> ## 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.

# Weaviate Hybrid Search

> Combine vector and keyword scores in one Weaviate query, tuned with hybrid_search_alpha.

## Code

```python weaviate_db_hybrid_search.py theme={null}
import typer
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
from rich.prompt import Prompt

vector_db = Weaviate(
    collection="recipes",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=False,  # Set to False if using Weaviate Cloud and True if using local instance
    # Adjust alpha for hybrid search (0.0-1.0, default is 0.5), where 0 is pure keyword search, 1 is pure vector search
    hybrid_search_alpha=0.6,
)

knowledge_base = Knowledge(
    name="Weaviate Hybrid Search",
    description="A knowledge base for Weaviate hybrid search",
    vector_db=vector_db,
)

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

def weaviate_agent(user: str = "user"):
    agent = Agent(
        user_id=user,
        knowledge=knowledge_base,
        search_knowledge=True,
    )

    while True:
        message = Prompt.ask(f"[bold]{user}[/bold]")
        if message in ("exit", "bye"):
            break
        agent.print_response(message)

if __name__ == "__main__":
    typer.run(weaviate_agent)
```

## Usage

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

**1. Install dependencies**

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

**2. Setup Weaviate**

```bash Weaviate Cloud theme={null}
# 1. Create account at https://console.weaviate.cloud/
# 2. Create a cluster and copy the "REST endpoint" and "Admin" API Key
# 3. Set environment variables:
export WCD_URL="your-cluster-url"
export WCD_API_KEY="your-api-key"
# 4. Set local=False in the code
```

```bash Local Development theme={null}
# 1. Install Docker from https://docs.docker.com/get-docker/
# 2. Run Weaviate locally:
docker run -d \
    -p 8080:8080 \
    -p 50051:50051 \
    --name weaviate \
    cr.weaviate.io/semitechnologies/weaviate:1.28.4
# 3. Set local=True in the code
```

**3. Set environment variables**

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

**4. Run Agent**

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


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