> ## 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.

# PgVector Vector Database

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

## Setup

```shell theme={null}
uv pip install -U sqlalchemy psycopg pgvector pypdf openai agno
```

Run PgVector with Docker:

```shell theme={null}
docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18
```

## Example

```python agent_with_knowledge.py theme={null}
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = Knowledge(
    vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)

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

    agent = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        knowledge=knowledge_base,
        # Add a tool to read chat history.
        read_chat_history=True,
        markdown=True,
        # debug_mode=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)
```

> **Async Support ⚡**
>
> PgVector also supports asynchronous operations, enabling concurrency and leading to better performance.
>
> ```python async_pgvector.py theme={null}
> import asyncio
>
> from agno.agent import Agent
> from agno.knowledge.knowledge import Knowledge
> from agno.vectordb.pgvector import PgVector
>
> db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
>
> vector_db = PgVector(table_name="recipes", db_url=db_url)
>
> knowledge_base = Knowledge(
>     vector_db=vector_db,
> )
>
> agent = Agent(knowledge=knowledge_base)
>
> if __name__ == "__main__":
>     # Load knowledge base asynchronously
>     asyncio.run(knowledge_base.ainsert(
>             url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
>         )
>     )
>
>     # Create and use the agent asynchronously
>     asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))
> ```
>
> > [!TIP]
> >
> > Use `ainsert()` and `aprint_response()` methods with `asyncio.run()` for non-blocking operations in high-throughput applications.

## PgVector Params

<Snippet file="vectordb_pgvector_params.mdx" />


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