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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/ollama/pyproject.toml
[build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-ollama" version = "0.2.1" description = "An integration package connecting Ollama and LangChain" authors = [] readme = "README.md" repository = "https://github.com/langchain-ai/langchain" license = "MIT" [tool.mypy] disallow_untyped_defs = "True" [tool.poetry.urls] "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/ollama" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-ollama%3D%3D0%22&expanded=true" [tool.poetry.dependencies] python = ">=3.9,<4.0" ollama = ">=0.3.0,<1" langchain-core = "^0.3.20" [tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort "T201", # print "D", # pydocstyle ] [tool.ruff.lint.pydocstyle] convention = "google" [tool.ruff.lint.per-file-ignores] "tests/**" = ["D"] # ignore docstring checks for tests [tool.coverage.run] omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" [tool.poetry.group.test] optional = true [tool.poetry.group.codespell] optional = true [tool.poetry.group.test_integration] optional = true [tool.poetry.group.lint] optional = true [tool.poetry.group.dev] optional = true [tool.poetry.group.test.dependencies] pytest = "^7.4.3" pytest-asyncio = "^0.23.2" syrupy = "^4.0.2" pytest-socket = "^0.7.0" pytest-watcher = "^0.3.4" [tool.poetry.group.codespell.dependencies] codespell = "^2.2.6" [tool.poetry.group.test_integration.dependencies] [tool.poetry.group.lint.dependencies] ruff = "^0.1.8" [tool.poetry.group.typing.dependencies] mypy = "^1.7.1" [tool.poetry.group.test.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.test.dependencies.langchain-tests] path = "../../standard-tests" develop = true [tool.poetry.group.typing.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.dev.dependencies.langchain-core] path = "../../core" develop = true
0
lc_public_repos/langchain/libs/partners/ollama
lc_public_repos/langchain/libs/partners/ollama/langchain_ollama/llms.py
"""Ollama large language models.""" from typing import ( Any, AsyncIterator, Dict, Iterator, List, Literal, Mapping, Optional, Union, ) from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.language_models import BaseLLM, LangSmithParams from langchain_core.outputs import GenerationChunk, LLMResult from ollama import AsyncClient, Client, Options from pydantic import PrivateAttr, model_validator from typing_extensions import Self class OllamaLLM(BaseLLM): """OllamaLLM large language models. Example: .. code-block:: python from langchain_ollama import OllamaLLM model = OllamaLLM(model="llama3") model.invoke("Come up with 10 names for a song about parrots") """ model: str """Model name to use.""" mirostat: Optional[int] = None """Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)""" mirostat_eta: Optional[float] = None """Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)""" mirostat_tau: Optional[float] = None """Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)""" num_ctx: Optional[int] = None """Sets the size of the context window used to generate the next token. (Default: 2048) """ num_gpu: Optional[int] = None """The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.""" num_thread: Optional[int] = None """Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).""" num_predict: Optional[int] = None """Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)""" repeat_last_n: Optional[int] = None """Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)""" repeat_penalty: Optional[float] = None """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)""" temperature: Optional[float] = None """The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)""" stop: Optional[List[str]] = None """Sets the stop tokens to use.""" tfs_z: Optional[float] = None """Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)""" top_k: Optional[int] = None """Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)""" top_p: Optional[float] = None """Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)""" format: Literal["", "json"] = "" """Specify the format of the output (options: json)""" keep_alive: Optional[Union[int, str]] = None """How long the model will stay loaded into memory.""" base_url: Optional[str] = None """Base url the model is hosted under.""" client_kwargs: Optional[dict] = {} """Additional kwargs to pass to the httpx Client. For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html) """ _client: Client = PrivateAttr(default=None) # type: ignore """ The client to use for making requests. """ _async_client: AsyncClient = PrivateAttr(default=None) # type: ignore """ The async client to use for making requests. """ def _generate_params( self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any, ) -> Dict[str, Any]: if self.stop is not None and stop is not None: raise ValueError("`stop` found in both the input and default params.") elif self.stop is not None: stop = self.stop options_dict = kwargs.pop( "options", { "mirostat": self.mirostat, "mirostat_eta": self.mirostat_eta, "mirostat_tau": self.mirostat_tau, "num_ctx": self.num_ctx, "num_gpu": self.num_gpu, "num_thread": self.num_thread, "num_predict": self.num_predict, "repeat_last_n": self.repeat_last_n, "repeat_penalty": self.repeat_penalty, "temperature": self.temperature, "stop": self.stop if stop is None else stop, "tfs_z": self.tfs_z, "top_k": self.top_k, "top_p": self.top_p, }, ) params = { "prompt": prompt, "stream": kwargs.pop("stream", True), "model": kwargs.pop("model", self.model), "format": kwargs.pop("format", self.format), "options": Options(**options_dict), "keep_alive": kwargs.pop("keep_alive", self.keep_alive), **kwargs, } return params @property def _llm_type(self) -> str: """Return type of LLM.""" return "ollama-llm" def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get standard params for tracing.""" params = super()._get_ls_params(stop=stop, **kwargs) if max_tokens := kwargs.get("num_predict", self.num_predict): params["ls_max_tokens"] = max_tokens return params @model_validator(mode="after") def _set_clients(self) -> Self: """Set clients to use for ollama.""" client_kwargs = self.client_kwargs or {} self._client = Client(host=self.base_url, **client_kwargs) self._async_client = AsyncClient(host=self.base_url, **client_kwargs) return self async def _acreate_generate_stream( self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any, ) -> AsyncIterator[Union[Mapping[str, Any], str]]: async for part in await self._async_client.generate( **self._generate_params(prompt, stop=stop, **kwargs) ): # type: ignore yield part # type: ignore def _create_generate_stream( self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any, ) -> Iterator[Union[Mapping[str, Any], str]]: yield from self._client.generate( **self._generate_params(prompt, stop=stop, **kwargs) ) # type: ignore async def _astream_with_aggregation( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, verbose: bool = False, **kwargs: Any, ) -> GenerationChunk: final_chunk = None async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs): if not isinstance(stream_resp, str): chunk = GenerationChunk( text=stream_resp["response"] if "response" in stream_resp else "", generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if final_chunk is None: final_chunk = chunk else: final_chunk += chunk if run_manager: await run_manager.on_llm_new_token( chunk.text, chunk=chunk, verbose=verbose, ) if final_chunk is None: raise ValueError("No data received from Ollama stream.") return final_chunk def _stream_with_aggregation( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, verbose: bool = False, **kwargs: Any, ) -> GenerationChunk: final_chunk = None for stream_resp in self._create_generate_stream(prompt, stop, **kwargs): if not isinstance(stream_resp, str): chunk = GenerationChunk( text=stream_resp["response"] if "response" in stream_resp else "", generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if final_chunk is None: final_chunk = chunk else: final_chunk += chunk if run_manager: run_manager.on_llm_new_token( chunk.text, chunk=chunk, verbose=verbose, ) if final_chunk is None: raise ValueError("No data received from Ollama stream.") return final_chunk def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: generations = [] for prompt in prompts: final_chunk = self._stream_with_aggregation( prompt, stop=stop, run_manager=run_manager, verbose=self.verbose, **kwargs, ) generations.append([final_chunk]) return LLMResult(generations=generations) # type: ignore[arg-type] async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: generations = [] for prompt in prompts: final_chunk = await self._astream_with_aggregation( prompt, stop=stop, run_manager=run_manager, verbose=self.verbose, **kwargs, ) generations.append([final_chunk]) return LLMResult(generations=generations) # type: ignore[arg-type] def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: for stream_resp in self._create_generate_stream(prompt, stop, **kwargs): if not isinstance(stream_resp, str): chunk = GenerationChunk( text=(stream_resp.get("response", "")), generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if run_manager: run_manager.on_llm_new_token( chunk.text, verbose=self.verbose, ) yield chunk async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs): if not isinstance(stream_resp, str): chunk = GenerationChunk( text=(stream_resp.get("response", "")), generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if run_manager: await run_manager.on_llm_new_token( chunk.text, verbose=self.verbose, ) yield chunk
0
lc_public_repos/langchain/libs/partners/ollama
lc_public_repos/langchain/libs/partners/ollama/langchain_ollama/chat_models.py
"""Ollama chat models.""" import json from typing import ( Any, AsyncIterator, Callable, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Type, Union, cast, ) from uuid import uuid4 from langchain_core.callbacks import ( CallbackManagerForLLMRun, ) from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun from langchain_core.exceptions import OutputParserException from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams from langchain_core.messages import ( AIMessage, AIMessageChunk, BaseMessage, HumanMessage, SystemMessage, ToolCall, ToolMessage, ) from langchain_core.messages.ai import UsageMetadata from langchain_core.messages.tool import tool_call from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool from ollama import AsyncClient, Client, Message, Options from pydantic import PrivateAttr, model_validator from typing_extensions import Self def _get_usage_metadata_from_generation_info( generation_info: Optional[Mapping[str, Any]], ) -> Optional[UsageMetadata]: """Get usage metadata from ollama generation info mapping.""" if generation_info is None: return None input_tokens: Optional[int] = generation_info.get("prompt_eval_count") output_tokens: Optional[int] = generation_info.get("eval_count") if input_tokens is not None and output_tokens is not None: return UsageMetadata( input_tokens=input_tokens, output_tokens=output_tokens, total_tokens=input_tokens + output_tokens, ) return None def _parse_json_string( json_string: str, raw_tool_call: dict[str, Any], skip: bool ) -> Any: """Attempt to parse a JSON string for tool calling. Args: json_string: JSON string to parse. skip: Whether to ignore parsing errors and return the value anyways. raw_tool_call: Raw tool call to include in error message. Returns: The parsed JSON string. Raises: OutputParserException: If the JSON string wrong invalid and skip=False. """ try: return json.loads(json_string) except json.JSONDecodeError as e: if skip: return json_string msg = ( f"Function {raw_tool_call['function']['name']} arguments:\n\n" f"{raw_tool_call['function']['arguments']}\n\nare not valid JSON. " f"Received JSONDecodeError {e}" ) raise OutputParserException(msg) from e except TypeError as e: if skip: return json_string msg = ( f"Function {raw_tool_call['function']['name']} arguments:\n\n" f"{raw_tool_call['function']['arguments']}\n\nare not a string or a " f"dictionary. Received TypeError {e}" ) raise OutputParserException(msg) from e def _parse_arguments_from_tool_call( raw_tool_call: dict[str, Any], ) -> Optional[dict[str, Any]]: """Parse arguments by trying to parse any shallowly nested string-encoded JSON. Band-aid fix for issue in Ollama with inconsistent tool call argument structure. Should be removed/changed if fixed upstream. See https://github.com/ollama/ollama/issues/6155 """ if "function" not in raw_tool_call: return None arguments = raw_tool_call["function"]["arguments"] parsed_arguments = {} if isinstance(arguments, dict): for key, value in arguments.items(): if isinstance(value, str): parsed_arguments[key] = _parse_json_string( value, skip=True, raw_tool_call=raw_tool_call ) else: parsed_arguments[key] = value else: parsed_arguments = _parse_json_string( arguments, skip=False, raw_tool_call=raw_tool_call ) return parsed_arguments def _get_tool_calls_from_response( response: Mapping[str, Any], ) -> List[ToolCall]: """Get tool calls from ollama response.""" tool_calls = [] if "message" in response: if raw_tool_calls := response["message"].get("tool_calls"): for tc in raw_tool_calls: tool_calls.append( tool_call( id=str(uuid4()), name=tc["function"]["name"], args=_parse_arguments_from_tool_call(tc) or {}, ) ) return tool_calls def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict: return { "type": "function", "id": tool_call["id"], "function": { "name": tool_call["name"], "arguments": tool_call["args"], }, } class ChatOllama(BaseChatModel): r"""Ollama chat model integration. .. dropdown:: Setup :open: Install ``langchain-ollama`` and download any models you want to use from ollama. .. code-block:: bash ollama pull mistral:v0.3 pip install -U langchain-ollama Key init args — completion params: model: str Name of Ollama model to use. temperature: float Sampling temperature. Ranges from 0.0 to 1.0. num_predict: Optional[int] Max number of tokens to generate. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_ollama import ChatOllama llm = ChatOllama( model = "llama3", temperature = 0.8, num_predict = 256, # other params ... ) Invoke: .. code-block:: python messages = [ ("system", "You are a helpful translator. Translate the user sentence to French."), ("human", "I love programming."), ] llm.invoke(messages) .. code-block:: python AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0') Stream: .. code-block:: python messages = [ ("human", "Return the words Hello World!"), ] for chunk in llm.stream(messages): print(chunk) .. code-block:: python content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1' content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1' content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1' content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1' .. code-block:: python stream = llm.stream(messages) full = next(stream) for chunk in stream: full += chunk full .. code-block:: python AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890') Async: .. code-block:: python messages = [ ("human", "Hello how are you!"), ] await llm.ainvoke(messages) .. code-block:: python AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0') .. code-block:: python messages = [ ("human", "Say hello world!"), ] async for chunk in llm.astream(messages): print(chunk.content) .. code-block:: python HEL LO WORLD ! .. code-block:: python messages = [ ("human", "Say hello world!"), ("human","Say goodbye world!") ] await llm.abatch(messages) .. code-block:: python [AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'), AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')] JSON mode: .. code-block:: python json_llm = ChatOllama(format="json") messages = [ ("human", "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only."), ] llm.invoke(messages).content .. code-block:: python '{"location": "Pune, India", "time_of_day": "morning"}' Tool Calling: .. warning:: Ollama currently does not support streaming for tools .. code-block:: python from langchain_ollama import ChatOllama from pydantic import BaseModel, Field class Multiply(BaseModel): a: int = Field(..., description="First integer") b: int = Field(..., description="Second integer") ans = await chat.invoke("What is 45*67") ans.tool_calls .. code-block:: python [{'name': 'Multiply', 'args': {'a': 45, 'b': 67}, 'id': '420c3f3b-df10-4188-945f-eb3abdb40622', 'type': 'tool_call'}] """ # noqa: E501 model: str """Model name to use.""" mirostat: Optional[int] = None """Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)""" mirostat_eta: Optional[float] = None """Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)""" mirostat_tau: Optional[float] = None """Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)""" num_ctx: Optional[int] = None """Sets the size of the context window used to generate the next token. (Default: 2048) """ num_gpu: Optional[int] = None """The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.""" num_thread: Optional[int] = None """Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).""" num_predict: Optional[int] = None """Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)""" repeat_last_n: Optional[int] = None """Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)""" repeat_penalty: Optional[float] = None """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)""" temperature: Optional[float] = None """The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)""" seed: Optional[int] = None """Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt.""" stop: Optional[List[str]] = None """Sets the stop tokens to use.""" tfs_z: Optional[float] = None """Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)""" top_k: Optional[int] = None """Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)""" top_p: Optional[float] = None """Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)""" format: Literal["", "json"] = "" """Specify the format of the output (options: json)""" keep_alive: Optional[Union[int, str]] = None """How long the model will stay loaded into memory.""" base_url: Optional[str] = None """Base url the model is hosted under.""" client_kwargs: Optional[dict] = {} """Additional kwargs to pass to the httpx Client. For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html) """ _client: Client = PrivateAttr(default=None) # type: ignore """ The client to use for making requests. """ _async_client: AsyncClient = PrivateAttr(default=None) # type: ignore """ The async client to use for making requests. """ def _chat_params( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, **kwargs: Any, ) -> Dict[str, Any]: ollama_messages = self._convert_messages_to_ollama_messages(messages) if self.stop is not None and stop is not None: raise ValueError("`stop` found in both the input and default params.") elif self.stop is not None: stop = self.stop options_dict = kwargs.pop( "options", { "mirostat": self.mirostat, "mirostat_eta": self.mirostat_eta, "mirostat_tau": self.mirostat_tau, "num_ctx": self.num_ctx, "num_gpu": self.num_gpu, "num_thread": self.num_thread, "num_predict": self.num_predict, "repeat_last_n": self.repeat_last_n, "repeat_penalty": self.repeat_penalty, "temperature": self.temperature, "seed": self.seed, "stop": self.stop if stop is None else stop, "tfs_z": self.tfs_z, "top_k": self.top_k, "top_p": self.top_p, }, ) tools = kwargs.get("tools") default_stream = not bool(tools) params = { "messages": ollama_messages, "stream": kwargs.pop("stream", default_stream), "model": kwargs.pop("model", self.model), "format": kwargs.pop("format", self.format), "options": Options(**options_dict), "keep_alive": kwargs.pop("keep_alive", self.keep_alive), **kwargs, } if tools: params["tools"] = tools return params @model_validator(mode="after") def _set_clients(self) -> Self: """Set clients to use for ollama.""" client_kwargs = self.client_kwargs or {} self._client = Client(host=self.base_url, **client_kwargs) self._async_client = AsyncClient(host=self.base_url, **client_kwargs) return self def _convert_messages_to_ollama_messages( self, messages: List[BaseMessage] ) -> Sequence[Message]: ollama_messages: List = [] for message in messages: role: Literal["user", "assistant", "system", "tool"] tool_call_id: Optional[str] = None tool_calls: Optional[List[Dict[str, Any]]] = None if isinstance(message, HumanMessage): role = "user" elif isinstance(message, AIMessage): role = "assistant" tool_calls = ( [ _lc_tool_call_to_openai_tool_call(tool_call) for tool_call in message.tool_calls ] if message.tool_calls else None ) elif isinstance(message, SystemMessage): role = "system" elif isinstance(message, ToolMessage): role = "tool" tool_call_id = message.tool_call_id else: raise ValueError("Received unsupported message type for Ollama.") content = "" images = [] if isinstance(message.content, str): content = message.content else: for content_part in cast(List[Dict], message.content): if content_part.get("type") == "text": content += f"\n{content_part['text']}" elif content_part.get("type") == "tool_use": continue elif content_part.get("type") == "image_url": image_url = None temp_image_url = content_part.get("image_url") if isinstance(temp_image_url, str): image_url = temp_image_url elif ( isinstance(temp_image_url, dict) and "url" in temp_image_url and isinstance(temp_image_url["url"], str) ): image_url = temp_image_url["url"] else: raise ValueError( "Only string image_url or dict with string 'url' " "inside content parts are supported." ) image_url_components = image_url.split(",") # Support data:image/jpeg;base64,<image> format # and base64 strings if len(image_url_components) > 1: images.append(image_url_components[1]) else: images.append(image_url_components[0]) else: raise ValueError( "Unsupported message content type. " "Must either have type 'text' or type 'image_url' " "with a string 'image_url' field." ) # Should convert to ollama.Message once role includes tool, and tool_call_id is in Message # noqa: E501 msg: dict = { "role": role, "content": content, "images": images, } if tool_calls: msg["tool_calls"] = tool_calls # type: ignore if tool_call_id: msg["tool_call_id"] = tool_call_id ollama_messages.append(msg) return ollama_messages async def _acreate_chat_stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, **kwargs: Any, ) -> AsyncIterator[Union[Mapping[str, Any], str]]: chat_params = self._chat_params(messages, stop, **kwargs) if chat_params["stream"]: async for part in await self._async_client.chat(**chat_params): yield part else: yield await self._async_client.chat(**chat_params) def _create_chat_stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, **kwargs: Any, ) -> Iterator[Union[Mapping[str, Any], str]]: chat_params = self._chat_params(messages, stop, **kwargs) if chat_params["stream"]: yield from self._client.chat(**chat_params) else: yield self._client.chat(**chat_params) def _chat_stream_with_aggregation( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, verbose: bool = False, **kwargs: Any, ) -> ChatGenerationChunk: final_chunk = None for stream_resp in self._create_chat_stream(messages, stop, **kwargs): if not isinstance(stream_resp, str): chunk = ChatGenerationChunk( message=AIMessageChunk( content=( stream_resp["message"]["content"] if "message" in stream_resp and "content" in stream_resp["message"] else "" ), usage_metadata=_get_usage_metadata_from_generation_info( stream_resp ), tool_calls=_get_tool_calls_from_response(stream_resp), ), generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if final_chunk is None: final_chunk = chunk else: final_chunk += chunk if run_manager: run_manager.on_llm_new_token( chunk.text, chunk=chunk, verbose=verbose, ) if final_chunk is None: raise ValueError("No data received from Ollama stream.") return final_chunk async def _achat_stream_with_aggregation( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, verbose: bool = False, **kwargs: Any, ) -> ChatGenerationChunk: final_chunk = None async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs): if not isinstance(stream_resp, str): chunk = ChatGenerationChunk( message=AIMessageChunk( content=( stream_resp["message"]["content"] if "message" in stream_resp and "content" in stream_resp["message"] else "" ), usage_metadata=_get_usage_metadata_from_generation_info( stream_resp ), tool_calls=_get_tool_calls_from_response(stream_resp), ), generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if final_chunk is None: final_chunk = chunk else: final_chunk += chunk if run_manager: await run_manager.on_llm_new_token( chunk.text, chunk=chunk, verbose=verbose, ) if final_chunk is None: raise ValueError("No data received from Ollama stream.") return final_chunk def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get standard params for tracing.""" params = self._get_invocation_params(stop=stop, **kwargs) ls_params = LangSmithParams( ls_provider="ollama", ls_model_name=self.model, ls_model_type="chat", ls_temperature=params.get("temperature", self.temperature), ) if ls_stop := stop or params.get("stop", None) or self.stop: ls_params["ls_stop"] = ls_stop return ls_params def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: final_chunk = self._chat_stream_with_aggregation( messages, stop, run_manager, verbose=self.verbose, **kwargs ) generation_info = final_chunk.generation_info chat_generation = ChatGeneration( message=AIMessage( content=final_chunk.text, usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata, tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls, ), generation_info=generation_info, ) return ChatResult(generations=[chat_generation]) def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: for stream_resp in self._create_chat_stream(messages, stop, **kwargs): if not isinstance(stream_resp, str): chunk = ChatGenerationChunk( message=AIMessageChunk( content=( stream_resp["message"]["content"] if "message" in stream_resp and "content" in stream_resp["message"] else "" ), usage_metadata=_get_usage_metadata_from_generation_info( stream_resp ), tool_calls=_get_tool_calls_from_response(stream_resp), ), generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if run_manager: run_manager.on_llm_new_token( chunk.text, verbose=self.verbose, ) yield chunk async def _astream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs): if not isinstance(stream_resp, str): chunk = ChatGenerationChunk( message=AIMessageChunk( content=( stream_resp["message"]["content"] if "message" in stream_resp and "content" in stream_resp["message"] else "" ), usage_metadata=_get_usage_metadata_from_generation_info( stream_resp ), tool_calls=_get_tool_calls_from_response(stream_resp), ), generation_info=( dict(stream_resp) if stream_resp.get("done") is True else None ), ) if run_manager: await run_manager.on_llm_new_token( chunk.text, verbose=self.verbose, ) yield chunk async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: final_chunk = await self._achat_stream_with_aggregation( messages, stop, run_manager, verbose=self.verbose, **kwargs ) generation_info = final_chunk.generation_info chat_generation = ChatGeneration( message=AIMessage( content=final_chunk.text, usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata, tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls, ), generation_info=generation_info, ) return ChatResult(generations=[chat_generation]) @property def _llm_type(self) -> str: """Return type of chat model.""" return "chat-ollama" def bind_tools( self, tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]], *, tool_choice: Optional[Union[dict, str, Literal["auto", "any"], bool]] = None, **kwargs: Any, ) -> Runnable[LanguageModelInput, BaseMessage]: """Bind tool-like objects to this chat model. Assumes model is compatible with OpenAI tool-calling API. Args: tools: A list of tool definitions to bind to this chat model. Supports any tool definition handled by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`. tool_choice: If provided, which tool for model to call. **This parameter is currently ignored as it is not supported by Ollama.** kwargs: Any additional parameters are passed directly to ``self.bind(**kwargs)``. """ # noqa: E501 formatted_tools = [convert_to_openai_tool(tool) for tool in tools] return super().bind(tools=formatted_tools, **kwargs)
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lc_public_repos/langchain/libs/partners/ollama
lc_public_repos/langchain/libs/partners/ollama/langchain_ollama/embeddings.py
"""Ollama embeddings models.""" from typing import ( List, Optional, ) from langchain_core.embeddings import Embeddings from ollama import AsyncClient, Client from pydantic import ( BaseModel, ConfigDict, PrivateAttr, model_validator, ) from typing_extensions import Self class OllamaEmbeddings(BaseModel, Embeddings): """Ollama embedding model integration. Set up a local Ollama instance: Install the Ollama package and set up a local Ollama instance using the instructions here: https://github.com/ollama/ollama . You will need to choose a model to serve. You can view a list of available models via the model library (https://ollama.com/library). To fetch a model from the Ollama model library use ``ollama pull <name-of-model>``. For example, to pull the llama3 model: .. code-block:: bash ollama pull llama3 This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model. * On Mac, the models will be downloaded to ~/.ollama/models * On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models You can specify the exact version of the model of interest as such ``ollama pull vicuna:13b-v1.5-16k-q4_0``. To view pulled models: .. code-block:: bash ollama list To start serving: .. code-block:: bash ollama serve View the Ollama documentation for more commands. .. code-block:: bash ollama help Install the langchain-ollama integration package: .. code-block:: bash pip install -U langchain_ollama Key init args — completion params: model: str Name of Ollama model to use. base_url: Optional[str] Base url the model is hosted under. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_ollama import OllamaEmbeddings embed = OllamaEmbeddings( model="llama3" ) Embed single text: .. code-block:: python input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3]) .. code-block:: python [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] Embed multiple texts: .. code-block:: python input_texts = ["Document 1...", "Document 2..."] vectors = embed.embed_documents(input_texts) print(len(vectors)) # The first 3 coordinates for the first vector print(vectors[0][:3]) .. code-block:: python 2 [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] Async: .. code-block:: python vector = await embed.aembed_query(input_text) print(vector[:3]) # multiple: # await embed.aembed_documents(input_texts) .. code-block:: python [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188] """ # noqa: E501 model: str """Model name to use.""" base_url: Optional[str] = None """Base url the model is hosted under.""" client_kwargs: Optional[dict] = {} """Additional kwargs to pass to the httpx Client. For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html) """ _client: Client = PrivateAttr(default=None) # type: ignore """ The client to use for making requests. """ _async_client: AsyncClient = PrivateAttr(default=None) # type: ignore """ The async client to use for making requests. """ model_config = ConfigDict( extra="forbid", ) @model_validator(mode="after") def _set_clients(self) -> Self: """Set clients to use for ollama.""" client_kwargs = self.client_kwargs or {} self._client = Client(host=self.base_url, **client_kwargs) self._async_client = AsyncClient(host=self.base_url, **client_kwargs) return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" embedded_docs = self._client.embed(self.model, texts)["embeddings"] return embedded_docs def embed_query(self, text: str) -> List[float]: """Embed query text.""" return self.embed_documents([text])[0] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" embedded_docs = (await self._async_client.embed(self.model, texts))[ "embeddings" ] return embedded_docs async def aembed_query(self, text: str) -> List[float]: """Embed query text.""" return (await self.aembed_documents([text]))[0]
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lc_public_repos/langchain/libs/partners/ollama
lc_public_repos/langchain/libs/partners/ollama/langchain_ollama/__init__.py
