"""VectorStore agent.""" from typing import Any from langchain_core._api import deprecated from langchain_core.callbacks.base import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_classic.agents.agent import AgentExecutor from langchain_classic.agents.agent_toolkits.vectorstore.prompt import ( PREFIX, ROUTER_PREFIX, ) from langchain_classic.agents.agent_toolkits.vectorstore.toolkit import ( VectorStoreRouterToolkit, VectorStoreToolkit, ) from langchain_classic.agents.mrkl.base import ZeroShotAgent from langchain_classic.chains.llm import LLMChain @deprecated( since="0.2.13", removal="1.0", message=( "This function will continue to be supported, but it is recommended for new " "use cases to be built with LangGraph. LangGraph offers a more flexible and " "full-featured framework for building agents, including support for " "tool-calling, persistence of state, and human-in-the-loop workflows. " "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/docs/how_to/qa_chat_history_how_to/#agents" ), ) def create_vectorstore_agent( llm: BaseLanguageModel, toolkit: VectorStoreToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = PREFIX, verbose: bool = False, # noqa: FBT001,FBT002 agent_executor_kwargs: dict[str, Any] | None = 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: ```bash pip install -U langgraph ``` ```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 model = 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(model, [tool]) for step in agent.stream( {"messages": [("human", "What are dogs known for?")]}, stream_mode="values", ): step["messages"][-1].pretty_print() ``` Args: llm: LLM that will be used by the agent toolkit: Set of tools for the agent callback_manager: Object to handle the callback prefix: The prefix prompt for the agent. verbose: If you want to see the content of the scratchpad. agent_executor_kwargs: If there is any other parameter you want to send to the agent. 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. """ 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=( "This function will continue to be supported, but it is recommended for new " "use cases to be built with LangGraph. LangGraph offers a more flexible and " "full-featured framework for building agents, including support for " "tool-calling, persistence of state, and human-in-the-loop workflows. " "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/docs/how_to/qa_chat_history_how_to/#agents" ), ) def create_vectorstore_router_agent( llm: BaseLanguageModel, toolkit: VectorStoreRouterToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = ROUTER_PREFIX, verbose: bool = False, # noqa: FBT001,FBT002 agent_executor_kwargs: dict[str, Any] | None = 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: ```bash pip install -U langgraph ``` ```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 model = 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(model, tools) for step in agent.stream( {"messages": [("human", "Tell me about carrots.")]}, stream_mode="values", ): step["messages"][-1].pretty_print() ``` Args: llm: LLM that will be used by the agent toolkit: Set of tools for the agent which have routing capability with multiple vector stores callback_manager: Object to handle the callback prefix: The prefix prompt for the router agent. If not provided uses default `ROUTER_PREFIX`. verbose: If you want to see the content of the scratchpad. agent_executor_kwargs: If there is any other parameter you want to send to the agent. 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. """ 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 {}), )