path to the sqlite database file, or a pd.DataFrame.\n :param visible_rows_num: the number of rows to be displayed in each table.\n :return: A dictionary with the string database representations in different formats.\n \"\"\"","source_hash":"5341e373080d2974e900e97bad0610dcf75ea450b7542cc75b4b2a927032871a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","uri":"program://OpenAgents/file/real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","kind":"file","name":"real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","path":"real_agents/adapters/data_model/templates/skg_templates/knowledge_graph_templates.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import subprocess\nimport sys\nfrom typing import Dict, List, Tuple\n\n\ndef convert(kg_input: List[Tuple], name_space: str = \"\") -> Dict[str, str]:\n \"\"\"\n Convert knowledge graph data to string representations in different formats.\n\n :param kg_input: the list of knowledge graph triples.\n :param name_space: of the knowledge graph.\n :return: A dictionary with the string knowledge graph representations in different formats.\n \"\"\"\n\n def install_required_packages() -> None:\n packages = [\"rdflib\", \"rdflib-jsonld\"]\n\n for package in packages:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n # Call the function to install the required packages","source_hash":"5d9e3d58bbe95d3d47840d56ea85f0a6194cc4c57f432ad1cf1b942d057bb9ea","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/base.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/base.py","kind":"file","name":"real_agents/adapters/executors/base.py","path":"real_agents/adapters/executors/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":10,"code":"from abc import ABC, abstractmethod\nfrom typing import Any, Dict, Optional\n\nfrom real_agents.adapters.schema import SQLDatabase\n\n\nclass BaseExecutor(ABC):\n @abstractmethod\n def run(self, user_intent: str, grounding_source: Optional[SQLDatabase]) -> Dict[str, Any]:\n \"\"\"Run the executor.\"\"\"","source_hash":"9c6a48a6c4d5038792b1387868571b2a4188032eef384fde4b9283e1455985b9","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/chat_executor.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/chat_executor.py","kind":"file","name":"real_agents/adapters/executors/chat_executor.py","path":"real_agents/adapters/executors/chat_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.prompts import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\nfrom langchain.chains import ConversationChain\n\nfrom real_agents.adapters.executors.base import BaseExecutor\nfrom real_agents.adapters.memory import ConversationBufferMemory\n\n\nclass ChatExecutor(BaseExecutor):\n \"\"\"Chat Executor.\"\"\"\n\n _DEFAULT_TEMPLATE = \"The following is a friendly conversation between a human and an AI. \\\n The AI is talkative and provides lots of specific details from its context. \\\n If the AI does not know the answer to a question, it truthfully says it does not know.\"","source_hash":"32ba2516f69e7e7ed64264232f16f9d5c18d0874870d1f7fc06f5cf90024e51a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/__init__.py","kind":"file","name":"real_agents/adapters/executors/__init__.py","path":"real_agents/adapters/executors/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":7,"code":"from real_agents.adapters.executors.base import BaseExecutor\nfrom real_agents.adapters.executors.chat_executor import ChatExecutor\nfrom real_agents.adapters.executors.question_suggestion.question_suggestion_executor import (\n QuestionSuggestionExecutor,\n QuestionSuggestionChainChatMemory,\n QuestionSuggestionChainBase,\n)","source_hash":"630f481f68f197ac99777c14ea8fd7f4c5cf694d113777dbf6144ad7da5ffd05","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/question_suggestion/chat_memory.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/question_suggestion/chat_memory.py","kind":"file","name":"real_agents/adapters/executors/question_suggestion/chat_memory.py","path":"real_agents/adapters/executors/question_suggestion/chat_memory.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY\n\n\nclass QuestionSuggestionChainChatMemory(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainChatMemory:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_CHAT_MEMORY)\n return cls(llm_chain=llm_chain)","source_hash":"d30cd4754b287bb9573920c6929414cf41112828cc5fe6c6d6785a486aabfa80","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/question_suggestion/base.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/question_suggestion/base.py","kind":"file","name":"real_agents/adapters/executors/question_suggestion/base.py","path":"real_agents/adapters/executors/question_suggestion/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nfrom typing import Dict, List, Optional\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains.base import Chain\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_BASE\n\n\nclass QuestionSuggestionChainBase(Chain, BaseModel):\n \"\"\"Question Suggestion by Language Models.\"\"\"\n\n llm_chain: LLMChain\n output_key: str = \"questions\"\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"","source_hash":"4b5fc3a67ac5eaf85baeb7041ebebe81de95fef49cad9824ba3ddea1b2718bc1","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/question_suggestion/user_profile.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/question_suggestion/user_profile.py","kind":"file","name":"real_agents/adapters/executors/question_suggestion/user_profile.py","path":"real_agents/adapters/executors/question_suggestion/user_profile.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":15,"code":"from __future__ import annotations\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains.llm import LLMChain\n\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.prompts import QUESTION_SUGGESTION_PROMPT_USER_PROFILE\n\n\nclass QuestionSuggestionChainUserProfile(QuestionSuggestionChainBase):\n @classmethod\n def from_prompt(cls, llm: BaseLanguageModel) -> QuestionSuggestionChainUserProfile:\n \"\"\"Load from user profile prompt.\"\"\"\n llm_chain = LLMChain(llm=llm, prompt=QUESTION_SUGGESTION_PROMPT_USER_PROFILE)\n return cls(llm_chain=llm_chain)","source_hash":"309949ef2d126a8a35edd88f80ef37e7f640d8a05a202c7fa0fe6f63960ed9b6","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/question_suggestion/prompts.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/question_suggestion/prompts.py","kind":"file","name":"real_agents/adapters/executors/question_suggestion/prompts.py","path":"real_agents/adapters/executors/question_suggestion/prompts.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from langchain import PromptTemplate\n\ntemplate_base = (\n \"{input_string}\\nPlease provide {num_questions} natural language questions related to the above contents, \"\n \"but very different from each other. These questions should be diverse, challenging, \"\n \"and targeted towards different perspectives. \"\n \"You should ask these questions like you would ask a human, \"\n \"but strictly follow the style of your role-playing character.