Agentic RAG With Lightrag

Ingest a PDF, a Wikipedia topic, and a URL into a LightRag-backed knowledge base and query it with an async agent.

Load a PDF, a Wikipedia topic, and a URL into a LightRAG-backed knowledge base and query it with an agent.

agentic_rag_with_lightrag.py

"""
Agentic Rag With Lightrag
=============================

Demonstrates an agentic RAG flow backed by LightRAG (relocated integration example).
"""

import asyncio
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

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
vector_db = LightRag(api_key=getenv("LIGHTRAG_API_KEY"))

knowledge = Knowledge(
    name="My LightRag Knowledge Base",
    description="Knowledge base using a LightRag vector database",
    vector_db=vector_db,
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
    read_chat_history=False,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    asyncio.run(
        knowledge.ainsert(
            name="Recipes",
            path="cookbook/07_knowledge/testing_resources/cv_1.pdf",
            metadata={"doc_type": "recipe_book"},
        )
    )

    asyncio.run(
        knowledge.ainsert(
            name="Recipes",
            topics=["Manchester United"],
            reader=WikipediaReader(),
        )
    )

    asyncio.run(
        knowledge.ainsert(
            name="Recipes",
            url="https://en.wikipedia.org/wiki/Manchester_United_F.C.",
        )
    )

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

    asyncio.run(
        agent.aprint_response(
            "In what year did Manchester United change their name?", markdown=True
        )
    )

Run the Example

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U agno beautifulsoup4 openai pypdf wikipedia
   
3. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
4. Clone Agno

   Clone the repository and run the remaining commands from its root:

   git clone --branch v2.7.4 --depth 1 https://github.com/agno-agi/agno.git
   cd agno
   
5. Start LightRAG

   Start a LightRAG server at http://localhost:9621 before running the example. Set LIGHTRAG_API_KEY only if the server requires authentication.

6. Run the example

   Run the example from the repository root:

   python cookbook/07_knowledge/05_integrations/rag/agentic_rag_with_lightrag.py
   

Full source: cookbook/07_knowledge/05_integrations/rag/agentic_rag_with_lightrag.py