id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
0bc2fb9d19be-1 | if agent_scratchpad:
return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _valid... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
0bc2fb9d19be-2 | format_instructions = format_instructions.format(tool_names=tool_names)
template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages =... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
0bc2fb9d19be-3 | )
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@property
de... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
27e1c6445686-0 | Source code for langchain.agents.structured_chat.output_parser
from __future__ import annotations
import json
import logging
import re
from typing import Optional, Union
from pydantic import Field
from langchain.agents.agent import AgentOutputParser
from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTION... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
27e1c6445686-1 | def _type(self) -> str:
return "structured_chat"
[docs]class StructuredChatOutputParserWithRetries(AgentOutputParser):
"""Output parser with retries for the structured chat agent."""
base_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
"""The base parser to use."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
27e1c6445686-2 | return cls()
@property
def _type(self) -> str:
return "structured_chat_with_retries" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/output_parser.html |
2e658f0dcbb8-0 | Source code for langchain.agents.openai_functions_agent.base
"""Module implements an agent that uses OpenAI's APIs function enabled API."""
import json
from dataclasses import dataclass
from json import JSONDecodeError
from typing import Any, List, Optional, Sequence, Tuple, Union
from pydantic import root_validator
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-1 | _create_function_message(agent_action, observation)
]
else:
return [AIMessage(content=agent_action.log)]
def _create_function_message(
agent_action: AgentAction, observation: str
) -> FunctionMessage:
"""Convert agent action and observation into a function message.
Args:
agent_ac... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-2 | if function_call:
function_name = function_call["name"]
try:
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
raise OutputParserException(
f"Could not parse tool input: {function_call} because "
f"the `arguments`... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-3 | prompt: The prompt for this agent, should support agent_scratchpad as one
of the variables. For an easy way to construct this prompt, use
`OpenAIFunctionsAgent.create_prompt(...)`
"""
llm: BaseLanguageModel
tools: Sequence[BaseTool]
prompt: BasePromptTemplate
[docs] def get_al... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-4 | """Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
agent_scratchpad = _format_intermediate_steps(intermediate_st... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-5 | }
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
prompt = self.prompt.format_prompt(**full_inputs)
messages = prompt.to_messages()
predicted_message = await self.llm.apredict_messages(
messages, functions=self.functions, callbacks=callbacks
)... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-6 | ),
extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
) -> BasePromptTemplate:
"""Create prompt for this agent.
Args:
system_message: Message to use as the system message that will be the
first in the prompt.
extra_prompt_messages... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
2e658f0dcbb8-7 | system_message=system_message,
)
return cls(
llm=llm,
prompt=prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
ebf88a3ca73f-0 | Source code for langchain.agents.openai_functions_agent.agent_token_buffer_memory
"""Memory used to save agent output AND intermediate steps."""
from typing import Any, Dict, List
from langchain.agents.openai_functions_agent.base import _format_intermediate_steps
from langchain.memory.chat_memory import BaseChatMemory
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/agent_token_buffer_memory.html |
ebf88a3ca73f-1 | )
return {self.memory_key: final_buffer}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
"""Save context from this conversation to buffer. Pruned."""
input_str, output_str = self._get_input_output(inputs, outputs)
self.chat_memory.add_user_messa... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/agent_token_buffer_memory.html |
04d14d462bdc-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
04d14d462bdc-1 | """Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
hum... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
04d14d462bdc-2 | def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Option... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
5279ede0bb05-0 | Source code for langchain.agents.conversational.output_parser
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class ConvoOutpu... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/output_parser.html |
3d963fc1d07e-0 | Source code for langchain.agents.agent_toolkits.base
"""Toolkits for agents."""
from abc import ABC, abstractmethod
from typing import List
from pydantic import BaseModel
from langchain.tools import BaseTool
[docs]class BaseToolkit(BaseModel, ABC):
"""Base Toolkit representing a collection of related tools."""