\\n\"\n \"Do not explicitly mention the provided contents; \"\n \"instead use natural language descriptions for them. \"\n \"The final result should be a numbered list.\".strip() + \"\\n\"\n)\n\nQUESTION_SUGGESTION_PROMPT_BASE = PromptTemplate(\n input_variables=[\"input_string\", \"num_questions\"], template=template_base\n)\n\ntemplate_user_profile = (\n \"{input_string}\\n--------------------\\n\"\n \"{user_description}\\n\"\n \"From now on, you should speak in a style that fully conforms to the given role. \\n\"","source_hash":"e41d54786895dc71f9d40174dd0f5ba1338660e6554811a5ff970ce8090da2df","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","uri":"program://OpenAgents/file/real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","kind":"file","name":"real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","path":"real_agents/adapters/executors/question_suggestion/question_suggestion_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from typing import Any, Dict\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.schema import AIMessage, HumanMessage\n\nfrom real_agents.adapters.memory import ConversationReActBufferMemory\nfrom real_agents.adapters.executors.question_suggestion.chat_memory import QuestionSuggestionChainChatMemory\nfrom real_agents.adapters.executors.question_suggestion.base import QuestionSuggestionChainBase\nfrom real_agents.adapters.executors.question_suggestion.user_profile import QuestionSuggestionChainUserProfile\n\n\nclass QuestionSuggestionExecutor:\n def run(\n self,\n user_intent: str,\n llm: BaseLanguageModel,\n num_questions: int = 3,\n mode: str = \"\",\n user_profile: str = \"\",\n chat_memory: ConversationReActBufferMemory = ConversationReActBufferMemory(),\n ) -> Dict[str, Any]:","source_hash":"4ab05b3161254739021aa7a1ad92a2290bbdea3addf67f61a571713b6d427982","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/agent_helpers/agent.py","uri":"program://OpenAgents/file/real_agents/adapters/agent_helpers/agent.py","kind":"file","name":"real_agents/adapters/agent_helpers/agent.py","path":"real_agents/adapters/agent_helpers/agent.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Chain that takes in an input and produces an action and action input.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom abc import abstractmethod\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union\nimport yaml\nfrom pydantic import BaseModel, root_validator\n\nfrom langchain.agents.agent_types import AgentType\nfrom langchain.agents.tools import InvalidTool\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.base import BaseCallbackManager\nfrom langchain.callbacks.manager import (\n AsyncCallbackManagerForToolRun,\n CallbackManagerForChainRun,\n CallbackManagerForToolRun,\n Callbacks,","source_hash":"431aaf03745ba34e26fffd9c1572ff2f4d423ec893e2ebbc5198572878326555","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/agent_helpers/output_parser.py","uri":"program://OpenAgents/file/real_agents/adapters/agent_helpers/output_parser.py","kind":"file","name":"real_agents/adapters/agent_helpers/output_parser.py","path":"real_agents/adapters/agent_helpers/output_parser.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from __future__ import annotations\n\nfrom typing import Optional, Union\nfrom pydantic import Extra\n\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n)\nfrom real_agents.adapters.agent_helpers.agent import AgentOutputParser\nfrom real_agents.adapters.schema import AgentTransition\n\n\nclass ConversationOutputParser(AgentOutputParser):\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n extra = Extra.allow\n arbitrary_types_allowed = True\n\n def get_format_instructions(self, app_name=\"copilot\") -> str:","source_hash":"8c129c1b903969833aa271e1cfad58438498e64b1e7b60c93b9587dd2de6170e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/agent_helpers/__init__.py","uri":"program://OpenAgents/file/real_agents/adapters/agent_helpers/__init__.py","kind":"file","name":"real_agents/adapters/agent_helpers/__init__.py","path":"real_agents/adapters/agent_helpers/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Interface for agents.\"\"\"\nfrom real_agents.adapters.agent_helpers.agent import (\n Agent,\n AgentExecutor,\n AgentOutputParser,\n BaseSingleActionAgent,\n)\nfrom real_agents.adapters.agent_helpers.tools import Tool, tool\n\n__all__ = [\n \"AgentExecutor\",\n \"Agent\",\n \"Tool\",\n \"tool\",\n \"AgentOutputParser\",\n \"BaseSingleActionAgent\",\n]","source_hash":"6fee3fc3821d6730c785f0c660f5895c107d9aa68db13f89794c83d25aa4dfb2","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/adapters/agent_helpers/tools.py","uri":"program://OpenAgents/file/real_agents/adapters/agent_helpers/tools.py","kind":"file","name":"real_agents/adapters/agent_helpers/tools.py","path":"real_agents/adapters/agent_helpers/tools.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Interface for tools.\"\"\"\nfrom inspect import signature\nfrom typing import Any, Awaitable, Callable, Dict, Optional, Type, Union\nfrom pydantic import BaseModel, validate_arguments\n\nfrom langchain.tools.base import BaseTool\n\nfrom real_agents.adapters.data_model import DataModel\nfrom real_agents.adapters.callbacks.manager import (\n CallbackManager,\n Callbacks,\n)\n\n\nclass Tool(BaseTool):\n \"\"\"Tool that takes in function or coroutine directly.\"\"\"\n\n description: str = \"\"\n func: Callable[..., str]\n \"\"\"The function to run when the tool is called.\"\"\"\n coroutine: Optional[Callable[..., Awaitable[str]]] = None","source_hash":"7a42ce1b8f39d057213e13607f46051311dfbc9760402c036e6012f04717cafa","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugin_prompt.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugin_prompt.py","kind":"file","name":"real_agents/plugins_agent/plugin_prompt.py","path":"real_agents/plugins_agent/plugin_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# flake8: noqa\nimport datetime\n\nPREFIX = (\n \"\"\"You are XLang Plugins Agent, a friendly and intuitive assistant developed by the XLang Team to guide you through every aspects of your work and your daily life. XLang Agent is always at your fingertips through our interactive chat system.\n\nYou can aware of what plugins you have, and use the plugins properly in right order to finish what user wants.\n\nToday is\n\"\"\".strip() + \" \"\n + datetime.datetime.now().strftime(\"%Y-%m-%d\")\n + \"\"\", and you should adapt the input to fit into the date, for example, seasonal information, or today's date as coordinate, etc.