RAG-Powered Document Intelligence System
Executive Summary
This demo demonstrates how Retrieval-Augmented Generation (RAG) can transform enterprise knowledge management, providing instant access to organizational information with high accuracy and context awareness.
The Challenge
Organizations struggle with information retrieval from large document repositories, leading to hours spent searching through documents, inconsistent answers to questions, and knowledge workers unable to access critical information quickly. Traditional search methods lack context and accuracy.
Key Pain Points:
- • Hours spent searching through document repositories
- • Inconsistent answers to the same questions
- • Knowledge workers unable to access critical information quickly
- • Traditional search lacks context and accuracy
The Solution
Built a comprehensive Retrieval-Augmented Generation (RAG) system that processes documents through a complete pipeline: cleaning and parsing PDFs, intelligent chunking with overlap strategies, vector embedding generation using OpenAI, storage in Supabase vector database, semantic retrieval with similarity search, and context-aware answer generation. The solution leverages LangChain for advanced document processing, chain orchestration, and intelligent retrieval strategies. The system includes a React frontend for natural language queries, OpenAI API integration for advanced language understanding, and a Python backend with LangChain orchestrating the entire RAG workflow.
Technical Architecture:
- • Complete RAG pipeline: PDF cleaning, parsing, and chunking
- • OpenAI API integration for embedding generation
- • Supabase vector database for semantic storage and retrieval
- • Intelligent chunking with overlap strategies
- • FastAPI backend orchestrating the entire workflow
- • React frontend with natural language query interface
RAG Pipeline Architecture
The solution implements a complete RAG pipeline that transforms raw documents into intelligent, searchable knowledge:
Document Processing Pipeline:
- • PDF Cleaning: Text extraction, formatting normalization, and noise removal
- • Intelligent Parsing: Structure-aware document breakdown and metadata extraction
- • Smart Chunking: Context-aware text segmentation with overlap strategies for continuity
- • Embedding Generation: OpenAI API integration for high-dimensional vector creation
- • Vector Storage: Supabase vector database for efficient similarity search
- • LangChain Integration: Advanced document processing, chain orchestration, and intelligent retrieval strategies
- • Semantic Retrieval: Context-aware document retrieval using cosine similarity
- • Answer Generation: OpenAI GPT integration for context-aware responses
Technical Implementation:
- • PDF Processing: PyPDF2 and pdfplumber for robust text extraction
- • Chunking Strategy: Recursive character splitting with configurable overlap
- • LangChain Framework: Document loaders, text splitters, and retrieval chains for robust RAG implementation
- • Vector Database: Supabase pgvector extension for PostgreSQL
- • Similarity Search: Cosine similarity with configurable threshold
- • Context Assembly: Dynamic context window assembly for optimal responses
Business Impact & Results
Demonstrates potential to reduce information retrieval time from hours to seconds, provide 90%+ accuracy in answers, and enable instant access to organizational knowledge. The solution scales to handle enterprise document volumes while maintaining context and relevance.
Cloud Infrastructure
The solution leverages Supabase's modern cloud infrastructure with built-in vector database capabilities, enterprise-grade security, and automatic scaling. The architecture follows modern SaaS best practices for rapid development and deployment.
Supabase Services & Architecture:
- • Supabase Database: PostgreSQL with pgvector extension for vector storage
- • Supabase Auth: Built-in authentication and user management
- • Supabase Storage: File storage for document uploads and management
Development & Deployment:
- • Database Management: Automatic backups, migrations, and monitoring
- • API Management: Auto-generated REST and GraphQL APIs
Implementation Approach
The project was delivered using an agile methodology over 3 weeks, focusing on rapid prototyping and iterative development to validate the concept with stakeholders.
Development Phases:
- • Week 1: Core RAG engine and vector embeddings
- • Week 2: AWS infrastructure setup and backend API
- • Week 3: Frontend interface, testing, and deployment
Project Details
Technical Stack
Ready to Build Something Similar?
This demo demonstrates the potential for RAG-powered knowledge management in your organization.
Let's Discuss Your Project