base

This class is deprecated. See below for a replacement that uses tool calling methods and LangGraph. Install LangGraph with:

pip install -U langgraph

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()

This class is deprecated. See below for a replacement that uses tool calling methods and LangGraph. Install LangGraph with:

pip install -U langgraph

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()