AI Extraction Pipeline and Chat RAG

I designed and developed Document.Intelligence, a next-generation AI-powered document intelligence platform that transforms any uploaded document (PDFs, Word, Excel, PowerPoint, scanned images, or photos) into structured, clean Markdown reports with surgical precision.

The system combines a high-performance AI Extraction Pipeline with a powerful Chat RAG (Retrieval-Augmented Generation) assistant, allowing users to upload documents once and instantly query, summarize, analyze, and interact with their content through natural language.

Key Features Shown in the Screenshots:

Smart Document Upload Interface: Supports PDF, Word, Excel, PowerPoint, JPG/PNG, TIFF/BMP, and scanned PDFs. One-click upload with drag & drop.

AI Chat Assistant (RAG): Intelligent conversational interface that answers questions about uploaded documents, explains value added, and provides professional insights (e.g., “What is the value added to my website?” or “How many files do we have in the system?”).

Multi-Document Intelligence: Automatically detects and summarizes all files in the system (Excel reports, Daily Drilling Reports, CVs, salary realignments, etc.) with clear metadata and key points.

Extraction Vault (Archive): Complete history of all processed documents with status (Completed), Job ID, and one-click access.

Production-Grade AI Pipeline: Real-time background processing using multiple workers, Redis for queuing, Celery for task management, and ChromaDB as the vector database for semantic search and RAG.

Full Backend Monitoring: Live terminal view showing Redis, ChromaDB, 4 AI extraction workers, backend, and frontend services running in parallel with background saving and database operations.

Seamless Integration: The same engine powers advanced analytics features (such as Annulus Pressure Status Summary) by extracting and interpreting complex technical documents.

Technologies & Architecture:

Backend: Python

Task Queue & Background Jobs: Redis + Celery

Vector Database (RAG): ChromaDB (with OpenAI embeddings)

Frontend: Modern React-based interface

AI Layer: Multi-model LLM integration + Retrieval-Augmented Generation

Infrastructure: Multi-worker pipeline architecture for high scalability and parallel document processing

Impact:

This platform turns raw, unstructured documents into actionable intelligence instantly. It is currently used to power intelligent document workflows in technical and professional environments, including Oil & Gas daily reports, professional profiles, infrastructure analysis, and more. It significantly reduces manual reading time while delivering accurate, context-aware AI insights.