# PgVector: PostgreSQL Vector Search (/examples/knowledge/integrations/vector-dbs/pgvector)



```python title="pgvector.py"
"""
PgVector: PostgreSQL Vector Search
====================================
PgVector adds vector similarity search to PostgreSQL, giving you
vectors alongside your existing relational data in one database.

Features:
- Vector, keyword, and hybrid search
- Full SQL capabilities for complex queries
- HNSW and IVFFlat indexing
- Reranking support
- Battle-tested PostgreSQL reliability

Setup: ./cookbook/scripts/run_pgvector.sh
Requires: pip install pgvector psycopg[binary]

See also: 01_qdrant.py for recommended default, 02_local.py for local dev.
"""

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.openai import OpenAIResponses
from agno.vectordb.pgvector import PgVector
from agno.vectordb.search import SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

# --- Basic PgVector setup ---
knowledge_basic = Knowledge(
    vector_db=PgVector(
        table_name="pgvector_basic",
        db_url=db_url,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

# --- Hybrid search with reranking ---
knowledge_hybrid = Knowledge(
    vector_db=PgVector(
        table_name="pgvector_hybrid",
        db_url=db_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
    # Reranking belongs on Knowledge: it applies to every vector db and can
    # widen the candidate pool for rerankers that need one.
    reranker=CohereReranker(model="rerank-multilingual-v3.0"),
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pdf_url = "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"

    # --- Basic vector search ---
    print("\n" + "=" * 60)
    print("PgVector: Basic vector search")
    print("=" * 60 + "\n")

    knowledge_basic.insert(url=pdf_url)
    agent = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        knowledge=knowledge_basic,
        search_knowledge=True,
        markdown=True,
    )
    agent.print_response("What Thai recipes do you know?", stream=True)

    # --- Hybrid search with reranking ---
    print("\n" + "=" * 60)
    print("PgVector: Hybrid search + Cohere reranking")
    print("=" * 60 + "\n")

    knowledge_hybrid.insert(url=pdf_url)
    agent_hybrid = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        knowledge=knowledge_hybrid,
        search_knowledge=True,
        markdown=True,
    )
    agent_hybrid.print_response("What Thai desserts are available?", stream=True)
```

## Run the Example [#run-the-example]

<Steps>
    <Step title="Set up your virtual environment">
      <CodeBlockTabs defaultValue="Mac">
        <CodeBlockTabsList>
          <CodeBlockTabsTrigger value="Mac">
            Mac
          </CodeBlockTabsTrigger>

          <CodeBlockTabsTrigger value="Windows">
            Windows
          </CodeBlockTabsTrigger>
        </CodeBlockTabsList>

        <CodeBlockTab value="Mac">
          ```bash
          uv venv --python 3.12
          source .venv/bin/activate
          ```
        </CodeBlockTab>

        <CodeBlockTab value="Windows">
          ```bash
          uv venv --python 3.12
          .venv\Scripts\activate
          ```
        </CodeBlockTab>
      </CodeBlockTabs>
    </Step>

  <Step title="Install dependencies">
    ```bash
    uv pip install -U agno "psycopg[binary]" cohere openai pgvector pypdf sqlalchemy
    ```
  </Step>

  <Step title="Export your API keys">
    <CodeBlockTabs defaultValue="Mac/Linux">
      <CodeBlockTabsList>
        <CodeBlockTabsTrigger value="Mac/Linux">
          Mac/Linux
        </CodeBlockTabsTrigger>

        <CodeBlockTabsTrigger value="Windows">
          Windows
        </CodeBlockTabsTrigger>
      </CodeBlockTabsList>

      <CodeBlockTab value="Mac/Linux">
        ```bash
        export CO_API_KEY="your_co_api_key_here"
        export OPENAI_API_KEY="your_openai_api_key_here"
        ```
      </CodeBlockTab>

      <CodeBlockTab value="Windows">
        ```bash
        $Env:CO_API_KEY="your_co_api_key_here"
        $Env:OPENAI_API_KEY="your_openai_api_key_here"
        ```
      </CodeBlockTab>
    </CodeBlockTabs>
  </Step>

    <Step title="Run PgVector">
      <CodeBlockTabs defaultValue="macOS / Linux">
        <CodeBlockTabsList>
          <CodeBlockTabsTrigger value="macOS / Linux">
            macOS / Linux
          </CodeBlockTabsTrigger>

          <CodeBlockTabsTrigger value="Windows">
            Windows
          </CodeBlockTabsTrigger>
        </CodeBlockTabsList>

        <CodeBlockTab value="macOS / Linux">
          ```bash
          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
          ```
        </CodeBlockTab>

        <CodeBlockTab value="Windows">
          ```powershell
          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
          ```
        </CodeBlockTab>
      </CodeBlockTabs>
    </Step>

  <Step title="Run the example">
    Save the code above as `pgvector.py`, then run:

    ```bash
    python pgvector.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/05\_integrations/vector\_dbs/04\_pgvector.py](https://github.com/agno-agi/agno/blob/8063a7ec3ac6ab4902bdc7966870d3b5c5f51ffc/cookbook/07_knowledge/05_integrations/vector_dbs/04_pgvector.py)