> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Qdrant: Recommended Vector Database

> Qdrant is the recommended vector database for production use.

Qdrant is the recommended vector database for production use. It provides fast, scalable vector search with rich filtering capabilities, hybrid search, and reranking support.

```python qdrant.py theme={null}
"""
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

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno cohere fastembed openai qdrant-client
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run Qdrant">
    ```bash theme={null}
    docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
    ```
  </Step>

  <Step title="Run the example">
    Save the code above as `qdrant.py`, then run:

    ```bash theme={null}
    python qdrant.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/05\_integrations/vector\_dbs/01\_qdrant.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/05_integrations/vector_dbs/01_qdrant.py)