"""VectorStore agent.""" from typing import Any, Dict, Optional from langchain_core._api import deprecated 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 @deprecated( since="0.2.13", removal="1.0", message=( "See API reference for this function for a replacement implementation: " "https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_agent.html " # noqa: E501 "Read more here on how to create agents that query vector stores: " "https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/#agents" ), ) 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. Note: this class is deprecated. See below for a replacement that uses tool calling methods and LangGraph. Install LangGraph with: .. code-block:: bash pip install -U langgraph .. code-block:: python from langchain_core.tools import create_retriever_tool from langchain_core.vectorstores import InMemoryVectorStore from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langgraph.prebuilt import create_react_agent llm = ChatOpenAI(model="gpt-4o-mini", temperature=0) vector_store = InMemoryVectorStore.from_texts( [ "Dogs are great companions, known for their loyalty and friendliness.", "Cats are independent pets that often enjoy their own space.", ], OpenAIEmbeddings(), ) tool = create_retriever_tool( vector_store.as_retriever(), "pet_information_retriever", "Fetches information about pets.", ) agent = create_react_agent(llm, [tool]) for step in agent.stream( {"messages": [("human", "What are dogs known for?")]}, stream_mode="values", ): step["messages"][-1].pretty_print() 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 {}), ) @deprecated( since="0.2.13", removal="1.0", message=( "See API reference for this function for a replacement implementation: " "https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent.html " # noqa: E501 "Read more here on how to create agents that query vector stores: " "https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/#agents" ), ) 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. Note: this class is deprecated. See below for a replacement that uses tool calling methods and LangGraph. Install LangGraph with: .. code-block:: bash pip install -U langgraph .. code-block:: python from langchain_core.tools import create_retriever_tool from langchain_core.vectorstores import InMemoryVectorStore from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langgraph.prebuilt import create_react_agent llm = ChatOpenAI(model="gpt-4o-mini", temperature=0) pet_vector_store = InMemoryVectorStore.from_texts( [ "Dogs are great companions, known for their loyalty and friendliness.", "Cats are independent pets that often enjoy their own space.", ], OpenAIEmbeddings(), ) food_vector_store = InMemoryVectorStore.from_texts( [ "Carrots are orange and delicious.", "Apples are red and delicious.", ], OpenAIEmbeddings(), ) tools = [ create_retriever_tool( pet_vector_store.as_retriever(), "pet_information_retriever", "Fetches information about pets.", ), create_retriever_tool( food_vector_store.as_retriever(), "food_information_retriever", "Fetches information about food.", ) ] agent = create_react_agent(llm, tools) for step in agent.stream( {"messages": [("human", "Tell me about carrots.")]}, stream_mode="values", ): step["messages"][-1].pretty_print() 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 {}), )