> ## 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.

# Valkey Async

## Code

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

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.valkey import ValkeyDB
from agno.vectordb.search import SearchType

# Initialize Valkey Vector DB
vector_db = ValkeyDB(
    index_name="agno_docs",
    host="localhost",
    port=6379,
    search_type=SearchType.vector,
)

# Build a Knowledge base backed by Valkey
knowledge = Knowledge(
    name="My Valkey Knowledge Base",
    description="Knowledge base using Valkey as the vector store",
    vector_db=vector_db,
)


async def main():
    # Add content (async)
    await knowledge.add_content_async(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"category": "recipe_book"},
        skip_if_exists=True,
    )

    # Query with an Agent (async)
    agent = Agent(knowledge=knowledge)
    await agent.aprint_response("List down the ingredients to make Massaman Gai", markdown=True)


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

## Usage

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U valkey-glide-sync pypdf openai agno
    ```
  </Step>

  <Step title="Run Valkey">
    ```bash theme={null}
    docker run -d --name my-valkey -p 6379:6379 valkey/valkey-bundle
    ```
  </Step>

  <Step title="Run Agent">
    ```bash theme={null}
    python async_valkey_db.py
    ```
  </Step>
</Steps>