[doc... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/base.html |
85b71b065fdf-0 | Source code for langchain.agents.agent_toolkits.azure_cognitive_services
from __future__ import annotations
import sys
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools.azure_cognitive_services import (
AzureCogsFormRecognizerTool,
AzureCogsImageAnalysisTo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/azure_cognitive_services.html |
03e5a4f6134d-0 | Source code for langchain.agents.agent_toolkits.playwright.toolkit
"""Playwright web browser toolkit."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Type, cast
from pydantic import Extra, root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
03e5a4f6134d-1 | """Check that the arguments are valid."""
lazy_import_playwright_browsers()
if values.get("async_browser") is None and values.get("sync_browser") is None:
raise ValueError("Either async_browser or sync_browser must be specified.")
return values
[docs] def get_tools(self) -> List[B... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
1b15bd794de8-0 | Source code for langchain.agents.agent_toolkits.csv.base
from typing import Any, List, Optional, Union
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.schema.language_model import BaseLanguageModel
[docs]def create_csv... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
e458a41424af-0 | Source code for langchain.agents.agent_toolkits.file_management.toolkit
from __future__ import annotations
from typing import List, Optional
from pydantic import root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.file_management.copy imp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
e458a41424af-1 | )
return values
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
allowed_tools = self.selected_tools or _FILE_TOOLS.keys()
tools: List[BaseTool] = []
for tool in allowed_tools:
tool_cls = _FILE_TOOLS[tool]
tools.append(t... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
45c72cb886eb-0 | Source code for langchain.agents.agent_toolkits.jira.toolkit
from typing import Dict, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.jira.prompt import (
JIRA_CATCH_ALL_PROMPT,
JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
JIRA_GET_ALL_PROJE... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
45c72cb886eb-1 | },
]
tools = [
JiraAction(
name=action["name"],
description=action["description"],
mode=action["mode"],
api_wrapper=jira_api_wrapper,
)
for action in operations
]
return cls(tools=tools)
[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
217a5d1ea88c-0 | Source code for langchain.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.python.prompt import PREFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langch... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
217a5d1ea88c-1 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_m... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
ae8fde4dddde-0 | Source code for langchain.agents.agent_toolkits.spark_sql.base
"""Spark SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.spark_sql.toolkit i... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
ae8fde4dddde-1 | llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
too... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
dfbccf728dc0-0 | Source code for langchain.agents.agent_toolkits.spark_sql.toolkit
"""Toolkit for interacting with Spark SQL."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.schema.language_model import BaseLanguageModel
from langchain.tools import BaseTo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
c733535c4ff6-0 | Source code for langchain.agents.agent_toolkits.multion.toolkit
"""MultiOn agent."""
from __future__ import annotations
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.multion.create_session import MultionCreateSession
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/multion/toolkit.html |
df0debf8d833-0 | Source code for langchain.agents.agent_toolkits.powerbi.chat_base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent import AgentOutputParser
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
P... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
df0debf8d833-1 | """
if toolkit is None:
if powerbi is None:
raise ValueError("Must provide either a toolkit or powerbi dataset")
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
tables = powerbi.table_names if powerbi else toolkit.powerbi.table_na... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
f9e5d1248b8a-0 | Source code for langchain.agents.agent_toolkits.powerbi.base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
from langchain.agents.agent_toolkits.powerbi.t... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
f9e5d1248b8a-1 | tools = toolkit.get_tools()
tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
agent = ZeroShotAgent(
llm_chain=LLMChain(
llm=llm,
prompt=ZeroShotAgent.create_prompt(
tools,
prefix=prefix.format(top_k=top_k).format(tables=tabl... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
2158dc18d256-0 | Source code for langchain.agents.agent_toolkits.powerbi.toolkit
"""Toolkit for interacting with a Power BI dataset."""
