AI-Powered Document Automation Platform: A RAG Journey 🚀

This repository documents my technical evolution from writing basic LLM prompts to engineering a production-ready Retrieval-Augmented Generation (RAG) Proof of Concept (PoC). Each folder and notebook represents a critical milestone in mastering LlamaIndex, open-source model deployment, and intelligent document orchestration.

🎯 Foundational Concepts

The foundational concepts to building a fully functional RAG application:

• RAG Fundamentals: Gain a deep understanding of how RAG pipelines work and the core concepts of information retrieval and processing in the context of LLMs.

• LlamaIndex Mastery: Learn to effectively use LlamaIndex for organizing, indexing, and efficiently searching through large, unstructured document sets.

• Model Integration: Practice integrating open-source Large Language Models (LLMs) into the RAG workflow.

• Practical Application: Build a functional, simple chatbot that uses the RAG pipeline to retrieve and synthesize relevant data for accurate, context-aware responses.

🗺️ The Development Roadmap

Phase 1: Foundations of LLM & RAG 🏗️

Focus: Establishing basic interaction, initial RAG logic, and environment setup.

• Simple Chatbot with LlamaIndex CoLab Notebook

  • Core Logic: Configuring the LLM engine (LlamaIndex + Gemini) for basic chat loops.

  • Review Demo: WATCH HERE

• Build And Optimize A RAG Pipeline For Document Retrieval

  • Core Logic: Moving from simple chat to basic document-grounded answers using local data.

  • Review Data: HERE

Phase 2: Retrieval Science & Benchmarking 🧪

Focus: Optimizing how data is processed, stored, and retrieved.

• RAG Optimization: Implementing Chunking & Embeddings in LlamaIndex and Gemini

  • Core Logic: Evaluating how various chunk sizes and embedding strategies impact accuracy.

• Comparing Open-Source Embedding Models for RAG

  • Core Logic: Systematic comparison of open-source vs. proprietary embeddings for domain-specific tasks.

  • Review Data: HERE

• Advanced PDF Retrieval and Optimization with LlamaIndex

  • Core Logic: Implementing query expansion and hybrid search for complex PDF layouts.

  • Review Data: HERE

• Optimized RAG Pipeline with Interactive RAG Chatbot for Document Retrieval

  • Core Logic: Integrating PyMuPDF and HuggingFace embeddings for high-speed retrieval.

  • Review Data: HERE

  • Review Demo: WATCH HERE

Phase 3: Intelligent Routing & Privacy 🔓

Focus: Managing multi-document "blobs," metadata, and local open-source deployment.

• RAG with Open-Source Model: Mistral 7B

  • Core Logic: Transitioning to self-contained, local GGUF models on GPU to ensure data privacy.

  • Review Data: HERE

• LLM Evaluation for RAG

  • Core Logic: Comparative accuracy testing across Gemini, Mistral, Phi-2, and TinyLlama.

  • Review Data: HERE

• Designing A Page-Level Detection Strategy Using RAG

  • Core Logic: Developing the logic to separate and classify different documents inside a single PDF.

  • Review Data: HERE

• Tagging Chunks with Metadata in LlamaIndex

  • Core Logic: Enhancing retrieval precision through document attributes (page number, doc type).

  • Review Data: HERE

• Routing Queries

  • Core Logic: Building a "Router" to automatically direct questions to specific document types.

  • Review Data:

    • Pay Statement
    • COE Sample
    • Lender Fees Worksheet
    • Sample Contract
    • Functional Sample
    • Pay Slip 1752803610
    • PaySlip 1752804713
    • Sample Contract

• End-to-End RAG Pipeline with Page-Level

  • Core Logic: Finalizing the back-end architecture for multi-document stream processing.

  • Review Data: HERE

Phase 4: UI Development & Lite Implementation 🎨

Focus: Creating user-friendly interfaces for research and deployment.

• Intro To Gradio

  • Core Logic: Learning to map Python functions to web-based UI components.

• Gradio Chatbot with Lite RAG Implementation

  • Core Logic: Creating a minimalist workspace for high-speed indexing and keyword-based retrieval.

Phase 5: Production PoC Deliverable 🏆

Focus: Integrating all modules into a unified, enterprise-grade platform.

• Full RAG Pipeline with Interactive Gradio Chatbot

  • View Presentation: HERE

  • Core Logic: Merging the high-performance retrieval back-end with the interactive Gradio front-end.

• POC - AI-Powered Document Automation Platform

  • A complete system featuring parallel ingestion, computer vision preprocessing, and semantic routing for complex document portfolios.

  • POC Presentation: HERE

  • View Web-based POC: HERE

  • To see additional enhancements and final deliverable for AI-Powered Document Intelligence Automation Platform, view repo: AI-Powered Document Automation Platform

🛠️ Tech Stack & Skills

• Orchestration: LlamaIndex

• Models: Google Gemini, Mistral 7B (Local), Phi-2, TinyLlama

• Vector DB/Indices: FAISS, VectorStoreIndex

• Embeddings: BGE, HuggingFace Transformers

• UI/UX: Gradio 5.x

• Processing: PyMuPDF, OpenCV, Multithreading

🎯 Final POC Feature Highlights

Feature	Technical Solution	Benefit
Context Isolation	Semantic Routing via Gemini	Prevents "Context Contamination" from irrelevant docs.
High-Speed Ingestion	Parallel ThreadPool Processing	5x faster parsing of 100+ page documents.
Source Trust	Metadata Tagging & Citations	Real-time source badges for every AI response.
Open-Source Ready	GGUF & LlamaCPP Integration	Zero-cost, private deployment options.