Traditional RAG with PgVector

For a string-input run, set add_knowledge_to_context=True to retrieve before the first model call.

traditional_rag_pgvector.py

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.pgvector import PgVector, SearchType

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

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    add_knowledge_to_context=True,
    search_knowledge=False,
    markdown=True,
)

if __name__ == "__main__":
    knowledge.insert(
        name="Thai Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    )
    agent.print_response(
        "How do I make chicken and galangal in coconut milk soup?",
        stream=True,
    )

Run the Agent

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

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

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

Export the API key

export OPENAI_API_KEY=your_openai_api_key_here

Run the agent

python traditional_rag_pgvector.py

How It Works

add_knowledge_to_context=True searches with the run’s string input.
Returned documents are appended to the user message inside a <references> block.
search_knowledge=False removes the model-callable search tool.

Next Steps

Task	Guide
Let the model choose when to search	Agentic RAG with PgVector
Tune hybrid ranking	Hybrid Search
Apply metadata filters	Filtering

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Traditional RAG with PgVector

Run the Agent

How It Works

Next Steps

​Run the Agent

​How It Works

​Next Steps

Run the Agent

How It Works

Next Steps