Custom RAG Document Chat System Recently, I built a Custom Retrieval-Augmented Generation (RAG) s...Custom RAG Document Chat System Recently, I built a Custom Retrieval-Augmented Generation (RAG) s...
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Muhammad Dawood
• 1d
Custom RAG Document Chat System
Recently, I built a Custom Retrieval-Augmented Generation (RAG) system that allows users to upload their own documents and interact with them through natural-language questions.
The goal was to understand how modern AI document-chat systems work internally — from document processing and chunking to embeddings, vector search, and LLM-based responses.
What I Built
📄 Document Upload — Users can upload PDF and DOCX documents.
🔍 Text Extraction — Extracts readable text from uploaded documents.
✂️ Document Chunking — Splits large documents into smaller overlapping chunks for better retrieval.
🧠 Embeddings — Converts document chunks into vector representations.
🗄️ Vector Storage — Uses Qdrant to store and search document embeddings.
🔎 Semantic Search — Retrieves the most relevant document chunks based on the user's question.
🤖 LLM Integration — Uses retrieved context to generate grounded answers.
💬 Document-Based Chat — Users can ask questions and receive answers based on their uploaded documents.
RAG Pipeline
Upload → Extract → Chunk → Embed → Store → Retrieve → Generate
This project helped me understand the complete RAG pipeline rather than treating RAG as just an API call to an LLM.
Tech Stack
Backend: ASP.NET Core Web API, C# Document Processing: PDF & DOCX text extraction Embeddings: Ollama + nomic-embed-text Vector Database: Qdrant AI: Retrieval-Augmented Generation (RAG), LLM API Testing: Swagger Frontend: React.js
AI Engineer Dot Net Backend DevelopmentFrontend DevelopmentASP.NET CoreOllamaReact
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