\n\nTo make your response informative, always speak includes the following information in MARKDOWN format when responding a message, that is:\n1. Natural language explanation, that make explain the API output in a human readable way;\n2. Organized information such as bullet points or MARKDOWN tables, followed by the links to the items (that in the API output), news etc. if API output contains the information;\n3. The links should in MARKDOWN format and have value in it. If reference information is provided in the API output, like links to the items, news etc. Your explanation MUST provide the links on each items and links can be clicked on when API output contains the information. The links better attach on some natural language explanation through MARKDOWN syntax, for example, - [Renewable Energy - Center for Climate and Energy Solutions](https://www.c2es.org/content/renewable-energy/);\n4. If there are image we would like to display, please use MARKDOWN syntax to display it, for example, ![image](https://www.c2es.org/content/renewable-energy/);\n5. Try to speak more and show all the information you got in a organized way, that will make you a better assistant, especially when you are giving the final answer.\n\nPLUGINS","source_hash":"91df179697c5736562a29082a4d8874e8a87ba66582f1c446f3800db6c7da8b1","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugin.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugin.py","kind":"file","name":"real_agents/plugins_agent/plugin.py","path":"real_agents/plugins_agent/plugin.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"An agent designed to hold a conversation in addition to using tools. (Specially designed for plugins model)\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, List, Optional, Sequence, Tuple, Union\nfrom pydantic import Extra, Field\nfrom typing_extensions import override\n\nfrom langchain.agents.agent import AgentOutputParser\nfrom langchain.agents.utils import validate_tools_single_input\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.chains import LLMChain\nfrom langchain.schema import (\n AgentAction,\n AgentFinish,\n AIMessage,\n BaseMessage,\n BaseOutputParser,\n HumanMessage\n)\nfrom langchain.callbacks.manager import (\n Callbacks","source_hash":"c2dc2ffa9e70d8b2afd3758bc878bf42bf0c43932de48e3cf2fe9967cb646d85","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/__init__.py","kind":"file","name":"real_agents/plugins_agent/__init__.py","path":"real_agents/plugins_agent/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"from real_agents.plugins_agent.plugin import ConversationalPluginChatAgent\nfrom real_agents.plugins_agent.api_calling.base import APICallingChain\nfrom real_agents.plugins_agent.executors.plugin_executor import PluginExecutor","source_hash":"e2fb7c2b30a1d51ae29a754a1f477ef3210053a7e81237ef23a0d351f824ba3e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/plugin_names.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/plugin_names.py","kind":"file","name":"real_agents/plugins_agent/plugins/plugin_names.py","path":"real_agents/plugins_agent/plugins/plugin_names.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"The .py file to control the plugin we use in the chatbot\"\"\"\nimport os\nfrom enum import Enum\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass PluginName(str, Enum):\n \"\"\"\n Enum class for plugin names\n each name is a plugin name 🔌 , each value is the folder name 📁 of the plugin\n \"\"\"\n KLARNA = \"klarna\"\n ZAPIER = \"zapier\"\n COURSERA = \"Coursera\"\n JOBSEARCH = \"jobsearch\"\n SHOW_ME = \"show_me\"\n SPEAK = \"speak\"\n CREATE_QR_CODE = \"create_qr_code\"\n MAPS = \"maps\"\n ASKYOURPDF = \"askyourpdf\"","source_hash":"e534a5981fc840bae9714838f671aeac7ca69ba60ab660ccebcd3174eea50f75","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/__init__.py","path":"real_agents/plugins_agent/plugins/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":16,"code":"from langchain.tools.base import BaseTool\nfrom langchain.tools.ifttt import IFTTTWebhook\nfrom langchain.tools.openapi.utils.api_models import APIOperation\nfrom langchain.tools.openapi.utils.openapi_utils import OpenAPISpec\nfrom langchain.tools.plugin import AIPluginTool\n\nfrom real_agents.plugins_agent.plugins.plugin_names import PluginName\n\n__all__ = [\n \"BaseTool\",\n \"IFTTTWebhook\",\n \"AIPluginTool\",\n \"OpenAPISpec\",\n \"APIOperation\",\n \"PluginName\",\n]","source_hash":"364664c844d7dd787a610a26576830a90000cfca494f263f5200577df26e0703","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/utils.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/utils.py","kind":"file","name":"real_agents/plugins_agent/plugins/utils.py","path":"real_agents/plugins_agent/plugins/utils.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Utils for plugins (loading and more to add)\"\"\"\nimport importlib\nimport json\nimport os\nimport sys\nfrom collections import defaultdict\nfrom typing import Any\nimport yaml\nfrom tqdm import tqdm\n\nfrom real_agents.adapters.data_model import APIYamlModel, SpecModel\nfrom real_agents.plugins_agent.plugins.plugin_names import PluginName\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\n\nAI_PLUGIN_FILE = \"ai-plugin.json\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nPATH_FOLDER = \"paths\"\n\n\ndef _load_module(name: str, file_path: str) -> Any:","source_hash":"718cbc3c87bdf85e7b089c964d08e319be8ec467c9fe5e1a97a16211de323524","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/tool_selector.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/tool_selector.py","kind":"file","name":"real_agents/plugins_agent/plugins/tool_selector.py","path":"real_agents/plugins_agent/plugins/tool_selector.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Implementation of Tool Selector that automate the selection of tools for the question (or sub-question).\"\"\"\nimport os\nimport pickle\n\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom tqdm import tqdm\nfrom typing import Any\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom langchain.embeddings.huggingface import HuggingFaceInstructEmbeddings\n\nDEFAULT_TOOL_INSTRUCTION = \"Represent the tool description for retrieval:\"\nDEFAULT_QUERY_INSTRUCTION = \"Represent the question for retrieving tools that can be used to solve the question:\"\nPLUGIN_SPEC_FILE = \"openapi.yaml\"\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\nEMBEDDING_CACHE_PATH = os.path.join(CURRENT_PATH, \"..\", \"..\", \"..\", \"backend\", \"static\", \"tool_embeddings\")\nif not os.path.exists(EMBEDDING_CACHE_PATH):\n os.makedirs(EMBEDDING_CACHE_PATH)\n\n","source_hash":"a43f4ce94d94b2c100a01f073f5a88bfc7a34e899b40634ae6fd0b89045b3988","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/klarna/paths/products.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/klarna/paths/products.py","kind":"file","name":"real_agents/plugins_agent/plugins/klarna/paths/products.py","path":"real_agents/plugins_agent/plugins/klarna/paths/products.