> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain Vector Database

> Search an existing LangChain vectorstore or retriever from your Knowledge Base.

## Setup

```shell theme={null}
uv pip install -U langchain langchain-community langchain-openai langchain-chroma openai agno
```

## Example

```python agent_with_knowledge.py theme={null}
import pathlib

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.langchaindb import LangChainVectorDb
from langchain_text_splitters import CharacterTextSplitter
from langchain_chroma import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings

chroma_db_dir = pathlib.Path("./chroma_db")

# Load, split, and embed the documents with LangChain
raw_documents = TextLoader("data/state_of_the_union.txt", encoding="utf-8").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
Chroma.from_documents(
    documents, OpenAIEmbeddings(), persist_directory=str(chroma_db_dir)
)

# Point Agno at the existing vectorstore
db = Chroma(
    embedding_function=OpenAIEmbeddings(), persist_directory=str(chroma_db_dir)
)
knowledge = Knowledge(
    vector_db=LangChainVectorDb(knowledge_retriever=db.as_retriever())
)

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

`LangChainVectorDb` wraps a vectorstore you already manage with LangChain. Pass a retriever with `knowledge_retriever`, or pass the vectorstore itself with `vectorstore` and Agno creates a retriever on the first search.

<Note>
  `LangChainVectorDb` is search-only. `insert()` and `upsert()` raise `NotImplementedError`. Load documents through LangChain, then wire the retriever to Agno. Filter expressions are not supported. Dictionary filters apply only when Agno builds the retriever from `vectorstore`; a retriever passed with `knowledge_retriever` ignores them.
</Note>

## LangChainVectorDb Params

<Snippet file="vectordb_langchaindb_params.mdx" />