Source code for langchain.agents.agent_toolkits.vectorstore.base

"""VectorStore agent."""
from typing import Any, Dict, Optional

from langchain_core.callbacks.base import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel

from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit import (
    VectorStoreRouterToolkit,
    VectorStoreToolkit,
)
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.chains.llm import LLMChain


[docs]def create_vectorstore_agent( llm: BaseLanguageModel, toolkit: VectorStoreToolkit, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = PREFIX, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> AgentExecutor: """Construct a VectorStore agent from an LLM and tools. Args: llm (BaseLanguageModel): LLM that will be used by the agent toolkit (VectorStoreToolkit): Set of tools for the agent callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ] prefix (str, optional): The prefix prompt for the agent. If not provided uses default PREFIX. verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ] agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ] **kwargs: Additional named parameters to pass to the ZeroShotAgent. Returns: AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response """ # noqa: E501 tools = toolkit.get_tools() prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix) 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, tools=tools, callback_manager=callback_manager, verbose=verbose, **(agent_executor_kwargs or {}), )
[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: Any, ) -> AgentExecutor: """Construct a VectorStore router agent from an LLM and tools. Args: llm (BaseLanguageModel): LLM that will be used by the agent toolkit (VectorStoreRouterToolkit): Set of tools for the agent which have routing capability with multiple vector stores callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ] prefix (str, optional): The prefix prompt for the router agent. If not provided uses default ROUTER_PREFIX. verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ] agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ] **kwargs: Additional named parameters to pass to the ZeroShotAgent. Returns: AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response. """ # noqa: E501 tools = toolkit.get_tools() prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix) 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, tools=tools, callback_manager=callback_manager, verbose=verbose, **(agent_executor_kwargs or {}), )