Agentic RAG with PgVector

This example demonstrates how to implement Agentic RAG using PgVector (PostgreSQL with vector extensions) for storing and searching embeddings with hybrid search capabilities.

Code

agentic_rag_pgvector.py

"""
1. Run: `./cookbook/run_pgvector.sh` to start a postgres container with pgvector
2. Run: `pip install openai sqlalchemy 'psycopg[binary]' pgvector agno` to install the dependencies
3. Run: `python cookbook/rag/02_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 OpenAIChat
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.add_content(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    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. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U agno openai sqlalchemy psycopg2-binary pgvector
   
3. Setup PgVector

   # Start PostgreSQL with pgvector extension
   # Update connection string in the code as needed
   
4. Export your OpenAI API key

   Mac/Linux

     export OPENAI_API_KEY="your_openai_api_key_here"
   
5. Create a Python file

   Create a Python file and add the above code.

   touch agentic_rag_pgvector.py
   
6. Run Agent

   Mac

   python agentic_rag_pgvector.py
   
7. Find All Cookbooks

   Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

   Agno Cookbooks on GitHub