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Search for products by keyword, price range, and size.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://www.klarna.com/us/shopping/public/openai/v0/products\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Accept\": \"application/json\"}\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"q\": \"nike shoes\",\n# \"size\": 10,\n# \"min_price\": 50,","source_hash":"ea6bb7aa683e46462cecc3e2edde025fd72fcbb9e686f37b933e49d8825ecb31","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/klarna/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/klarna/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/klarna/paths/__init__.py","path":"real_agents/plugins_agent/plugins/klarna/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"path_dict = {\n \"products\": \"/public/openai/v0/products\",\n}","source_hash":"c75cefca1240ac47461547fd89fb4027daa05cad41ed9fa81514540128449f74","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/Coursera/paths/search.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/Coursera/paths/search.py","kind":"file","name":"real_agents/plugins_agent/plugins/Coursera/paths/search.py","path":"real_agents/plugins_agent/plugins/Coursera/paths/search.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Search Coursera API for courses matching a query.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://www.coursera.org/api/rest/v1/search\", json=input_json)\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"7b630acf4b7c89ae7f7bd7cd9413445840534eb35c7da07bc2747c7bf191a371","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/Coursera/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/Coursera/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/Coursera/paths/__init__.py","path":"real_agents/plugins_agent/plugins/Coursera/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"path_dict = {\"search\": \"/api/rest/v1/search\"}","source_hash":"cfd61fa59cd51ee733858210abc185831941680f6f4dd4ce2ff14a9f3461f888","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/nba_stats/paths/basketball_stats.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/nba_stats/paths/basketball_stats.py","kind":"file","name":"real_agents/plugins_agent/plugins/nba_stats/paths/basketball_stats.py","path":"real_agents/plugins_agent/plugins/nba_stats/paths/basketball_stats.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"\"\"\"NBA stats API path.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://nba-gpt-prod.onrender.com/basketball_stats\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"b81243f5258e0e039c069529f0278205e79ade5bd4189bf97ea7802e25e9477a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/nba_stats/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/nba_stats/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/nba_stats/paths/__init__.py","path":"real_agents/plugins_agent/plugins/nba_stats/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"path_dict = {\n \"basketball_stats\": \"/basketball_stats\"\n}","source_hash":"63eb7a8e5759d5be1f7855ce69cab507f4fdda83afc7fe334ed79dbbdc2d8617","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/askyourpdf/paths/download_pdf.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/askyourpdf/paths/download_pdf.py","kind":"file","name":"real_agents/plugins_agent/plugins/askyourpdf/paths/download_pdf.py","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/download_pdf.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Download PDF from AskYourPDF API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://plugin.askyourpdf.com/api/download_pdf\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"46c3316702bb92057472ca05230eaa0ad9603e8e21233302d5aee257a0fb6fb9","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/askyourpdf/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/askyourpdf/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/askyourpdf/paths/__init__.py","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":4,"code":"path_dict = {\n \"download_pdf\": \"/api/download_pdf\",\n \"perform_query\": \"/query\"\n}","source_hash":"6da606736467ce0d3ecbbafcc705bed0ca57403600c81be2f0fab8362a666c98","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/askyourpdf/paths/perform_query.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/askyourpdf/paths/perform_query.py","kind":"file","name":"real_agents/plugins_agent/plugins/askyourpdf/paths/perform_query.py","path":"real_agents/plugins_agent/plugins/askyourpdf/paths/perform_query.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Perform query to AskYourPDF API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://plugin.askyourpdf.com/query\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"85b14f55580aa32960a81b00108b04e00f37b53842d92bf053a75eb13266e487","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/wolfram/paths/llm_api.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/wolfram/paths/llm_api.py","kind":"file","name":"real_agents/plugins_agent/plugins/wolfram/paths/llm_api.py","path":"real_agents/plugins_agent/plugins/wolfram/paths/llm_api.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"LLM API for Wolfram Alpha\"\"\"\nimport requests\n\n\ndef call_api(input_json, api_key):\n input_json[\"appid\"] = api_key\n response = requests.get(\"https://www.wolframalpha.com/api/v1/llm-api\", params=input_json)\n\n if response.status_code == 200:\n return response.content.decode(\"utf-8\")\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"a361623fa8b8727ae5d124b2b41efca3770d4cffb607c35abf59f61e005f603e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/wolfram/paths/cloud_plugin.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/wolfram/paths/cloud_plugin.py","kind":"file","name":"real_agents/plugins_agent/plugins/wolfram/paths/cloud_plugin.py","path":"real_agents/plugins_agent/plugins/wolfram/paths/cloud_plugin.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Cloud plugin for Wolfram Alpha API\"\"\"\nimport requests\n\n\ndef call_api(input_json, api_key):\n input_json[\"appid\"] = api_key\n response = requests.get(\"https://www.wolframalpha.com/api/v1/cloud-plugin\", params=input_json)\n\n if response.status_code == 200:\n return response.content.decode(\"utf-8\")\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"2e6a8a9c25e8ed7cf0349f4278e6ad6471d9c449897eb9aff331dd89c2d75904","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/wolfram/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/wolfram/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/wolfram/paths/__init__.py","path":"real_agents/plugins_agent/plugins/wolfram/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":4,"code":"path_dict = {\n \"cloud_plugin\": \"/api/v1/cloud-plugin\",\n \"llm_api\": \"/api/v1/llm-api\"\n}","source_hash":"fdbfb34d062805565fbd657c221473157b57c8e568e4b36334787f40e4a04235","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/show_me/paths/render_diagram.