reranking.py
"""
Reranking: Improving Search Quality
=====================================
Reranking is a two-stage retrieval process:
1. First, retrieve candidate results using vector/hybrid search
2. Then, a reranker model scores and reorders results by relevance
This dramatically improves result quality, especially for complex queries.
Supported rerankers:
- CohereReranker: Cohere's rerank models (recommended)
- SentenceTransformerReranker: Local reranking with BAAI/bge models
- InfinityReranker: Self-hosted reranking
- BedrockReranker: AWS Bedrock reranking
See also: 02_hybrid_search.py for search type options.
"""
import asyncio
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.qdrant import Qdrant
from agno.vectordb.search import SearchType
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
qdrant_url = "http://localhost:6333"
# Knowledge with hybrid search + Cohere reranking
knowledge = Knowledge(
vector_db=Qdrant(
collection="reranking_demo",
url=qdrant_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
reranker=CohereReranker(model="rerank-multilingual-v3.0"),
),
)
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
search_knowledge=True,
instructions=[
"Always search your knowledge base before answering.",
"Include sources in your response.",
],
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
async def main():
await knowledge.ainsert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
print("\n" + "=" * 60)
print("Hybrid search + Cohere reranking")
print("=" * 60 + "\n")
agent.print_response(
"What are some good Thai dessert recipes?",
stream=True,
)
asyncio.run(main())
Run the Example
1
Set up your virtual environment
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 fastembed openai pypdf qdrant-client
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run Qdrant
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
5
Run the example
Save the code above as
reranking.py, then run:python reranking.py