RAG Monorepo Template

Overview

This is a comprehensive RAG (Retrieval-Augmented Generation) monorepo template that allows users to query information from their document collections. This tool provides AI-powered knowledge extraction and question-answering capabilities for various document types and domains.

Supported Document Types

The assistant can work with various document formats including:

• PDF Documents: Technical specifications, manuals, research papers
• Text Files: Documentation, articles, reports
• Word Documents: Policies, procedures, guides
• Markdown Files: Technical documentation, wikis

Getting Started

Prerequisites

• Docker and Docker Compose installed on your system

Running with Docker Compose

1. Clone the repository:

   git clone <your-repo-url>
   cd RAG-monorepo

2. Start the application using Docker Compose:

   docker-compose up --build

3. Access the application through your web browser at:

   http://localhost:3002
   

Features

• Document Upload: Support for PDF, TXT, DOC, DOCX, and MD files
• Intelligent Querying: AI-powered question answering based on document content
• Document Comparison: Compare two documents and analyze differences/similarities
• Multi-language Support: Interface available in multiple languages
• Real-time Streaming: Live response streaming for better user experience
• Collection Management: Organize documents into collections for better organization

Configuration

Setting the Model

Choose the appropriate language model for your use case:

Recommended models include:

• mistral
• llama
• deepseek

Environment Configuration

Before running the application, you need to set up your environment variables:

1. Create your environment files from the templates:

   cp .env.sample .env
   cp backend/rag/.env.sample backend/rag/.env

2. Edit both files according to your environment:

   • Main .env file: Configure security settings, database connections, and server ports.
   • RAG .env file: Set embedding providers, models, retrieval parameters, and the ChromaDB collection name.

3. Place your ChromaDB data folder into the backend/rag/ directory. The path to this folder should also be configured in the backend/rag/.env file if it's not the default.

These configurations are essential for proper functioning of the application and must be adjusted to match your infrastructure and requirements.

Usage

To interact with the RAG system:

1. Type your question in the chat interface
2. Upload documents to build your knowledge base
3. Format questions clearly, for example:
   • "What are the key points in this document?"
   • "Compare the requirements between these two documents"
   • "Explain the process described in the manual"

The system will retrieve relevant information from your document collection and provide accurate responses based on the content you've uploaded.