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/show_me/paths/render_diagram.py","kind":"file","name":"real_agents/plugins_agent/plugins/show_me/paths/render_diagram.py","path":"real_agents/plugins_agent/plugins/show_me/paths/render_diagram.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Render diagram path for Show Me plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/render/\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"2245b15fd30b105697d483558e6f59a396e78783bee0774dffc7935b49871bdc","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/show_me/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/show_me/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/show_me/paths/__init__.py","path":"real_agents/plugins_agent/plugins/show_me/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":5,"code":"path_dict = {\n \"diagram_guidelines\": \"/diagram-guidelines\",\n \"render_diagram\": \"/render\",\n \"show_carousel\": \"/show-carousel\"\n}","source_hash":"ba91402ee17dfd50f81851b737a42d7c9d4d37f9009c3149e5a28f8be1a2fa0c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/show_me/paths/show_carousel.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/show_me/paths/show_carousel.py","kind":"file","name":"real_agents/plugins_agent/plugins/show_me/paths/show_carousel.py","path":"real_agents/plugins_agent/plugins/show_me/paths/show_carousel.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Show Carousel path for Show Me plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/show-carousel\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"94a06975dbb473d3689749138fc9e29fc419ab92927e054ac92f7ef6c58c1627","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/show_me/paths/diagram_guidelines.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/show_me/paths/diagram_guidelines.py","kind":"file","name":"real_agents/plugins_agent/plugins/show_me/paths/diagram_guidelines.py","path":"real_agents/plugins_agent/plugins/show_me/paths/diagram_guidelines.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Guidelines for diagramming\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://showme.redstarplugin.com/diagram-guidelines\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"00e8cff7cafa9c40e4493e9bbd27f162bd0cde920125500fddac6cdefeff8995","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/XWeather/paths/get_radar.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/XWeather/paths/get_radar.py","kind":"file","name":"real_agents/plugins_agent/plugins/XWeather/paths/get_radar.py","path":"real_agents/plugins_agent/plugins/XWeather/paths/get_radar.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Radars API path.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n location = input_json['location']\n response = requests.get(f\"https://openai-plugin.xweather.com/radar/{location}\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4cbe4b545793e9f8668ad8df1762ed0d2b2f12880d927a284efa138e29b35344","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/XWeather/paths/weather_summary.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/XWeather/paths/weather_summary.py","kind":"file","name":"real_agents/plugins_agent/plugins/XWeather/paths/weather_summary.py","path":"real_agents/plugins_agent/plugins/XWeather/paths/weather_summary.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Weather summary path for XWeather plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://openai-plugin.xweather.com/weather/summary/{}\".format(input_json['location'])\n response = requests.get(url)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bd534023f21a6cc46b1e407a3a9506256e1e8c3d33fc90690ed26c5a209e3e32","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/XWeather/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/XWeather/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/XWeather/paths/__init__.py","path":"real_agents/plugins_agent/plugins/XWeather/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":5,"code":"path_dict = {\n \"get_radar\": \"/radar/{location}\",\n \"weather_forecast\": \"/weather/forecast/{location}\",\n \"weather_summary\": \"/weather/summary/{location}\"\n}","source_hash":"947ec11338adc365230684a465b31bd7acacf4edd8e5dcd7dcf13dbe93e002bc","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/XWeather/paths/weather_forecast.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/XWeather/paths/weather_forecast.py","kind":"file","name":"real_agents/plugins_agent/plugins/XWeather/paths/weather_forecast.py","path":"real_agents/plugins_agent/plugins/XWeather/paths/weather_forecast.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":14,"code":"\"\"\"Weather Forecast API Path.\"\"\"\nfrom typing import Dict, Any\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n location = input_json[\"location\"]\n url = f\"https://openai-plugin.xweather.com/weather/forecast/{location}\"\n response = requests.get(url)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"25b87494b7ba2a6a116e03987ec2b7f9c4d2ca112e11001b588ab7310ff8b44a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/biztoc/paths/search_news.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/biztoc/paths/search_news.py","kind":"file","name":"real_agents/plugins_agent/plugins/biztoc/paths/search_news.py","path":"real_agents/plugins_agent/plugins/biztoc/paths/search_news.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Search news from Biztoc API.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://ai.biztoc.com/ai/news\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"334841dd12ea6cd3307f732fa38bd6373d77b2a7bf205dc5f9d3c62704332e5c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/biztoc/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/biztoc/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/biztoc/paths/__init__.py","path":"real_agents/plugins_agent/plugins/biztoc/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"path_dict = {\n \"search_news\": \"/ai/news\",\n}","source_hash":"7a9a4547cb7e7e3c9c377d64bf9b9948b9239aa850484704267c8c928ad9891b","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/web_scraper/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/web_scraper/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/web_scraper/paths/__init__.py","path":"real_agents/plugins_agent/plugins/web_scraper/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"path_dict = {\n \"scraper\": \"/scrape\"\n}","source_hash":"b97faeebdda7243cc446590935d67a0a190aab078c888bfba5c70170e563195a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/web_scraper/paths/scraper.