"""This is the langchain_ollama package. It provides infrastructure for interacting with the Ollama service. """ from importlib import metadata from langchain_ollama.chat_models import ChatOllama from langchain_ollama.embeddings import OllamaEmbeddings from langchain_ollama.llms import OllamaLLM try: __version__ = metadata.version(__package__) except metadata.PackageNotFoundError: # Case where package metadata is not available. __version__ = "" del metadata # optional, avoids polluting the results of dir(__package__) __all__ = [ "ChatOllama", "OllamaLLM", "OllamaEmbeddings", "__version__", ]
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests/test_embeddings.py
"""Test Ollama embeddings.""" from typing import Type from langchain_tests.integration_tests import EmbeddingsIntegrationTests from langchain_ollama.embeddings import OllamaEmbeddings class TestOllamaEmbeddings(EmbeddingsIntegrationTests): @property def embeddings_class(self) -> Type[OllamaEmbeddings]: return OllamaEmbeddings @property def embedding_model_params(self) -> dict: return {"model": "llama3:latest"}
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests/test_llms.py
"""Test OllamaLLM llm.""" from langchain_ollama.llms import OllamaLLM MODEL_NAME = "llama3" def test_stream() -> None: """Test streaming tokens from OpenAI.""" llm = OllamaLLM(model=MODEL_NAME) for token in llm.stream("I'm Pickle Rick"): assert isinstance(token, str) async def test_astream() -> None: """Test streaming tokens from OpenAI.""" llm = OllamaLLM(model=MODEL_NAME) async for token in llm.astream("I'm Pickle Rick"): assert isinstance(token, str) async def test_abatch() -> None: """Test streaming tokens from OllamaLLM.""" llm = OllamaLLM(model=MODEL_NAME) result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token, str) async def test_abatch_tags() -> None: """Test batch tokens from OllamaLLM.""" llm = OllamaLLM(model=MODEL_NAME) result = await llm.abatch( ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} ) for token in result: assert isinstance(token, str) def test_batch() -> None: """Test batch tokens from OllamaLLM.""" llm = OllamaLLM(model=MODEL_NAME) result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token, str) async def test_ainvoke() -> None: """Test invoke tokens from OllamaLLM.""" llm = OllamaLLM(model=MODEL_NAME) result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) assert isinstance(result, str) def test_invoke() -> None: """Test invoke tokens from OllamaLLM.""" llm = OllamaLLM(model=MODEL_NAME) result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) assert isinstance(result, str)
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests/test_compile.py
import pytest @pytest.mark.compile def test_placeholder() -> None: """Used for compiling integration tests without running any real tests.""" pass
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lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests
lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py
"""Ollama specific chat model integration tests""" from typing import List, Optional import pytest from pydantic import BaseModel, Field from langchain_ollama import ChatOllama @pytest.mark.parametrize(("model"), [("llama3.1")]) def test_structured_output_deeply_nested(model: str) -> None: """Test to verify structured output with a nested objects.""" llm = ChatOllama(model=model, temperature=0) class Person(BaseModel): """Information about a person.""" name: Optional[str] = Field(default=None, description="The name of the person") hair_color: Optional[str] = Field( default=None, description="The color of the person's hair if known" ) height_in_meters: Optional[str] = Field( default=None, description="Height measured in meters" ) class Data(BaseModel): """Extracted data about people.""" people: List[Person] chat = llm.with_structured_output(Data) # type: ignore[arg-type] text = ( "Alan Smith is 6 feet tall and has blond hair." "Alan Poe is 3 feet tall and has grey hair." ) result = chat.invoke(text) assert isinstance(result, Data) for chunk in chat.stream(text): assert isinstance(chunk, Data)
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lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests
lc_public_repos/langchain/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_standard.py
"""Test chat model integration using standard integration tests.""" from typing import Type import pytest from langchain_core.language_models import BaseChatModel from langchain_tests.integration_tests import ChatModelIntegrationTests from langchain_ollama.chat_models import ChatOllama class TestChatOllama(ChatModelIntegrationTests): @property def chat_model_class(self) -> Type[ChatOllama]: return ChatOllama @property def chat_model_params(self) -> dict: return {"model": "llama3.1"} @property def supports_image_inputs(self) -> bool: return True @pytest.mark.xfail( reason=( "Fails with 'AssertionError'. Ollama does not support 'tool_choice' yet." ) ) def test_structured_output(self, model: BaseChatModel) -> None: super().test_structured_output(model) @pytest.mark.xfail( reason=( "Fails with 'AssertionError'. Ollama does not support 'tool_choice' yet." ) ) def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None: super().test_structured_output_pydantic_2_v1(model)
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/unit_tests/test_chat_models.py
"""Test chat model integration.""" from typing import Dict, Type from langchain_tests.unit_tests import ChatModelUnitTests from langchain_ollama.chat_models import ChatOllama class TestChatOllama(ChatModelUnitTests): @property def chat_model_class(self) -> Type[ChatOllama]: return ChatOllama @property def chat_model_params(self) -> Dict: return {"model": "llama3-groq-tool-use"}
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/unit_tests/test_imports.py
from langchain_ollama import __all__ EXPECTED_ALL = [ "OllamaLLM", "ChatOllama", "OllamaEmbeddings", "__version__", ] def test_all_imports() -> None: assert sorted(EXPECTED_ALL) == sorted(__all__)
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/unit_tests/test_embeddings.py
"""Test embedding model integration.""" from langchain_ollama.embeddings import OllamaEmbeddings def test_initialization() -> None: """Test embedding model initialization.""" OllamaEmbeddings(model="llama3")
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lc_public_repos/langchain/libs/partners/ollama/tests
lc_public_repos/langchain/libs/partners/ollama/tests/unit_tests/test_llms.py
"""Test Ollama Chat API wrapper.""" from langchain_ollama import OllamaLLM def test_initialization() -> None: """Test integration initialization.""" OllamaLLM(model="llama3") def test_model_params() -> None: # Test standard tracing params llm = OllamaLLM(model="llama3") ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "ollama", "ls_model_type": "llm", "ls_model_name": "llama3", } llm = OllamaLLM(model="llama3", num_predict=3) ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "ollama", "ls_model_type": "llm", "ls_model_name": "llama3", "ls_max_tokens": 3, }
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lc_public_repos/langchain/libs/partners/ollama
lc_public_repos/langchain/libs/partners/ollama/scripts/lint_imports.sh
#!/bin/bash set -eu # Initialize a variable to keep track of errors errors=0 # make sure not importing from langchain, langchain_experimental, or langchain_community git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) # Decide on an exit status based on the errors if [ "$errors" -gt 0 ]; then exit 1 else exit 0 fi
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lc_public_repos/langchain/libs/partners/ollama
lc_public_repos/langchain/libs/partners/ollama/scripts/check_imports.py
"""load multiple Python files specified as command line arguments.""" import sys import traceback from importlib.machinery import SourceFileLoader if __name__ == "__main__": files = sys.argv[1:] has_failure = False for file in files: try: SourceFileLoader("x", file).load_module() except Exception: has_failure = True print(file) # noqa: T201 traceback.print_exc() print() # noqa: T201 sys.exit(1 if has_failure else 0)
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/milvus/README.md
This package has moved! https://github.com/langchain-ai/langchain-milvus/tree/main/libs/milvus
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/ai21/README.md
This package has moved! https://github.com/langchain-ai/langchain-ai21/tree/main/libs/ai21
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/exa/Makefile
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests # Default target executed when no arguments are given to make. all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ integration_tests: TEST_FILE=tests/integration_tests/ test integration_tests: poetry run pytest $(TEST_FILE) tests: poetry run pytest $(TEST_FILE) test_watch: poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) ###################### # LINTING AND FORMATTING ###################### # Define a variable for Python and notebook files. PYTHON_FILES=. MYPY_CACHE=.mypy_cache lint format: PYTHON_FILES=. lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/exa --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint_package: PYTHON_FILES=langchain_exa lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml spell_fix: poetry run codespell --toml pyproject.toml -w check_imports: $(shell find langchain_exa -name '*.py') poetry run python ./scripts/check_imports.py $^ ###################### # HELP ###################### help: @echo '----' @echo 'check_imports - check imports' @echo 'format - run code formatters' @echo 'lint - run linters' @echo 'test - run unit tests' @echo 'tests - run unit tests' @echo 'test TEST_FILE=<test_file> - run all tests in file'
0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/exa/LICENSE
MIT License Copyright (c) 2023 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/exa/poetry.lock
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0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/exa/README.md
# langchain-exa This package contains the LangChain integrations for Exa Cloud generative models. ## Installation ```bash pip install -U langchain-exa ``` ## Exa Search Retriever You can retrieve search results as follows ```python from langchain_exa import ExaSearchRetriever exa_api_key = "YOUR API KEY" # Create a new instance of the ExaSearchRetriever exa = ExaSearchRetriever(exa_api_key=exa_api_key) # Search for a query and save the results results = exa.invoke("What is the capital of France?") # Print the results print(results) ``` ## Exa Search Results You can run the ExaSearchResults module as follows ```python from langchain_exa import ExaSearchResults # Initialize the ExaSearchResults tool search_tool = ExaSearchResults(exa_api_key="YOUR API KEY") # Perform a search query search_results = search_tool._run( query="When was the last time the New York Knicks won the NBA Championship?", num_results=5, text_contents_options=True, highlights=True ) print("Search Results:", search_results) ``` ## Exa Find Similar Results You can run the ExaFindSimilarResults module as follows ```python from langchain_exa import ExaFindSimilarResults # Initialize the ExaFindSimilarResults tool find_similar_tool = ExaFindSimilarResults(exa_api_key="YOUR API KEY") # Find similar results based on a URL similar_results = find_similar_tool._run( url="http://espn.com", num_results=5, text_contents_options=True, highlights=True ) print("Similar Results:", similar_results) ```
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/exa/pyproject.toml
[build-system] requires = [ "poetry-core>=1.0.0",] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-exa" version = "0.2.1" description = "An integration package connecting Exa and LangChain" authors = [] readme = "README.md" repository = "https://github.com/langchain-ai/langchain" license = "MIT" [tool.mypy] disallow_untyped_defs = "True" [tool.poetry.urls] "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/exa" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-exa%3D%3D0%22&expanded=true" [tool.poetry.dependencies] python = ">=3.9,<4.0" langchain-core = "^0.3.15" exa-py = "^1.0.8" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] [tool.coverage.run] omit = [ "tests/*",] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them",] asyncio_mode = "auto" [tool.poetry.group.test] optional = true [tool.poetry.group.codespell] optional = true [tool.poetry.group.lint] optional = true [tool.poetry.group.dev] optional = true [tool.poetry.group.test_integration] optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" [tool.poetry.group.codespell.dependencies] codespell = "^2.2.0" [tool.poetry.group.lint.dependencies] ruff = "^0.5" [tool.poetry.group.test_integration.dependencies] [tool.poetry.group.typing.dependencies] mypy = "^1.10" [tool.poetry.group.test.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.dev.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.typing.dependencies.langchain-core] path = "../../core" develop = true
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lc_public_repos/langchain/libs/partners/exa/tests
lc_public_repos/langchain/libs/partners/exa/tests/integration_tests/test_search_tool.py
from langchain_exa import ( ExaSearchResults, # type: ignore[import-not-found, import-not-found] ) def test_search_tool() -> None: tool = ExaSearchResults() res = tool.invoke({"query": "best time to visit japan", "num_results": 5}) print(res) # noqa: T201 assert not isinstance(res, str) # str means error for this tool\
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lc_public_repos/langchain/libs/partners/exa/tests
lc_public_repos/langchain/libs/partners/exa/tests/integration_tests/test_retriever.py
from langchain_core.documents import ( Document, # type: ignore[import-not-found, import-not-found] ) from langchain_exa import ExaSearchRetriever def test_exa_retriever() -> None: retriever = ExaSearchRetriever() res = retriever.invoke("best time to visit japan") print(res) # noqa: T201 assert len(res) == 10 # default k assert isinstance(res, list) assert isinstance(res[0], Document) def test_exa_retriever_highlights() -> None: retriever = ExaSearchRetriever(highlights=True) res = retriever.invoke("best time to visit japan") print(res) # noqa: T201 assert isinstance(res, list) assert isinstance(res[0], Document) highlights = res[0].metadata["highlights"] highlight_scores = res[0].metadata["highlight_scores"] assert isinstance(highlights, list) assert isinstance(highlight_scores, list) assert isinstance(highlights[0], str) assert isinstance(highlight_scores[0], float)
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lc_public_repos/langchain/libs/partners/exa/tests
lc_public_repos/langchain/libs/partners/exa/tests/integration_tests/test_compile.py
import pytest # type: ignore[import-not-found, import-not-found] @pytest.mark.compile def test_placeholder() -> None: """Used for compiling integration tests without running any real tests.""" pass
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lc_public_repos/langchain/libs/partners/exa/tests
lc_public_repos/langchain/libs/partners/exa/tests/integration_tests/test_find_similar_tool.py
from langchain_exa import ( ExaFindSimilarResults, # type: ignore[import-not-found, import-not-found] ) def test_similarity_tool() -> None: tool = ExaFindSimilarResults() res = tool.invoke( { "url": "https://boutiquejapan.com/when-is-the-best-time-of-year-to-visit-japan/", "num_results": 5, } ) print(res) # noqa: T201 assert not isinstance(res, str) # str means error for this tool
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lc_public_repos/langchain/libs/partners/exa/tests
lc_public_repos/langchain/libs/partners/exa/tests/unit_tests/test_imports.py
from langchain_exa import __all__ # type: ignore[import-not-found, import-not-found] EXPECTED_ALL = [ "ExaSearchResults", "ExaSearchRetriever", "HighlightsContentsOptions", "TextContentsOptions", "ExaFindSimilarResults", ] def test_all_imports() -> None: assert sorted(EXPECTED_ALL) == sorted(__all__)
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lc_public_repos/langchain/libs/partners/exa
lc_public_repos/langchain/libs/partners/exa/langchain_exa/retrievers.py
from typing import Any, Dict, List, Literal, Optional, Union from exa_py import Exa # type: ignore[untyped-import] from exa_py.api import ( HighlightsContentsOptions, # type: ignore[untyped-import] TextContentsOptions, # type: ignore[untyped-import] ) from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever from pydantic import Field, SecretStr, model_validator from langchain_exa._utilities import initialize_client def _get_metadata(result: Any) -> Dict[str, Any]: """Get the metadata from a result object.""" metadata = { "title": result.title, "url": result.url, "id": result.id, "score": result.score, "published_date": result.published_date, "author": result.author, } if getattr(result, "highlights"): metadata["highlights"] = result.highlights if getattr(result, "highlight_scores"): metadata["highlight_scores"] = result.highlight_scores return metadata class ExaSearchRetriever(BaseRetriever): """Exa Search retriever.""" k: int = 10 # num_results """The number of search results to return.""" include_domains: Optional[List[str]] = None """A list of domains to include in the search.""" exclude_domains: Optional[List[str]] = None """A list of domains to exclude from the search.""" start_crawl_date: Optional[str] = None """The start date for the crawl (in YYYY-MM-DD format).""" end_crawl_date: Optional[str] = None """The end date for the crawl (in YYYY-MM-DD format).""" start_published_date: Optional[str] = None """The start date for when the document was published (in YYYY-MM-DD format).""" end_published_date: Optional[str] = None """The end date for when the document was published (in YYYY-MM-DD format).""" use_autoprompt: Optional[bool] = None """Whether to use autoprompt for the search.""" type: str = "neural" """The type of search, 'keyword' or 'neural'. Default: neural""" highlights: Optional[Union[HighlightsContentsOptions, bool]] = None """Whether to set the page content to the highlights of the results.""" text_contents_options: Union[TextContentsOptions, Literal[True]] = True """How to set the page content of the results""" client: Exa = Field(default=None) exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None @model_validator(mode="before") @classmethod def validate_environment(cls, values: Dict) -> Any: """Validate the environment.""" values = initialize_client(values) return values def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: response = self.client.search_and_contents( # type: ignore[misc] query, num_results=self.k, text=self.text_contents_options, highlights=self.highlights, # type: ignore include_domains=self.include_domains, exclude_domains=self.exclude_domains, start_crawl_date=self.start_crawl_date, end_crawl_date=self.end_crawl_date, start_published_date=self.start_published_date, end_published_date=self.end_published_date, use_autoprompt=self.use_autoprompt, ) results = response.results return [ Document( page_content=(result.text), metadata=_get_metadata(result), ) for result in results ]
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lc_public_repos/langchain/libs/partners/exa
lc_public_repos/langchain/libs/partners/exa/langchain_exa/_utilities.py
import os # type: ignore[import-not-found] from typing import Dict from exa_py import Exa # type: ignore from langchain_core.utils import convert_to_secret_str def initialize_client(values: Dict) -> Dict: """Initialize the client.""" exa_api_key = values.get("exa_api_key") or os.environ.get("EXA_API_KEY") or "" values["exa_api_key"] = convert_to_secret_str(exa_api_key) args = { "api_key": values["exa_api_key"].get_secret_value(), } if values.get("exa_base_url"): args["base_url"] = values["exa_base_url"] values["client"] = Exa(**args) return values
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lc_public_repos/langchain/libs/partners/exa
lc_public_repos/langchain/libs/partners/exa/langchain_exa/tools.py
"""Tool for the Exa Search API.""" from typing import Any, Dict, List, Optional, Union from exa_py import Exa # type: ignore[untyped-import] from exa_py.api import ( HighlightsContentsOptions, # type: ignore[untyped-import] TextContentsOptions, # type: ignore[untyped-import] ) from langchain_core.callbacks import ( CallbackManagerForToolRun, ) from langchain_core.tools import BaseTool from pydantic import Field, SecretStr, model_validator from langchain_exa._utilities import initialize_client class ExaSearchResults(BaseTool): """Exa Search tool. Setup: Install ``langchain-exa`` and set environment variable ``EXA_API_KEY``. .. code-block:: bash pip install -U langchain-exa export EXA_API_KEY="your-api-key" Instantiation: .. code-block:: python from langchain-exa import ExaSearchResults tool = ExaSearchResults() Invocation with args: .. code-block:: python tool.invoke({"query":"what is the weather in SF","num_results":1}) .. code-block:: python SearchResponse(results=[Result(url='https://www.wunderground.com/weather/37.8,-122.4', id='https://www.wunderground.com/weather/37.8,-122.4', title='San Francisco, CA Weather Conditionsstar_ratehome', score=0.1843988299369812, published_date='2023-02-23T01:17:06.594Z', author=None, text='The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.', highlights=None, highlight_scores=None, summary=None)], autoprompt_string=None) Invocation with ToolCall: .. code-block:: python tool.invoke({"args": {"query":"what is the weather in SF","num_results":1}, "id": "1", "name": tool.name, "type": "tool_call"}) .. code-block:: python ToolMessage(content='Title: San Francisco, CA Weather Conditionsstar_ratehome\nURL: https://www.wunderground.com/weather/37.8,-122.4\nID: https://www.wunderground.com/weather/37.8,-122.4\nScore: 0.1843988299369812\nPublished Date: 2023-02-23T01:17:06.594Z\nAuthor: None\nText: The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.\nHighlights: None\nHighlight Scores: None\nSummary: None\n', name='exa_search_results_json', tool_call_id='1') """ # noqa: E501 name: str = "exa_search_results_json" description: str = ( "A wrapper around Exa Search. " "Input should be an Exa-optimized query. " "Output is a JSON array of the query results" ) client: Exa = Field(default=None) exa_api_key: SecretStr = Field(default=None) @model_validator(mode="before") @classmethod def validate_environment(cls, values: Dict) -> Any: """Validate the environment.""" values = initialize_client(values) return values def _run( self, query: str, num_results: int, text_contents_options: Optional[Union[TextContentsOptions, bool]] = None, highlights: Optional[Union[HighlightsContentsOptions, bool]] = None, include_domains: Optional[List[str]] = None, exclude_domains: Optional[List[str]] = None, start_crawl_date: Optional[str] = None, end_crawl_date: Optional[str] = None, start_published_date: Optional[str] = None, end_published_date: Optional[str] = None, use_autoprompt: Optional[bool] = None, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> Union[List[Dict], str]: """Use the tool.""" try: return self.client.search_and_contents( query, num_results=num_results, text=text_contents_options, # type: ignore highlights=highlights, # type: ignore include_domains=include_domains, exclude_domains=exclude_domains, start_crawl_date=start_crawl_date, end_crawl_date=end_crawl_date, start_published_date=start_published_date, end_published_date=end_published_date, use_autoprompt=use_autoprompt, ) # type: ignore except Exception as e: return repr(e) class ExaFindSimilarResults(BaseTool): """Tool that queries the Metaphor Search API and gets back json.""" name: str = "exa_find_similar_results_json" description: str = ( "A wrapper around Exa Find Similar. " "Input should be an Exa-optimized query. " "Output is a JSON array of the query results" ) client: Exa = Field(default=None) exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None @model_validator(mode="before") @classmethod def validate_environment(cls, values: Dict) -> Any: """Validate the environment.""" values = initialize_client(values) return values def _run( self, url: str, num_results: int, text_contents_options: Optional[Union[TextContentsOptions, bool]] = None, highlights: Optional[Union[HighlightsContentsOptions, bool]] = None, include_domains: Optional[List[str]] = None, exclude_domains: Optional[List[str]] = None, start_crawl_date: Optional[str] = None, end_crawl_date: Optional[str] = None, start_published_date: Optional[str] = None, end_published_date: Optional[str] = None, exclude_source_domain: Optional[bool] = None, category: Optional[str] = None, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> Union[List[Dict], str]: """Use the tool.""" try: return self.client.find_similar_and_contents( url, num_results=num_results, text=text_contents_options, # type: ignore highlights=highlights, # type: ignore include_domains=include_domains, exclude_domains=exclude_domains, start_crawl_date=start_crawl_date, end_crawl_date=end_crawl_date, start_published_date=start_published_date, end_published_date=end_published_date, exclude_source_domain=exclude_source_domain, category=category, ) # type: ignore except Exception as e: return repr(e)
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lc_public_repos/langchain/libs/partners/exa
lc_public_repos/langchain/libs/partners/exa/langchain_exa/__init__.py
from exa_py.api import ( # type: ignore # type: ignore[import-not-found, import-not-found] HighlightsContentsOptions, TextContentsOptions, ) from langchain_exa.retrievers import ExaSearchRetriever from langchain_exa.tools import ExaFindSimilarResults, ExaSearchResults __all__ = [ "ExaSearchResults", "ExaSearchRetriever", "HighlightsContentsOptions", "TextContentsOptions", "ExaFindSimilarResults", ]
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lc_public_repos/langchain/libs/partners/exa
lc_public_repos/langchain/libs/partners/exa/scripts/lint_imports.sh
#!/bin/bash set -eu # Initialize a variable to keep track of errors errors=0 # make sure not importing from langchain or langchain_experimental git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) # Decide on an exit status based on the errors if [ "$errors" -gt 0 ]; then exit 1 else exit 0 fi
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lc_public_repos/langchain/libs/partners/exa
lc_public_repos/langchain/libs/partners/exa/scripts/check_imports.py
import sys import traceback from importlib.machinery import SourceFileLoader if __name__ == "__main__": files = sys.argv[1:] has_failure = False for file in files: try: SourceFileLoader("x", file).load_module() except Exception: has_failure = True print(file) # noqa: T201 traceback.print_exc() print() # noqa: T201 sys.exit(1 if has_failure else 0)
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/fireworks/Makefile
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests # Default target executed when no arguments are given to make. all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) test_watch: poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) ###################### # LINTING AND FORMATTING ###################### # Define a variable for Python and notebook files. PYTHON_FILES=. MYPY_CACHE=.mypy_cache lint format: PYTHON_FILES=. lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/fireworks --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint_package: PYTHON_FILES=langchain_fireworks lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml spell_fix: poetry run codespell --toml pyproject.toml -w check_imports: $(shell find langchain_fireworks -name '*.py') poetry run python ./scripts/check_imports.py $^ ###################### # HELP ###################### help: @echo '----' @echo 'check_imports - check imports' @echo 'format - run code formatters' @echo 'lint - run linters' @echo 'test - run unit tests' @echo 'tests - run unit tests' @echo 'test TEST_FILE=<test_file> - run all tests in file'
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/fireworks/LICENSE
MIT License Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/fireworks/poetry.lock
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/fireworks/README.md
# LangChain-Fireworks This is the partner package for tying Fireworks.ai and LangChain. Fireworks really strive to provide good support for LangChain use cases, so if you run into any issues please let us know. You can reach out to us [in our Discord channel](https://discord.com/channels/1137072072808472616/) ## Installation To use the `langchain-fireworks` package, follow these installation steps: ```bash pip install langchain-fireworks ``` ## Basic usage ### Setting up 1. Sign in to [Fireworks AI](http://fireworks.ai/) to obtain an API Key to access the models, and make sure it is set as the `FIREWORKS_API_KEY` environment variable. Once you've signed in and obtained an API key, follow these steps to set the `FIREWORKS_API_KEY` environment variable: - **Linux/macOS:** Open your terminal and execute the following command: ```bash export FIREWORKS_API_KEY='your_api_key' ``` **Note:** To make this environment variable persistent across terminal sessions, add the above line to your `~/.bashrc`, `~/.bash_profile`, or `~/.zshrc` file. - **Windows:** For Command Prompt, use: ```cmd set FIREWORKS_API_KEY=your_api_key ``` 2. Set up your model using a model id. If the model is not set, the default model is `fireworks-llama-v2-7b-chat`. See the full, most up-to-date model list on [fireworks.ai](https://fireworks.ai/models). ```python import getpass import os # Initialize a Fireworks model llm = Fireworks( model="accounts/fireworks/models/mixtral-8x7b-instruct", base_url="https://api.fireworks.ai/inference/v1/completions", ) ``` ### Calling the Model Directly You can call the model directly with string prompts to get completions. ```python # Single prompt output = llm.invoke("Who's the best quarterback in the NFL?") print(output) ``` ```python # Calling multiple prompts output = llm.generate( [ "Who's the best cricket player in 2016?", "Who's the best basketball player in the league?", ] ) print(output.generations) ``` ## Advanced usage ### Tool use: LangChain Agent + Fireworks function calling model Please checkout how to teach Fireworks function calling model to use a [calculator here](https://github.com/fw-ai/cookbook/blob/main/examples/function_calling/fireworks_langchain_tool_usage.ipynb). Fireworks focus on delivering the best experience for fast model inference as well as tool use. You can check out [our blog](https://fireworks.ai/blog/firefunction-v1-gpt-4-level-function-calling) for more details on how it fares compares to GPT-4, the punchline is that it is on par with GPT-4 in terms just function calling use cases, but it is way faster and much cheaper. ### RAG: LangChain agent + Fireworks function calling model + MongoDB + Nomic AI embeddings Please check out the [cookbook here](https://github.com/fw-ai/cookbook/blob/main/examples/rag/mongodb_agent.ipynb) for an end to end flow
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/fireworks/pyproject.toml
[build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-fireworks" version = "0.2.5" description = "An integration package connecting Fireworks and LangChain" authors = [] readme = "README.md" repository = "https://github.com/langchain-ai/langchain" license = "MIT" [tool.mypy] disallow_untyped_defs = "True" [tool.poetry.urls] "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/fireworks" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-fireworks%3D%3D0%22&expanded=true" [tool.poetry.dependencies] python = ">=3.9,<4.0" langchain-core = "^0.3.15" fireworks-ai = ">=0.13.0" openai = "^1.10.0" requests = "^2" aiohttp = "^3.9.1" [tool.ruff.lint] select = ["E", "F", "I", "T201"] [tool.coverage.run] omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" [tool.poetry.group.test] optional = true [tool.poetry.group.codespell] optional = true [tool.poetry.group.test_integration] optional = true [tool.poetry.group.lint] optional = true [tool.poetry.group.dev] optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" [tool.poetry.group.codespell.dependencies] codespell = "^2.2.0" [tool.poetry.group.test_integration.dependencies] [tool.poetry.group.lint.dependencies] ruff = "^0.5" [tool.poetry.group.typing.dependencies] mypy = "^1.10" types-requests = "^2" [tool.poetry.group.test.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.test.dependencies.langchain-tests] path = "../../standard-tests" develop = true [tool.poetry.group.dev.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.typing.dependencies.langchain-core] path = "../../core" develop = true
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/integration_tests/test_standard.py
