qdrant.py
"""
Qdrant: Recommended Vector Database
=====================================
Qdrant is the recommended vector database for production use.
It provides fast, scalable vector search with rich filtering
capabilities, hybrid search, and reranking support.
Features:
- Vector, keyword, and hybrid search
- Reranking support
- Rich metadata filtering
- Cloud or self-hosted deployment options
Setup: ./cookbook/scripts/run_qdrant.sh
See also: 02_local.py for local dev, 03_managed.py for Pinecone, 04_pgvector.py for PostgreSQL.
"""
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, SearchType
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
# --- Basic Qdrant setup ---
knowledge_basic = Knowledge(
vector_db=Qdrant(
collection="qdrant_basic",
url="http://localhost:6333",
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# --- Hybrid search with reranking ---
knowledge_advanced = Knowledge(
vector_db=Qdrant(
collection="qdrant_advanced",
url="http://localhost:6333",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
reranker=CohereReranker(model="rerank-multilingual-v3.0"),
),
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# --- Basic vector search ---
print("\n" + "=" * 60)
print("Qdrant: Basic vector search")
print("=" * 60 + "\n")
knowledge_basic.insert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge_basic,
search_knowledge=True,
markdown=True,
)
agent.print_response("What Thai recipes do you know?", stream=True)
# --- Hybrid search with reranking ---
print("\n" + "=" * 60)
print("Qdrant: Hybrid search + Cohere reranking")
print("=" * 60 + "\n")
knowledge_advanced.insert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
agent_advanced = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge_advanced,
search_knowledge=True,
markdown=True,
)
agent_advanced.print_response("What Thai desserts are available?", stream=True)
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
qdrant.py, then run:python qdrant.py