Agentic RAG with LanceDB

Agentic RAG with LanceDB as the vector store and OpenAI embeddings.

Implement Agentic RAG using the LanceDB vector database with OpenAI embeddings. The agent searches the knowledge base and retrieves relevant information dynamically.

Code

agentic_rag_lancedb.py
"""
1. Run: `pip install openai lancedb pypdf agno` to install the dependencies
2. Run: `python 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

Set up your virtual environment

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

Install dependencies

uv pip install -U agno openai lancedb pypdf

Export your OpenAI API key

export OPENAI_API_KEY=your_openai_api_key_here

Run Agent

python agentic_rag_lancedb.py

Next Steps

TaskGuide
Retrieve before the first model call insteadTraditional RAG with LanceDB
Change the retrieval signalSearch and Retrieval
Apply metadata filtersFiltering