"""Standard LangChain interface tests""" from typing import Type import pytest from langchain_core.language_models import BaseChatModel from langchain_tests.integration_tests import ( # type: ignore[import-not-found] ChatModelIntegrationTests, # type: ignore[import-not-found] ) from langchain_fireworks import ChatFireworks class TestFireworksStandard(ChatModelIntegrationTests): @property def chat_model_class(self) -> Type[BaseChatModel]: return ChatFireworks @property def chat_model_params(self) -> dict: return { "model": "accounts/fireworks/models/firefunction-v2", "temperature": 0, } @pytest.mark.xfail(reason="Not yet implemented.") def test_tool_message_histories_list_content(self, model: BaseChatModel) -> None: super().test_tool_message_histories_list_content(model)
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/integration_tests/test_chat_models.py
"""Test ChatFireworks API wrapper You will need FIREWORKS_API_KEY set in your environment to run these tests. """ import json from typing import Optional from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessageChunk from pydantic import BaseModel from langchain_fireworks import ChatFireworks def test_chat_fireworks_call() -> None: """Test valid call to fireworks.""" llm = ChatFireworks( # type: ignore[call-arg] model="accounts/fireworks/models/firefunction-v1", temperature=0 ) resp = llm.invoke("Hello!") assert isinstance(resp, AIMessage) assert len(resp.content) > 0 def test_tool_choice() -> None: """Test that tool choice is respected.""" llm = ChatFireworks( # type: ignore[call-arg] model="accounts/fireworks/models/firefunction-v1", temperature=0 ) class MyTool(BaseModel): name: str age: int with_tool = llm.bind_tools([MyTool], tool_choice="MyTool") resp = with_tool.invoke("Who was the 27 year old named Erick?") assert isinstance(resp, AIMessage) assert resp.content == "" # should just be tool call tool_calls = resp.additional_kwargs["tool_calls"] assert len(tool_calls) == 1 tool_call = tool_calls[0] assert tool_call["function"]["name"] == "MyTool" assert json.loads(tool_call["function"]["arguments"]) == { "age": 27, "name": "Erick", } assert tool_call["type"] == "function" assert isinstance(resp.tool_calls, list) assert len(resp.tool_calls) == 1 tool_call = resp.tool_calls[0] assert tool_call["name"] == "MyTool" assert tool_call["args"] == {"age": 27, "name": "Erick"} def test_tool_choice_bool() -> None: """Test that tool choice is respected just passing in True.""" llm = ChatFireworks( # type: ignore[call-arg] model="accounts/fireworks/models/firefunction-v1", temperature=0 ) class MyTool(BaseModel): name: str age: int with_tool = llm.bind_tools([MyTool], tool_choice=True) resp = with_tool.invoke("Who was the 27 year old named Erick?") assert isinstance(resp, AIMessage) assert resp.content == "" # should just be tool call tool_calls = resp.additional_kwargs["tool_calls"] assert len(tool_calls) == 1 tool_call = tool_calls[0] assert tool_call["function"]["name"] == "MyTool" assert json.loads(tool_call["function"]["arguments"]) == { "age": 27, "name": "Erick", } assert tool_call["type"] == "function" def test_stream() -> None: """Test streaming tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] for token in llm.stream("I'm Pickle Rick"): assert isinstance(token.content, str) async def test_astream() -> None: """Test streaming tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] full: Optional[BaseMessageChunk] = None chunks_with_token_counts = 0 async for token in llm.astream("I'm Pickle Rick"): assert isinstance(token, AIMessageChunk) assert isinstance(token.content, str) full = token if full is None else full + token if token.usage_metadata is not None: chunks_with_token_counts += 1 if chunks_with_token_counts != 1: raise AssertionError( "Expected exactly one chunk with token counts. " "AIMessageChunk aggregation adds counts. Check that " "this is behaving properly." ) assert isinstance(full, AIMessageChunk) assert full.usage_metadata is not None assert full.usage_metadata["input_tokens"] > 0 assert full.usage_metadata["output_tokens"] > 0 assert ( full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"] == full.usage_metadata["total_tokens"] ) async def test_abatch() -> None: """Test abatch tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token.content, str) async def test_abatch_tags() -> None: """Test batch tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] result = await llm.abatch( ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} ) for token in result: assert isinstance(token.content, str) def test_batch() -> None: """Test batch tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token.content, str) async def test_ainvoke() -> None: """Test invoke tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) assert isinstance(result.content, str) def test_invoke() -> None: """Test invoke tokens from ChatFireworks.""" llm = ChatFireworks() # type: ignore[call-arg] result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) assert isinstance(result.content, str)
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/integration_tests/test_embeddings.py
"""Test Fireworks embeddings.""" from langchain_fireworks.embeddings import FireworksEmbeddings def test_langchain_fireworks_embedding_documents() -> None: """Test Fireworks hosted embeddings.""" documents = ["foo bar"] embedding = FireworksEmbeddings(model="nomic-ai/nomic-embed-text-v1.5") output = embedding.embed_documents(documents) assert len(output) == 1 assert len(output[0]) > 0 def test_langchain_fireworks_embedding_query() -> None: """Test Fireworks hosted embeddings.""" document = "foo bar" embedding = FireworksEmbeddings(model="nomic-ai/nomic-embed-text-v1.5") output = embedding.embed_query(document) assert len(output) > 0
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/integration_tests/test_llms.py
"""Test Fireworks API wrapper. In order to run this test, you need to have an Fireworks api key. You can get it by registering for free at https://api.fireworks.ai/. A test key can be found at https://api.fireworks.ai/settings/api-keys You'll then need to set FIREWORKS_API_KEY environment variable to your api key. """ import pytest as pytest from langchain_fireworks import Fireworks def test_fireworks_call() -> None: """Test simple call to fireworks.""" llm = Fireworks( model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) output = llm.invoke("Say foo:") assert llm._llm_type == "fireworks" assert isinstance(output, str) assert len(output) > 0 async def test_fireworks_acall() -> None: """Test simple call to fireworks.""" llm = Fireworks( model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) output = await llm.agenerate(["Say foo:"], stop=["bar"]) assert llm._llm_type == "fireworks" output_text = output.generations[0][0].text assert isinstance(output_text, str) assert output_text.count("bar") <= 1 def test_stream() -> None: """Test streaming tokens from OpenAI.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") for token in llm.stream("I'm Pickle Rick"): assert isinstance(token, str) async def test_astream() -> None: """Test streaming tokens from OpenAI.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") async for token in llm.astream("I'm Pickle Rick"): assert isinstance(token, str) async def test_abatch() -> None: """Test streaming tokens from Fireworks.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token, str) async def test_abatch_tags() -> None: """Test batch tokens from Fireworks.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") result = await llm.abatch( ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} ) for token in result: assert isinstance(token, str) def test_batch() -> None: """Test batch tokens from Fireworks.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token, str) async def test_ainvoke() -> None: """Test invoke tokens from Fireworks.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) assert isinstance(result, str) def test_invoke() -> None: """Test invoke tokens from Fireworks.""" llm = Fireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct") result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) assert isinstance(result, str)
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/integration_tests/test_compile.py
import pytest @pytest.mark.compile def test_placeholder() -> None: """Used for compiling integration tests without running any real tests.""" pass
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests/test_standard.py
"""Standard LangChain interface tests""" from typing import Tuple, Type from langchain_core.language_models import BaseChatModel from langchain_tests.unit_tests import ( # type: ignore[import-not-found] ChatModelUnitTests, # type: ignore[import-not-found] ) from langchain_fireworks import ChatFireworks class TestFireworksStandard(ChatModelUnitTests): @property def chat_model_class(self) -> Type[BaseChatModel]: return ChatFireworks @property def chat_model_params(self) -> dict: return {"api_key": "test_api_key"} @property def init_from_env_params(self) -> Tuple[dict, dict, dict]: return ( { "FIREWORKS_API_KEY": "api_key", "FIREWORKS_API_BASE": "https://base.com", }, {}, { "fireworks_api_key": "api_key", "fireworks_api_base": "https://base.com", }, )
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests/test_imports.py
from langchain_fireworks import __all__ EXPECTED_ALL = [ "__version__", "ChatFireworks", "Fireworks", "FireworksEmbeddings", ] def test_all_imports() -> None: assert sorted(EXPECTED_ALL) == sorted(__all__)
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests/test_embeddings.py
"""Test embedding model integration.""" from langchain_fireworks.embeddings import FireworksEmbeddings def test_initialization() -> None: """Test embedding model initialization.""" FireworksEmbeddings(model="nomic-ai/nomic-embed-text-v1.5")
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests/test_llms.py
"""Test Fireworks LLM""" from typing import cast from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_fireworks import Fireworks def test_fireworks_api_key_is_secret_string() -> None: """Test that the API key is stored as a SecretStr.""" llm = Fireworks( # type: ignore[call-arg] fireworks_api_key="secret-api-key", model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) assert isinstance(llm.fireworks_api_key, SecretStr) # Test api_key alias llm = Fireworks( api_key="secret-api-key", # type: ignore[arg-type] model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) assert isinstance(llm.fireworks_api_key, SecretStr) def test_fireworks_api_key_masked_when_passed_from_env( monkeypatch: MonkeyPatch, capsys: CaptureFixture ) -> None: """Test that the API key is masked when passed from an environment variable.""" monkeypatch.setenv("FIREWORKS_API_KEY", "secret-api-key") llm = Fireworks( model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) print(llm.fireworks_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" def test_fireworks_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: """Test that the API key is masked when passed via the constructor.""" llm = Fireworks( # type: ignore[call-arg] fireworks_api_key="secret-api-key", model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) print(llm.fireworks_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" def test_fireworks_uses_actual_secret_value_from_secretstr() -> None: """Test that the actual secret value is correctly retrieved.""" llm = Fireworks( # type: ignore[call-arg] fireworks_api_key="secret-api-key", model="accounts/fireworks/models/mixtral-8x7b-instruct", temperature=0.2, max_tokens=250, ) assert cast(SecretStr, llm.fireworks_api_key).get_secret_value() == "secret-api-key" def test_fireworks_model_params() -> None: # Test standard tracing params llm = Fireworks(model="foo", api_key="secret-api-key") # type: ignore[arg-type] ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "fireworks", "ls_model_type": "llm", "ls_model_name": "foo", } llm = Fireworks( model="foo", api_key="secret-api-key", # type: ignore[arg-type] max_tokens=10, temperature=0.1, ) ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "fireworks", "ls_model_type": "llm", "ls_model_name": "foo", "ls_max_tokens": 10, "ls_temperature": 0.1, }
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lc_public_repos/langchain/libs/partners/fireworks/tests
lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests/test_embeddings_standard.py
"""Standard LangChain interface tests""" from typing import Tuple, Type from langchain_core.embeddings import Embeddings from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests from langchain_fireworks import FireworksEmbeddings class TestFireworksStandard(EmbeddingsUnitTests): @property def embeddings_class(self) -> Type[Embeddings]: return FireworksEmbeddings @property def embeddings_params(self) -> dict: return {"api_key": "test_api_key"} @property def init_from_env_params(self) -> Tuple[dict, dict, dict]: return ( { "FIREWORKS_API_KEY": "api_key", }, {}, { "fireworks_api_key": "api_key", }, )
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lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests
lc_public_repos/langchain/libs/partners/fireworks/tests/unit_tests/__snapshots__/test_standard.ambr
# serializer version: 1 # name: TestFireworksStandard.test_serdes[serialized] dict({ 'id': list([ 'langchain', 'chat_models', 'fireworks', 'ChatFireworks', ]), 'kwargs': dict({ 'fireworks_api_key': dict({ 'id': list([ 'FIREWORKS_API_KEY', ]), 'lc': 1, 'type': 'secret', }), 'max_retries': 2, 'max_tokens': 100, 'model_name': 'accounts/fireworks/models/mixtral-8x7b-instruct', 'n': 1, 'request_timeout': 60.0, 'stop': list([ ]), }), 'lc': 1, 'name': 'ChatFireworks', 'type': 'constructor', }) # ---
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/langchain_fireworks/version.py
"""Main entrypoint into package.""" from importlib import metadata try: __version__ = metadata.version(__package__) except metadata.PackageNotFoundError: # Case where package metadata is not available. __version__ = ""
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/langchain_fireworks/llms.py
"""Wrapper around Fireworks AI's Completion API.""" import logging from typing import Any, Dict, List, Optional import requests from aiohttp import ClientSession from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM from langchain_core.utils import get_pydantic_field_names from langchain_core.utils.utils import _build_model_kwargs, secret_from_env from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_fireworks.version import __version__ logger = logging.getLogger(__name__) class Fireworks(LLM): """LLM models from `Fireworks`. To use, you'll need an API key which you can find here: https://fireworks.ai This can be passed in as init param ``fireworks_api_key`` or set as environment variable ``FIREWORKS_API_KEY``. Fireworks AI API reference: https://readme.fireworks.ai/ Example: .. code-block:: python response = fireworks.generate(["Tell me a joke."]) """ base_url: str = "https://api.fireworks.ai/inference/v1/completions" """Base inference API URL.""" fireworks_api_key: SecretStr = Field( alias="api_key", default_factory=secret_from_env( "FIREWORKS_API_KEY", error_message=( "You must specify an api key. " "You can pass it an argument as `api_key=...` or " "set the environment variable `FIREWORKS_API_KEY`." ), ), ) """Fireworks API key. Automatically read from env variable `FIREWORKS_API_KEY` if not provided. """ model: str """Model name. Available models listed here: https://readme.fireworks.ai/ """ temperature: Optional[float] = None """Model temperature.""" top_p: Optional[float] = None """Used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities. A value of 1 will always yield the same output. A temperature less than 1 favors more correctness and is appropriate for question answering or summarization. A value greater than 1 introduces more randomness in the output. """ model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" top_k: Optional[int] = None """Used to limit the number of choices for the next predicted word or token. It specifies the maximum number of tokens to consider at each step, based on their probability of occurrence. This technique helps to speed up the generation process and can improve the quality of the generated text by focusing on the most likely options. """ max_tokens: Optional[int] = None """The maximum number of tokens to generate.""" repetition_penalty: Optional[float] = None """A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition. """ logprobs: Optional[int] = None """An integer that specifies how many top token log probabilities are included in the response for each token generation step. """ model_config = ConfigDict( extra="forbid", populate_by_name=True, ) @model_validator(mode="before") @classmethod def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) values = _build_model_kwargs(values, all_required_field_names) return values @property def _llm_type(self) -> str: """Return type of model.""" return "fireworks" def _format_output(self, output: dict) -> str: return output["choices"][0]["text"] @staticmethod def get_user_agent() -> str: return f"langchain-fireworks/{__version__}" @property def default_params(self) -> Dict[str, Any]: return { "model": self.model, "temperature": self.temperature, "top_p": self.top_p, "top_k": self.top_k, "max_tokens": self.max_tokens, "repetition_penalty": self.repetition_penalty, } def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Fireworks's text generation endpoint. Args: prompt: The prompt to pass into the model. Returns: The string generated by the model.. """ headers = { "Authorization": f"Bearer {self.fireworks_api_key.get_secret_value()}", "Content-Type": "application/json", } stop_to_use = stop[0] if stop and len(stop) == 1 else stop payload: Dict[str, Any] = { **self.default_params, "prompt": prompt, "stop": stop_to_use, **kwargs, } # filter None values to not pass them to the http payload payload = {k: v for k, v in payload.items() if v is not None} response = requests.post(url=self.base_url, json=payload, headers=headers) if response.status_code >= 500: raise Exception(f"Fireworks Server: Error {response.status_code}") elif response.status_code >= 400: raise ValueError(f"Fireworks received an invalid payload: {response.text}") elif response.status_code != 200: raise Exception( f"Fireworks returned an unexpected response with status " f"{response.status_code}: {response.text}" ) data = response.json() output = self._format_output(data) return output async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call Fireworks model to get predictions based on the prompt. Args: prompt: The prompt to pass into the model. Returns: The string generated by the model. """ headers = { "Authorization": f"Bearer {self.fireworks_api_key.get_secret_value()}", "Content-Type": "application/json", } stop_to_use = stop[0] if stop and len(stop) == 1 else stop payload: Dict[str, Any] = { **self.default_params, "prompt": prompt, "stop": stop_to_use, **kwargs, } # filter None values to not pass them to the http payload payload = {k: v for k, v in payload.items() if v is not None} async with ClientSession() as session: async with session.post( self.base_url, json=payload, headers=headers ) as response: if response.status >= 500: raise Exception(f"Fireworks Server: Error {response.status}") elif response.status >= 400: raise ValueError( f"Fireworks received an invalid payload: {response.text}" ) elif response.status != 200: raise Exception( f"Fireworks returned an unexpected response with status " f"{response.status}: {response.text}" ) response_json = await response.json() output = self._format_output(response_json) return output
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/langchain_fireworks/chat_models.py
"""Fireworks chat wrapper.""" from __future__ import annotations import json import logging from operator import itemgetter from typing import ( Any, AsyncIterator, Callable, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Tuple, Type, TypedDict, Union, cast, ) from fireworks.client import AsyncFireworks, Fireworks # type: ignore from langchain_core._api import deprecated from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import ( BaseChatModel, LangSmithParams, agenerate_from_stream, generate_from_stream, ) from langchain_core.messages import ( AIMessage, AIMessageChunk, BaseMessage, BaseMessageChunk, ChatMessage, ChatMessageChunk, FunctionMessage, FunctionMessageChunk, HumanMessage, HumanMessageChunk, InvalidToolCall, SystemMessage, SystemMessageChunk, ToolCall, ToolMessage, ToolMessageChunk, ) from langchain_core.messages.tool import ( ToolCallChunk, ) from langchain_core.messages.tool import ( tool_call_chunk as create_tool_call_chunk, ) from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser from langchain_core.output_parsers.base import OutputParserLike from langchain_core.output_parsers.openai_tools import ( JsonOutputKeyToolsParser, PydanticToolsParser, make_invalid_tool_call, parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import ( get_pydantic_field_names, ) from langchain_core.utils.function_calling import ( convert_to_openai_function, convert_to_openai_tool, ) from langchain_core.utils.pydantic import is_basemodel_subclass from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env from pydantic import ( BaseModel, ConfigDict, Field, SecretStr, model_validator, ) from typing_extensions import Self logger = logging.getLogger(__name__) def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: """Convert a dictionary to a LangChain message. Args: _dict: The dictionary. Returns: The LangChain message. """ role = _dict.get("role") if role == "user": return HumanMessage(content=_dict.get("content", "")) elif role == "assistant": # Fix for azure # Also Fireworks returns None for tool invocations content = _dict.get("content", "") or "" additional_kwargs: Dict = {} if function_call := _dict.get("function_call"): additional_kwargs["function_call"] = dict(function_call) tool_calls = [] invalid_tool_calls = [] if raw_tool_calls := _dict.get("tool_calls"): additional_kwargs["tool_calls"] = raw_tool_calls for raw_tool_call in raw_tool_calls: try: tool_calls.append(parse_tool_call(raw_tool_call, return_id=True)) except Exception as e: invalid_tool_calls.append( dict(make_invalid_tool_call(raw_tool_call, str(e))) ) return AIMessage( content=content, additional_kwargs=additional_kwargs, tool_calls=tool_calls, invalid_tool_calls=invalid_tool_calls, ) elif role == "system": return SystemMessage(content=_dict.get("content", "")) elif role == "function": return FunctionMessage( content=_dict.get("content", ""), name=_dict.get("name", "") ) elif role == "tool": additional_kwargs = {} if "name" in _dict: additional_kwargs["name"] = _dict["name"] return ToolMessage( content=_dict.get("content", ""), tool_call_id=_dict.get("tool_call_id", ""), additional_kwargs=additional_kwargs, ) else: return ChatMessage(content=_dict.get("content", ""), role=role or "") def _convert_message_to_dict(message: BaseMessage) -> dict: """Convert a LangChain message to a dictionary. Args: message: The LangChain message. Returns: The dictionary. """ message_dict: Dict[str, Any] if isinstance(message, ChatMessage): message_dict = {"role": message.role, "content": message.content} elif isinstance(message, HumanMessage): message_dict = {"role": "user", "content": message.content} elif isinstance(message, AIMessage): message_dict = {"role": "assistant", "content": message.content} if "function_call" in message.additional_kwargs: message_dict["function_call"] = message.additional_kwargs["function_call"] # If function call only, content is None not empty string if message_dict["content"] == "": message_dict["content"] = None if message.tool_calls or message.invalid_tool_calls: message_dict["tool_calls"] = [ _lc_tool_call_to_fireworks_tool_call(tc) for tc in message.tool_calls ] + [ _lc_invalid_tool_call_to_fireworks_tool_call(tc) for tc in message.invalid_tool_calls ] elif "tool_calls" in message.additional_kwargs: message_dict["tool_calls"] = message.additional_kwargs["tool_calls"] # If tool calls only, content is None not empty string if "tool_calls" in message_dict and message_dict["content"] == "": message_dict["content"] = None else: pass elif isinstance(message, SystemMessage): message_dict = {"role": "system", "content": message.content} elif isinstance(message, FunctionMessage): message_dict = { "role": "function", "content": message.content, "name": message.name, } elif isinstance(message, ToolMessage): message_dict = { "role": "tool", "content": message.content, "tool_call_id": message.tool_call_id, } else: raise TypeError(f"Got unknown type {message}") if "name" in message.additional_kwargs: message_dict["name"] = message.additional_kwargs["name"] return message_dict def _convert_chunk_to_message_chunk( chunk: Mapping[str, Any], default_class: Type[BaseMessageChunk] ) -> BaseMessageChunk: choice = chunk["choices"][0] _dict = choice["delta"] role = cast(str, _dict.get("role")) content = cast(str, _dict.get("content") or "") additional_kwargs: Dict = {} tool_call_chunks: List[ToolCallChunk] = [] if _dict.get("function_call"): function_call = dict(_dict["function_call"]) if "name" in function_call and function_call["name"] is None: function_call["name"] = "" additional_kwargs["function_call"] = function_call if raw_tool_calls := _dict.get("tool_calls"): additional_kwargs["tool_calls"] = raw_tool_calls for rtc in raw_tool_calls: try: tool_call_chunks.append( create_tool_call_chunk( name=rtc["function"].get("name"), args=rtc["function"].get("arguments"), id=rtc.get("id"), index=rtc.get("index"), ) ) except KeyError: pass if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content) elif role == "assistant" or default_class == AIMessageChunk: if usage := chunk.get("usage"): input_tokens = usage.get("prompt_tokens", 0) output_tokens = usage.get("completion_tokens", 0) usage_metadata = { "input_tokens": input_tokens, "output_tokens": output_tokens, "total_tokens": usage.get("total_tokens", input_tokens + output_tokens), } else: usage_metadata = None return AIMessageChunk( content=content, additional_kwargs=additional_kwargs, tool_call_chunks=tool_call_chunks, usage_metadata=usage_metadata, # type: ignore[arg-type] ) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content) elif role == "function" or default_class == FunctionMessageChunk: return FunctionMessageChunk(content=content, name=_dict["name"]) elif role == "tool" or default_class == ToolMessageChunk: return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"]) elif role or default_class == ChatMessageChunk: return ChatMessageChunk(content=content, role=role) else: return default_class(content=content) # type: ignore class _FunctionCall(TypedDict): name: str # This is basically a copy and replace for ChatFireworks, except # - I needed to gut out tiktoken and some of the token estimation logic # (not sure how important it is) # - Environment variable is different # we should refactor into some OpenAI-like class in the future class ChatFireworks(BaseChatModel): """`Fireworks` Chat large language models API. To use, you should have the environment variable ``FIREWORKS_API_KEY`` set with your API key. Any parameters that are valid to be passed to the fireworks.create call can be passed in, even if not explicitly saved on this class. Example: .. code-block:: python from langchain_fireworks.chat_models import ChatFireworks fireworks = ChatFireworks( model_name="accounts/fireworks/models/mixtral-8x7b-instruct") """ @property def lc_secrets(self) -> Dict[str, str]: return {"fireworks_api_key": "FIREWORKS_API_KEY"} @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "chat_models", "fireworks"] @property def lc_attributes(self) -> Dict[str, Any]: attributes: Dict[str, Any] = {} if self.fireworks_api_base: attributes["fireworks_api_base"] = self.fireworks_api_base return attributes @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return True client: Any = Field(default=None, exclude=True) #: :meta private: async_client: Any = Field(default=None, exclude=True) #: :meta private: model_name: str = Field( default="accounts/fireworks/models/mixtral-8x7b-instruct", alias="model" ) """Model name to use.""" temperature: float = 0.0 """What sampling temperature to use.""" stop: Optional[Union[str, List[str]]] = Field(default=None, alias="stop_sequences") """Default stop sequences.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" fireworks_api_key: SecretStr = Field( alias="api_key", default_factory=secret_from_env( "FIREWORKS_API_KEY", error_message=( "You must specify an api key. " "You can pass it an argument as `api_key=...` or " "set the environment variable `FIREWORKS_API_KEY`." ), ), ) """Fireworks API key. Automatically read from env variable `FIREWORKS_API_KEY` if not provided. """ fireworks_api_base: Optional[str] = Field( alias="base_url", default_factory=from_env("FIREWORKS_API_BASE", default=None) ) """Base URL path for API requests, leave blank if not using a proxy or service emulator.""" request_timeout: Union[float, Tuple[float, float], Any, None] = Field( default=None, alias="timeout" ) """Timeout for requests to Fireworks completion API. Can be float, httpx.Timeout or None.""" streaming: bool = False """Whether to stream the results or not.""" n: int = 1 """Number of chat completions to generate for each prompt.""" max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" max_retries: Optional[int] = None """Maximum number of retries to make when generating.""" model_config = ConfigDict( populate_by_name=True, ) @model_validator(mode="before") @classmethod def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) values = _build_model_kwargs(values, all_required_field_names) return values @model_validator(mode="after") def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" if self.n < 1: raise ValueError("n must be at least 1.") if self.n > 1 and self.streaming: raise ValueError("n must be 1 when streaming.") client_params = { "api_key": ( self.fireworks_api_key.get_secret_value() if self.fireworks_api_key else None ), "base_url": self.fireworks_api_base, "timeout": self.request_timeout, } if not self.client: self.client = Fireworks(**client_params).chat.completions if not self.async_client: self.async_client = AsyncFireworks(**client_params).chat.completions if self.max_retries: self.client._max_retries = self.max_retries self.async_client._max_retries = self.max_retries return self @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Fireworks API.""" params = { "model": self.model_name, "stream": self.streaming, "n": self.n, "temperature": self.temperature, "stop": self.stop, **self.model_kwargs, } if self.max_tokens is not None: params["max_tokens"] = self.max_tokens return params def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get standard params for tracing.""" params = self._get_invocation_params(stop=stop, **kwargs) ls_params = LangSmithParams( ls_provider="fireworks", ls_model_name=self.model_name, ls_model_type="chat", ls_temperature=params.get("temperature", self.temperature), ) if ls_max_tokens := params.get("max_tokens", self.max_tokens): ls_params["ls_max_tokens"] = ls_max_tokens if ls_stop := stop or params.get("stop", None): ls_params["ls_stop"] = ls_stop return ls_params def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: overall_token_usage: dict = {} system_fingerprint = None for output in llm_outputs: if output is None: # Happens in streaming continue token_usage = output["token_usage"] if token_usage is not None: for k, v in token_usage.items(): if k in overall_token_usage: overall_token_usage[k] += v else: overall_token_usage[k] = v if system_fingerprint is None: system_fingerprint = output.get("system_fingerprint") combined = {"token_usage": overall_token_usage, "model_name": self.model_name} if system_fingerprint: combined["system_fingerprint"] = system_fingerprint return combined def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk for chunk in self.client.create(messages=message_dicts, **params): if not isinstance(chunk, dict): chunk = chunk.model_dump() if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] message_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class) generation_info = {} if finish_reason := choice.get("finish_reason"): generation_info["finish_reason"] = finish_reason logprobs = choice.get("logprobs") if logprobs: generation_info["logprobs"] = logprobs default_chunk_class = message_chunk.