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/web_scraper/paths/scraper.py","kind":"file","name":"real_agents/plugins_agent/plugins/web_scraper/paths/scraper.py","path":"real_agents/plugins_agent/plugins/web_scraper/paths/scraper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Scrape data from a website using the Scraper API.\"\"\"\nimport requests\nfrom typing import Any, Dict\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://scraper.gafo.tech/scrape\", json=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"25f69297e08c968dcb59abfcf80bddbce48755b7129068381d49766ec20d0b7f","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/jobsearch/paths/jobs.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/jobsearch/paths/jobs.py","kind":"file","name":"real_agents/plugins_agent/plugins/jobsearch/paths/jobs.py","path":"real_agents/plugins_agent/plugins/jobsearch/paths/jobs.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Jobsearch API jobs path.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://jobsearch.vencio.de/jobs\"\n headers = {\n \"Content-Type\": \"application/json\"\n }\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"d0676f7f7f00c242be9d60bd4e5e1df922d93d9562f440ba5c38de8f431cc6a0","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/jobsearch/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/jobsearch/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/jobsearch/paths/__init__.py","path":"real_agents/plugins_agent/plugins/jobsearch/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"path_dict = {\n \"jobs\": \"/jobs\"\n}","source_hash":"113ee4c12622007fb1f7a4fad677e1fec99ca12f25cb858f783f575eabeb2491","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/maps/paths/generate_map.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/maps/paths/generate_map.py","kind":"file","name":"real_agents/plugins_agent/plugins/maps/paths/generate_map.py","path":"real_agents/plugins_agent/plugins/maps/paths/generate_map.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":14,"code":"\"\"\"Maps plugin for generating maps from latlng coordinates.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n query_param = input_json[\"latlng\"]\n response = requests.get(f\"https://maps.smoothplugins.com/?latlng={query_param}\")\n\n if response.status_code == 200:\n return {\"result\": response.content.decode()}\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4979327a1d23ac44c2d3e5f5902f0273f7854a07ab469455955ca9ee8e60e59d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/maps/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/maps/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/maps/paths/__init__.py","path":"real_agents/plugins_agent/plugins/maps/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"path_dict = {\"generate_map\": \"/\"}","source_hash":"3c917b45abc1720263b04cbe8781617825679d766a6b81fa01d5879e9623cea4","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/speak/paths/explain_phrase.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/speak/paths/explain_phrase.py","kind":"file","name":"real_agents/plugins_agent/plugins/speak/paths/explain_phrase.py","path":"real_agents/plugins_agent/plugins/speak/paths/explain_phrase.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Explains a foreign phrase in the context of a full query.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/explain-phrase\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"foreign_phrase\": \"no mames\",\n# \"learning_language\": \"Spanish\",\n# \"native_language\": \"English\",","source_hash":"0aa193e2f4582a52f653b9f80f182c671be5610da262cc9285f38ce59a97398c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/speak/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/speak/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/speak/paths/__init__.py","path":"real_agents/plugins_agent/plugins/speak/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":5,"code":"path_dict = {\n \"translate\": \"/v1/public/openai/translate\",\n \"explain_task\": \"/v1/public/openai/explain-task\",\n \"explain_phrase\": \"/v1/public/openai/explain-phrase\",\n}","source_hash":"94b6d35e1f3d72491ce06dcbd968fc76f6ace5d5289dcc3dc7d6a06d87618919","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/speak/paths/translate.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/speak/paths/translate.py","kind":"file","name":"real_agents/plugins_agent/plugins/speak/paths/translate.py","path":"real_agents/plugins_agent/plugins/speak/paths/translate.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Translate a phrase from one language to another.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/translate\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"phrase_to_translate\": \"Hello, how are you?\",\n# \"learning_language\": \"Spanish\",\n# \"native_language\": \"English\",","source_hash":"a027cc9ed769781310acf189ecfb65e5a62550337e4764d5d7f83cdf30ed9fce","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/speak/paths/explain_task.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/speak/paths/explain_task.py","kind":"file","name":"real_agents/plugins_agent/plugins/speak/paths/explain_task.py","path":"real_agents/plugins_agent/plugins/speak/paths/explain_task.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Explain the task to the user.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\nurl = \"https://api.speak.com/v1/public/openai/explain-task\"\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n headers = {\"Content-Type\": \"application/json\"}\n response = requests.post(url, json=input_json, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}\n\n# input_json = {\n# \"task_description\": \"ask for directions,\n# \"learning_language\": \"French\",\n# \"native_language\": \"English\",","source_hash":"d7794f92e04fd54e60fd6ce949ea300b4dc55401f04e982b7282c1109f1e3d18","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/Outschool/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/Outschool/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/Outschool/paths/__init__.py","path":"real_agents/plugins_agent/plugins/Outschool/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":4,"code":"path_dict = {\n \"get_classes\": \"/classes\",\n \"search_teachers\": \"/teachers\"\n}","source_hash":"d96b1db987e82219790e06476c9b1ee9d091ab4098a5bd6308b846da89bfc4bb","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/Outschool/paths/search_teachers.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/Outschool/paths/search_teachers.py","kind":"file","name":"real_agents/plugins_agent/plugins/Outschool/paths/search_teachers.py","path":"real_agents/plugins_agent/plugins/Outschool/paths/search_teachers.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Search for teachers on Outschool.