> ## 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.

# Search Types: Vector, Keyword, and Hybrid

> Knowledge supports three search types.

See also: 03\_reranking.py for improving search results with reranking.

```python hybrid_search.py theme={null}
"""
Search Types: Vector, Keyword, and Hybrid
===========================================
Knowledge supports three search types. Each has different strengths:

- Vector: Semantic similarity search. Finds conceptually related content
  even when exact words don't match.
- Keyword: Full-text search. Fast and precise for exact term matching.
- Hybrid: Combines vector + keyword. Best of both worlds. Recommended default.

See also: 03_reranking.py for improving search results with reranking.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

qdrant_url = "http://localhost:6333"
pdf_url = "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"


def create_knowledge(search_type: SearchType) -> Knowledge:
    return Knowledge(
        vector_db=Qdrant(
            collection="search_types_%s" % search_type.value,
            url=qdrant_url,
            search_type=search_type,
            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
        ),
    )


# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":

    async def main():
        search_types = [
            (SearchType.vector, "Vector (semantic similarity)"),
            (SearchType.keyword, "Keyword (full-text search)"),
            (SearchType.hybrid, "Hybrid (vector + keyword)"),
        ]

        for search_type, description in search_types:
            print("\n" + "=" * 60)
            print("SEARCH TYPE: %s" % description)
            print("=" * 60 + "\n")

            knowledge = create_knowledge(search_type)
            # skip_if_exists=True avoids re-processing if run multiple times
            await knowledge.ainsert(url=pdf_url, skip_if_exists=True)

            agent = Agent(
                model=OpenAIResponses(id="gpt-5.2"),
                knowledge=knowledge,
                search_knowledge=True,
                markdown=True,
            )
            agent.print_response(
                "How do I make pad thai?",
                stream=True,
            )

    asyncio.run(main())
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno 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 `hybrid_search.py`, then run:

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

Full source: [cookbook/07\_knowledge/02\_building\_blocks/02\_hybrid\_search.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/02_building_blocks/02_hybrid_search.py)