🧠 DocsInsight Engine: Enterprise RAG System

DocsInsight Engine is a high-performance, private Retrieval-Augmented Generation (RAG) platform. It allows users to upload complex documents and interact with them through a neural search interface powered by local Large Language Models (LLMs).

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✨ Key Features

• 📁 Multi-Format Support: Seamlessly process PDF, DOCX, XLSX, CSV, and TXT files.
• 🔒 Privacy-Centric: Fully local execution using Ollama. Your sensitive data never leaves your infrastructure.
• ⚡ Neural Retrieval: Uses ChromaDB for high-speed vector similarity search.
• 🎨 Modern Interface: A sleek, dark-themed "Glassmorphism" UI with real-time markdown rendering and code highlighting.
• 🛠️ Source Verification: Every answer comes with citations from the uploaded documents to prevent hallucinations.
• 🐳 One-Command Setup: Ready for production with Docker and Docker Compose.

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🏗️ Technical Architecture

Backend Stack

• Core: Python 3.11 with Flask.
• Orchestration: LangChain for managing document loaders, splitters, and LLM chains.
• Vector Database: ChromaDB for persistent document embeddings.
• LLM/Embeddings: Llama 3 (8B) via Ollama.

Frontend Stack

• UI: Vanilla JS with CSS Mesh Gradients and Backdrop Filters.
• Rendering: Marked.js for markdown and Highlight.js for code snippets.

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🚀 Getting Started

Prerequisites

• Docker and Docker Compose.
• Ollama installed and running on your host machine.
• Pull the required model: ollama pull llama3:8b.

Installation

1. Clone the repository:

   git clone [https://github.com/arfazrll/rag-docsinsight-engine.git](https://github.com/arfazrll/rag-docsinsight-engine.git)
   cd rag-docsinsight-engine
   

2. Launch with Docker:

   docker-compose up --build
   

3. Access the App: Open your browser and navigate to http://localhost:5000.

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📂 Project Structure

├── backend/
│   ├── app.py          # Flask API Endpoints
│   └── rag_core.py     # RAG Logic, Vector Store & Document Processing
├── web/
│   ├── index.html      # Frontend Structure
│   ├── style.css       # Glassmorphism Styling
│   └── script.js       # Client-side Logic
├── storage/            # Local Persistent Storage (Vector DB & Docs)
├── Dockerfile          # Container Configuration
└── docker-compose.yml  # Service Orchestration

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🛠️ Configuration

The system uses environment variables to communicate with the AI engine. In docker-compose.yml:

• Ollama_BASE_URL: Defaults to http://host.docker.internal:11434 for local communication.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2025 S. A. Almazril.

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💡 System Insights

• Scalability: The VectorStoreManager is designed to handle multiple documents simultaneously by filtering searches based on unique file hashes.
• Performance: Document chunking is optimized with a 1000 character size and 200 character overlap to maintain context window efficiency.
• Security: Includes a .dockerignore and .gitignore to prevent sensitive credentials (.env) or local databases from being leaked.