Traditional RAG with PgVector
Retrieve from PgVector for a string input and append references before the first model call.
For a string-input run, set add_knowledge_to_context=True to retrieve before the first model call.
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:18Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activateInstall dependencies
uv pip install -U agno openai pgvector psycopg pypdf sqlalchemyExport the API key
export OPENAI_API_KEY=your_openai_api_key_hereRun the agent
python traditional_rag_pgvector.pyHow It Works
add_knowledge_to_context=Truesearches with the run's string input.- Returned documents are appended to the user message inside a
<references>block. search_knowledge=Falseremoves 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 |