Private RAG Example

Watch the video tutorial here

This repository contains an example project for building a private Retrieval-Augmented Generation (RAG) application using Llama3.2, Ollama, and PostgreSQL. It demonstrates how to set up a RAG pipeline that does not rely on external API calls, ensuring that sensitive data remains within your infrastructure.

Prerequisites

• Docker
• Python, psycopg
• Ollama
• PostgreSQL, pgai

Docker Setup

• Create a network through which the Ollama and PostgreSQL containers will interact:

  docker network docker network create rag-net

• Ollama docker container: (Note: --network tag to make sure that the container runs on the network defined)

  docker run -d --network rag-net -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

  • Llama3.2: docker exec -it ollama ollama pull llama3.2
  • Nomic Embed v1.5: docker exec -it ollama ollama pull nomic-embed-text

• TimescaleDB: docker run -d --network rag-net --name timescaledb -p 5432:5432 -e POSTGRES_PASSWORD=password timescale/timescaledb-ha:pg16

  • pgai: CREATE EXTENSION IF NOT EXISTS ai CASCADE; (also installs pgvector and plpython3)