traditional_rag.py
"""
Traditional Rag
=============================
1. Run: `./cookbook/run_pgvector.sh` to start a postgres container with pgvector.
"""
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(
# Use PgVector as the vector database and store embeddings in the `ai.recipes` table
vector_db=PgVector(
table_name="recipes",
db_url=db_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
# Enable RAG by adding context from the `knowledge` to the user prompt.
add_knowledge_to_context=True,
# Set as False because Agents default to `search_knowledge=True`
search_knowledge=False,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
knowledge.insert(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 Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai pgvector psycopg-binary pypdf sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
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
5
Run the example
Save the code above as
traditional_rag.py, then run:python traditional_rag.py