Agentic RAG With Reranking

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

agentic_rag_with_reranking.py

"""
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

1. Set up your virtual environment

   Mac

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

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U agno cohere lancedb openai pyarrow
   
3. Export your API keys

   Mac/Linux

   export LANCEDB_API_KEY="your_lancedb_api_key_here"
   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:LANCEDB_API_KEY="your_lancedb_api_key_here"
   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
4. Run the example

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

   python agentic_rag_with_reranking.py
   

Full source: cookbook/02_agents/07_knowledge/agentic_rag_with_reranking.py