> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Valkey

> Add and query knowledge with ValkeyDB as the vector database.

## Code

```python valkey_db.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,
)

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

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

## Usage

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

**1. Install dependencies**

```bash theme={null}
uv pip install -U valkey-glide-sync pypdf openai agno
```

**2. Export your OpenAI API key**

```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"
```

**3. Run Valkey**

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

**4. Run Agent**

```bash theme={null}
python valkey_db.py
```


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