Insert, Search, and Delete with PgVector

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

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

Run the Example

Set up your virtual environment

Install dependencies

Run PgVector

Export the API key

Run the example

Next Steps

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