> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-service-account.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agentic RAG With Reranking

> Agentic RAG over LanceDB with hybrid search and a Cohere reranker ordering results.

```python agentic_rag_with_reranking.py theme={null}
"""
Agentic Rag With Reranking
=============================

1. Run: `uv pip install openai agno cohere lancedb sqlalchemy` to install the dependencies.
"""

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
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 `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(
            id="text-embedding-3-small"
        ),  # Use OpenAI for embeddings
        reranker=CohereReranker(
            model="rerank-multilingual-v3.0"
        ),  # Use Cohere for reranking
    ),
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    # Agentic RAG is enabled by default when `knowledge` is provided to the Agent.
    knowledge=knowledge,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    knowledge.insert(name="Agno Docs", url="https://docs.agno.com/introduction.md")
    agent.print_response("What are Agno's key features?")
```

## Run the Example

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

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

  <Step title="Export your API keys">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export LANCEDB_API_KEY="your_lancedb_api_key_here"
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:LANCEDB_API_KEY="your_lancedb_api_key_here"
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `agentic_rag_with_reranking.py`, then run:

    ```bash theme={null}
    python agentic_rag_with_reranking.py
    ```
  </Step>
</Steps>

Full source: [cookbook/02\_agents/07\_knowledge/agentic\_rag\_with\_reranking.py](https://github.com/agno-agi/agno/blob/main/cookbook/02_agents/07_knowledge/agentic_rag_with_reranking.py)