> ## 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 Async

> Load a document into a Chroma vectorstore with LangChain, then query it asynchronously with aprint_response.

## Code

```python async_langchain_db.py theme={null}
import asyncio
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")
state_of_the_union = pathlib.Path("data/state_of_the_union.txt")

# Load, split, and embed the document with LangChain
raw_documents = TextLoader(str(state_of_the_union), 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_retriever = db.as_retriever()

knowledge = Knowledge(
    vector_db=LangChainVectorDb(knowledge_retriever=knowledge_retriever)
)

agent = Agent(knowledge=knowledge)

if __name__ == "__main__":
    asyncio.run(
        agent.aprint_response("What did the president say?", markdown=True)
    )
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U langchain langchain-community langchain-openai langchain-chroma openai agno
    ```
  </Step>

  <Step title="Set environment variables">
    ```bash theme={null}
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="Run Agent">
    ```bash theme={null}
    python async_langchain_db.py
    ```
  </Step>
</Steps>