> ## 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 Vector Database

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

<Badge icon="code-branch" color="orange">
  <Tooltip tip="Introduced in v2.7.3" cta="View release notes" href="https://github.com/agno-agi/agno/releases/tag/v2.7.3">v2.7.3</Tooltip>
</Badge>

You can use Valkey as a vector database with Agno using the valkey-search module.

## Setup

Valkey vector search requires the valkey-search module. Use the `valkey/valkey-bundle` Docker image which includes it:

```shell theme={null}
docker run -d --name my-valkey \
  -p 6379:6379 \
  valkey/valkey-bundle
```

## Example

```python agent_with_knowledge.py theme={null}
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,
)

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

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

## Valkey Params

<Snippet file="vectordb_valkey_params.mdx" />