Manual Retrieval System

A full-stack application for uploading and querying PDF manuals using RAG (Retrieval-Augmented Generation) technology.

Features

• Admin Dashboard: Upload PDF manuals with company and product information
• User Interface: Chat with AI assistant to get answers from uploaded manuals
• Real-time Processing: Fast PDF processing and vector search
• Company Management: Organize manuals by company and product

Architecture

• Frontend: Next.js 14 with TypeScript, Tailwind CSS, and shadcn/ui components
• Backend: FastAPI with Python
• Vector Database: Qdrant for document embeddings
• Database: MongoDB for metadata storage
• AI: NIM AI Support

Setup Instructions

Backend Setup

1. Navigate to the backend directory:

   cd Rag_Manual_retrival/backend

2. Install Python dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the backend directory with:

   NIM_api_key=nim api key
   MONGODB_URI=your_mongodb_connection_string
   MONGODB_DB=datquest
   MONGODB_COLLECTION=uploads
   

4. Start Qdrant vector database:

   docker run -p 6333:6333 qdrant/qdrant

5. Run the backend server:

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

1. Install dependencies:

   npm install

2. Set up environment variables: Create a .env.local file in the root directory with:

   NEXT_PUBLIC_API_URL=http://localhost:8000
   

3. Run the development server:

   npm run dev

4. Open http://localhost:3000 in your browser

Usage

Admin Panel

• Access at /admin (use email containing "admin" to login)
• Upload PDF manuals with company name, product name, and product code
• View all uploaded manuals and their status
• Monitor system metrics

User Interface

• Access at /user (use any other email to login)
• Select company and product to load specific manual
• Ask questions about the manual content
• Upload new manuals if needed

API Endpoints

Backend API (Port 8000)

• POST /upload_pdf/ - Upload and process PDF
• POST /query/ - Query the AI assistant
• GET /companies/ - Get all companies
• GET /companies/{company}/models/ - Get models for a company
• GET /health/ - Health check

Frontend (Port 3000)

• / - Redirects to login
• /login - Login page
• /admin - Admin dashboard
• /user - User interface

Configuration

The backend URL can be configured in lib/config.ts or via the NEXT_PUBLIC_API_URL environment variable.

Troubleshooting

1. Backend not responding: Check if the backend server is running on port 8000
2. Qdrant connection failed: Ensure Qdrant is running on port 6333
3. MongoDB connection failed: Verify your MongoDB connection string
4. OpenAI API errors: Check your OpenAI API key and credits
5. Frontend errors: The app includes error boundaries and better error handling
6. Test backend connection: Run node test-backend.js to verify backend connectivity

Error Handling

The application includes comprehensive error handling:

• Error Boundaries: Catch React component errors gracefully
• API Error Handling: Proper error messages for all API calls
• Loading States: Visual feedback during data loading
• Validation: File type and size validation for uploads
• Fallback States: Graceful degradation when services are unavailable

Development

• Frontend: Next.js with TypeScript
• Backend: FastAPI with Python
• Styling: Tailwind CSS with shadcn/ui components
• State Management: React hooks
• API Communication: Fetch API with custom service layer