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Knowledge Intelligence System

The goal of this project is to build a Retrieval-Augmented Generation (RAG)–based Knowledge Intelligence System that allows users to ingest, organize, search, and converse with their internal documents and data sources using a conversational AI interface. The system will combine a vector-based retrieval layer with a large language model (LLM) to provide accurate, context-aware answers grounded in the user’s knowledge base, while offering an admin-friendly interface for managing content and monitoring usage.

11 lectures

What You Will Learn

Master building a RAG (Retrieval-Augmented Generation) pipeline using LangChain.
Implement document ingestion, chunking, and vector storage with ChromaDB.
Understand how to integrate OpenAI LLMs with a conversational retrieval chain.
Build a full-stack web application using Flask with HTML/CSS frontend.
Gain experience with AWS S3 for cloud-based document storage.
Develop a modular, production-ready Python application with service-oriented architecture.

System Architecture

Knowledge Intelligence System Architecture Diagram

High-level architecture overview of the Knowledge Intelligence System .

What You'll Build

  • A "Knowledge Intelligence System" that lets users upload documents and ask intelligent questions.
  • A RAG pipeline that processes PDFs and text files, stores embeddings in ChromaDB, and retrieves context-aware answers.
  • A Flask backend with REST API endpoints for document upload and question-answering.
  • A clean web interface for uploading documents and interacting with the AI-powered Q&A system.
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