__class__ generation_chunk = ChatGenerationChunk( message=message_chunk, generation_info=generation_info or None ) if run_manager: run_manager.on_llm_new_token( generation_chunk.text, chunk=generation_chunk, logprobs=logprobs ) yield generation_chunk def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: should_stream = stream if stream is not None else self.streaming if should_stream: stream_iter = self._stream( messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = { **params, **({"stream": stream} if stream is not None else {}), **kwargs, } response = self.client.create(messages=message_dicts, **params) return self._create_chat_result(response) def _create_message_dicts( self, messages: List[BaseMessage], stop: Optional[List[str]] ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: params = self._default_params if stop is not None: params["stop"] = stop message_dicts = [_convert_message_to_dict(m) for m in messages] return message_dicts, params def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult: generations = [] if not isinstance(response, dict): response = response.model_dump() token_usage = response.get("usage", {}) for res in response["choices"]: message = _convert_dict_to_message(res["message"]) if token_usage and isinstance(message, AIMessage): message.usage_metadata = { "input_tokens": token_usage.get("prompt_tokens", 0), "output_tokens": token_usage.get("completion_tokens", 0), "total_tokens": token_usage.get("total_tokens", 0), } generation_info = dict(finish_reason=res.get("finish_reason")) if "logprobs" in res: generation_info["logprobs"] = res["logprobs"] gen = ChatGeneration( message=message, generation_info=generation_info, ) generations.append(gen) llm_output = { "token_usage": token_usage, "model_name": self.model_name, "system_fingerprint": response.get("system_fingerprint", ""), } return ChatResult(generations=generations, llm_output=llm_output) async def _astream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk async for chunk in self.async_client.acreate(messages=message_dicts, **params): if not isinstance(chunk, dict): chunk = chunk.model_dump() if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] message_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class) generation_info = {} if finish_reason := choice.get("finish_reason"): generation_info["finish_reason"] = finish_reason logprobs = choice.get("logprobs") if logprobs: generation_info["logprobs"] = logprobs default_chunk_class = message_chunk.__class__ generation_chunk = ChatGenerationChunk( message=message_chunk, generation_info=generation_info or None ) if run_manager: await run_manager.on_llm_new_token( token=generation_chunk.text, chunk=generation_chunk, logprobs=logprobs, ) yield generation_chunk async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: should_stream = stream if stream is not None else self.streaming if should_stream: stream_iter = self._astream( messages, stop=stop, run_manager=run_manager, **kwargs ) return await agenerate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = { **params, **({"stream": stream} if stream is not None else {}), **kwargs, } response = await self.async_client.acreate(messages=message_dicts, **params) return self._create_chat_result(response) @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {"model_name": self.model_name, **self._default_params} def _get_invocation_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> Dict[str, Any]: """Get the parameters used to invoke the model.""" return { "model": self.model_name, **super()._get_invocation_params(stop=stop), **self._default_params, **kwargs, } @property def _llm_type(self) -> str: """Return type of chat model.""" return "fireworks-chat" @deprecated( since="0.2.1", alternative="langchain_fireworks.chat_models.ChatFireworks.bind_tools", removal="1.0.0", ) def bind_functions( self, functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], function_call: Optional[ Union[_FunctionCall, str, Literal["auto", "none"]] ] = None, **kwargs: Any, ) -> Runnable[LanguageModelInput, BaseMessage]: """Bind functions (and other objects) to this chat model. Assumes model is compatible with Fireworks function-calling API. NOTE: Using bind_tools is recommended instead, as the `functions` and `function_call` request parameters are officially marked as deprecated by Fireworks. Args: functions: A list of function definitions to bind to this chat model. Can be a dictionary, pydantic model, or callable. Pydantic models and callables will be automatically converted to their schema dictionary representation. function_call: Which function to require the model to call. Must be the name of the single provided function or "auto" to automatically determine which function to call (if any). **kwargs: Any additional parameters to pass to the :class:`~langchain.runnable.Runnable` constructor. """ formatted_functions = [convert_to_openai_function(fn) for fn in functions] if function_call is not None: function_call = ( {"name": function_call} if isinstance(function_call, str) and function_call not in ("auto", "none") else function_call ) if isinstance(function_call, dict) and len(formatted_functions) != 1: raise ValueError( "When specifying `function_call`, you must provide exactly one " "function." ) if ( isinstance(function_call, dict) and formatted_functions[0]["name"] != function_call["name"] ): raise ValueError( f"Function call {function_call} was specified, but the only " f"provided function was {formatted_functions[0]['name']}." ) kwargs = {**kwargs, "function_call": function_call} return super().bind( functions=formatted_functions, **kwargs, ) def bind_tools( self, tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], *, tool_choice: Optional[ Union[dict, str, Literal["auto", "any", "none"], bool] ] = None, **kwargs: Any, ) -> Runnable[LanguageModelInput, BaseMessage]: """Bind tool-like objects to this chat model. Assumes model is compatible with Fireworks tool-calling API. Args: tools: A list of tool definitions to bind to this chat model. Supports any tool definition handled by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`. tool_choice: Which tool to require the model to call. Must be the name of the single provided function, "auto" to automatically determine which function to call with the option to not call any function, "any" to enforce that some function is called, or a dict of the form: {"type": "function", "function": {"name": <<tool_name>>}}. **kwargs: Any additional parameters to pass to :meth:`~langchain_fireworks.chat_models.ChatFireworks.bind` """ formatted_tools = [convert_to_openai_tool(tool) for tool in tools] if tool_choice is not None and tool_choice: if isinstance(tool_choice, str) and ( tool_choice not in ("auto", "any", "none") ): tool_choice = {"type": "function", "function": {"name": tool_choice}} if isinstance(tool_choice, bool): if len(tools) > 1: raise ValueError( "tool_choice can only be True when there is one tool. Received " f"{len(tools)} tools." ) tool_name = formatted_tools[0]["function"]["name"] tool_choice = { "type": "function", "function": {"name": tool_name}, } kwargs["tool_choice"] = tool_choice return super().bind(tools=formatted_tools, **kwargs) def with_structured_output( self, schema: Optional[Union[Dict, Type[BaseModel]]] = None, *, method: Literal["function_calling", "json_mode"] = "function_calling", include_raw: bool = False, **kwargs: Any, ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]: """Model wrapper that returns outputs formatted to match the given schema. Args: schema: The output schema. Can be passed in as: - an OpenAI function/tool schema, - a JSON Schema, - a TypedDict class (support added in 0.1.7), - or a Pydantic class. If ``schema`` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or TypedDict class. .. versionchanged:: 0.1.7 Added support for TypedDict class. method: The method for steering model generation, either "function_calling" or "json_mode". If "function_calling" then the schema will be converted to an OpenAI function and the returned model will make use of the function-calling API. If "json_mode" then OpenAI's JSON mode will be used. Note that if using "json_mode" then you must include instructions for formatting the output into the desired schema into the model call. include_raw: If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys "raw", "parsed", and "parsing_error". Returns: A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`. If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict. If ``include_raw`` is True, then Runnable outputs a dict with keys: - ``"raw"``: BaseMessage - ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above. - ``"parsing_error"``: Optional[BaseException] Example: schema=Pydantic class, method="function_calling", include_raw=False: .. code-block:: python from typing import Optional from langchain_fireworks import ChatFireworks from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str # If we provide default values and/or descriptions for fields, these will be passed # to the model. This is an important part of improving a model's ability to # correctly return structured outputs. justification: Optional[str] = Field( default=None, description="A justification for the answer." ) llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> AnswerWithJustification( # answer='They weigh the same', # justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.' # ) Example: schema=Pydantic class, method="function_calling", include_raw=True: .. code-block:: python from langchain_fireworks import ChatFireworks from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, include_raw=True ) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}), # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'), # 'parsing_error': None # } Example: schema=TypedDict class, method="function_calling", include_raw=False: .. code-block:: python # IMPORTANT: If you are using Python <=3.8, you need to import Annotated # from typing_extensions, not from typing. from typing_extensions import Annotated, TypedDict from langchain_fireworks import ChatFireworks class AnswerWithJustification(TypedDict): '''An answer to the user question along with justification for the answer.''' answer: str justification: Annotated[ Optional[str], None, "A justification for the answer." ] llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } Example: schema=OpenAI function schema, method="function_calling", include_raw=False: .. code-block:: python from langchain_fireworks import ChatFireworks oai_schema = { 'name': 'AnswerWithJustification', 'description': 'An answer to the user question along with justification for the answer.', 'parameters': { 'type': 'object', 'properties': { 'answer': {'type': 'string'}, 'justification': {'description': 'A justification for the answer.', 'type': 'string'} }, 'required': ['answer'] } } llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0) structured_llm = llm.with_structured_output(oai_schema) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } Example: schema=Pydantic class, method="json_mode", include_raw=True: .. code-block:: from langchain_fireworks import ChatFireworks from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, method="json_mode", include_raw=True ) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n" "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'), # 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'), # 'parsing_error': None # } Example: schema=None, method="json_mode", include_raw=True: .. code-block:: structured_llm = llm.with_structured_output(method="json_mode", include_raw=True) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n" "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'), # 'parsed': { # 'answer': 'They are both the same weight.', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.' # }, # 'parsing_error': None # } """ # noqa: E501 if kwargs: raise ValueError(f"Received unsupported arguments {kwargs}") is_pydantic_schema = _is_pydantic_class(schema) if method == "function_calling": if schema is None: raise ValueError( "schema must be specified when method is 'function_calling'. " "Received None." ) tool_name = convert_to_openai_tool(schema)["function"]["name"] llm = self.bind_tools([schema], tool_choice=tool_name) if is_pydantic_schema: output_parser: OutputParserLike = PydanticToolsParser( tools=[schema], # type: ignore[list-item] first_tool_only=True, # type: ignore[list-item] ) else: output_parser = JsonOutputKeyToolsParser( key_name=tool_name, first_tool_only=True ) elif method == "json_mode": llm = self.bind(response_format={"type": "json_object"}) output_parser = ( PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] if is_pydantic_schema else JsonOutputParser() ) else: raise ValueError( f"Unrecognized method argument. Expected one of 'function_calling' or " f"'json_mode'. Received: '{method}'" ) if include_raw: parser_assign = RunnablePassthrough.assign( parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None ) parser_none = RunnablePassthrough.assign(parsed=lambda _: None) parser_with_fallback = parser_assign.with_fallbacks( [parser_none], exception_key="parsing_error" ) return RunnableMap(raw=llm) | parser_with_fallback else: return llm | output_parser def _is_pydantic_class(obj: Any) -> bool: return isinstance(obj, type) and is_basemodel_subclass(obj) def _lc_tool_call_to_fireworks_tool_call(tool_call: ToolCall) -> dict: return { "type": "function", "id": tool_call["id"], "function": { "name": tool_call["name"], "arguments": json.dumps(tool_call["args"]), }, } def _lc_invalid_tool_call_to_fireworks_tool_call( invalid_tool_call: InvalidToolCall, ) -> dict: return { "type": "function", "id": invalid_tool_call["id"], "function": { "name": invalid_tool_call["name"], "arguments": invalid_tool_call["args"], }, }
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/langchain_fireworks/embeddings.py
from typing import List from langchain_core.embeddings import Embeddings from langchain_core.utils import secret_from_env from openai import OpenAI from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator from typing_extensions import Self # type: ignore class FireworksEmbeddings(BaseModel, Embeddings): """Fireworks embedding model integration. Setup: Install ``langchain_fireworks`` and set environment variable ``FIREWORKS_API_KEY``. .. code-block:: bash pip install -U langchain_fireworks export FIREWORKS_API_KEY="your-api-key" Key init args — completion params: model: str Name of Fireworks model to use. Key init args — client params: fireworks_api_key: SecretStr Fireworks API key. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_fireworks import FireworksEmbeddings model = FireworksEmbeddings( model='nomic-ai/nomic-embed-text-v1.5' # Use FIREWORKS_API_KEY env var or pass it in directly # fireworks_api_key="..." ) Embed multiple texts: .. code-block:: python vectors = embeddings.embed_documents(['hello', 'goodbye']) # Showing only the first 3 coordinates print(len(vectors)) print(vectors[0][:3]) .. code-block:: python 2 [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] Embed single text: .. code-block:: python input_text = "The meaning of life is 42" vector = embeddings.embed_query('hello') print(vector[:3]) .. code-block:: python [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] """ client: OpenAI = Field(default=None, exclude=True) #: :meta private: fireworks_api_key: SecretStr = Field( alias="api_key", default_factory=secret_from_env( "FIREWORKS_API_KEY", default="", ), ) """Fireworks API key. Automatically read from env variable `FIREWORKS_API_KEY` if not provided. """ model: str = "nomic-ai/nomic-embed-text-v1.5" model_config = ConfigDict( populate_by_name=True, arbitrary_types_allowed=True, ) @model_validator(mode="after") def validate_environment(self) -> Self: """Validate environment variables.""" self.client = OpenAI( api_key=self.fireworks_api_key.get_secret_value(), base_url="https://api.fireworks.ai/inference/v1", ) return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" return [ i.embedding for i in self.client.embeddings.create(input=texts, model=self.model).data ] def embed_query(self, text: str) -> List[float]: """Embed query text.""" return self.embed_documents([text])[0]
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/langchain_fireworks/__init__.py
from langchain_fireworks.chat_models import ChatFireworks from langchain_fireworks.embeddings import FireworksEmbeddings from langchain_fireworks.llms import Fireworks from langchain_fireworks.version import __version__ __all__ = [ "__version__", "ChatFireworks", "Fireworks", "FireworksEmbeddings", ]
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/scripts/lint_imports.sh
#!/bin/bash set -eu # Initialize a variable to keep track of errors errors=0 # make sure not importing from langchain or langchain_experimental git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) # Decide on an exit status based on the errors if [ "$errors" -gt 0 ]; then exit 1 else exit 0 fi
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lc_public_repos/langchain/libs/partners/fireworks
lc_public_repos/langchain/libs/partners/fireworks/scripts/check_imports.py
import sys import traceback from importlib.machinery import SourceFileLoader if __name__ == "__main__": files = sys.argv[1:] has_failure = False for file in files: try: SourceFileLoader("x", file).load_module() except Exception: has_faillure = True print(file) # noqa: T201 traceback.print_exc() print() # noqa: T201 sys.exit(1 if has_failure else 0)
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/couchbase/Makefile
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests # Default target executed when no arguments are given to make. all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ integration_test integration_tests: TEST_FILE = tests/integration_tests/ # unit tests are run with the --disable-socket flag to prevent network calls test tests: poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) test_watch: poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) # integration tests are run without the --disable-socket flag to allow network calls integration_test integration_tests: poetry run pytest $(TEST_FILE) ###################### # LINTING AND FORMATTING ###################### # Define a variable for Python and notebook files. PYTHON_FILES=. MYPY_CACHE=.mypy_cache lint format: PYTHON_FILES=. lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/couchbase --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint_package: PYTHON_FILES=langchain_couchbase lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml spell_fix: poetry run codespell --toml pyproject.toml -w check_imports: $(shell find langchain_couchbase -name '*.py') poetry run python ./scripts/check_imports.py $^ ###################### # HELP ###################### help: @echo '----' @echo 'check_imports - check imports' @echo 'format - run code formatters' @echo 'lint - run linters' @echo 'test - run unit tests' @echo 'tests - run unit tests' @echo 'test TEST_FILE=<test_file> - run all tests in file'
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/couchbase/LICENSE
MIT License Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/couchbase/poetry.lock
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0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/couchbase/README.md
# langchain-couchbase This package contains the LangChain integration with Couchbase ## Installation ```bash pip install -U langchain-couchbase ``` ## Usage The `CouchbaseVectorStore` class exposes the connection to the Couchbase vector store. ```python from langchain_couchbase.vectorstores import CouchbaseVectorStore from couchbase.cluster import Cluster from couchbase.auth import PasswordAuthenticator from couchbase.options import ClusterOptions from datetime import timedelta auth = PasswordAuthenticator(username, password) options = ClusterOptions(auth) connect_string = "couchbases://localhost" cluster = Cluster(connect_string, options) # Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5)) embeddings = OpenAIEmbeddings() vectorstore = CouchbaseVectorStore( cluster=cluster, bucket_name="", scope_name="", collection_name="", embedding=embeddings, index_name="vector-search-index", ) ```
0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/couchbase/pyproject.toml
[build-system] requires = [ "poetry-core>=1.0.0",] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-couchbase" version = "0.2.2" description = "An integration package connecting Couchbase and LangChain" authors = [] readme = "README.md" repository = "https://github.com/langchain-ai/langchain" license = "MIT" [tool.mypy] disallow_untyped_defs = "True" ignore_missing_imports = "True" [tool.poetry.urls] "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/couchbase" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-couchbase%3D%3D0%22&expanded=true" [tool.poetry.dependencies] python = ">=3.9,<4.0" langchain-core = "^0.3.15" couchbase = "^4.3.2" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] [tool.coverage.run] omit = [ "tests/*",] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "compile: mark placeholder test used to compile integration tests without running them",] asyncio_mode = "auto" [tool.poetry.group.test] optional = true [tool.poetry.group.codespell] optional = true [tool.poetry.group.test_integration] optional = true [tool.poetry.group.lint] optional = true [tool.poetry.group.dev] optional = true [tool.poetry.group.test.dependencies] pytest = "^7.4.3" pytest-asyncio = "^0.23.2" pytest-socket = "^0.7.0" syrupy = "^4.0.2" langchain = "^0.3.0.dev" [tool.poetry.group.codespell.dependencies] codespell = "^2.2.6" [tool.poetry.group.test_integration.dependencies] [tool.poetry.group.lint.dependencies] ruff = "^0.5" [tool.poetry.group.typing.dependencies] mypy = "^1.10" [tool.poetry.group.test.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.dev.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.typing.dependencies.langchain-core] path = "../../core" develop = true
0
lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/langchain_couchbase/cache.py
""" LangChain Couchbase Caches Functions "_hash", "_loads_generations" and "_dumps_generations" are duplicated in this utility from modules: - "libs/community/langchain_community/cache.py" """ import hashlib import json import logging from datetime import timedelta from typing import Any, Dict, Optional, Union from couchbase.cluster import Cluster from langchain_core.caches import RETURN_VAL_TYPE, BaseCache from langchain_core.embeddings import Embeddings from langchain_core.load.dump import dumps from langchain_core.load.load import loads from langchain_core.outputs import Generation from langchain_couchbase.vectorstores import CouchbaseVectorStore logger = logging.getLogger(__file__) def _hash(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def _dumps_generations(generations: RETURN_VAL_TYPE) -> str: """ Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation` Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: a single string representing a list of generations. This function (+ its counterpart `_loads_generations`) rely on the dumps/loads pair with Reviver, so are able to deal with all subclasses of Generation. Each item in the list can be `dumps`ed to a string, then we make the whole list of strings into a json-dumped. """ return json.dumps([dumps(_item) for _item in generations]) def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]: """ Deserialization of a string into a generic RETURN_VAL_TYPE (i.e. a sequence of `Generation`). See `_dumps_generations`, the inverse of this function. Args: generations_str (str): A string representing a list of generations. Compatible with the legacy cache-blob format Does not raise exceptions for malformed entries, just logs a warning and returns none: the caller should be prepared for such a cache miss. Returns: RETURN_VAL_TYPE: A list of generations. """ try: generations = [loads(_item_str) for _item_str in json.loads(generations_str)] return generations except (json.JSONDecodeError, TypeError): # deferring the (soft) handling to after the legacy-format attempt pass try: gen_dicts = json.loads(generations_str) # not relying on `_load_generations_from_json` (which could disappear): generations = [Generation(**generation_dict) for generation_dict in gen_dicts] logger.warning( f"Legacy 'Generation' cached blob encountered: '{generations_str}'" ) return generations except (json.JSONDecodeError, TypeError): logger.warning( f"Malformed/unparsable cached blob encountered: '{generations_str}'" ) return None def _validate_ttl(ttl: Optional[timedelta]) -> None: """Validate the time to live""" if not isinstance(ttl, timedelta): raise ValueError(f"ttl should be of type timedelta but was {type(ttl)}.") if ttl <= timedelta(seconds=0): raise ValueError( f"ttl must be greater than 0 but was {ttl.total_seconds()} seconds." ) class CouchbaseCache(BaseCache): """Couchbase LLM Cache LLM Cache that uses Couchbase as the backend """ PROMPT = "prompt" LLM = "llm" RETURN_VAL = "return_val" def _check_bucket_exists(self) -> bool: """Check if the bucket exists in the linked Couchbase cluster""" bucket_manager = self._cluster.buckets() try: bucket_manager.get_bucket(self._bucket_name) return True except Exception: return False def _check_scope_and_collection_exists(self) -> bool: """Check if the scope and collection exists in the linked Couchbase bucket Raises a ValueError if either is not found""" scope_collection_map: Dict[str, Any] = {} # Get a list of all scopes in the bucket for scope in self._bucket.collections().get_all_scopes(): scope_collection_map[scope.name] = [] # Get a list of all the collections in the scope for collection in scope.collections: scope_collection_map[scope.name].append(collection.name) # Check if the scope exists if self._scope_name not in scope_collection_map.keys(): raise ValueError( f"Scope {self._scope_name} not found in Couchbase " f"bucket {self._bucket_name}" ) # Check if the collection exists in the scope if self._collection_name not in scope_collection_map[self._scope_name]: raise ValueError( f"Collection {self._collection_name} not found in scope " f"{self._scope_name} in Couchbase bucket {self._bucket_name}" ) return True def __init__( self, cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, ttl: Optional[timedelta] = None, **kwargs: Dict[str, Any], ) -> None: """Initialize the Couchbase LLM Cache Args: cluster (Cluster): couchbase cluster object with active connection. bucket_name (str): name of the bucket to store documents in. scope_name (str): name of the scope in bucket to store documents in. collection_name (str): name of the collection in the scope to store documents in. ttl (Optional[timedelta]): TTL or time for the document to live in the cache After this time, the document will get deleted from the cache. """ if not isinstance(cluster, Cluster): raise ValueError( f"cluster should be an instance of couchbase.Cluster, " f"got {type(cluster)}" ) self._cluster = cluster self._bucket_name = bucket_name self._scope_name = scope_name self._collection_name = collection_name self._ttl = None # Check if the bucket exists if not self._check_bucket_exists(): raise ValueError( f"Bucket {self._bucket_name} does not exist. " " Please create the bucket before searching." ) try: self._bucket = self._cluster.bucket(self._bucket_name) self._scope = self._bucket.scope(self._scope_name) self._collection = self._scope.collection(self._collection_name) except Exception as e: raise ValueError( "Error connecting to couchbase. " "Please check the connection and credentials." ) from e # Check if the scope and collection exists. Throws ValueError if they don't try: self._check_scope_and_collection_exists() except Exception as e: raise e # Check if the time to live is provided and valid if ttl is not None: _validate_ttl(ttl) self._ttl = ttl def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up from cache based on prompt and llm_string.""" try: doc = self._collection.get( self._generate_key(prompt, llm_string) ).content_as[dict] return _loads_generations(doc[self.RETURN_VAL]) except Exception: return None def _generate_key(self, prompt: str, llm_string: str) -> str: """Generate the key based on prompt and llm_string.""" return _hash(prompt + llm_string) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" doc = { self.PROMPT: prompt, self.LLM: llm_string, self.RETURN_VAL: _dumps_generations(return_val), } document_key = self._generate_key(prompt, llm_string) try: if self._ttl: self._collection.upsert( key=document_key, value=doc, expiry=self._ttl, ) else: self._collection.upsert(key=document_key, value=doc) except Exception: logger.error("Error updating cache") def clear(self, **kwargs: Any) -> None: """Clear the cache. This will delete all documents in the collection. This requires an index on the collection. """ try: query = f"DELETE FROM `{self._collection_name}`" self._scope.query(query).execute() except Exception: logger.error("Error clearing cache. Please check if you have an index.") class CouchbaseSemanticCache(BaseCache, CouchbaseVectorStore): """Couchbase Semantic Cache Cache backed by a Couchbase Server with Vector Store support """ LLM = "llm_string" RETURN_VAL = "return_val" def __init__( self, cluster: Cluster, embedding: Embeddings, bucket_name: str, scope_name: str, collection_name: str, index_name: str, score_threshold: Optional[float] = None, ttl: Optional[timedelta] = None, ) -> None: """Initialize the Couchbase LLM Cache Args: cluster (Cluster): couchbase cluster object with active connection. embedding (Embeddings): embedding model to use. bucket_name (str): name of the bucket to store documents in. scope_name (str): name of the scope in bucket to store documents in. collection_name (str): name of the collection in the scope to store documents in. index_name (str): name of the Search index to use. score_threshold (float): score threshold to use for filtering results. ttl (Optional[timedelta]): TTL or time for the document to live in the cache After this time, the document will get deleted from the cache. """ if not isinstance(cluster, Cluster): raise ValueError( f"cluster should be an instance of couchbase.Cluster, " f"got {type(cluster)}" ) self._cluster = cluster self._bucket_name = bucket_name self._scope_name = scope_name self._collection_name = collection_name self._ttl = None # Check if the bucket exists if not self._check_bucket_exists(): raise ValueError( f"Bucket {self._bucket_name} does not exist. " " Please create the bucket before searching." ) try: self._bucket = self._cluster.bucket(self._bucket_name) self._scope = self._bucket.scope(self._scope_name) self._collection = self._scope.collection(self._collection_name) except Exception as e: raise ValueError( "Error connecting to couchbase. " "Please check the connection and credentials." ) from e # Check if the scope and collection exists. Throws ValueError if they don't try: self._check_scope_and_collection_exists() except Exception as e: raise e self.score_threshold = score_threshold if ttl is not None: _validate_ttl(ttl) self._ttl = ttl # Initialize the vector store super().__init__( cluster=cluster, bucket_name=bucket_name, scope_name=scope_name, collection_name=collection_name, embedding=embedding, index_name=index_name, ) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up from cache based on the semantic similarity of the prompt""" search_results = self.similarity_search_with_score( prompt, k=1, search_options={f"metadata.{self.LLM}": llm_string} ) if search_results: selected_doc, score = search_results[0] else: return None # Check if the score is above the threshold if a threshold is provided if self.score_threshold: if score < self.score_threshold: return None # Note that the llm_string might not match the vector search result. # So if the llm_string does not match, do not return the result. if selected_doc.metadata["llm_string"] != llm_string: return None return _loads_generations(selected_doc.metadata[self.RETURN_VAL]) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on the prompt and llm_string""" try: self.add_texts( texts=[prompt], metadatas=[ { self.LLM: llm_string, self.RETURN_VAL: _dumps_generations(return_val), } ], ttl=self._ttl, ) except Exception: logger.error("Error updating cache") def clear(self, **kwargs: Any) -> None: """Clear the cache. This will delete all documents in the collection. This requires an index on the collection. """ try: query = f"DELETE FROM `{self._collection_name}`" self._scope.query(query).execute() except Exception: logger.error("Error clearing cache. Please check if you have an index.")
