> ## 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.

# Weaviate Vector Database

> Use Weaviate as a vector database for your Knowledge Base.

## Setup

Install Agno, the Weaviate client, the OpenAI client, and the PDF reader:

```shell theme={null}
uv pip install -U agno weaviate-client openai pypdf
```

Set your OpenAI API key for the default embedder and agent model:

```shell theme={null}
export OPENAI_API_KEY=xxx
```

Start Weaviate locally:

```shell theme={null}
docker run -d \
  -p 8080:8080 \
  -p 50051:50051 \
  --name weaviate \
  cr.weaviate.io/semitechnologies/weaviate:1.28.4
```

See Weaviate's [local Docker quickstart](https://docs.weaviate.io/weaviate/quickstart/local) for other deployment options.

## Example

```python agent_with_knowledge.py theme={null}
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=True,  # Set to False if using Weaviate Cloud and True if using local instance
)
# Create knowledge base
knowledge_base = Knowledge(
    vector_db=vector_db,
)

# Create and use the agent
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)

if __name__ == "__main__":
    knowledge_base.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )

    agent.print_response("How to make Thai curry?", markdown=True)
```

<Card title="Async Support">
  <div className="mt-2">
    <p>
      Use asynchronous operations when knowledge loading or search must not block the event loop.
    </p>

    ```python async_weaviate_db.py theme={null}
    import asyncio

    from agno.agent import Agent
    from agno.knowledge.knowledge import Knowledge
    from agno.vectordb.search import SearchType
    from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

    vector_db = Weaviate(
        collection="recipes_async",
        search_type=SearchType.hybrid,
        vector_index=VectorIndex.HNSW,
        distance=Distance.COSINE,
        local=True,  # Set to False if using Weaviate Cloud and True if using local instance
    )

    # Create knowledge base
    knowledge_base = Knowledge(
        vector_db=vector_db,
    )

    agent = Agent(
        knowledge=knowledge_base,
        search_knowledge=True,
    )

    async def main():
        await knowledge_base.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )
        await agent.aprint_response("How to make Tom Kha Gai", markdown=True)

    if __name__ == "__main__":
        asyncio.run(main())
    ```

    <Tip className="mt-4">
      The async methods use <code>WeaviateAsyncClient</code> for non-blocking vector operations.
    </Tip>
  </div>
</Card>

## Weaviate Params

<Snippet file="vectordb_weaviate_params.mdx" />