📄 Enterprise Document Intelligence System

LLM-Powered Knowledge Extraction & Retrieval (RAG)

🔗 Live API (Swagger UI):
👉 https://enterprise-docs-ai-production.up.railway.app/docs

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🚀 Overview

The Enterprise Document Intelligence System is a cloud-native, multi-tenant platform that enables organizations to ingest documents, extract knowledge, and perform secure, explainable, LLM-powered question answering over their internal data.

This system is designed for real enterprise use cases such as:

• Internal knowledge search
• Policy & compliance analysis
• Research intelligence
• Document-heavy decision support

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✨ Key Features

🔐 Authentication & RBAC

• JWT-based authentication
• Role-based access control (Admin / User)
• Strict per-user document isolation (multi-tenancy)

📥 Intelligent Document Ingestion

• PDF upload with automatic parsing
• Adaptive chunking strategy based on:
  • Document structure
  • End-use (Q&A focused retrieval)
• Rich metadata captured for traceability

🔍 Semantic Search + RAG

• High-performance vector search using Pinecone
• Retrieval-Augmented Generation (RAG)
• Groq-powered LLM inference for low-latency responses
• Answers strictly grounded in retrieved document context

📌 Source Citations

• Every response includes:
  • Source document name
  • Chunk index
  • Relevance score
• Enables explainability and enterprise trust

📊 Usage Analytics

• Per-user query tracking
• Usage logging for monitoring and future billing
• Designed for scalability and observability

☁️ Cloud-Native Architecture

• Fully Dockerized backend
• Deployed on Railway
• External managed services for reliability and scale

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🧠 System Architecture

Client (Swagger / UI)
        ↓
FastAPI Backend (Railway)
    ├── Auth & RBAC
    ├── Document Upload & Parsing
    ├── Adaptive Chunking Engine
    ├── Vector Store (Pinecone)
    ├── RAG Pipeline
    └── Usage Analytics
        ↓
External Services
    ├── Supabase Postgres (Metadata + Analytics)
    ├── Pinecone (Vector Search)
    └── Groq (LLM Inference)

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🗂 Backend Structure

backend/
├── app/
│   ├── api/
│   │   ├── auth.py              # Authentication endpoints
│   │   ├── upload.py            # Document ingestion
│   │   ├── query.py             # RAG query endpoint
│   │   ├── admin.py             # Admin operations
│   │   └── dependencies.py      # Security dependencies
│   │
│   ├── core/
│   │   ├── config.py            # Centralized configuration
│   │   ├── database.py          # SQLAlchemy setup
│   │   └── security.py          # JWT utilities
│   │
│   ├── models/
│   │   ├── user.py              # User schema
│   │   ├── document.py          # Document metadata
│   │   └── usage.py             # Query analytics
│   │
│   ├── services/
│   │   ├── auth.py              # Auth logic
│   │   ├── chunking.py          # Adaptive chunking
│   │   ├── vector_store.py      # Pinecone upsert logic
│   │   ├── retriever.py         # Semantic retrieval
│   │   ├── rag.py               # RAG orchestration
│   │   ├── llm.py               # Groq integration
│   │   └── analytics.py         # Usage tracking
│   │
│   ├── utils/
│   │   └── file_loader.py       # PDF text extraction
│   │
│   └── main.py                  # Application entry point
│
├── Dockerfile
└── requirements.txt

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🔍 RAG Execution Flow

1. User uploads a document
2. Document is parsed and chunked intelligently
3. Chunks are embedded and stored in Pinecone
4. User submits a query
5. Relevant chunks are retrieved semantically
6. Retrieved context is passed to the LLM
7. Final answer is generated with citations

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📌 Example API Response

{
  "answer": "The internship duration mentioned in the document is 6 months.",
  "sources": [
    {
      "filename": "Internship_Description.pdf",
      "chunk_index": 3,
      "score": 0.91
    }
  ]
}

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🛡️ Security & Isolation

• Namespace-based vector isolation per user
• JWT validation on every request
• No cross-user data leakage
• LLM responses restricted to retrieved context only

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📈 Designed for Scale

• Stateless backend
• External vector and database services
• Ready for hybrid dense + sparse search
• Easy extension to streaming and reranking