from typing import List, Optional, Union
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.callbacks.base import BaseCallbackManager
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
2158dc18d256-1 | return [
QueryPowerBITool(
llm_chain=self._get_chain(),
powerbi=self.powerbi,
examples=self.examples,
max_iterations=self.max_iterations,
output_token_limit=self.output_token_limit,
tiktoken_model_name=self.tikto... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
f856b847b6ed-0 | Source code for langchain.agents.agent_toolkits.gmail.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.gmail.create_draft import GmailCreateD... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
2f822648ea7a-0 | Source code for langchain.agents.agent_toolkits.zapier.toolkit
"""Zapier Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrappe... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
2f822648ea7a-1 | for action in actions
]
return cls(tools=tools)
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return self.tools | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
13a72f5be346-0 | Source code for langchain.agents.agent_toolkits.nla.tool
"""Tool for interacting with a single API with natural language definition."""
from typing import Any, Optional
from langchain.agents.tools import Tool
from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
from langchain.schema.language_model import... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/tool.html |
13a72f5be346-1 | api_operation = APIOperation.from_openapi_spec(spec, path, method)
chain = OpenAPIEndpointChain.from_api_operation(
api_operation,
llm,
requests=requests,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
**kwargs,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/tool.html |
421db8f2404b-0 | Source code for langchain.agents.agent_toolkits.nla.toolkit
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.nla.tool import NLATool
from langchain.schema.langu... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
421db8f2404b-1 | return http_operation_tools
[docs] @classmethod
def from_llm_and_spec(
cls,
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
) -> NLAToolkit:
"""Instantiate the toolkit by creating too... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
421db8f2404b-2 | # TODO: Merge optional Auth information with the `requests` argument
return cls.from_llm_and_spec(
llm=llm,
spec=spec,
requests=requests,
verbose=verbose,
**kwargs,
)
[docs] @classmethod
def from_llm_and_ai_plugin_url(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
6ff438d79f5f-0 | Source code for langchain.agents.agent_toolkits.xorbits.base
"""Agent for working with xorbits objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.xorbits.prompt import (
NP_PREFIX,
NP_SUFFIX,
PD_PREFIX,
PD_SUFFIX... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/xorbits/base.html |
6ff438d79f5f-1 | if not isinstance(data, (pd.DataFrame, np.ndarray)):
raise ValueError(
f"Expected Xorbits DataFrame or ndarray object, got {type(data)}"
)
if input_variables is None:
input_variables = ["data", "input", "agent_scratchpad"]
tools = [PythonAstREPLTool(locals={"data": data})]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/xorbits/base.html |
6ff438d79f5f-2 | max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/xorbits/base.html |
1d13fcfdc162-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
1d13fcfdc162-1 | )
[docs]def create_vectorstore_router_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreRouterToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = ROUTER_PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any]... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
a6f9e4056d9a-0 | Source code for langchain.agents.agent_toolkits.vectorstore.toolkit
"""Toolkit for interacting with a vector store."""
from typing import List
from pydantic import BaseModel, Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.llms.openai import OpenAI
from langchain.schema.language_model ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
a6f9e4056d9a-1 | self.vectorstore_info.name, self.vectorstore_info.description
)
qa_with_sources_tool = VectorStoreQAWithSourcesTool(
name=f"{self.vectorstore_info.name}_with_sources",
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
62a5557c1c3c-0 | Source code for langchain.agents.agent_toolkits.spark.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAge... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
62a5557c1c3c-1 | ) -> AgentExecutor:
"""Construct a Spark agent from an LLM and dataframe."""
if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
raise ImportError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scra... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
d9072948678e-0 | Source code for langchain.agents.agent_toolkits.conversational_retrieval.openai_functions
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
AgentTokenBufferMemory,
)
from langchain.agents.openai_fun... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.html |
d9072948678e-1 | steps or not. Intermediate steps refer to prior action/observation
pairs from previous questions. The benefit of remembering these is if
there is relevant information in there, the agent can use it to answer
follow up questions. The downside is it will take up more tokens.
me... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.html |
d9072948678e-2 | tools=tools,
memory=memory,
verbose=verbose,
return_intermediate_steps=remember_intermediate_steps,
**kwargs
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.html |
adf935124e84-0 | Source code for langchain.agents.agent_toolkits.conversational_retrieval.tool
from langchain.schema import BaseRetriever
from langchain.tools import Tool
[docs]def create_retriever_tool(
retriever: BaseRetriever, name: str, description: str
) -> Tool:
"""Create a tool to do retrieval of documents.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/conversational_retrieval/tool.html |
d050c7c8a3af-0 | Source code for langchain.agents.agent_toolkits.github.toolkit
"""GitHub Toolkit."""
from typing import Dict, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.github.prompt import (
COMMENT_ON_ISSUE_PROMPT,
CREATE_FILE_PROMPT,
CREATE... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/github/toolkit.html |
d050c7c8a3af-1 | "description": CREATE_FILE_PROMPT,
},
{
"mode": "read_file",
"name": "Read File",
"description": READ_FILE_PROMPT,
},
{
"mode": "update_file",
"name": "Update File",
"descripti... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/github/toolkit.html |
11b8e9bb2e26-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
11b8e9bb2e26-1 | return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
daab9c7194ea-0 | Source code for langchain.agents.agent_toolkits.json.toolkit
from __future__ import annotations
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.json.tool import JsonGetValueTool, JsonListKeysTool, JsonSpec
[docs]class JsonToo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/toolkit.html |
c8f09dfe4f2c-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
c8f09dfe4f2c-1 | input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
3fd96de0f1a2-0 | Source code for langchain.agents.agent_toolkits.openapi.spec
"""Quick and dirty representation for OpenAPI specs."""
from dataclasses import dataclass
from typing import Any, Dict, List, Tuple, Union
[docs]def dereference_refs(spec_obj: dict, full_spec: dict) -> Union[dict, list]:
"""Try to substitute $refs.
Th... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
3fd96de0f1a2-1 | obj_out[k] = _dereference_refs(v)
else:
obj_out[k] = v
return obj_out
elif isinstance(obj, list):
return [_dereference_refs(el) for el in obj]
else:
return obj
return _dereference_refs(spec_obj)
[docs]@dataclass(frozen=True)
cla... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
3fd96de0f1a2-2 | # 3. Strip docs down to required request args + happy path response.
def reduce_endpoint_docs(docs: dict) -> dict:
out = {}
if docs.get("description"):
out["description"] = docs.get("description")
if docs.get("parameters"):
out["parameters"] = [
parame... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
f62169aaf50d-0 | Source code for langchain.agents.agent_toolkits.openapi.planner
"""Agent that interacts with OpenAPI APIs via a hierarchical planning approach."""
import json
import re
from functools import partial
from typing import Any, Callable, Dict, List, Optional
import yaml
from pydantic import Field
from langchain.agents.agent... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-1 | # information in the response.
# However, the goal for now is to have only a single inference step.
MAX_RESPONSE_LENGTH = 5000
"""Maximum length of the response to be returned."""
def _get_default_llm_chain(prompt: BasePromptTemplate) -> LLMChain:
return LLMChain(
llm=OpenAI(),
prompt=prompt,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-2 | raise NotImplementedError()
[docs]class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests POST tool with LLM-instructed extraction of truncated responses."""
name = "requests_post"
"""Tool name."""
description = REQUESTS_POST_TOOL_DESCRIPTION
"""Tool description."""
response_l... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-3 | def _run(self, text: str) -> str:
try:
data = json.loads(text)
except json.JSONDecodeError as e:
raise e
response = self.requests_wrapper.patch(data["url"], data["data"])
response = response[: self.response_length]
return self.llm_chain.predict(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-4 | ) -> Tool:
endpoint_descriptions = [
f"{name} {description}" for name, description, _ in api_spec.endpoints
]
prompt = PromptTemplate(
template=API_PLANNER_PROMPT,
input_variables=["query"],
partial_variables={"endpoints": "- " + "- ".join(endpoint_descriptions)},
)
c... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-5 | [f"{tool.name}: {tool.description}" for tool in tools]
),
},
)
agent = ZeroShotAgent(
llm_chain=LLMChain(llm=llm, prompt=prompt),
allowed_tools=[tool.name for tool in tools],
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
def _crea... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-6 | return agent.run(plan_str)
return Tool(
name=API_CONTROLLER_TOOL_NAME,
func=_create_and_run_api_controller_agent,
description=API_CONTROLLER_TOOL_DESCRIPTION,
)
[docs]def create_openapi_agent(
api_spec: ReducedOpenAPISpec,
requests_wrapper: RequestsWrapper,
llm: BaseLanguageM... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
f62169aaf50d-7 | allowed_tools=[tool.name for tool in tools],
**kwargs,
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
b4f01bfe1743-0 | Source code for langchain.agents.agent_toolkits.openapi.toolkit
"""Requests toolkit."""