0
lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/langchain_couchbase/chat_message_histories.py
"""Couchbase Chat Message History""" import logging import time import uuid from datetime import timedelta from typing import Any, Dict, List, Optional, Sequence from couchbase.cluster import Cluster from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.messages import ( BaseMessage, message_to_dict, messages_from_dict, ) logger = logging.getLogger(__name__) DEFAULT_SESSION_ID_KEY = "session_id" DEFAULT_MESSAGE_KEY = "message" DEFAULT_TS_KEY = "ts" DEFAULT_INDEX_NAME = "LANGCHAIN_CHAT_HISTORY" DEFAULT_BATCH_SIZE = 100 def _validate_ttl(ttl: Optional[timedelta]) -> None: """Validate the time to live""" if not isinstance(ttl, timedelta): raise ValueError(f"ttl should be of type timedelta but was {type(ttl)}.") if ttl <= timedelta(seconds=0): raise ValueError( f"ttl must be greater than 0 but was {ttl.total_seconds()} seconds." ) class CouchbaseChatMessageHistory(BaseChatMessageHistory): """Couchbase Chat Message History Chat message history that uses Couchbase as the storage """ def _check_bucket_exists(self) -> bool: """Check if the bucket exists in the linked Couchbase cluster""" bucket_manager = self._cluster.buckets() try: bucket_manager.get_bucket(self._bucket_name) return True except Exception: return False def _check_scope_and_collection_exists(self) -> bool: """Check if the scope and collection exists in the linked Couchbase bucket Raises a ValueError if either is not found""" scope_collection_map: Dict[str, Any] = {} # Get a list of all scopes in the bucket for scope in self._bucket.collections().get_all_scopes(): scope_collection_map[scope.name] = [] # Get a list of all the collections in the scope for collection in scope.collections: scope_collection_map[scope.name].append(collection.name) # Check if the scope exists if self._scope_name not in scope_collection_map.keys(): raise ValueError( f"Scope {self._scope_name} not found in Couchbase " f"bucket {self._bucket_name}" ) # Check if the collection exists in the scope if self._collection_name not in scope_collection_map[self._scope_name]: raise ValueError( f"Collection {self._collection_name} not found in scope " f"{self._scope_name} in Couchbase bucket " f"{self._bucket_name}" ) return True def __init__( self, *, cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, session_id: str, session_id_key: str = DEFAULT_SESSION_ID_KEY, message_key: str = DEFAULT_MESSAGE_KEY, create_index: bool = True, ttl: Optional[timedelta] = None, ) -> None: """Initialize the Couchbase Chat Message History Args: cluster (Cluster): couchbase cluster object with active connection. bucket_name (str): name of the bucket to store documents in. scope_name (str): name of the scope in bucket to store documents in. collection_name (str): name of the collection in the scope to store documents in. session_id (str): value for the session used to associate messages from a single chat session. It is stored as a field in the chat message. session_id_key (str): name of the field to use for the session id. Set to "session_id" by default. message_key (str): name of the field to use for the messages Set to "message" by default. create_index (bool): create an index if True. Set to True by default. ttl (timedelta): time to live for the documents in the collection. When set, the documents are automatically deleted after the ttl expires. """ if not isinstance(cluster, Cluster): raise ValueError( f"cluster should be an instance of couchbase.Cluster, " f"got {type(cluster)}" ) self._cluster = cluster self._bucket_name = bucket_name self._scope_name = scope_name self._collection_name = collection_name self._ttl = None # Check if the bucket exists if not self._check_bucket_exists(): raise ValueError( f"Bucket {self._bucket_name} does not exist. " " Please create the bucket before searching." ) try: self._bucket = self._cluster.bucket(self._bucket_name) self._scope = self._bucket.scope(self._scope_name) self._collection = self._scope.collection(self._collection_name) except Exception as e: raise ValueError( "Error connecting to couchbase. " "Please check the connection and credentials." ) from e # Check if the scope and collection exists. Throws ValueError if they don't try: self._check_scope_and_collection_exists() except Exception as e: raise e self._session_id_key = session_id_key self._message_key = message_key self._create_index = create_index self._session_id = session_id self._ts_key = DEFAULT_TS_KEY if ttl is not None: _validate_ttl(ttl) self._ttl = ttl # Create an index if it does not exist if requested if create_index: index_fields = ( f"({self._session_id_key}, {self._ts_key}, {self._message_key})" ) index_creation_query = ( f"CREATE INDEX {DEFAULT_INDEX_NAME} IF NOT EXISTS ON " + f"{self._collection_name}{index_fields} " ) try: self._scope.query(index_creation_query).execute() except Exception as e: logger.error("Error creating index: ", e) def add_message(self, message: BaseMessage) -> None: """Add a message to the cache""" # Generate a UUID for the document key document_key = uuid.uuid4().hex # get utc timestamp for ordering the messages timestamp = time.time() message_content = message_to_dict(message) try: if self._ttl: self._collection.insert( document_key, value={ self._message_key: message_content, self._session_id_key: self._session_id, self._ts_key: timestamp, }, expiry=self._ttl, ) else: self._collection.insert( document_key, value={ self._message_key: message_content, self._session_id_key: self._session_id, self._ts_key: timestamp, }, ) except Exception as e: logger.error("Error adding message: ", e) def add_messages(self, messages: Sequence[BaseMessage]) -> None: """Add messages to the cache in a batched manner""" batch_size = DEFAULT_BATCH_SIZE messages_to_insert = [] for message in messages: document_key = uuid.uuid4().hex timestamp = time.time() message_content = message_to_dict(message) messages_to_insert.append( { document_key: { self._message_key: message_content, self._session_id_key: self._session_id, self._ts_key: timestamp, }, } ) # Add the messages to the cache in batches of batch_size try: for i in range(0, len(messages_to_insert), batch_size): batch = messages_to_insert[i : i + batch_size] # Convert list of dictionaries to a single dictionary to insert insert_batch = {list(d.keys())[0]: list(d.values())[0] for d in batch} if self._ttl: self._collection.insert_multi(insert_batch, expiry=self._ttl) else: self._collection.insert_multi(insert_batch) except Exception as e: logger.error("Error adding messages: ", e) def clear(self) -> None: """Clear the cache""" # Delete all documents in the collection with the session_id clear_query = ( f"DELETE FROM `{self._collection_name}`" + f"WHERE {self._session_id_key}=$session_id" ) try: self._scope.query(clear_query, session_id=self._session_id).execute() except Exception as e: logger.error("Error clearing cache: ", e) @property def messages(self) -> List[BaseMessage]: """Get all messages in the cache associated with the session_id""" fetch_query = ( f"SELECT {self._message_key} FROM `{self._collection_name}` " + f"where {self._session_id_key}=$session_id" + f" ORDER BY {self._ts_key} ASC" ) message_items = [] try: result = self._scope.query(fetch_query, session_id=self._session_id) for document in result: message_items.append(document[f"{self._message_key}"]) except Exception as e: logger.error("Error fetching messages: ", e) return messages_from_dict(message_items) @messages.setter def messages(self, messages: List[BaseMessage]) -> None: raise NotImplementedError( "Direct assignment to 'messages' is not allowed." " Use the 'add_messages' instead." )
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lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/langchain_couchbase/vectorstores.py
"""Couchbase vector stores.""" from __future__ import annotations import uuid from typing import ( Any, Dict, Iterable, List, Optional, Tuple, Type, ) import couchbase.search as search from couchbase.cluster import Cluster from couchbase.exceptions import DocumentExistsException, DocumentNotFoundException from couchbase.options import SearchOptions from couchbase.vector_search import VectorQuery, VectorSearch from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore class CouchbaseVectorStore(VectorStore): """__ModuleName__ vector store integration. Setup: Install ``langchain-couchbase`` and head over to the Couchbase [website](https://cloud.couchbase.com) and create a new connection, with a bucket, collection, and search index. .. code-block:: bash pip install -U langchain-couchbase .. code-block:: python import getpass COUCHBASE_CONNECTION_STRING = getpass.getpass("Enter the connection string for the Couchbase cluster: ") DB_USERNAME = getpass.getpass("Enter the username for the Couchbase cluster: ") DB_PASSWORD = getpass.getpass("Enter the password for the Couchbase cluster: ") Key init args — indexing params: embedding: Embeddings Embedding function to use. Key init args — client params: cluster: Cluster Couchbase cluster object with active connection. bucket_name: str Name of the bucket to store documents in. scope_name: str Name of the scope in the bucket to store documents in. collection_name: str Name of the collection in the scope to store documents in. index_name: str Name of the Search index to use. Instantiate: .. code-block:: python from datetime import timedelta from langchain_openai import OpenAIEmbeddings from couchbase.auth import PasswordAuthenticator from couchbase.cluster import Cluster from couchbase.options import ClusterOptions auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD) options = ClusterOptions(auth) cluster = Cluster(COUCHBASE_CONNECTION_STRING, options) # Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5)) BUCKET_NAME = "langchain_bucket" SCOPE_NAME = "_default" COLLECTION_NAME = "default" SEARCH_INDEX_NAME = "langchain-test-index" vector_store = CouchbaseVectorStore( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, embedding=embeddings, index_name=SEARCH_INDEX_NAME, ) Add Documents: .. code-block:: python from langchain_core.documents import Document document_1 = Document(page_content="foo", metadata={"baz": "bar"}) document_2 = Document(page_content="thud", metadata={"bar": "baz"}) document_3 = Document(page_content="i will be deleted :(") documents = [document_1, document_2, document_3] ids = ["1", "2", "3"] vector_store.add_documents(documents=documents, ids=ids) Delete Documents: .. code-block:: python vector_store.delete(ids=["3"]) # TODO: Fill out with example output. Search: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python # TODO: Example output # TODO: Fill out with relevant variables and example output. Search with filter: .. code-block:: python # TODO: Update filter to correct format results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python # TODO: Example output # TODO: Fill out with example output. Search with score: .. code-block:: python results = vector_store.similarity_search_with_score(query="qux",k=1) for doc, score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python # TODO: Example output # TODO: Fill out with example output. Async: .. code-block:: python # add documents # await vector_store.aadd_documents(documents=documents, ids=ids) # delete documents # await vector_store.adelete(ids=["3"]) # search # results = vector_store.asimilarity_search(query="thud",k=1) # search with score results = await vector_store.asimilarity_search_with_score(query="qux",k=1) for doc,score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python # TODO: Example output # TODO: Fill out with example output. Use as Retriever: .. code-block:: python retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud") .. code-block:: python # TODO: Example output """ # noqa: E501 # Default batch size DEFAULT_BATCH_SIZE = 100 _metadata_key = "metadata" _default_text_key = "text" _default_embedding_key = "embedding" def _check_bucket_exists(self) -> bool: """Check if the bucket exists in the linked Couchbase cluster""" bucket_manager = self._cluster.buckets() try: bucket_manager.get_bucket(self._bucket_name) return True except Exception: return False def _check_scope_and_collection_exists(self) -> bool: """Check if the scope and collection exists in the linked Couchbase bucket Raises a ValueError if either is not found""" scope_collection_map: Dict[str, Any] = {} # Get a list of all scopes in the bucket for scope in self._bucket.collections().get_all_scopes(): scope_collection_map[scope.name] = [] # Get a list of all the collections in the scope for collection in scope.collections: scope_collection_map[scope.name].append(collection.name) # Check if the scope exists if self._scope_name not in scope_collection_map.keys(): raise ValueError( f"Scope {self._scope_name} not found in Couchbase " f"bucket {self._bucket_name}" ) # Check if the collection exists in the scope if self._collection_name not in scope_collection_map[self._scope_name]: raise ValueError( f"Collection {self._collection_name} not found in scope " f"{self._scope_name} in Couchbase bucket {self._bucket_name}" ) return True def _check_index_exists(self) -> bool: """Check if the Search index exists in the linked Couchbase cluster Raises a ValueError if the index does not exist""" if self._scoped_index: all_indexes = [ index.name for index in self._scope.search_indexes().get_all_indexes() ] if self._index_name not in all_indexes: raise ValueError( f"Index {self._index_name} does not exist. " " Please create the index before searching." ) else: all_indexes = [ index.name for index in self._cluster.search_indexes().get_all_indexes() ] if self._index_name not in all_indexes: raise ValueError( f"Index {self._index_name} does not exist. " " Please create the index before searching." ) return True def __init__( self, cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, embedding: Embeddings, index_name: str, *, text_key: Optional[str] = _default_text_key, embedding_key: Optional[str] = _default_embedding_key, scoped_index: bool = True, ) -> None: """ Initialize the Couchbase Vector Store. Args: cluster (Cluster): couchbase cluster object with active connection. bucket_name (str): name of bucket to store documents in. scope_name (str): name of scope in the bucket to store documents in. collection_name (str): name of collection in the scope to store documents in embedding (Embeddings): embedding function to use. index_name (str): name of the Search index to use. text_key (optional[str]): key in document to use as text. Set to text by default. embedding_key (optional[str]): key in document to use for the embeddings. Set to embedding by default. scoped_index (optional[bool]): specify whether the index is a scoped index. Set to True by default. """ if not isinstance(cluster, Cluster): raise ValueError( f"cluster should be an instance of couchbase.Cluster, " f"got {type(cluster)}" ) self._cluster = cluster if not embedding: raise ValueError("Embeddings instance must be provided.") if not bucket_name: raise ValueError("bucket_name must be provided.") if not scope_name: raise ValueError("scope_name must be provided.") if not collection_name: raise ValueError("collection_name must be provided.") if not index_name: raise ValueError("index_name must be provided.") self._bucket_name = bucket_name self._scope_name = scope_name self._collection_name = collection_name self._embedding_function = embedding self._text_key = text_key self._embedding_key = embedding_key self._index_name = index_name self._scoped_index = scoped_index # Check if the bucket exists if not self._check_bucket_exists(): raise ValueError( f"Bucket {self._bucket_name} does not exist. " " Please create the bucket before searching." ) try: self._bucket = self._cluster.bucket(self._bucket_name) self._scope = self._bucket.scope(self._scope_name) self._collection = self._scope.collection(self._collection_name) except Exception as e: raise ValueError( "Error connecting to couchbase. " "Please check the connection and credentials." ) from e # Check if the scope and collection exists. Throws ValueError if they don't try: self._check_scope_and_collection_exists() except Exception as e: raise e # Check if the index exists. Throws ValueError if it doesn't try: self._check_index_exists() except Exception as e: raise e def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: Optional[int] = None, **kwargs: Any, ) -> List[str]: """Run texts through the embeddings and persist in vectorstore. If the document IDs are passed, the existing documents (if any) will be overwritten with the new ones. Args: texts (Iterable[str]): Iterable of strings to add to the vectorstore. metadatas (Optional[List[Dict]]): Optional list of metadatas associated with the texts. ids (Optional[List[str]]): Optional list of ids associated with the texts. IDs have to be unique strings across the collection. If it is not specified uuids are generated and used as ids. batch_size (Optional[int]): Optional batch size for bulk insertions. Default is 100. Returns: List[str]:List of ids from adding the texts into the vectorstore. """ if not batch_size: batch_size = self.DEFAULT_BATCH_SIZE doc_ids: List[str] = [] if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] # Check if TTL is provided ttl = kwargs.get("ttl", None) embedded_texts = self._embedding_function.embed_documents(list(texts)) documents_to_insert = [ { id: { self._text_key: text, self._embedding_key: vector, self._metadata_key: metadata, } for id, text, vector, metadata in zip( ids, texts, embedded_texts, metadatas ) } ] # Insert in batches for i in range(0, len(documents_to_insert), batch_size): batch = documents_to_insert[i : i + batch_size] try: # Insert with TTL if provided if ttl: result = self._collection.upsert_multi(batch[0], expiry=ttl) else: result = self._collection.upsert_multi(batch[0]) if result.all_ok: doc_ids.extend(batch[0].keys()) except DocumentExistsException as e: raise ValueError(f"Document already exists: {e}") return doc_ids def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: """Delete documents from the vector store by ids. Args: ids (List[str]): List of IDs of the documents to delete. batch_size (Optional[int]): Optional batch size for bulk deletions. Returns: bool: True if all the documents were deleted successfully, False otherwise. """ if ids is None: raise ValueError("No document ids provided to delete.") batch_size = kwargs.get("batch_size", self.DEFAULT_BATCH_SIZE) deletion_status = True # Delete in batches for i in range(0, len(ids), batch_size): batch = ids[i : i + batch_size] try: result = self._collection.remove_multi(batch) except DocumentNotFoundException as e: deletion_status = False raise ValueError(f"Document not found: {e}") deletion_status &= result.all_ok return deletion_status @property def embeddings(self) -> Embeddings: """Return the query embedding object.""" return self._embedding_function def _format_metadata(self, row_fields: Dict[str, Any]) -> Dict[str, Any]: """Helper method to format the metadata from the Couchbase Search API. Args: row_fields (Dict[str, Any]): The fields to format. Returns: Dict[str, Any]: The formatted metadata. """ metadata = {} for key, value in row_fields.items(): # Couchbase Search returns the metadata key with a prefix # `metadata.` We remove it to get the original metadata key if key.startswith(self._metadata_key): new_key = key.split(self._metadata_key + ".")[-1] metadata[new_key] = value else: metadata[key] = value return metadata def similarity_search( self, query: str, k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any, ) -> List[Document]: """Return documents most similar to embedding vector with their scores. Args: query (str): Query to look up for similar documents k (int): Number of Documents to return. Defaults to 4. search_options (Optional[Dict[str, Any]]): Optional search options that are passed to Couchbase search. Defaults to empty dictionary fields (Optional[List[str]]): Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to all the fields stored in the index. Returns: List of Documents most similar to the query. """ query_embedding = self.embeddings.embed_query(query) docs_with_scores = self.similarity_search_with_score_by_vector( query_embedding, k, search_options, **kwargs ) return [doc for doc, _ in docs_with_scores] def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs most similar to embedding vector with their scores. Args: embedding (List[float]): Embedding vector to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. search_options (Optional[Dict[str, Any]]): Optional search options that are passed to Couchbase search. Defaults to empty dictionary. fields (Optional[List[str]]): Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to all the fields stored in the index. Returns: List of (Document, score) that are the most similar to the query vector. """ fields = kwargs.get("fields", ["*"]) # Document text field needs to be returned from the search if fields != ["*"] and self._text_key not in fields: fields.append(self._text_key) search_req = search.SearchRequest.create( VectorSearch.from_vector_query( VectorQuery( self._embedding_key, embedding, k, ) ) ) try: if self._scoped_index: search_iter = self._scope.search( self._index_name, search_req, SearchOptions( limit=k, fields=fields, raw=search_options, ), ) else: search_iter = self._cluster.search( self._index_name, search_req, SearchOptions(limit=k, fields=fields, raw=search_options), ) docs_with_score = [] # Parse the results for row in search_iter.rows(): text = row.fields.pop(self._text_key, "") id = row.id # Format the metadata from Couchbase metadata = self._format_metadata(row.fields) score = row.score doc = Document(id=id, page_content=text, metadata=metadata) docs_with_score.append((doc, score)) except Exception as e: raise ValueError(f"Search failed with error: {e}") return docs_with_score def similarity_search_with_score( self, query: str, k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return documents that are most similar to the query with their scores. Args: query (str): Query to look up for similar documents k (int): Number of Documents to return. Defaults to 4. search_options (Optional[Dict[str, Any]]): Optional search options that are passed to Couchbase search. Defaults to empty dictionary. fields (Optional[List[str]]): Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to text and metadata fields. Returns: List of (Document, score) that are most similar to the query. """ query_embedding = self.embeddings.embed_query(query) docs_with_score = self.similarity_search_with_score_by_vector( query_embedding, k, search_options, **kwargs ) return docs_with_score def similarity_search_by_vector( self, embedding: List[float], k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any, ) -> List[Document]: """Return documents that are most similar to the vector embedding. Args: embedding (List[float]): Embedding to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. search_options (Optional[Dict[str, Any]]): Optional search options that are passed to Couchbase search. Defaults to empty dictionary. fields (Optional[List[str]]): Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to document text and metadata fields. Returns: List of Documents most similar to the query. """ docs_with_score = self.similarity_search_with_score_by_vector( embedding, k, search_options, **kwargs ) return [doc for doc, _ in docs_with_score] @classmethod def _from_kwargs( cls: Type[CouchbaseVectorStore], embedding: Embeddings, **kwargs: Any, ) -> CouchbaseVectorStore: """Initialize the Couchbase vector store from keyword arguments for the vector store. Args: embedding: Embedding object to use to embed text. **kwargs: Keyword arguments to initialize the vector store with. Accepted arguments are: - cluster - bucket_name - scope_name - collection_name - index_name - text_key - embedding_key - scoped_index """ cluster = kwargs.get("cluster", None) bucket_name = kwargs.get("bucket_name", None) scope_name = kwargs.get("scope_name", None) collection_name = kwargs.get("collection_name", None) index_name = kwargs.get("index_name", None) text_key = kwargs.get("text_key", cls._default_text_key) embedding_key = kwargs.get("embedding_key", cls._default_embedding_key) scoped_index = kwargs.get("scoped_index", True) return cls( embedding=embedding, cluster=cluster, bucket_name=bucket_name, scope_name=scope_name, collection_name=collection_name, index_name=index_name, text_key=text_key, embedding_key=embedding_key, scoped_index=scoped_index, ) @classmethod def from_texts( cls: Type[CouchbaseVectorStore], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> CouchbaseVectorStore: """Construct a Couchbase vector store from a list of texts. Example: .. code-block:: python from langchain_couchbase import CouchbaseVectorStore from langchain_openai import OpenAIEmbeddings from couchbase.cluster import Cluster from couchbase.auth import PasswordAuthenticator from couchbase.options import ClusterOptions from datetime import timedelta auth = PasswordAuthenticator(username, password) options = ClusterOptions(auth) connect_string = "couchbases://localhost" cluster = Cluster(connect_string, options) # Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5)) embeddings = OpenAIEmbeddings() texts = ["hello", "world"] vectorstore = CouchbaseVectorStore.from_texts( texts, embedding=embeddings, cluster=cluster, bucket_name="", scope_name="", collection_name="", index_name="vector-index", ) Args: texts (List[str]): list of texts to add to the vector store. embedding (Embeddings): embedding function to use. metadatas (optional[List[Dict]): list of metadatas to add to documents. **kwargs: Keyword arguments used to initialize the vector store with and/or passed to `add_texts` method. Check the constructor and/or `add_texts` for the list of accepted arguments. Returns: A Couchbase vector store. """ vector_store = cls._from_kwargs(embedding, **kwargs) batch_size = kwargs.get("batch_size", vector_store.DEFAULT_BATCH_SIZE) ids = kwargs.get("ids", None) vector_store.add_texts( texts, metadatas=metadatas, ids=ids, batch_size=batch_size ) return vector_store
0
lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/langchain_couchbase/__init__.py
from langchain_couchbase.cache import CouchbaseCache, CouchbaseSemanticCache from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory from langchain_couchbase.vectorstores import CouchbaseVectorStore __all__ = [ "CouchbaseVectorStore", "CouchbaseCache", "CouchbaseSemanticCache", "CouchbaseChatMessageHistory", ]
0
lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/tests/utils.py
