LangChain Vector DB

Install dependencies: - uv pip install langchain langchain-community langchain-openai langchain-chroma agno.

Install dependencies: - uv pip install langchain langchain-community langchain-openai langchain-chroma agno

"""
LangChain Vector DB
===================

Install dependencies:
- uv pip install langchain langchain-community langchain-openai langchain-chroma agno
"""

import asyncio
import pathlib

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.langchaindb import LangChainVectorDb
from langchain.text_splitter import CharacterTextSplitter
from langchain_chroma import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
chroma_db_dir = pathlib.Path("./chroma_db")
state_of_the_union = pathlib.Path(
    "cookbook/07_knowledge/testing_resources/state_of_the_union.txt"
)


# ---------------------------------------------------------------------------
# Create Knowledge Base
# ---------------------------------------------------------------------------
def create_documents():
    raw_documents = TextLoader(str(state_of_the_union), encoding="utf-8").load()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    return text_splitter.split_documents(raw_documents)


def create_knowledge() -> Knowledge:
    db = Chroma(
        embedding_function=OpenAIEmbeddings(),
        persist_directory=str(chroma_db_dir),
    )
    knowledge_retriever = db.as_retriever()
    return Knowledge(
        vector_db=LangChainVectorDb(knowledge_retriever=knowledge_retriever)
    )


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
def create_agent(knowledge: Knowledge) -> Agent:
    return Agent(model=OpenAIChat("gpt-5.2"), knowledge=knowledge)


# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
def run_sync() -> None:
    documents = create_documents()
    Chroma.from_documents(
        documents,
        OpenAIEmbeddings(),
        persist_directory=str(chroma_db_dir),
    )

    knowledge = create_knowledge()
    agent = create_agent(knowledge)
    agent.print_response(
        "What did the president say about broadcasting and the State of the Union?",
        markdown=True,
    )


async def run_async() -> None:
    documents = create_documents()
    await asyncio.get_event_loop().run_in_executor(
        None,
        lambda: Chroma.from_documents(
            documents,
            OpenAIEmbeddings(),
            persist_directory=str(chroma_db_dir),
        ),
    )

    knowledge = create_knowledge()
    agent = create_agent(knowledge)
    await agent.aprint_response(
        "What did the president say about broadcasting and the State of the Union?",
        markdown=True,
    )


if __name__ == "__main__":
    run_sync()
    asyncio.run(run_async())

Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/vector_db/langchain

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python langchain_db.py