"""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
[docs]
@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: 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 {}),
)
[docs]
@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: 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 {}),
)
