langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_agent¶
langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_agent(llm: BaseLanguageModel, toolkit: VectorStoreToolkit, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.\nIf the question does not seem relevant to any of the tools provided, just return "I don\'t know" as the answer.\n', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → AgentExecutor[source]¶
Construct a VectorStore agent from an LLM and tools.
Parameters
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
Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response
Return type
AgentExecutor
Examples using create_vectorstore_agent¶
Vectorstore