> ## 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.

# Upstash Vector Database

> Use Upstash Vector as a serverless vector database for your Knowledge Base.

## Setup

```shell theme={null}
uv pip install -U upstash-vector pypdf openai agno
```

Create an index in the [Upstash Console](https://console.upstash.com), then copy its REST URL and token.

```shell theme={null}
export UPSTASH_VECTOR_REST_URL="your-index-url"
export UPSTASH_VECTOR_REST_TOKEN="your-index-token"
```

The example uses OpenAI for the agent model, so set your API key:

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

## Example

```python agent_with_knowledge.py theme={null}
import os

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.upstashdb import UpstashVectorDb

vector_db = UpstashVectorDb(
    url=os.getenv("UPSTASH_VECTOR_REST_URL"),
    token=os.getenv("UPSTASH_VECTOR_REST_TOKEN"),
)

knowledge = Knowledge(
    name="Upstash Knowledge Base",
    vector_db=vector_db,
)

knowledge.insert(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
)

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

<Note>
  Without an `embedder`, `UpstashVectorDb` uses Upstash's hosted embedding models. Create your index with an embedding model in the Upstash Console. To embed locally instead, pass an `embedder` and make sure its dimension matches the index.
</Note>

<Card title="Async Support ⚡">
  <div className="mt-2">
    <p>
      Upstash also supports asynchronous operations, enabling concurrency and leading to better performance.
    </p>

    ```python async_upstash_db.py theme={null}
    import asyncio
    import os

    from agno.agent import Agent
    from agno.knowledge.embedder.openai import OpenAIEmbedder
    from agno.knowledge.knowledge import Knowledge
    from agno.vectordb.upstashdb import UpstashVectorDb

    vector_db = UpstashVectorDb(
        url=os.getenv("UPSTASH_VECTOR_REST_URL"),
        token=os.getenv("UPSTASH_VECTOR_REST_TOKEN"),
        dimension=1536,
        embedder=OpenAIEmbedder(enable_batch=True),
    )

    knowledge = Knowledge(
        vector_db=vector_db,
    )

    agent = Agent(knowledge=knowledge)

    if __name__ == "__main__":
        asyncio.run(
            knowledge.ainsert(
                url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
            )
        )

        asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))
    ```

    <Tip className="mt-4">
      Use <code>ainsert()</code> and <code>aprint\_response()</code> methods with <code>asyncio.run()</code> for non-blocking operations in high-throughput applications.
    </Tip>
  </div>
</Card>

## UpstashVectorDb Params

<Snippet file="vectordb_upstashdb_params.mdx" />