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

# Agentic RAG with Hybrid Search and Reranking

This example demonstrates how to implement Agentic RAG using Hybrid Search and Reranking with LanceDB, Cohere embeddings, and Cohere reranking for enhanced document retrieval and response generation.

## Code

```python agentic_rag.py theme={null}
"""This cookbook shows how to implement Agentic RAG using Hybrid Search and Reranking.
1. Run: `pip install agno anthropic cohere lancedb tantivy sqlalchemy` to install the dependencies
2. Export your ANTHROPIC_API_KEY and CO_API_KEY
3. Run: `python cookbook/agent_concepts/agentic_search/agentic_rag.py` to run the agent
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.cohere import CohereEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.anthropic import Claude
from agno.vectordb.lancedb import LanceDb, SearchType

knowledge = Knowledge(
    # Use LanceDB as the vector database, store embeddings in the `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=CohereEmbedder(id="embed-v4.0"),
        reranker=CohereReranker(model="rerank-v3.5"),
    ),
)

asyncio.run(
    knowledge.add_content_async(url="https://docs.agno.com/introduction/agents.md")
)

agent = Agent(
    model=Claude(id="claude-3-7-sonnet-latest"),
    # Agentic RAG is enabled by default when `knowledge` is provided to the Agent.
    knowledge=knowledge,
    # search_knowledge=True gives the Agent the ability to search on demand
    # search_knowledge is True by default
    search_knowledge=True,
    instructions=[
        "Include sources in your response.",
        "Always search your knowledge before answering the question.",
    ],
    markdown=True,
)

if __name__ == "__main__":
    agent.print_response("What are Agents?", stream=True)
```

## Usage

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

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U agno anthropic cohere lancedb tantivy sqlalchemy
    ```
  </Step>

  <Step title="Export your ANTHROPIC API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
        export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
      ```

      ```bash Windows theme={null}
        $Env:ANTHROPIC_API_KEY="your_anthropic_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Create a Python file">
    Create a Python file and add the above code.

    ```bash theme={null}
    touch agentic_rag.py
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python agentic_rag.py
      ```

      ```bash Windows theme={null}
      python agentic_rag.py
      ```
    </CodeGroup>
  </Step>

  <Step title="Find All Cookbooks">
    Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

    <Link href="https://github.com/agno-agi/agno/tree/main/cookbook/agents/agentic_search" target="_blank">
      Agno Cookbooks on GitHub
    </Link>
  </Step>
</Steps>