Insert, Search, and Delete with PgVector

pgvector_db.py

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

vector_db = PgVector(
    table_name="vectors",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
)
knowledge = Knowledge(vector_db=vector_db)
agent = Agent(knowledge=knowledge, search_knowledge=True)

if __name__ == "__main__":
    knowledge.insert(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"doc_type": "recipe_book"},
    )

    agent.print_response("How do I make pad thai?", markdown=True)

    vector_db.delete_by_name("Recipes")
    vector_db.delete_by_metadata({"doc_type": "recipe_book"})

delete_by_name() removes rows whose name matches exactly. delete_by_metadata() removes rows whose JSON metadata contains the supplied fields.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

uv venv --python 3.12
.venv\Scripts\activate

Install dependencies

uv pip install -U agno openai pgvector psycopg pypdf sqlalchemy

Run PgVector

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

Export the API key

export OPENAI_API_KEY=your_openai_api_key_here

Run the example

python pgvector_db.py

Next Steps

Task	Guide
Run async Agno methods	Async PgVector usage
Configure hybrid search	PgVector hybrid search

Overview Async PgVector Usage

⌘I

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

Insert, Search, and Delete with PgVector

Run the Example

Next Steps

​Run the Example

​Next Steps

Run the Example

Next Steps