Agentic RAG with LanceDB

This example demonstrates how to implement Agentic RAG using LanceDB vector database with OpenAI embeddings, enabling the agent to search and retrieve relevant information dynamically.

Code

agentic_rag_lancedb.py

"""
1. Run: `pip install openai lancedb tantivy pypdf sqlalchemy agno` to install the dependencies
2. Run: `python cookbook/rag/04_agentic_rag_lancedb.py` to run the agent
"""

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.lancedb import LanceDb, SearchType

knowledge = Knowledge(
    # Use LanceDB as the vector database and store embeddings in the `recipes` table
    vector_db=LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",
        search_type=SearchType.vector,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    # Add a tool to search the knowledge base which enables agentic RAG.
    # This is enabled by default when `knowledge` is provided to the Agent.
    search_knowledge=True,
    markdown=True,
)
agent.print_response(
    "How do I make chicken and galangal in coconut milk soup", stream=True
)

Usage

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 openai lancedb tantivy pypdf sqlalchemy
   
3. Export your OpenAI API key

   export OPENAI_API_KEY=your_openai_api_key_here
   
4. Run Agent

   python agentic_rag_lancedb.py