> ## 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.

# 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

```python agentic_rag_lancedb.py theme={null}
"""
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

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

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

  <Step title="Export your OpenAI API key">
    ```bash theme={null}
    export OPENAI_API_KEY=your_openai_api_key_here
    ```
  </Step>

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

## Next Steps

| Task                                         | Guide                                                                     |
| -------------------------------------------- | ------------------------------------------------------------------------- |
| Retrieve before the first model call instead | [Traditional RAG with LanceDB](/knowledge/agents/traditional-rag-lancedb) |
| Change the retrieval signal                  | [Search and Retrieval](/knowledge/concepts/search-and-retrieval/overview) |
| Apply metadata filters                       | [Filtering](/knowledge/concepts/filters/overview)                         |