​
Code

cookbook/agent_concepts/vector_dbs/pg_vector.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector

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

vector_db = PgVector(table_name="recipes", db_url=db_url)

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)
knowledge_base.load(recreate=False)  # Comment out after first run

agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Thai curry?", markdown=True)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.

2

Start PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:16
3

Install libraries

pip install -U sqlalchemy pgvector psycopg pypdf openai agno
4

Run Agent

MongoDB IntegrationPinecone Integration