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Code

cookbook/08_knowledge/vector_db/pgvector/pgvector_db.py
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="vectors", db_url=db_url)

knowledge = Knowledge(
    name="My PG Vector Knowledge Base",
    description="This is a knowledge base that uses a PG Vector DB",
    vector_db=vector_db,
)
knowledge.insert(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
)

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
    read_chat_history=True,
)

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"})

​
Usage

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Install dependencies

uv pip install -U psycopg2-binary pgvector pypdf openai agno
3
Run PgVector on Docker
4
Create a file resources.py with the following contents:
5
from agno.docker.app.postgres import PgVectorDb
from agno.docker.resources import DockerResources

# -*- PgVector running on port 5432:5432
vector_db = PgVectorDb(
    pg_user="ai",
    pg_password="ai",
    pg_database="ai",
    debug_mode=True,
)

# -*- DockerResources
dev_docker_resources = DockerResources(apps=[vector_db])
6
Start resources using:
7
Mac
ag start resources.py
Windows
ag start resources.py
8
Press Enter to confirm and verify container status on the docker dashboard.
9

Set environment variables

export OPENAI_API_KEY=xxx
10

Run Agent

python cookbook/08_knowledge/vector_db/pgvector/pgvector_db.py