"""Utilities for testing purposes.""" import hashlib from datetime import datetime from typing import Any, Dict, List, Mapping, Optional, cast from couchbase.cluster import Cluster from couchbase.options import GetOptions from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.embeddings import Embeddings from langchain_core.language_models.llms import LLM class FakeEmbeddings(Embeddings): """Fake embeddings functionality for testing.""" def embed_documents(self, texts: List[str]) -> List[List[float]]: """Return simple embeddings. Embeddings encode each text as its index.""" return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: return self.embed_documents(texts) def embed_query(self, text: str) -> List[float]: """Return constant query embeddings. Embeddings are identical to embed_documents(texts)[0]. Distance to each text will be that text's index, as it was passed to embed_documents.""" return [float(1.0)] * 9 + [float(0.0)] async def aembed_query(self, text: str) -> List[float]: return self.embed_query(text) class ConsistentFakeEmbeddings(FakeEmbeddings): """Fake embeddings which remember all the texts seen so far to return consistent vectors for the same texts.""" def __init__(self, dimensionality: int = 10) -> None: self.known_texts: List[str] = [] self.dimensionality = dimensionality def embed_documents(self, texts: List[str]) -> List[List[float]]: """Return consistent embeddings for each text seen so far.""" out_vectors = [] for text in texts: if text not in self.known_texts: self.known_texts.append(text) vector = [float(1.0)] * (self.dimensionality - 1) + [ float(self.known_texts.index(text)) ] out_vectors.append(vector) return out_vectors def embed_query(self, text: str) -> List[float]: """Return consistent embeddings for the text, if seen before, or a constant one if the text is unknown.""" return self.embed_documents([text])[0] class FakeLLM(LLM): """Fake LLM wrapper for testing purposes.""" queries: Optional[Mapping] = None sequential_responses: Optional[bool] = False response_index: int = 0 def get_num_tokens(self, text: str) -> int: """Return number of tokens.""" return len(text.split()) @property def _llm_type(self) -> str: """Return type of llm.""" return "fake" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: if self.sequential_responses: return self._get_next_response_in_sequence if self.queries is not None: return self.queries[prompt] if stop is None: return "foo" else: return "bar" @property def _identifying_params(self) -> Dict[str, Any]: return {} @property def _get_next_response_in_sequence(self) -> str: queries = cast(Mapping, self.queries) response = queries[list(queries.keys())[self.response_index]] self.response_index = self.response_index + 1 return response def cache_key_hash_function(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def fetch_document_expiry_time( cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, document_key: str, ) -> datetime: """Fetch the document's expiry time from the database.""" collection = ( cluster.bucket(bucket_name).scope(scope_name).collection(collection_name) ) result = collection.get(document_key, GetOptions(with_expiry=True)) return result.expiryTime def get_document_keys( cluster: Cluster, bucket_name: str, scope_name: str, query: str ) -> List[str]: """Get the document key from the database based on the query using meta().id.""" scope = cluster.bucket(bucket_name).scope(scope_name) result = scope.query(query).execute() document_keys = [row["id"] for row in result] return document_keys
0
lc_public_repos/langchain/libs/partners/couchbase/tests
lc_public_repos/langchain/libs/partners/couchbase/tests/integration_tests/test_cache.py
"""Test Couchbase Cache functionality""" import os from datetime import datetime, timedelta from typing import Any import pytest from couchbase.auth import PasswordAuthenticator from couchbase.cluster import Cluster from couchbase.options import ClusterOptions from langchain_core.globals import get_llm_cache, set_llm_cache from langchain_core.outputs import Generation from langchain_couchbase.cache import CouchbaseCache, CouchbaseSemanticCache from tests.utils import ( FakeEmbeddings, FakeLLM, cache_key_hash_function, fetch_document_expiry_time, get_document_keys, ) CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") CACHE_COLLECTION_NAME = os.getenv("COUCHBASE_CACHE_COLLECTION_NAME", "") SEMANTIC_CACHE_COLLECTION_NAME = os.getenv( "COUCHBASE_SEMANTIC_CACHE_COLLECTION_NAME", "" ) USERNAME = os.getenv("COUCHBASE_USERNAME", "") PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") INDEX_NAME = os.getenv("COUCHBASE_SEMANTIC_CACHE_INDEX_NAME", "") def set_all_env_vars() -> bool: """Check if all environment variables are set""" return all( [ CONNECTION_STRING, BUCKET_NAME, SCOPE_NAME, CACHE_COLLECTION_NAME, USERNAME, PASSWORD, INDEX_NAME, ] ) def get_cluster() -> Any: """Get a couchbase cluster object""" auth = PasswordAuthenticator(USERNAME, PASSWORD) options = ClusterOptions(auth) connect_string = CONNECTION_STRING cluster = Cluster(connect_string, options) # Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5)) return cluster @pytest.fixture() def cluster() -> Any: """Get a couchbase cluster object""" return get_cluster() @pytest.mark.skipif( not set_all_env_vars(), reason="Missing Couchbase environment variables" ) class TestCouchbaseCache: def test_cache(self, cluster: Any) -> None: """Test standard LLM cache functionality""" set_llm_cache( CouchbaseCache( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=CACHE_COLLECTION_NAME, ) ) llm = FakeLLM() params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) cache_output = get_llm_cache().lookup("foo", llm_string) assert cache_output == [Generation(text="fizz")] get_llm_cache().clear() output = get_llm_cache().lookup("bar", llm_string) assert output != [Generation(text="fizz")] def test_cache_with_ttl(self, cluster: Any) -> None: """Test standard LLM cache functionality with TTL""" ttl = timedelta(minutes=10) set_llm_cache( CouchbaseCache( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=CACHE_COLLECTION_NAME, ttl=ttl, ) ) llm = FakeLLM() params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) cache_output = get_llm_cache().lookup("foo", llm_string) assert cache_output == [Generation(text="fizz")] # Check the document's expiry time by fetching it from the database document_key = cache_key_hash_function("foo" + llm_string) document_expiry_time = fetch_document_expiry_time( cluster, BUCKET_NAME, SCOPE_NAME, CACHE_COLLECTION_NAME, document_key ) current_time = datetime.now() time_to_expiry = document_expiry_time - current_time assert time_to_expiry < ttl def test_semantic_cache(self, cluster: Any) -> None: """Test semantic LLM cache functionality""" set_llm_cache( CouchbaseSemanticCache( cluster=cluster, embedding=FakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=SEMANTIC_CACHE_COLLECTION_NAME, ) ) llm = FakeLLM() params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) get_llm_cache().update( "foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")] ) # foo and bar will have the same embedding produced by FakeEmbeddings cache_output = get_llm_cache().lookup("bar", llm_string) assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")] # clear the cache get_llm_cache().clear() output = get_llm_cache().lookup("bar", llm_string) assert output != [Generation(text="fizz"), Generation(text="Buzz")] def test_semantic_cache_with_ttl(self, cluster: Any) -> None: """Test semantic LLM cache functionality with TTL""" ttl = timedelta(minutes=10) set_llm_cache( CouchbaseSemanticCache( cluster=cluster, embedding=FakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=SEMANTIC_CACHE_COLLECTION_NAME, ttl=ttl, ) ) llm = FakeLLM() params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) # Add a document to the cache seed_prompt = "foo" get_llm_cache().update( seed_prompt, llm_string, [Generation(text="fizz"), Generation(text="Buzz")] ) # foo and bar will have the same embedding produced by FakeEmbeddings cache_output = get_llm_cache().lookup("bar", llm_string) assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")] # Check the document's expiry time by fetching it from the database fetch_document_query = ( f"SELECT meta().id, * from `{SEMANTIC_CACHE_COLLECTION_NAME}` doc " f"WHERE doc.text = '{seed_prompt}'" ) document_keys = get_document_keys( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, query=fetch_document_query, ) assert len(document_keys) == 1 document_expiry_time = fetch_document_expiry_time( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=SEMANTIC_CACHE_COLLECTION_NAME, document_key=document_keys[0], ) current_time = datetime.now() time_to_expiry = document_expiry_time - current_time assert time_to_expiry < ttl
0
lc_public_repos/langchain/libs/partners/couchbase/tests
lc_public_repos/langchain/libs/partners/couchbase/tests/integration_tests/test_compile.py
import pytest @pytest.mark.compile def test_placeholder() -> None: """Used for compiling integration tests without running any real tests.""" pass
0
lc_public_repos/langchain/libs/partners/couchbase/tests
lc_public_repos/langchain/libs/partners/couchbase/tests/integration_tests/test_chat_message_history.py
"""Test Couchbase Chat Message History functionality""" import os import time from datetime import datetime, timedelta from typing import Any import pytest from couchbase.auth import PasswordAuthenticator from couchbase.cluster import Cluster from couchbase.options import ClusterOptions from langchain.memory import ConversationBufferMemory from langchain_core.messages import AIMessage, HumanMessage from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory from tests.utils import fetch_document_expiry_time, get_document_keys CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") MESSAGE_HISTORY_COLLECTION_NAME = os.getenv( "COUCHBASE_CHAT_HISTORY_COLLECTION_NAME", "" ) USERNAME = os.getenv("COUCHBASE_USERNAME", "") PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") SLEEP_DURATION = 0.2 def set_all_env_vars() -> bool: """Check if all environment variables are set""" return all( [ CONNECTION_STRING, BUCKET_NAME, SCOPE_NAME, MESSAGE_HISTORY_COLLECTION_NAME, USERNAME, PASSWORD, ] ) def get_cluster() -> Any: """Get a couchbase cluster object""" auth = PasswordAuthenticator(USERNAME, PASSWORD) options = ClusterOptions(auth) connect_string = CONNECTION_STRING cluster = Cluster(connect_string, options) # Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5)) return cluster @pytest.fixture() def cluster() -> Any: """Get a couchbase cluster object""" return get_cluster() @pytest.mark.skipif( not set_all_env_vars(), reason="Missing Couchbase environment variables" ) class TestCouchbaseCache: def test_memory_with_message_store(self, cluster: Any) -> None: """Test chat message history with a message store""" message_history = CouchbaseChatMessageHistory( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, session_id="test-session", ) memory = ConversationBufferMemory( memory_key="baz", chat_memory=message_history, return_messages=True ) # clear the memory memory.chat_memory.clear() # wait for the messages to be cleared time.sleep(SLEEP_DURATION) assert memory.chat_memory.messages == [] # add some messages ai_message = AIMessage(content="Hello, how are you doing ?") user_message = HumanMessage(content="I'm good, how are you?") memory.chat_memory.add_messages([ai_message, user_message]) # wait until the messages can be retrieved time.sleep(SLEEP_DURATION) # check that the messages are in the memory messages = memory.chat_memory.messages assert len(messages) == 2 # check that the messages are in the order of creation assert messages == [ai_message, user_message] # clear the memory memory.chat_memory.clear() time.sleep(SLEEP_DURATION) assert memory.chat_memory.messages == [] def test_memory_with_separate_sessions(self, cluster: Any) -> None: """Test the chat message history with multiple sessions""" message_history_a = CouchbaseChatMessageHistory( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, session_id="test-session-a", ) message_history_b = CouchbaseChatMessageHistory( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, session_id="test-session-b", ) memory_a = ConversationBufferMemory( memory_key="a", chat_memory=message_history_a, return_messages=True ) memory_b = ConversationBufferMemory( memory_key="b", chat_memory=message_history_b, return_messages=True ) # clear the memory memory_a.chat_memory.clear() memory_b.chat_memory.clear() # add some messages ai_message = AIMessage(content="Hello, how are you doing ?") user_message = HumanMessage(content="I'm good, how are you?") memory_a.chat_memory.add_ai_message(ai_message) memory_b.chat_memory.add_user_message(user_message) # wait until the messages can be retrieved time.sleep(SLEEP_DURATION) # check that the messages are in the memory messages_a = memory_a.chat_memory.messages messages_b = memory_b.chat_memory.messages assert len(messages_a) == 1 assert len(messages_b) == 1 assert messages_a == [ai_message] assert messages_b == [user_message] # clear the memory memory_a.chat_memory.clear() time.sleep(SLEEP_DURATION) # ensure that only the session that is cleared is empty assert memory_a.chat_memory.messages == [] assert memory_b.chat_memory.messages == [user_message] # clear the other session's memory memory_b.chat_memory.clear() time.sleep(SLEEP_DURATION) assert memory_b.chat_memory.messages == [] def test_memory_message_with_ttl(self, cluster: Any) -> None: """Test chat message history with a message being saved with a TTL""" ttl = timedelta(minutes=5) session_id = "test-session-ttl" message_history = CouchbaseChatMessageHistory( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, session_id=session_id, ttl=ttl, ) memory = ConversationBufferMemory( memory_key="baz", chat_memory=message_history, return_messages=True ) # clear the memory memory.chat_memory.clear() # wait for the messages to be cleared time.sleep(SLEEP_DURATION) assert memory.chat_memory.messages == [] # add some messages ai_message = AIMessage(content="Hello, how are you doing ?") memory.chat_memory.add_ai_message(ai_message) # wait until the messages can be retrieved time.sleep(SLEEP_DURATION) # check that the messages are in the memory messages = memory.chat_memory.messages assert len(messages) == 1 # check that the messages are in the order of creation assert messages == [ai_message] # Check the document's expiry time by fetching it from the database fetch_documents_query = ( f"SELECT meta().id, * from `{MESSAGE_HISTORY_COLLECTION_NAME}` doc" f" WHERE doc.session_id = '{session_id}'" ) document_keys = get_document_keys( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, query=fetch_documents_query, ) assert len(document_keys) == 1 # Ensure that the document will expire within the TTL document_expiry_time = fetch_document_expiry_time( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, document_key=document_keys[0], ) current_time = datetime.now() assert document_expiry_time - current_time < ttl def test_memory_messages_with_ttl(self, cluster: Any) -> None: """Test chat message history with messages being stored with a TTL""" ttl = timedelta(minutes=5) session_id = "test-session-ttl" message_history = CouchbaseChatMessageHistory( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, session_id=session_id, ttl=ttl, ) memory = ConversationBufferMemory( memory_key="baz", chat_memory=message_history, return_messages=True ) # clear the memory memory.chat_memory.clear() # wait for the messages to be cleared time.sleep(SLEEP_DURATION) assert memory.chat_memory.messages == [] # add some messages ai_message = AIMessage(content="Hello, how are you doing ?") user_message = HumanMessage(content="I'm good, how are you?") memory.chat_memory.add_messages([ai_message, user_message]) # wait until the messages can be retrieved time.sleep(SLEEP_DURATION) # check that the messages are in the memory messages = memory.chat_memory.messages assert len(messages) == 2 # check that the messages are in the order of creation assert messages == [ai_message, user_message] # Check the documents' expiry time by fetching the documents from the database fetch_documents_query = ( f"SELECT meta().id, * from `{MESSAGE_HISTORY_COLLECTION_NAME}` doc" f" WHERE doc.session_id = '{session_id}'" ) document_keys = get_document_keys( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, query=fetch_documents_query, ) assert len(document_keys) == 2 # Ensure that each document will expire within the TTL for document_key in document_keys: document_expiry_time = fetch_document_expiry_time( cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=MESSAGE_HISTORY_COLLECTION_NAME, document_key=document_key, ) current_time = datetime.now() assert document_expiry_time - current_time < ttl
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lc_public_repos/langchain/libs/partners/couchbase/tests
lc_public_repos/langchain/libs/partners/couchbase/tests/integration_tests/test_vector_store.py
"""Test Couchbase Vector Store functionality""" import os import time from typing import Any import pytest from langchain_core.documents import Document from langchain_couchbase import CouchbaseVectorStore from tests.utils import ( ConsistentFakeEmbeddings, ) CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") COLLECTION_NAME = os.getenv("COUCHBASE_COLLECTION_NAME", "") USERNAME = os.getenv("COUCHBASE_USERNAME", "") PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") INDEX_NAME = os.getenv("COUCHBASE_INDEX_NAME", "") SLEEP_DURATION = 1 def set_all_env_vars() -> bool: return all( [ CONNECTION_STRING, BUCKET_NAME, SCOPE_NAME, COLLECTION_NAME, USERNAME, PASSWORD, INDEX_NAME, ] ) def get_cluster() -> Any: """Get a couchbase cluster object""" from datetime import timedelta from couchbase.auth import PasswordAuthenticator from couchbase.cluster import Cluster from couchbase.options import ClusterOptions auth = PasswordAuthenticator(USERNAME, PASSWORD) options = ClusterOptions(auth) connect_string = CONNECTION_STRING cluster = Cluster(connect_string, options) # Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5)) return cluster @pytest.fixture() def cluster() -> Any: """Get a couchbase cluster object""" return get_cluster() def delete_documents( cluster: Any, bucket_name: str, scope_name: str, collection_name: str ) -> None: """Delete all the documents in the collection""" query = f"DELETE FROM `{bucket_name}`.`{scope_name}`.`{collection_name}`" cluster.query(query).execute() @pytest.mark.skipif( not set_all_env_vars(), reason="Missing Couchbase environment variables" ) class TestCouchbaseVectorStore: @classmethod def setup_method(self) -> None: cluster = get_cluster() # Delete all the documents in the collection delete_documents(cluster, BUCKET_NAME, SCOPE_NAME, COLLECTION_NAME) def test_from_documents(self, cluster: Any) -> None: """Test end to end search using a list of documents.""" documents = [ Document(page_content="foo", metadata={"page": 1}), Document(page_content="bar", metadata={"page": 2}), Document(page_content="baz", metadata={"page": 3}), ] vectorstore = CouchbaseVectorStore.from_documents( documents, ConsistentFakeEmbeddings(), cluster=cluster, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, index_name=INDEX_NAME, ) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("baz", k=1) assert output[0].page_content == "baz" assert output[0].metadata["page"] == 3 def test_from_texts(self, cluster: Any) -> None: """Test end to end search using a list of texts.""" texts = [ "foo", "bar", "baz", ] vectorstore = CouchbaseVectorStore.from_texts( texts, ConsistentFakeEmbeddings(), cluster=cluster, index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("foo", k=1) assert len(output) == 1 assert output[0].page_content == "foo" def test_from_texts_with_metadatas(self, cluster: Any) -> None: """Test end to end search using a list of texts and metadatas.""" texts = [ "foo", "bar", "baz", ] metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] vectorstore = CouchbaseVectorStore.from_texts( texts, ConsistentFakeEmbeddings(), metadatas=metadatas, cluster=cluster, index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("baz", k=1) assert output[0].page_content == "baz" assert output[0].metadata["c"] == 3 def test_add_texts_with_ids_and_metadatas(self, cluster: Any) -> None: """Test end to end search by adding a list of texts, ids and metadatas.""" texts = [ "foo", "bar", "baz", ] ids = ["a", "b", "c"] metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) results = vectorstore.add_texts( texts, ids=ids, metadatas=metadatas, ) assert results == ids # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("foo", k=1) assert output[0].id == "a" assert output[0].page_content == "foo" assert output[0].metadata["a"] == 1 def test_delete_texts_with_ids(self, cluster: Any) -> None: """Test deletion of documents by ids.""" texts = [ "foo", "bar", "baz", ] ids = ["a", "b", "c"] metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) results = vectorstore.add_texts( texts, ids=ids, metadatas=metadatas, ) assert results == ids assert vectorstore.delete(ids) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("foo", k=1) assert len(output) == 0 def test_similarity_search_with_scores(self, cluster: Any) -> None: """Test similarity search with scores.""" texts = ["foo", "bar", "baz"] metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) vectorstore.add_texts(texts, metadatas=metadatas) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search_with_score("foo", k=2) assert len(output) == 2 assert output[0][0].page_content == "foo" # check if the scores are sorted assert output[0][0].metadata["a"] == 1 assert output[0][1] > output[1][1] def test_similarity_search_by_vector(self, cluster: Any) -> None: """Test similarity search by vector.""" texts = ["foo", "bar", "baz"] metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) vectorstore.add_texts(texts, metadatas=metadatas) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) vector = ConsistentFakeEmbeddings().embed_query("foo") vector_output = vectorstore.similarity_search_by_vector(vector, k=1) assert vector_output[0].page_content == "foo" similarity_output = vectorstore.similarity_search("foo", k=1) assert similarity_output == vector_output def test_output_fields(self, cluster: Any) -> None: """Test that output fields are set correctly.""" texts = [ "foo", "bar", "baz", ] metadatas = [{"page": 1, "a": 1}, {"page": 2, "b": 2}, {"page": 3, "c": 3}] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) ids = vectorstore.add_texts(texts, metadatas) assert len(ids) == len(texts) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("foo", k=1, fields=["metadata.page"]) assert output[0].page_content == "foo" assert output[0].metadata["page"] == 1 assert "a" not in output[0].metadata def test_hybrid_search(self, cluster: Any) -> None: """Test hybrid search.""" texts = [ "foo", "bar", "baz", ] metadatas = [ {"section": "index"}, {"section": "glossary"}, {"section": "appendix"}, ] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) vectorstore.add_texts(texts, metadatas=metadatas) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) result, score = vectorstore.similarity_search_with_score("foo", k=1)[0] # Wait for the documents to be indexed for hybrid search time.sleep(SLEEP_DURATION) hybrid_result, hybrid_score = vectorstore.similarity_search_with_score( "foo", k=1, search_options={"query": {"match": "index", "field": "metadata.section"}}, )[0] assert result == hybrid_result assert score <= hybrid_score def test_id_in_results(self, cluster: Any) -> None: """Test that the id is returned in the result documents.""" texts = [ "foo", "bar", "baz", ] metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] vectorstore = CouchbaseVectorStore( cluster=cluster, embedding=ConsistentFakeEmbeddings(), index_name=INDEX_NAME, bucket_name=BUCKET_NAME, scope_name=SCOPE_NAME, collection_name=COLLECTION_NAME, ) ids = vectorstore.add_texts(texts, metadatas=metadatas) assert len(ids) == len(texts) # Wait for the documents to be indexed time.sleep(SLEEP_DURATION) output = vectorstore.similarity_search("foo", k=1) assert output[0].id == ids[0]
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lc_public_repos/langchain/libs/partners/couchbase/tests
lc_public_repos/langchain/libs/partners/couchbase/tests/unit_tests/test_imports.py
from langchain_couchbase import __all__ EXPECTED_ALL = [ "CouchbaseVectorStore", "CouchbaseCache", "CouchbaseSemanticCache", "CouchbaseChatMessageHistory", ] def test_all_imports() -> None: assert sorted(EXPECTED_ALL) == sorted(__all__)
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lc_public_repos/langchain/libs/partners/couchbase/tests
lc_public_repos/langchain/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py
0
lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/scripts/lint_imports.sh
#!/bin/bash set -eu # Initialize a variable to keep track of errors errors=0 # make sure not importing from langchain, langchain_experimental, or langchain_community git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) # Decide on an exit status based on the errors if [ "$errors" -gt 0 ]; then exit 1 else exit 0 fi
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lc_public_repos/langchain/libs/partners/couchbase
lc_public_repos/langchain/libs/partners/couchbase/scripts/check_imports.py
import sys import traceback from importlib.machinery import SourceFileLoader if __name__ == "__main__": files = sys.argv[1:] has_failure = False for file in files: try: SourceFileLoader("x", file).load_module() except Exception: has_failure = True print(file) # noqa: T201 traceback.print_exc() print() # noqa: T201 sys.exit(1 if has_failure else 0)
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/azure-dynamic-sessions/README.md
This package has moved! https://github.com/langchain-ai/langchain-azure/tree/main/libs/azure-dynamic-sessions
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/xai/Makefile
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests # Default target executed when no arguments are given to make. all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) ###################### # LINTING AND FORMATTING ###################### # Define a variable for Python and notebook files. PYTHON_FILES=. MYPY_CACHE=.mypy_cache lint format: PYTHON_FILES=. lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/xai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint_package: PYTHON_FILES=langchain_xai lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml spell_fix: poetry run codespell --toml pyproject.toml -w check_imports: $(shell find langchain_xai -name '*.py') poetry run python ./scripts/check_imports.py $^ ###################### # HELP ###################### help: @echo '----' @echo 'check_imports - check imports' @echo 'format - run code formatters' @echo 'lint - run linters' @echo 'test - run unit tests' @echo 'tests - run unit tests' @echo 'test TEST_FILE=<test_file> - run all tests in file'
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/xai/LICENSE
MIT License Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/xai/poetry.lock
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0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/xai/README.md
# langchain-xai This package contains the LangChain integrations for [xAI](https://x.ai/) through their [APIs](https://console.x.ai). ## Installation and Setup - Install the LangChain partner package ```bash pip install -U langchain-xai ``` - Get your xAI api key from the [xAI Dashboard](https://console.x.ai) and set it as an environment variable (`XAI_API_KEY`) ## Chat Completions This package contains the `ChatXAI` class, which is the recommended way to interface with xAI chat models.
