> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.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

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

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno beautifulsoup4 cohere lancedb openai pyarrow
```

**2. Export your API keys**

```bash Mac/Linux theme={null}
export CO_API_KEY="your_co_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
```

```bash Windows theme={null}
$Env:CO_API_KEY="your_co_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
```

**3. Run the example**

Save the code above as `agentic_rag_with_reranking.py`, then run:

```bash theme={null}
python agentic_rag_with_reranking.py
```

Full source: [cookbook/02\_agents/07\_knowledge/agentic\_rag\_with\_reranking.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/02_agents/07_knowledge/agentic_rag_with_reranking.py)


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