> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# PgVector Hybrid Search

## Code

```python theme={null}
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.models.openai import OpenAIChat
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(
        table_name="recipes", db_url=db_url, search_type=SearchType.hybrid
    ),
)
# Load the knowledge base: Comment out after first run
knowledge_base.load(recreate=False)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=knowledge_base,
    search_knowledge=True,
    read_chat_history=True,
    markdown=True,
)
agent.print_response(
    "How do I make chicken and galangal in coconut milk soup", stream=True
)
agent.print_response("What was my last question?", stream=True)
```

## Usage

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

  <Step title="Set your API key">
    ```bash theme={null}
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U pgvector pypdf "psycopg[binary]" sqlalchemy openai agno
    ```
  </Step>

  <Step title="Run PgVector">
    ```bash theme={null}
    ./cookbook/scripts/run_pgvector.sh
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/agent_concepts/hybrid_search/pgvector/agent.py
      ```

      ```bash Windows theme={null}
      python cookbook/agent_concepts/hybrid_search/pgvector/agent.py
      ```
    </CodeGroup>
  </Step>
</Steps>