0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/xai/pyproject.toml
[build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-xai" version = "0.1.0" description = "An integration package connecting xAI and LangChain" authors = [] readme = "README.md" repository = "https://github.com/langchain-ai/langchain" license = "MIT" [tool.mypy] disallow_untyped_defs = "True" [tool.poetry.urls] "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/xai" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-xai%3D%3D0%22&expanded=true" [tool.poetry.dependencies] python = ">=3.9,<4.0" langchain-openai = ">=0.2,<0.3" langchain-core = ">=0.3.18,<0.4" requests = ">=2,<3" aiohttp = ">=3.9.1,<4" [tool.ruff.lint] select = ["E", "F", "I", "D"] [tool.coverage.run] omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" [tool.poetry.group.test] optional = true [tool.poetry.group.codespell] optional = true [tool.poetry.group.test_integration] optional = true [tool.poetry.group.lint] optional = true [tool.poetry.group.typing] optional = true [tool.poetry.group.dev] optional = true [tool.ruff.lint.pydocstyle] convention = "google" [tool.ruff.lint.per-file-ignores] "tests/**" = ["D"] [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" docarray = "^0.32.1" langchain-openai = { path = "../openai", develop = true } langchain-core = { path = "../../core", develop = true } langchain-tests = { path = "../../standard-tests", develop = true } [tool.poetry.group.codespell.dependencies] codespell = "^2.2.0" [tool.poetry.group.test_integration.dependencies] [[tool.poetry.group.test_integration.dependencies.numpy]] version = "^1" python = "<3.12" [[tool.poetry.group.test_integration.dependencies.numpy]] version = "^1.26.0" python = ">=3.12" [tool.poetry.group.lint.dependencies] ruff = "^0.5" [tool.poetry.group.typing.dependencies] mypy = "^1.10" types-requests = "^2" langchain-core = { path = "../../core", develop = true } [tool.poetry.group.dev.dependencies] langchain-core = { path = "../../core", develop = true }
0
lc_public_repos/langchain/libs/partners/xai
lc_public_repos/langchain/libs/partners/xai/langchain_xai/chat_models.py
"""Wrapper around xAI's Chat Completions API.""" from typing import ( Any, Dict, List, Optional, ) import openai from langchain_core.language_models.chat_models import LangSmithParams from langchain_core.utils import secret_from_env from langchain_openai.chat_models.base import BaseChatOpenAI from pydantic import ConfigDict, Field, SecretStr, model_validator from typing_extensions import Self class ChatXAI(BaseChatOpenAI): # type: ignore[override] r"""ChatXAI chat model. Setup: Install ``langchain-xai`` and set environment variable ``XAI_API_KEY``. .. code-block:: bash pip install -U langchain-xai export XAI_API_KEY="your-api-key" Key init args — completion params: model: str Name of model to use. temperature: float Sampling temperature. max_tokens: Optional[int] Max number of tokens to generate. logprobs: Optional[bool] Whether to return logprobs. Key init args — client params: timeout: Union[float, Tuple[float, float], Any, None] Timeout for requests. max_retries: int Max number of retries. api_key: Optional[str] xAI API key. If not passed in will be read from env var `XAI_API_KEY`. Instantiate: .. code-block:: python from langchain_xai import ChatXAI llm = ChatXAI( model="grok-beta", temperature=0, max_tokens=None, timeout=None, max_retries=2, # api_key="...", # other params... ) Invoke: .. code-block:: python messages = [ ( "system", "You are a helpful translator. Translate the user sentence to French.", ), ("human", "I love programming."), ] llm.invoke(messages) .. code-block:: python AIMessage( content="J'adore la programmation.", response_metadata={ 'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41}, 'model_name': 'grok-beta', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None }, id='run-168dceca-3b8b-4283-94e3-4c739dbc1525-0', usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41}) Stream: .. code-block:: python for chunk in llm.stream(messages): print(chunk) .. code-block:: python content='J' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content="'" id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content='ad' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content='ore' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content=' la' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content=' programm' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content='ation' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content='.' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' content='' response_metadata={'finish_reason': 'stop', 'model_name': 'grok-beta'} id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9' Async: .. code-block:: python await llm.ainvoke(messages) # stream: # async for chunk in (await llm.astream(messages)) # batch: # await llm.abatch([messages]) .. code-block:: python AIMessage( content="J'adore la programmation.", response_metadata={ 'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41}, 'model_name': 'grok-beta', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None }, id='run-09371a11-7f72-4c53-8e7c-9de5c238b34c-0', usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41}) Tool calling: .. code-block:: python from pydantic import BaseModel, Field llm = ChatXAI(model="grok-beta") class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) ai_msg = llm_with_tools.invoke( "Which city is bigger: LA or NY?" ) ai_msg.tool_calls .. code-block:: python [ { 'name': 'GetPopulation', 'args': {'location': 'NY'}, 'id': 'call_m5tstyn2004pre9bfuxvom8x', 'type': 'tool_call' }, { 'name': 'GetPopulation', 'args': {'location': 'LA'}, 'id': 'call_0vjgq455gq1av5sp9eb1pw6a', 'type': 'tool_call' } ] Structured output: .. code-block:: python from typing import Optional from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10") structured_llm = llm.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats") .. code-block:: python Joke( setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=7 ) Token usage: .. code-block:: python ai_msg = llm.invoke(messages) ai_msg.usage_metadata .. code-block:: python {'input_tokens': 37, 'output_tokens': 6, 'total_tokens': 43} Logprobs: .. code-block:: python logprobs_llm = llm.bind(logprobs=True) messages=[("human","Say Hello World! Do not return anything else.")] ai_msg = logprobs_llm.invoke(messages) ai_msg.response_metadata["logprobs"] .. code-block:: python { 'content': None, 'token_ids': [22557, 3304, 28808, 2], 'tokens': [' Hello', ' World', '!', '</s>'], 'token_logprobs': [-4.7683716e-06, -5.9604645e-07, 0, -0.057373047] } Response metadata .. code-block:: python ai_msg = llm.invoke(messages) ai_msg.response_metadata .. code-block:: python { 'token_usage': { 'completion_tokens': 4, 'prompt_tokens': 19, 'total_tokens': 23 }, 'model_name': 'grok-beta', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None } """ # noqa: E501 model_name: str = Field(alias="model") """Model name to use.""" xai_api_key: Optional[SecretStr] = Field( alias="api_key", default_factory=secret_from_env("XAI_API_KEY", default=None), ) """xAI API key. Automatically read from env variable `XAI_API_KEY` if not provided. """ xai_api_base: str = Field(default="https://api.x.ai/v1/") """Base URL path for API requests.""" openai_api_key: Optional[SecretStr] = None openai_api_base: Optional[str] = None model_config = ConfigDict( populate_by_name=True, ) @property def lc_secrets(self) -> Dict[str, str]: """A map of constructor argument names to secret ids. For example, {"xai_api_key": "XAI_API_KEY"} """ return {"xai_api_key": "XAI_API_KEY"} @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain_xai", "chat_models"] @property def lc_attributes(self) -> Dict[str, Any]: """List of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. """ attributes: Dict[str, Any] = {} if self.xai_api_base: attributes["xai_api_base"] = self.xai_api_base return attributes @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return True @property def _llm_type(self) -> str: """Return type of chat model.""" return "xai-chat" def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get the parameters used to invoke the model.""" params = super()._get_ls_params(stop=stop, **kwargs) params["ls_provider"] = "xai" return params @model_validator(mode="after") def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" if self.n < 1: raise ValueError("n must be at least 1.") if self.n > 1 and self.streaming: raise ValueError("n must be 1 when streaming.") client_params: dict = { "api_key": ( self.xai_api_key.get_secret_value() if self.xai_api_key else None ), "base_url": self.xai_api_base, "timeout": self.request_timeout, "max_retries": self.max_retries, "default_headers": self.default_headers, "default_query": self.default_query, } if client_params["api_key"] is None: raise ValueError( "xAI API key is not set. Please set it in the `xai_api_key` field or " "in the `XAI_API_KEY` environment variable." ) if not (self.client or None): sync_specific: dict = {"http_client": self.http_client} self.client = openai.OpenAI( **client_params, **sync_specific ).chat.completions if not (self.async_client or None): async_specific: dict = {"http_client": self.http_async_client} self.async_client = openai.AsyncOpenAI( **client_params, **async_specific ).chat.completions return self
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lc_public_repos/langchain/libs/partners/xai
lc_public_repos/langchain/libs/partners/xai/langchain_xai/__init__.py
"""This package provides the xAI integration for LangChain.""" from langchain_xai.chat_models import ChatXAI __all__ = ["ChatXAI"]
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/integration_tests/test_compile.py
import pytest # type: ignore[import-not-found] @pytest.mark.compile def test_placeholder() -> None: """Used for compiling integration tests without running any real tests.""" pass
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py
"""Standard LangChain interface tests""" from typing import Optional, Type import pytest # type: ignore[import-not-found] from langchain_core.language_models import BaseChatModel from langchain_core.rate_limiters import InMemoryRateLimiter from langchain_tests.integration_tests import ( # type: ignore[import-not-found] ChatModelIntegrationTests, # type: ignore[import-not-found] ) from langchain_xai import ChatXAI # Initialize the rate limiter in global scope, so it can be re-used # across tests. rate_limiter = InMemoryRateLimiter( requests_per_second=0.5, ) class TestXAIStandard(ChatModelIntegrationTests): @property def chat_model_class(self) -> Type[BaseChatModel]: return ChatXAI @property def chat_model_params(self) -> dict: return { "model": "grok-beta", "rate_limiter": rate_limiter, } @property def tool_choice_value(self) -> Optional[str]: """Value to use for tool choice when used in tests.""" return "tool_name" @pytest.mark.xfail(reason="Not yet supported.") def test_usage_metadata_streaming(self, model: BaseChatModel) -> None: super().test_usage_metadata_streaming(model) @pytest.mark.xfail(reason="Can't handle AIMessage with empty content.") def test_tool_message_error_status(self, model: BaseChatModel) -> None: super().test_tool_message_error_status(model) @pytest.mark.xfail(reason="Can't handle AIMessage with empty content.") def test_structured_few_shot_examples(self, model: BaseChatModel) -> None: super().test_structured_few_shot_examples(model) @pytest.mark.xfail(reason="Can't handle AIMessage with empty content.") def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None: super().test_tool_message_histories_string_content(model)
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/unit_tests/test_secrets.py
from langchain_xai import ChatXAI def test_chat_xai_secrets() -> None: o = ChatXAI(model="grok-beta", xai_api_key="foo") # type: ignore[call-arg] s = str(o) assert "foo" not in s
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/unit_tests/test_chat_models.py
import json import pytest # type: ignore[import-not-found] from langchain_core.messages import ( AIMessage, FunctionMessage, HumanMessage, SystemMessage, ToolMessage, ) from langchain_openai.chat_models.base import ( _convert_dict_to_message, _convert_message_to_dict, ) from langchain_xai import ChatXAI def test_initialization() -> None: """Test chat model initialization.""" ChatXAI(model="grok-beta") def test_xai_model_param() -> None: llm = ChatXAI(model="foo") assert llm.model_name == "foo" llm = ChatXAI(model_name="foo") # type: ignore[call-arg] assert llm.model_name == "foo" ls_params = llm._get_ls_params() assert ls_params["ls_provider"] == "xai" def test_chat_xai_invalid_streaming_params() -> None: """Test that streaming correctly invokes on_llm_new_token callback.""" with pytest.raises(ValueError): ChatXAI( model="grok-beta", max_tokens=10, streaming=True, temperature=0, n=5, ) def test_chat_xai_extra_kwargs() -> None: """Test extra kwargs to chat xai.""" # Check that foo is saved in extra_kwargs. llm = ChatXAI(model="grok-beta", foo=3, max_tokens=10) # type: ignore[call-arg] assert llm.max_tokens == 10 assert llm.model_kwargs == {"foo": 3} # Test that if extra_kwargs are provided, they are added to it. llm = ChatXAI(model="grok-beta", foo=3, model_kwargs={"bar": 2}) # type: ignore[call-arg] assert llm.model_kwargs == {"foo": 3, "bar": 2} # Test that if provided twice it errors with pytest.raises(ValueError): ChatXAI(model="grok-beta", foo=3, model_kwargs={"foo": 2}) # type: ignore[call-arg] def test_function_dict_to_message_function_message() -> None: content = json.dumps({"result": "Example #1"}) name = "test_function" result = _convert_dict_to_message( { "role": "function", "name": name, "content": content, } ) assert isinstance(result, FunctionMessage) assert result.name == name assert result.content == content def test_convert_dict_to_message_human() -> None: message = {"role": "user", "content": "foo"} result = _convert_dict_to_message(message) expected_output = HumanMessage(content="foo") assert result == expected_output assert _convert_message_to_dict(expected_output) == message def test__convert_dict_to_message_human_with_name() -> None: message = {"role": "user", "content": "foo", "name": "test"} result = _convert_dict_to_message(message) expected_output = HumanMessage(content="foo", name="test") assert result == expected_output assert _convert_message_to_dict(expected_output) == message def test_convert_dict_to_message_ai() -> None: message = {"role": "assistant", "content": "foo"} result = _convert_dict_to_message(message) expected_output = AIMessage(content="foo") assert result == expected_output assert _convert_message_to_dict(expected_output) == message def test_convert_dict_to_message_ai_with_name() -> None: message = {"role": "assistant", "content": "foo", "name": "test"} result = _convert_dict_to_message(message) expected_output = AIMessage(content="foo", name="test") assert result == expected_output assert _convert_message_to_dict(expected_output) == message def test_convert_dict_to_message_system() -> None: message = {"role": "system", "content": "foo"} result = _convert_dict_to_message(message) expected_output = SystemMessage(content="foo") assert result == expected_output assert _convert_message_to_dict(expected_output) == message def test_convert_dict_to_message_system_with_name() -> None: message = {"role": "system", "content": "foo", "name": "test"} result = _convert_dict_to_message(message) expected_output = SystemMessage(content="foo", name="test") assert result == expected_output assert _convert_message_to_dict(expected_output) == message def test_convert_dict_to_message_tool() -> None: message = {"role": "tool", "content": "foo", "tool_call_id": "bar"} result = _convert_dict_to_message(message) expected_output = ToolMessage(content="foo", tool_call_id="bar") assert result == expected_output assert _convert_message_to_dict(expected_output) == message
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/unit_tests/test_imports.py
from langchain_xai import __all__ EXPECTED_ALL = ["ChatXAI"] def test_all_imports() -> None: assert sorted(EXPECTED_ALL) == sorted(__all__)
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/unit_tests/test_chat_models_standard.py
"""Standard LangChain interface tests""" from typing import Tuple, Type from langchain_core.language_models import BaseChatModel from langchain_tests.unit_tests import ( # type: ignore[import-not-found] ChatModelUnitTests, # type: ignore[import-not-found] ) from langchain_xai import ChatXAI class TestXAIStandard(ChatModelUnitTests): @property def chat_model_class(self) -> Type[BaseChatModel]: return ChatXAI @property def chat_model_params(self) -> dict: return {"model": "grok-beta"} @property def init_from_env_params(self) -> Tuple[dict, dict, dict]: return ( { "XAI_API_KEY": "api_key", }, { "model": "grok-beta", }, { "xai_api_key": "api_key", "xai_api_base": "https://api.x.ai/v1/", }, )
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lc_public_repos/langchain/libs/partners/xai/tests
lc_public_repos/langchain/libs/partners/xai/tests/unit_tests/__init__.py
import os os.environ["XAI_API_KEY"] = "test"
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lc_public_repos/langchain/libs/partners/xai/tests/unit_tests
lc_public_repos/langchain/libs/partners/xai/tests/unit_tests/__snapshots__/test_chat_models_standard.ambr
# serializer version: 1 # name: TestXAIStandard.test_serdes[serialized] dict({ 'id': list([ 'langchain_xai', 'chat_models', 'ChatXAI', ]), 'kwargs': dict({ 'max_retries': 2, 'max_tokens': 100, 'model_name': 'grok-beta', 'n': 1, 'request_timeout': 60.0, 'stop': list([ ]), 'temperature': 0.0, 'xai_api_base': 'https://api.x.ai/v1/', 'xai_api_key': dict({ 'id': list([ 'XAI_API_KEY', ]), 'lc': 1, 'type': 'secret', }), }), 'lc': 1, 'name': 'ChatXAI', 'type': 'constructor', }) # ---
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lc_public_repos/langchain/libs/partners/xai
lc_public_repos/langchain/libs/partners/xai/scripts/lint_imports.sh
#!/bin/bash set -eu # Initialize a variable to keep track of errors errors=0 # make sure not importing from langchain or langchain_experimental git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) # Decide on an exit status based on the errors if [ "$errors" -gt 0 ]; then exit 1 else exit 0 fi
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lc_public_repos/langchain/libs/partners/xai
lc_public_repos/langchain/libs/partners/xai/scripts/check_imports.py
"""This module checks if the given python files can be imported without error.""" import sys import traceback from importlib.machinery import SourceFileLoader if __name__ == "__main__": files = sys.argv[1:] has_failure = False for file in files: try: SourceFileLoader("x", file).load_module() except Exception: has_failure = True print(file) traceback.print_exc() print() sys.exit(1 if has_failure else 0)
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/mistralai/Makefile
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests # Default target executed when no arguments are given to make. all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ INTEGRATION_TEST_FILE ?= tests/integration_tests/ integration_test integration_tests: TEST_FILE=$(INTEGRATION_TEST_FILE) test tests: poetry run pytest $(TEST_FILE) test_watch: poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) integration_test integration_tests: poetry run pytest $(TEST_FILE) ###################### # LINTING AND FORMATTING ###################### # Define a variable for Python and notebook files. PYTHON_FILES=. MYPY_CACHE=.mypy_cache lint format: PYTHON_FILES=. lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/mistral --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint_package: PYTHON_FILES=langchain_mistralai lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml spell_fix: poetry run codespell --toml pyproject.toml -w check_imports: $(shell find langchain_mistralai -name '*.py') poetry run python ./scripts/check_imports.py $^ ###################### # HELP ###################### help: @echo '----' @echo 'check_imports - check imports' @echo 'format - run code formatters' @echo 'lint - run linters' @echo 'test - run unit tests' @echo 'tests - run unit tests' @echo 'test TEST_FILE=<test_file> - run all tests in file'
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/mistralai/LICENSE
MIT License Copyright (c) 2023 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/mistralai/poetry.lock
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lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/mistralai/README.md
# langchain-mistralai This package contains the LangChain integrations for [MistralAI](https://docs.mistral.ai) through their [mistralai](https://pypi.org/project/mistralai/) SDK. ## Installation ```bash pip install -U langchain-mistralai ``` ## Chat Models This package contains the `ChatMistralAI` class, which is the recommended way to interface with MistralAI models. To use, install the requirements, and configure your environment. ```bash export MISTRAL_API_KEY=your-api-key ``` Then initialize ```python from langchain_core.messages import HumanMessage from langchain_mistralai.chat_models import ChatMistralAI chat = ChatMistralAI(model="mistral-small") messages = [HumanMessage(content="say a brief hello")] chat.invoke(messages) ``` `ChatMistralAI` also supports async and streaming functionality: ```python # For async... await chat.ainvoke(messages) # For streaming... for chunk in chat.stream(messages): print(chunk.content, end="", flush=True) ``` ## Embeddings With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available. ### Choose model `embedding.model = 'mistral-embed'` ### Simple query `res_query = embedding.embed_query("The test information")` ### Documents `res_document = embedding.embed_documents(["test1", "another test"])`
0
lc_public_repos/langchain/libs/partners
lc_public_repos/langchain/libs/partners/mistralai/pyproject.toml
[build-system] requires = [ "poetry-core>=1.0.0",] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-mistralai" version = "0.2.3" description = "An integration package connecting Mistral and LangChain" authors = [] readme = "README.md" repository = "https://github.com/langchain-ai/langchain" license = "MIT" [tool.mypy] disallow_untyped_defs = "True" [tool.poetry.urls] "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/mistralai" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-mistralai%3D%3D0%22&expanded=true" [tool.poetry.dependencies] python = ">=3.9,<4.0" langchain-core = "^0.3.21" tokenizers = ">=0.15.1,<1" httpx = ">=0.25.2,<1" httpx-sse = ">=0.3.1,<1" pydantic = ">=2,<3" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] [tool.coverage.run] omit = [ "tests/*",] [tool.pytest.ini_options] addopts = "--strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them",] asyncio_mode = "auto" [tool.poetry.group.test] optional = true [tool.poetry.group.test_integration] optional = true [tool.poetry.group.codespell] optional = true [tool.poetry.group.lint] optional = true [tool.poetry.group.dev] optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" pytest-asyncio = "^0.21.1" pytest-watcher = "^0.3.4" [tool.poetry.group.test_integration.dependencies] [tool.poetry.group.codespell.dependencies] codespell = "^2.2.0" [tool.poetry.group.lint.dependencies] ruff = "^0.5" [tool.poetry.group.typing.dependencies] mypy = "^1.10" [tool.poetry.group.test.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.test.dependencies.langchain-tests] path = "../../standard-tests" develop = true [tool.poetry.group.dev.dependencies.langchain-core] path = "../../core" develop = true [tool.poetry.group.typing.dependencies.langchain-core] path = "../../core" develop = true
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lc_public_repos/langchain/libs/partners/mistralai
lc_public_repos/langchain/libs/partners/mistralai/langchain_mistralai/chat_models.py
