Back to publicationsDec 21, 2025●43 readsRAG_Basic_ImplementationsShivam KumarLikeBookmarkShareTable of contents 📋 Project Overview ✅ Implementation Summary Document Loading ✅ Text Chunking ✅ Vector Database Integration ✅ RAG Pipeline ✅ 🚀 How to Run 📋 Project Overview Project: Module 1 - RAG System Implementation ✅ Implementation Summary 1. Document Loading ✅ Implemented flexible document loader supporting .pdf files 2. Text Chunking ✅ Implemented intelligent text splitting using sentence boundaries Configurable chunk size with overlap for context preservation 3. Vector Database Integration ✅ Successfully integrated ChromaDB for vector storage Implemented efficient embedding generation and storage 4. RAG Pipeline ✅ Designed effective prompt templates for context-aware responses 🚀 How to Run Install dependencies: pip install -r requirements.txt Configure API key in .env: GROQ_API_KEY=your_key_here Run the application: python src/app.py Table of contents 📋 Project Overview ✅ Implementation Summary Document Loading ✅ Text Chunking ✅ Vector Database Integration ✅ RAG Pipeline ✅ 🚀 How to Run