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://chatgpt-plugin.outschool.com/api/teachers\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4a3dc66cfd59adecb4d2e1b7d6ac01b92fa5cc39f19c731ff72757e835b1a64a","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/Outschool/paths/get_classes.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/Outschool/paths/get_classes.py","kind":"file","name":"real_agents/plugins_agent/plugins/Outschool/paths/get_classes.py","path":"real_agents/plugins_agent/plugins/Outschool/paths/get_classes.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Get classes from Outschool API.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.get(\"https://chatgpt-plugin.outschool.com/api/classes\", params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"dccddafedb33e6cd847f43b31ed26b5e633d4e04b203ef7975c35b9f9ac79b8e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/DreamInterpreter/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/DreamInterpreter/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/__init__.py","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":4,"code":"path_dict = {\n \"dream_interpreter\": \"/getDream/{DreamText}\",\n \"data\": \"/api/data\",\n}","source_hash":"f18c03570fbbdaaa719d3b1cc3f2ad91183a225c605fc97aa01c3cc96cf1bae3","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/DreamInterpreter/paths/data.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/DreamInterpreter/paths/data.py","kind":"file","name":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/data.py","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/data.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Data path for DreamInterpreter plugin.\"\"\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n response = requests.post(\"https://dreamplugin.bgnetmobile.com/api/data\", json=input_json)\n\n if response.status_code == 200:\n return response.content\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"4145ab9eeb3faa08bfc243f4686606e840f23f82c533951bf1494d1b316adf18","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/DreamInterpreter/paths/dream_interpreter.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/DreamInterpreter/paths/dream_interpreter.py","kind":"file","name":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/dream_interpreter.py","path":"real_agents/plugins_agent/plugins/DreamInterpreter/paths/dream_interpreter.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"\"\"\"Dream Interpreter plugin.\"\"\"\nfrom typing import Any, Dict\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n dream_text = input_json[\"DreamText\"]\n response = requests.get(f\"https://dreamplugin.bgnetmobile.com/getDream/{dream_text}\")\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"320e469d83f64c26397e4df79fcdce472c14779d719395affd974a477eee191f","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/personnel.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/personnel.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/personnel.py","path":"real_agents/plugins_agent/plugins/zapier/personnel.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"The Zapier plugin personnel openapi.yaml handling, since it is special case, we need to handle it separately\"\"\"\nimport os\nimport requests\n\nFILE_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\n# You need to manage your actions first in https://nla.zapier.com/providers/\n\n# Reload the openapi\ndef reload_openapi(api_key, openapi_json):\n # Original data\n headers = {\"X-API-Key\": api_key, }\n # Call read the openapi\n url = \"https://nla.zapier.com/api/v1/exposed/\"\n\n data = None\n while True:\n try:\n response = requests.get(url, headers=headers)\n data = response.json()","source_hash":"19453d3a371326bf33bb5253a96c8a562ced11ef47150120967797d189dca242","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/__init__.py","path":"real_agents/plugins_agent/plugins/zapier/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"\"\"\"Zapier Tool.\"\"\"","source_hash":"3138c19e9a8da4675cfd539b3fabf68241e97cba7c5ef2526fb962e38f126c3e","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/paths/exposed.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/paths/exposed.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/paths/exposed.py","path":"real_agents/plugins_agent/plugins/zapier/paths/exposed.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Exposed paths for the Zapier plugin.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/exposed/\"\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"934c56342bd51cec80340176673ee4868ae25d24846367bc40aee37175e3b390","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/paths/configuration_link.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/paths/configuration_link.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/paths/configuration_link.py","path":"real_agents/plugins_agent/plugins/zapier/paths/configuration_link.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Configuration Link Path.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/configuration-link/\"\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"6898f8dce698eaf5a059d4b1379cca11fb3712da1e2088a6dde4beb6f702b05c","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/paths/preview_a_zap.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/paths/preview_a_zap.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/paths/preview_a_zap.py","path":"real_agents/plugins_agent/plugins/zapier/paths/preview_a_zap.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":18,"code":"\"\"\"Preview a Zapier Zap.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n \"Content-Type\": \"application/json\"\n }\n url = \"https://nla.zapier.com/api/v1/preview-a-zap/\"\n response = requests.post(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bbff868c4711c486bb0248e4a3c14345e07a0c09def8e5f26119e2a0768c0c3d","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/paths/execution_log.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/paths/execution_log.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/paths/execution_log.py","path":"real_agents/plugins_agent/plugins/zapier/paths/execution_log.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Execution log path for Zapier plugin.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/\" + input_json['execution-log']\n response = requests.get(url, headers=headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"bdfe51174c63eacd443955c8ed76308dd5254cf0892e432e6bd12ebb175024a5","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/paths/search_actions.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/paths/search_actions.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/paths/search_actions.py","path":"real_agents/plugins_agent/plugins/zapier/paths/search_actions.