Agentic Rag With Reranking

Agentic RAG with result reranking for better relevance.

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

"""
Agentic Rag With Reranking
=============================

1. Run: `uv pip install openai agno cohere lancedb tantivy 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

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/02_agents/07_knowledge

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python agentic_rag_with_reranking.py