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

# PgVector: PostgreSQL Vector Search

> PgVector adds vector similarity search to PostgreSQL, giving you vectors alongside your existing relational data in one database.

```python pgvector.py theme={null}
"""
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"),
        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

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno cohere openai pgvector psycopg-binary sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```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"
      ```
    </CodeGroup>
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

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

    ```bash theme={null}
    python pgvector.py
    ```
  </Step>
</Steps>

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