from __future__ import annotations import hashlib import json import logging import os import re import uuid from operator import itemgetter from typing import ( Any, AsyncContextManager, AsyncIterator, Callable, Dict, Iterator, List, Literal, Optional, Sequence, Tuple, Type, Union, cast, ) import httpx from httpx_sse import EventSource, aconnect_sse, connect_sse from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import ( BaseChatModel, LangSmithParams, agenerate_from_stream, generate_from_stream, ) from langchain_core.language_models.llms import create_base_retry_decorator from langchain_core.messages import ( AIMessage, AIMessageChunk, BaseMessage, BaseMessageChunk, ChatMessage, ChatMessageChunk, HumanMessage, HumanMessageChunk, InvalidToolCall, SystemMessage, SystemMessageChunk, ToolCall, ToolMessage, ) from langchain_core.messages.tool import tool_call_chunk from langchain_core.output_parsers import ( JsonOutputParser, PydanticOutputParser, ) from langchain_core.output_parsers.base import OutputParserLike from langchain_core.output_parsers.openai_tools import ( JsonOutputKeyToolsParser, PydanticToolsParser, make_invalid_tool_call, parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import secret_from_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass from pydantic import ( BaseModel, ConfigDict, Field, SecretStr, model_validator, ) from typing_extensions import Self logger = logging.getLogger(__name__) # Mistral enforces a specific pattern for tool call IDs TOOL_CALL_ID_PATTERN = re.compile(r"^[a-zA-Z0-9]{9}$") def _create_retry_decorator( llm: ChatMistralAI, run_manager: Optional[ Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] ] = None, ) -> Callable[[Any], Any]: """Returns a tenacity retry decorator, preconfigured to handle exceptions""" errors = [httpx.RequestError, httpx.StreamError] return create_base_retry_decorator( error_types=errors, max_retries=llm.max_retries, run_manager=run_manager ) def _is_valid_mistral_tool_call_id(tool_call_id: str) -> bool: """Check if tool call ID is nine character string consisting of a-z, A-Z, 0-9""" return bool(TOOL_CALL_ID_PATTERN.match(tool_call_id)) def _base62_encode(num: int) -> str: """Encodes a number in base62 and ensures result is of a specified length.""" base62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" if num == 0: return base62[0] arr = [] base = len(base62) while num: num, rem = divmod(num, base) arr.append(base62[rem]) arr.reverse() return "".join(arr) def _convert_tool_call_id_to_mistral_compatible(tool_call_id: str) -> str: """Convert a tool call ID to a Mistral-compatible format""" if _is_valid_mistral_tool_call_id(tool_call_id): return tool_call_id else: hash_bytes = hashlib.sha256(tool_call_id.encode()).digest() hash_int = int.from_bytes(hash_bytes, byteorder="big") base62_str = _base62_encode(hash_int) if len(base62_str) >= 9: return base62_str[:9] else: return base62_str.rjust(9, "0") def _convert_mistral_chat_message_to_message( _message: Dict, ) -> BaseMessage: role = _message["role"] assert role == "assistant", f"Expected role to be 'assistant', got {role}" content = cast(str, _message["content"]) additional_kwargs: Dict = {} tool_calls = [] invalid_tool_calls = [] if raw_tool_calls := _message.get("tool_calls"): additional_kwargs["tool_calls"] = raw_tool_calls for raw_tool_call in raw_tool_calls: try: parsed: dict = cast( dict, parse_tool_call(raw_tool_call, return_id=True) ) if not parsed["id"]: parsed["id"] = uuid.uuid4().hex[:] tool_calls.append(parsed) except Exception as e: invalid_tool_calls.append(make_invalid_tool_call(raw_tool_call, str(e))) return AIMessage( content=content, additional_kwargs=additional_kwargs, tool_calls=tool_calls, invalid_tool_calls=invalid_tool_calls, ) def _raise_on_error(response: httpx.Response) -> None: """Raise an error if the response is an error.""" if httpx.codes.is_error(response.status_code): error_message = response.read().decode("utf-8") raise httpx.HTTPStatusError( f"Error response {response.status_code} " f"while fetching {response.url}: {error_message}", request=response.request, response=response, ) async def _araise_on_error(response: httpx.Response) -> None: """Raise an error if the response is an error.""" if httpx.codes.is_error(response.status_code): error_message = (await response.aread()).decode("utf-8") raise httpx.HTTPStatusError( f"Error response {response.status_code} " f"while fetching {response.url}: {error_message}", request=response.request, response=response, ) async def _aiter_sse( event_source_mgr: AsyncContextManager[EventSource], ) -> AsyncIterator[Dict]: """Iterate over the server-sent events.""" async with event_source_mgr as event_source: await _araise_on_error(event_source.response) async for event in event_source.aiter_sse(): if event.data == "[DONE]": return yield event.json() async def acompletion_with_retry( llm: ChatMistralAI, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Any: """Use tenacity to retry the async completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @retry_decorator async def _completion_with_retry(**kwargs: Any) -> Any: if "stream" not in kwargs: kwargs["stream"] = False stream = kwargs["stream"] if stream: event_source = aconnect_sse( llm.async_client, "POST", "/chat/completions", json=kwargs ) return _aiter_sse(event_source) else: response = await llm.async_client.post(url="/chat/completions", json=kwargs) await _araise_on_error(response) return response.json() return await _completion_with_retry(**kwargs) def _convert_chunk_to_message_chunk( chunk: Dict, default_class: Type[BaseMessageChunk] ) -> BaseMessageChunk: _delta = chunk["choices"][0]["delta"] role = _delta.get("role") content = _delta.get("content") or "" if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content) elif role == "assistant" or default_class == AIMessageChunk: additional_kwargs: Dict = {} if raw_tool_calls := _delta.get("tool_calls"): additional_kwargs["tool_calls"] = raw_tool_calls try: tool_call_chunks = [] for raw_tool_call in raw_tool_calls: if not raw_tool_call.get("index") and not raw_tool_call.get("id"): tool_call_id = uuid.uuid4().hex[:] else: tool_call_id = raw_tool_call.get("id") tool_call_chunks.append( tool_call_chunk( name=raw_tool_call["function"].get("name"), args=raw_tool_call["function"].get("arguments"), id=tool_call_id, index=raw_tool_call.get("index"), ) ) except KeyError: pass else: tool_call_chunks = [] if token_usage := chunk.get("usage"): usage_metadata = { "input_tokens": token_usage.get("prompt_tokens", 0), "output_tokens": token_usage.get("completion_tokens", 0), "total_tokens": token_usage.get("total_tokens", 0), } else: usage_metadata = None return AIMessageChunk( content=content, additional_kwargs=additional_kwargs, tool_call_chunks=tool_call_chunks, # type: ignore[arg-type] usage_metadata=usage_metadata, # type: ignore[arg-type] ) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content) elif role or default_class == ChatMessageChunk: return ChatMessageChunk(content=content, role=role) else: return default_class(content=content) # type: ignore[call-arg] def _format_tool_call_for_mistral(tool_call: ToolCall) -> dict: """Format Langchain ToolCall to dict expected by Mistral.""" result: Dict[str, Any] = { "function": { "name": tool_call["name"], "arguments": json.dumps(tool_call["args"]), } } if _id := tool_call.get("id"): result["id"] = _convert_tool_call_id_to_mistral_compatible(_id) return result def _format_invalid_tool_call_for_mistral(invalid_tool_call: InvalidToolCall) -> dict: """Format Langchain InvalidToolCall to dict expected by Mistral.""" result: Dict[str, Any] = { "function": { "name": invalid_tool_call["name"], "arguments": invalid_tool_call["args"], } } if _id := invalid_tool_call.get("id"): result["id"] = _convert_tool_call_id_to_mistral_compatible(_id) return result def _convert_message_to_mistral_chat_message( message: BaseMessage, ) -> Dict: if isinstance(message, ChatMessage): return dict(role=message.role, content=message.content) elif isinstance(message, HumanMessage): return dict(role="user", content=message.content) elif isinstance(message, AIMessage): message_dict: Dict[str, Any] = {"role": "assistant"} tool_calls = [] if message.tool_calls or message.invalid_tool_calls: for tool_call in message.tool_calls: tool_calls.append(_format_tool_call_for_mistral(tool_call)) for invalid_tool_call in message.invalid_tool_calls: tool_calls.append( _format_invalid_tool_call_for_mistral(invalid_tool_call) ) elif "tool_calls" in message.additional_kwargs: for tc in message.additional_kwargs["tool_calls"]: chunk = { "function": { "name": tc["function"]["name"], "arguments": tc["function"]["arguments"], } } if _id := tc.get("id"): chunk["id"] = _id tool_calls.append(chunk) else: pass if tool_calls: # do not populate empty list tool_calls message_dict["tool_calls"] = tool_calls if tool_calls and message.content: # Assistant message must have either content or tool_calls, but not both. # Some providers may not support tool_calls in the same message as content. # This is done to ensure compatibility with messages from other providers. message_dict["content"] = "" else: message_dict["content"] = message.content return message_dict elif isinstance(message, SystemMessage): return dict(role="system", content=message.content) elif isinstance(message, ToolMessage): return { "role": "tool", "content": message.content, "name": message.name, "tool_call_id": _convert_tool_call_id_to_mistral_compatible( message.tool_call_id ), } else: raise ValueError(f"Got unknown type {message}") class ChatMistralAI(BaseChatModel): """A chat model that uses the MistralAI API.""" # The type for client and async_client is ignored because the type is not # an Optional after the model is initialized and the model_validator # is run. client: httpx.Client = Field( # type: ignore # : meta private: default=None, exclude=True ) async_client: httpx.AsyncClient = Field( # type: ignore # : meta private: default=None, exclude=True ) #: :meta private: mistral_api_key: Optional[SecretStr] = Field( alias="api_key", default_factory=secret_from_env("MISTRAL_API_KEY", default=None), ) endpoint: Optional[str] = Field(default=None, alias="base_url") max_retries: int = 5 timeout: int = 120 max_concurrent_requests: int = 64 model: str = Field(default="mistral-small", alias="model_name") temperature: float = 0.7 max_tokens: Optional[int] = None top_p: float = 1 """Decode using nucleus sampling: consider the smallest set of tokens whose probability sum is at least top_p. Must be in the closed interval [0.0, 1.0].""" random_seed: Optional[int] = None safe_mode: Optional[bool] = None streaming: bool = False model_config = ConfigDict( populate_by_name=True, arbitrary_types_allowed=True, ) @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling the API.""" defaults = { "model": self.model, "temperature": self.temperature, "max_tokens": self.max_tokens, "top_p": self.top_p, "random_seed": self.random_seed, "safe_prompt": self.safe_mode, } filtered = {k: v for k, v in defaults.items() if v is not None} return filtered def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get standard params for tracing.""" params = self._get_invocation_params(stop=stop, **kwargs) ls_params = LangSmithParams( ls_provider="mistral", ls_model_name=self.model, ls_model_type="chat", ls_temperature=params.get("temperature", self.temperature), ) if ls_max_tokens := params.get("max_tokens", self.max_tokens): ls_params["ls_max_tokens"] = ls_max_tokens if ls_stop := stop or params.get("stop", None): ls_params["ls_stop"] = ls_stop return ls_params @property def _client_params(self) -> Dict[str, Any]: """Get the parameters used for the client.""" return self._default_params def completion_with_retry( self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any ) -> Any: """Use tenacity to retry the completion call.""" # retry_decorator = _create_retry_decorator(self, run_manager=run_manager) # @retry_decorator def _completion_with_retry(**kwargs: Any) -> Any: if "stream" not in kwargs: kwargs["stream"] = False stream = kwargs["stream"] if stream: def iter_sse() -> Iterator[Dict]: with connect_sse( self.client, "POST", "/chat/completions", json=kwargs ) as event_source: _raise_on_error(event_source.response) for event in event_source.iter_sse(): if event.data == "[DONE]": return yield event.json() return iter_sse() else: response = self.client.post(url="/chat/completions", json=kwargs) _raise_on_error(response) return response.json() rtn = _completion_with_retry(**kwargs) return rtn def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: overall_token_usage: dict = {} for output in llm_outputs: if output is None: # Happens in streaming continue token_usage = output["token_usage"] if token_usage is not None: for k, v in token_usage.items(): if k in overall_token_usage: overall_token_usage[k] += v else: overall_token_usage[k] = v combined = {"token_usage": overall_token_usage, "model_name": self.model} return combined @model_validator(mode="after") def validate_environment(self) -> Self: """Validate api key, python package exists, temperature, and top_p.""" if isinstance(self.mistral_api_key, SecretStr): api_key_str: Optional[str] = self.mistral_api_key.get_secret_value() else: api_key_str = self.mistral_api_key # todo: handle retries base_url_str = ( self.endpoint or os.environ.get("MISTRAL_BASE_URL") or "https://api.mistral.ai/v1" ) self.endpoint = base_url_str if not self.client: self.client = httpx.Client( base_url=base_url_str, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, timeout=self.timeout, ) # todo: handle retries and max_concurrency if not self.async_client: self.async_client = httpx.AsyncClient( base_url=base_url_str, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, timeout=self.timeout, ) if self.temperature is not None and not 0 <= self.temperature <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") if self.top_p is not None and not 0 <= self.top_p <= 1: raise ValueError("top_p must be in the range [0.0, 1.0]") return self def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: should_stream = stream if stream is not None else self.streaming if should_stream: stream_iter = self._stream( messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} response = self.completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) def _create_chat_result(self, response: Dict) -> ChatResult: generations = [] token_usage = response.get("usage", {}) for res in response["choices"]: finish_reason = res.get("finish_reason") message = _convert_mistral_chat_message_to_message(res["message"]) if token_usage and isinstance(message, AIMessage): message.usage_metadata = { "input_tokens": token_usage.get("prompt_tokens", 0), "output_tokens": token_usage.get("completion_tokens", 0), "total_tokens": token_usage.get("total_tokens", 0), } gen = ChatGeneration( message=message, generation_info={"finish_reason": finish_reason}, ) generations.append(gen) llm_output = {"token_usage": token_usage, "model": self.model} return ChatResult(generations=generations, llm_output=llm_output) def _create_message_dicts( self, messages: List[BaseMessage], stop: Optional[List[str]] ) -> Tuple[List[Dict], Dict[str, Any]]: params = self._client_params if stop is not None or "stop" in params: if "stop" in params: params.pop("stop") logger.warning( "Parameter `stop` not yet supported (https://docs.mistral.ai/api)" ) message_dicts = [_convert_message_to_mistral_chat_message(m) for m in messages] return message_dicts, params def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk for chunk in self.completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["choices"]) == 0: continue new_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class) # make future chunks same type as first chunk default_chunk_class = new_chunk.__class__ gen_chunk = ChatGenerationChunk(message=new_chunk) if run_manager: run_manager.on_llm_new_token( token=cast(str, new_chunk.content), chunk=gen_chunk ) yield gen_chunk async def _astream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk async for chunk in await acompletion_with_retry( self, messages=message_dicts, run_manager=run_manager, **params ): if len(chunk["choices"]) == 0: continue new_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class) # make future chunks same type as first chunk default_chunk_class = new_chunk.__class__ gen_chunk = ChatGenerationChunk(message=new_chunk) if run_manager: await run_manager.on_llm_new_token( token=cast(str, new_chunk.content), chunk=gen_chunk ) yield gen_chunk async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, stream: Optional[bool] = None, **kwargs: Any, ) -> ChatResult: should_stream = stream if stream is not None else self.streaming if should_stream: stream_iter = self._astream( messages=messages, stop=stop, run_manager=run_manager, **kwargs ) return await agenerate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} response = await acompletion_with_retry( self, messages=message_dicts, run_manager=run_manager, **params ) return self._create_chat_result(response) def bind_tools( self, tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]], **kwargs: Any, ) -> Runnable[LanguageModelInput, BaseMessage]: """Bind tool-like objects to this chat model. Assumes model is compatible with OpenAI tool-calling API. Args: tools: A list of tool definitions to bind to this chat model. Supports any tool definition handled by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`. tool_choice: Which tool to require the model to call. Must be the name of the single provided function or "auto" to automatically determine which function to call (if any), or a dict of the form: {"type": "function", "function": {"name": <<tool_name>>}}. kwargs: Any additional parameters are passed directly to ``self.bind(**kwargs)``. """ formatted_tools = [convert_to_openai_tool(tool) for tool in tools] return super().bind(tools=formatted_tools, **kwargs) def with_structured_output( self, schema: Optional[Union[Dict, Type]] = None, *, method: Literal["function_calling", "json_mode"] = "function_calling", include_raw: bool = False, **kwargs: Any, ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]: """Model wrapper that returns outputs formatted to match the given schema. Args: schema: The output schema. Can be passed in as: - an OpenAI function/tool schema, - a JSON Schema, - a TypedDict class (support added in 0.1.12), - or a Pydantic class. If ``schema`` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or TypedDict class. .. versionchanged:: 0.1.12 Added support for TypedDict class. method: The method for steering model generation, either "function_calling" or "json_mode". If "function_calling" then the schema will be converted to an OpenAI function and the returned model will make use of the function-calling API. If "json_mode" then OpenAI's JSON mode will be used. Note that if using "json_mode" then you must include instructions for formatting the output into the desired schema into the model call. include_raw: If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys "raw", "parsed", and "parsing_error". Returns: A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`. If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict. If ``include_raw`` is True, then Runnable outputs a dict with keys: - ``"raw"``: BaseMessage - ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above. - ``"parsing_error"``: Optional[BaseException] Example: schema=Pydantic class, method="function_calling", include_raw=False: .. code-block:: python from typing import Optional from langchain_mistralai import ChatMistralAI from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str # If we provide default values and/or descriptions for fields, these will be passed # to the model. This is an important part of improving a model's ability to # correctly return structured outputs. justification: Optional[str] = Field( default=None, description="A justification for the answer." ) llm = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> AnswerWithJustification( # answer='They weigh the same', # justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.' # ) Example: schema=Pydantic class, method="function_calling", include_raw=True: .. code-block:: python from langchain_mistralai import ChatMistralAI from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, include_raw=True ) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}), # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'), # 'parsing_error': None # } Example: schema=TypedDict class, method="function_calling", include_raw=False: .. code-block:: python # IMPORTANT: If you are using Python <=3.8, you need to import Annotated # from typing_extensions, not from typing. from typing_extensions import Annotated, TypedDict from langchain_mistralai import ChatMistralAI class AnswerWithJustification(TypedDict): '''An answer to the user question along with justification for the answer.''' answer: str justification: Annotated[ Optional[str], None, "A justification for the answer." ] llm = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } Example: schema=OpenAI function schema, method="function_calling", include_raw=False: .. code-block:: python from langchain_mistralai import ChatMistralAI oai_schema = { 'name': 'AnswerWithJustification', 'description': 'An answer to the user question along with justification for the answer.', 'parameters': { 'type': 'object', 'properties': { 'answer': {'type': 'string'}, 'justification': {'description': 'A justification for the answer.', 'type': 'string'} }, 'required': ['answer'] } } llm = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_llm = llm.with_structured_output(oai_schema) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } Example: schema=Pydantic class, method="json_mode", include_raw=True: .. code-block:: from langchain_mistralai import ChatMistralAI from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str llm = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, method="json_mode", include_raw=True ) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n" "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'), # 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'), # 'parsing_error': None # } Example: schema=None, method="json_mode", include_raw=True: .. code-block:: structured_llm = llm.with_structured_output(method="json_mode", include_raw=True) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n" "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'), # 'parsed': { # 'answer': 'They are both the same weight.', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.' # }, # 'parsing_error': None # } """ # noqa: E501 if kwargs: raise ValueError(f"Received unsupported arguments {kwargs}") is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema) if method == "function_calling": if schema is None: raise ValueError( "schema must be specified when method is 'function_calling'. " "Received None." ) # TODO: Update to pass in tool name as tool_choice if/when Mistral supports # specifying a tool. llm = self.bind_tools([schema], tool_choice="any") if is_pydantic_schema: output_parser: OutputParserLike = PydanticToolsParser( tools=[schema], # type: ignore[list-item] first_tool_only=True, # type: ignore[list-item] ) else: key_name = convert_to_openai_tool(schema)["function"]["name"] output_parser = JsonOutputKeyToolsParser( key_name=key_name, first_tool_only=True ) elif method == "json_mode": llm = self.bind(response_format={"type": "json_object"}) output_parser = ( PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] if is_pydantic_schema else JsonOutputParser() ) if include_raw: parser_assign = RunnablePassthrough.assign( parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None ) parser_none = RunnablePassthrough.assign(parsed=lambda _: None) parser_with_fallback = parser_assign.with_fallbacks( [parser_none], exception_key="parsing_error" ) return RunnableMap(raw=llm) | parser_with_fallback else: return llm | output_parser @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return self._default_params @property def _llm_type(self) -> str: """Return type of chat model.""" return "mistralai-chat" @property def lc_secrets(self) -> Dict[str, str]: return {"mistral_api_key": "MISTRAL_API_KEY"} @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return True @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "chat_models", "mistralai"]
0
lc_public_repos/langchain/libs/partners/mistralai
lc_public_repos/langchain/libs/partners/mistralai/langchain_mistralai/embeddings.py
import asyncio import logging import warnings from typing import Iterable, List import httpx from langchain_core.embeddings import Embeddings from langchain_core.utils import ( secret_from_env, ) from pydantic import ( BaseModel, ConfigDict, Field, SecretStr, model_validator, ) from tokenizers import Tokenizer # type: ignore from typing_extensions import Self logger = logging.getLogger(__name__) MAX_TOKENS = 16_000 """A batching parameter for the Mistral API. This is NOT the maximum number of tokens accepted by the embedding model for each document/chunk, but rather the maximum number of tokens that can be sent in a single request to the Mistral API (across multiple documents/chunks)""" class DummyTokenizer: """Dummy tokenizer for when tokenizer cannot be accessed (e.g., via Huggingface)""" def encode_batch(self, texts: List[str]) -> List[List[str]]: return [list(text) for text in texts] class MistralAIEmbeddings(BaseModel, Embeddings): """MistralAI embedding model integration. Setup: Install ``langchain_mistralai`` and set environment variable ``MISTRAL_API_KEY``. .. code-block:: bash pip install -U langchain_mistralai export MISTRAL_API_KEY="your-api-key" Key init args — completion params: model: str Name of MistralAI model to use. Key init args — client params: api_key: Optional[SecretStr] The API key for the MistralAI API. If not provided, it will be read from the environment variable `MISTRAL_API_KEY`. max_retries: int The number of times to retry a request if it fails. timeout: int The number of seconds to wait for a response before timing out. max_concurrent_requests: int The maximum number of concurrent requests to make to the Mistral API. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from __module_name__ import MistralAIEmbeddings embed = MistralAIEmbeddings( model="mistral-embed", # api_key="...", # other params... ) Embed single text: .. code-block:: python input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3]) .. code-block:: python [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] Embed multiple text: .. code-block:: python input_texts = ["Document 1...", "Document 2..."] vectors = embed.embed_documents(input_texts) print(len(vectors)) # The first 3 coordinates for the first vector print(vectors[0][:3]) .. code-block:: python 2 [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] Async: .. code-block:: python vector = await embed.aembed_query(input_text) print(vector[:3]) # multiple: # await embed.aembed_documents(input_texts) .. code-block:: python [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188] """ # The type for client and async_client is ignored because the type is not # an Optional after the model is initialized and the model_validator # is run. client: httpx.Client = Field(default=None) # type: ignore # : :meta private: async_client: httpx.AsyncClient = Field( # type: ignore # : meta private: default=None ) mistral_api_key: SecretStr = Field( alias="api_key", default_factory=secret_from_env("MISTRAL_API_KEY", default=""), ) endpoint: str = "https://api.mistral.ai/v1/" max_retries: int = 5 timeout: int = 120 max_concurrent_requests: int = 64 tokenizer: Tokenizer = Field(default=None) model: str = "mistral-embed" model_config = ConfigDict( extra="forbid", arbitrary_types_allowed=True, populate_by_name=True, ) @model_validator(mode="after") def validate_environment(self) -> Self: """Validate configuration.""" api_key_str = self.mistral_api_key.get_secret_value() # todo: handle retries if not self.client: self.client = httpx.Client( base_url=self.endpoint, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, timeout=self.timeout, ) # todo: handle retries and max_concurrency if not self.async_client: self.async_client = httpx.AsyncClient( base_url=self.endpoint, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, timeout=self.timeout, ) if self.tokenizer is None: try: self.tokenizer = Tokenizer.from_pretrained( "mistralai/Mixtral-8x7B-v0.1" ) except IOError: # huggingface_hub GatedRepoError warnings.warn( "Could not download mistral tokenizer from Huggingface for " "calculating batch sizes. Set a Huggingface token via the " "HF_TOKEN environment variable to download the real tokenizer. " "Falling back to a dummy tokenizer that uses `len()`." ) self.tokenizer = DummyTokenizer() return self def _get_batches(self, texts: List[str]) -> Iterable[List[str]]: """Split a list of texts into batches of less than 16k tokens for Mistral API.""" batch: List[str] = [] batch_tokens = 0 text_token_lengths = [ len(encoded) for encoded in self.tokenizer.encode_batch(texts) ] for text, text_tokens in zip(texts, text_token_lengths): if batch_tokens + text_tokens > MAX_TOKENS: if len(batch) > 0: # edge case where first batch exceeds max tokens # should not yield an empty batch. yield batch batch = [text] batch_tokens = text_tokens else: batch.append(text) batch_tokens += text_tokens if batch: yield batch def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ try: batch_responses = ( self.client.post( url="/embeddings", json=dict( model=self.model, input=batch, ), ) for batch in self._get_batches(texts) ) return [ list(map(float, embedding_obj["embedding"])) for response in batch_responses for embedding_obj in response.json()["data"] ] except Exception as e: logger.error(f"An error occurred with MistralAI: {e}") raise async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ try: batch_responses = await asyncio.gather( *[ self.async_client.post( url="/embeddings", json=dict( model=self.model, input=batch, ), ) for batch in self._get_batches(texts) ] ) return [ list(map(float, embedding_obj["embedding"])) for response in batch_responses for embedding_obj in response.json()["data"] ] except Exception as e: logger.error(f"An error occurred with MistralAI: {e}") raise def embed_query(self, text: str) -> List[float]: """Embed a single query text. Args: text: The text to embed. Returns: Embedding for the text. """ return self.embed_documents([text])[0] async def aembed_query(self, text: str) -> List[float]: """Embed a single query text. Args: text: The text to embed. Returns: Embedding for the text. """ return (await self.aembed_documents([text]))[0]