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"\"\"\"Search Actions\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any], api_key) -> Dict[str, Any]:\n headers = {\n \"X-API-Key\": api_key,\n }\n url = \"https://nla.zapier.com/api/v1/search/actions/\"\n response = requests.get(url, headers=headers, params=input_json)\n\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"04a7d703852560b97735097cd73ccb97b5319eaaaa261e7002946dc133776db7","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/zapier/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/zapier/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/zapier/paths/__init__.py","path":"real_agents/plugins_agent/plugins/zapier/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":7,"code":"path_dict = {\n \"search_actions\": \"/api/v1/search/actions/\",\n \"preview_a_zap\": \"/api/v1/preview-a-zap/\",\n \"configuration_link\": \"/api/v1/configuration-link/\",\n \"exposed\": \"/api/v1/exposed/\",\n \"execution_log\": \"/api/v1/execution-log/{execution_log_id}/\",\n}","source_hash":"3f112792ac5afc16d3b2952cadf200c452dfc6c8166d3efa82c3b131688ed854","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/create_qr_code/paths/__init__.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/create_qr_code/paths/__init__.py","kind":"file","name":"real_agents/plugins_agent/plugins/create_qr_code/paths/__init__.py","path":"real_agents/plugins_agent/plugins/create_qr_code/paths/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"path_dict = {\"create_qr\": \"/create-qr-code\"}","source_hash":"35b66ed6348a04be50587ebd321e4bef754fe9c37643d72f6a9bf2104c6528ef","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/plugins/create_qr_code/paths/create_qr.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/plugins/create_qr_code/paths/create_qr.py","kind":"file","name":"real_agents/plugins_agent/plugins/create_qr_code/paths/create_qr.py","path":"real_agents/plugins_agent/plugins/create_qr_code/paths/create_qr.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":13,"code":"\"\"\"Create QR Code API.\"\"\"\nfrom typing import Any, Dict\n\nimport requests\n\n\ndef call_api(input_json: Dict[str, Any]) -> Dict[str, Any]:\n url = \"https://create-qr-code.modelxy.com/create-qr-code\"\n response = requests.get(url, params=input_json)\n if response.status_code == 200:\n return response.json()\n else:\n return {\"status_code\": response.status_code, \"text\": response.text}","source_hash":"c2bb5f1a83c2828dcbae2c0adf52764441326b4544d160d22b911d0472b11506","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/executors/plugin_executor.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/executors/plugin_executor.py","kind":"file","name":"real_agents/plugins_agent/executors/plugin_executor.py","path":"real_agents/plugins_agent/executors/plugin_executor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Executor that manage the plugins calling\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any, Callable, Dict, Union\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\n\nfrom real_agents.adapters.data_model import SpecModel\nfrom real_agents.plugins_agent import APICallingChain\nfrom real_agents.plugins_agent.plugins.utils import load_plugin_elements_by_name\nfrom real_agents.adapters.data_model.utils import indent_multiline_string\n\n\nclass PluginExecutor(BaseModel):\n \"\"\"Executor to call plugins that handle the spec showing, endpoint calling and output modeling.\"\"\"\n name: str\n description: str\n spec_model: SpecModel\n meta_info: Dict[str, Any]\n endpoint2caller: Dict[str, Callable]","source_hash":"4633a125486b25b4839125fc293f1357f3c493660c872f318f3667b20637a110","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/api_calling/base.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/api_calling/base.py","kind":"file","name":"real_agents/plugins_agent/api_calling/base.py","path":"real_agents/plugins_agent/api_calling/base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"Implement API calling.\"\"\"\n\nfrom __future__ import annotations\n\nimport re\nimport traceback\nfrom typing import Any, Callable, Dict, List, Optional\nimport backoff\nimport json5\nfrom fuzzywuzzy import process\nfrom pydantic import BaseModel, Extra\n\nfrom langchain.base_language import BaseLanguageModel\nfrom langchain.callbacks.manager import CallbackManagerForChainRun\nfrom langchain.chains import LLMChain\nfrom langchain.chains.base import Chain\nfrom langchain.prompts.base import BasePromptTemplate\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,","source_hash":"fee078b01a505f30fb5a782da9147909342fd757909faae002a3bec8d912e9e1","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/api_calling/prompt.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/api_calling/prompt.py","kind":"file","name":"real_agents/plugins_agent/api_calling/prompt.py","path":"real_agents/plugins_agent/api_calling/prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"SYSTEM_PROMPT = (\n \"\"\"You are acting like plugin system that understand user's needs and call APIs precisely for them.\"\"\".strip()\n + \"\\n\"\n)\n\nUSER_PROMPT = (\n \"\"\"\nHere are the endpoints specs:\n```\n{specs_str}\n```\nHere is the input string:\n```\n{input_str}\n```\nSelect the right endpoint called that can process the input string.\nYou need to wrap the input str into a json object, so that it could be fed into the function you selected. \nDuring wrapping, you should:\n1. modify the value of each key so that it satisfies the requirements in function specs.For example, if the type of the value should be a number, then you should modify it into a number;\n2. ignore the information that is not useful or not applicable to the function you selected.\nYou fill values into some slots in the input_json, and then call the API. If the API returns a valid output, then you succeed. Otherwise, you fail.","source_hash":"a9f3cb2981535f746e141a21c65787e0d96f07c38256d5f36333c4bf74495b22","truncated":false} {"repo_id":"OpenAgents","entity_id":"file:real_agents/plugins_agent/api_calling/custom_exceptions.py","uri":"program://OpenAgents/file/real_agents/plugins_agent/api_calling/custom_exceptions.py","kind":"file","name":"real_agents/plugins_agent/api_calling/custom_exceptions.py","path":"real_agents/plugins_agent/api_calling/custom_exceptions.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":16,"code":"\"\"\"Customize exceptions for API calling.\"\"\"\n\n\nclass ParsingError(BaseException):\n \"\"\"Error occur when parsing.\"\"\"\n\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when parsing: {message}\")\n\n\nclass APICallingError(BaseException):\n \"\"\"Error occur when calling API.\"\"\"\n def __init__(self, message: str):\n self.message = message\n super().__init__(f\"Error occur when calling API: {message}\")","source_hash":"608a58a8035b81b6d94ba47a58d85d9e90eaccfae6d6292aea3e249d8a370c7e","truncated":false}