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

# LangChain KnowledgeBase

## Example

```python langchain_kb.py theme={null}
from phi.agent import Agent
from phi.knowledge.langchain import LangChainKnowledgeBase

from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma

chroma_db_dir = "./chroma_db"


def load_vector_store():
    state_of_the_union = ws_settings.ws_root.joinpath("data/demo/state_of_the_union.txt")
    # -*- Load the document
    raw_documents = TextLoader(str(state_of_the_union)).load()
    # -*- Split it into chunks
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    documents = text_splitter.split_documents(raw_documents)
    # -*- Embed each chunk and load it into the vector store
    Chroma.from_documents(documents, OpenAIEmbeddings(), persist_directory=str(chroma_db_dir))


# -*- Get the vectordb
db = Chroma(embedding_function=OpenAIEmbeddings(), persist_directory=str(chroma_db_dir))
# -*- Create a retriever from the vector store
retriever = db.as_retriever()

# -*- Create a knowledge base from the vector store
knowledge_base = LangChainKnowledgeBase(retriever=retriever)

agent = Agent(knowledge_base=knowledge_base, add_references_to_prompt=True)
conv.print_response("What did the president say about technology?")
```

## LangChainKnowledgeBase Params

| Parameter       | Type                 | Default | Description                                                               |
| --------------- | -------------------- | ------- | ------------------------------------------------------------------------- |
| `loader`        | `Optional[Callable]` | `None`  | LangChain loader.                                                         |
| `vectorstore`   | `Optional[Any]`      | `None`  | LangChain vector store used to create a retriever.                        |
| `search_kwargs` | `Optional[dict]`     | `None`  | Search kwargs when creating a retriever using the langchain vector store. |
| `retriever`     | `Optional[Any]`      | `None`  | LangChain retriever.                                                      |

## AgentKnowledge Params

`LangChainKnowledgeBase` is a subclass of the `AgentKnowledge` class and has access to the same params

<Snippet file="kb-base-reference.mdx" />