> ## 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.

# LightRAG

> Insert a PDF and a Wikipedia page into a LightRAG knowledge base and query it with an agent.

## Code

```python lightrag_db.py theme={null}
from os import getenv

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.wikipedia_reader import WikipediaReader
from agno.vectordb.lightrag import LightRag

vector_db = LightRag(
    api_key=getenv("LIGHTRAG_API_KEY"),
)

knowledge = Knowledge(
    name="My LightRag Knowledge Base",
    description="This is a knowledge base that uses a LightRag Vector DB",
    vector_db=vector_db,
)

knowledge.insert(
    name="CV",
    path="data/cv_1.pdf",
    metadata={"doc_type": "cv"},
)

knowledge.insert(
    name="Manchester United",
    topics=["Manchester United"],
    reader=WikipediaReader(),
)

knowledge.insert(
    name="Manchester United",
    url="https://en.wikipedia.org/wiki/Manchester_United_F.C.",
)

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
    read_chat_history=False,
)

agent.print_response("What skills does Jordan Mitchell have?", markdown=True)

agent.print_response(
    "In what year did Manchester United change their name?", markdown=True
)
```

## Usage

<Note>
  This example requires a running LightRAG server. `LightRag` connects to `http://localhost:9621` by default. Pass `server_url` to use a different address.
</Note>

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno pypdf wikipedia beautifulsoup4 openai
    ```
  </Step>

  <Step title="Set environment variables">
    ```bash theme={null}
    export LIGHTRAG_API_KEY="your-lightrag-api-key"
    export OPENAI_API_KEY=xxx
    ```
  </Step>

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