from __future__ import annotations
from typing import Any, List
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.json.base import crea... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
b4f01bfe1743-1 | name="json_explorer",
func=self.json_agent.run,
description=DESCRIPTION,
)
request_toolkit = RequestsToolkit(requests_wrapper=self.requests_wrapper)
return [*request_toolkit.get_tools(), json_agent_tool]
[docs] @classmethod
def from_llm(
cls,
llm: B... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
79e5854eea85-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
79e5854eea85-1 | **kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct an SQL agent from an LLM and tools."""
tools = toolkit.get_tools()
prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt = ZeroShotAge... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
79e5854eea85-2 | tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
06ad740505e3-0 | Source code for langchain.agents.agent_toolkits.sql.toolkit
"""Toolkit for interacting with an SQL database."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.schema.language_model import BaseLanguageModel
from langchain.tools import BaseTo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
06ad740505e3-1 | )
query_sql_database_tool_description = (
"Input to this tool is a detailed and correct SQL query, output is a "
"result from the database. If the query is not correct, an error message "
"will be returned. If an error is returned, rewrite the query, check the "
"... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
d0222ea7096e-0 | Source code for langchain.agents.agent_toolkits.amadeus.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.amadeus.closest_airport import Amade... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/amadeus/toolkit.html |
67bc67c9a133-0 | Source code for langchain.agents.agent_toolkits.office365.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.office365.create_draft_message imp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/office365/toolkit.html |
ba0b8d6b9dbd-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional, Tuple
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.pandas.prompt import (
FUNCTIONS_WITH_DF,
FUNC... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-1 | else:
suffix_to_use = SUFFIX_NO_DF
include_dfs_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad", "num_dfs"]
if include_dfs_head:
input_variables += ["dfs_head"]
if prefix is None:
prefix = MULTI_DF_PREFIX
df_locals = ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-2 | suffix_to_use = SUFFIX_WITH_DF
include_df_head = True
else:
suffix_to_use = SUFFIX_NO_DF
include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables += ["df_head"]
if prefix is Non... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-3 | for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_multi_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_pr... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-4 | tools = [PythonAstREPLTool(locals={"df": df})]
system_message = SystemMessage(content=prefix + suffix_to_use)
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
return prompt, tools
def _get_functions_multi_prompt(
dfs: Any,
prefix: Optional[str] = None,
suffix: Optional[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-5 | return prompt, tools
def _get_functions_prompt_and_tools(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
number_of_head_rows: int = 5,
) -> Tuple[BasePromptTemplate, List[PythonAstREPL... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-6 | )
[docs]def create_pandas_dataframe_agent(
llm: BaseLanguageModel,
df: Any,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
ba0b8d6b9dbd-7 | callback_manager=callback_manager,
**kwargs,
)
elif agent_type == AgentType.OPENAI_FUNCTIONS:
_prompt, tools = _get_functions_prompt_and_tools(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variables,
include_df_in_pro... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
55f6e020fda4-0 | Source code for langchain.agents.chat.base
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.chat.output_parser import ChatOutputParser
from langchain.agents.chat.prompt import (
FORMAT_INSTRUCTIONS,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/base.html |
55f6e020fda4-1 | return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _get_default_output_parser(cls, **kwarg... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/base.html |
55f6e020fda4-2 | ]
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/chat/base.html |
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