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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
This project demonstrates a Retrieval-Augmented Generation (RAG) architecture using open-source tools. It solves the "knowledge cutoff" problem of LLMs by giving them access to live tools.
How it works:
Intent Classification: The user's input is first processed by a local Llama 3 instance, which acts as a semantic router. It analyzes the prompt to determine if external data is required.
Tool Execution: If a search intent is detected, the Python backend executes a query against the SerpApi (Google Shopping) endpoint. Data Structuring: The raw JSON response from the API is parsed, cleaned, and structured into standardized product objects.
UI Rendering: The structured data is dynamically rendered into interactive product cards using Streamlit's component system, allowing users to view images and click direct purchase links without leaving the chat interface.
Been working on this Saas dashboard 🔥
