• faizan
• Uncategorized

Document Intelligence Portal – Advanced RAG Pipeline

AI that understands, remembers, and compares your documents — even when they’re messy.

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🚀 Project Snapshot

• Type: Full-stack AI Application

• Focus: Retrieval-Augmented Generation (RAG), Semantic Search, Multi-Doc Chat, Comparison

• Status: Production-ready (Local & AWS Deployable)

• Impact: Cuts document retrieval & analysis time from hours to seconds

📍 Why it’s different:
Most “AI doc apps” break under messy PDFs. This one thrives on them — delivering relevant answers, remembering context, and comparing documents at scale.

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🎯 Problem

In real-world document workflows:

• Knowledge is scattered across systems (SharePoint, email, local drives)

• Search is shallow, stuck on keywords

• AI chat tools forget context after a few turns

Goal: Build a system that:

1. Understands meaning, not just words

2. Remembers conversations

3. Compares documents instantly

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🛠 My Approach

1. RAG Pipeline Foundations

• Architecture: Ingestion → Retriever → Generator

• Chunking: LangChain splitters tuned for logical sections

• Embeddings: Benchmarked Hugging Face, OpenAI, BGE

• Vector Stores: FAISS (local), Pinecone (cloud)

2. Advanced Enhancements

• Query rewriting & context condensation (RAG Fusion, ReAct)

• Reranking with MMR to improve result relevance

• Similarity metrics: L2, Cosine, Euclidean, Jaccard

3. Conversational Intelligence

• Single-doc deep Q&A

• Multi-doc retrieval across all sources

• Side-by-side diff detection for comparisons

4. Performance Optimizations

• Local LLMs with vLLM & Groq

• Cache-Augmented Generation (CAG) for repeated queries

5. Deployment Like a Product

• Frontend: Streamlit

• Backend: FastAPI (async, scalable)

• CI/CD: GitHub Actions → AWS Fargate

• Security: AWS Secrets Manager

• Quality: SonarQube static code analysis

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📷 Screenshots & Demos

(Placeholder — swap with real images)

Main Interface

 

Document Comparison View

 

Architecture Diagram

 

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⚙️ Tech Stack

• AI/RAG: LangChain, FAISS / Pinecone, Hugging Face / OpenAI / BGE

• Backend: FastAPI

• Frontend: Streamlit

• Infra: AWS Fargate, ECR, IAM, GitHub Actions

• Performance: vLLM, Groq

• Quality/Security: SonarQube, AWS Secrets Manager

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💡 Key Features

• Semantic Search: Finds meaning, not just keywords

• Memory-Powered Chat: Remembers entire session context

• Multi-Document Chat: Pulls from multiple sources at once

• Version Comparison: Side-by-side diff with semantic matching

• Optimized Performance: Local LLM + caching for speed

• Cloud-Ready: One-click deploy to AWS Fargate

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📈 Impact

• Reduced document lookup time by 90%+

• Enabled multi-source insights without manual search

• Created a scalable architecture ready for enterprise integration

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🔗 Try It Live / View Code

• Live Demo: [Your Link Here]

• GitHub Repo: [Your Link Here]

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📜 Lessons Learned

• Architecture-first beats library-first — tools change, design principles don’t.

• Semantic search quality is as much about data prep as the LLM.

• Caching isn’t an optimization — it’s a feature when done right.

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Next Steps

• Add OCR pipeline for scanned docs

• Integrate multi-language support

• Role-based access controls for enterprise use