Traditional RAG with PgVector

This example demonstrates traditional RAG implementation using PgVector database with OpenAI embeddings, where knowledge context is automatically added to prompts without search functionality.

Code

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 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,
    # 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,
)
agent.print_response(
    "How do I make chicken and galangal in coconut milk soup", 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 psycopg pgvector
   
3. Setup PgVector

   # Start PostgreSQL container with pgvector
   ./cookbook/run_pgvector.sh
   
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 traditional_rag_pgvector.py
   
6. Run Agent

   Mac

   python traditional_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