Notion RAG

Project Overview

Notion RAG is a system that implements Retrieval-Augmented Generation (RAG) to provide AI-powered answers from Notion databases. The project integrates Notion’s API, vector search capabilities, and language models to create an intelligent knowledge retrieval system.

Features Implemented

• Notion Integration - Seamless indexing of Notion databases and content
• Vector Search - Efficient retrieval using Qdrant for vector similarity search
• RAG Pipeline - Combines retrieved documents with AI generation for accurate answers
• API Endpoints - Flask-based REST API for search and question answering
• Command-Line Interface - Simple CLI for direct interaction

Implementation Details

The system consists of several key components:

1. Notion Indexer - Extracts and processes content from Notion databases
2. Embedding Generator - Creates vector representations of text content
3. Vector Database - Stores and indexes embeddings for efficient retrieval
4. RAG Engine - Combines retrieval results with language model generation
5. API Server - Provides endpoints for search and question answering
6. CLI Tool - Enables command-line access to the system’s capabilities

Learning Outcomes

This project allowed me to:

• Gain hands-on experience with modern RAG architectures
• Develop skills in vector embeddings and similarity search
• Implement efficient data processing pipelines for unstructured text
• Create a production-ready API for NLP applications

GitHub Repository

The complete source code is available on my GitHub repository: github.com/apratim-mishra/notion_