Code
agentic_rag_pgvector.py
"""
1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
2. Run: `pip install openai sqlalchemy psycopg pgvector pypdf agno` to install the dependencies
3. Run: `python agentic_rag_pgvector.py` to run the agent
"""
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"),
),
)
knowledge.insert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
# Add a tool to search the knowledge base which enables agentic RAG.
# This is enabled by default when `knowledge` is provided to the Agent.
search_knowledge=True,
markdown=True,
)
agent.print_response(
"How do I make chicken and galangal in coconut milk soup", stream=True
)
# agent.print_response(
# "Hi, i want to make a 3 course meal. Can you recommend some recipes. "
# "I'd like to start with a soup, then im thinking a thai curry for the main course and finish with a dessert",
# stream=True,
# )
Usage
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 sqlalchemy psycopg pgvector pypdf
3
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
4
Export your OpenAI API key
export OPENAI_API_KEY=your_openai_api_key_here
5
Run Agent
python agentic_rag_pgvector.py
Next Steps
| Task | Guide |
|---|---|
| Retrieve before the first model call instead | Traditional RAG with PgVector |
| Tune hybrid ranking | Hybrid Search |
| Apply metadata filters | Filtering |