AI - Trainer-Teacher

AI-powered educational platform built with Angular 19 and Firebase. Features intelligent RAG-based learning assistant, adaptive exam system, and comprehensive admin dashboard for content management.

🎯 What's Inside

This repository demonstrates a complete AI-driven learning platform with three core layers:

1. AI-Powered Learning (RAG System): Context-aware chat with PDF-based knowledge, section-specific retrieval, and session persistence (see implementation →)
2. Knowledge Assessment: Timed exams with auto-save, resume capability, and intelligent question shuffling to validate learning
3. Content Management: Admin dashboard for managing Teachers (modules), Courses, Exams, and student analytics

🧑‍🎓 User Roles

Role	Capabilities	Routes
Admin	Full CRUD on Teachers, Courses, Exams + Dashboard access	/crud, /course-crud, /exam-crud, /main
Students	View educational content, take exams, chat with AI teacher	/teacher/:id, /exam/:id, /pdf-viewer

🎓 Key Features

Exam Taking System

• Smart Shuffling: Questions and answers randomized per attempt
• Live Progress Tracking: Every answer auto-saved to Firestore
• Resume Capability: Interrupted exams continue with correct time calculation
• Timer Management:
  • Exam duration (timeToDoTheExam)
  • Retry cooldown (timeToWait)
• Instant Results: Pass/fail based on configurable passingPercentage

Admin Dashboard

• Teachers Management: Educational modules with PDF content and section mapping
• Courses Management: Difficulty levels, date ranges, teacher assignments
• Exams Management: Multiple-choice questions with 2-6 answer options
• Results Analytics: Student performance tracking across exams

Content Delivery

• PDF viewer for educational materials
• Section-based navigation with page mapping

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🧠 Advanced Features: Intelligent RAG System

This platform integrates with a sophisticated RAG (Retrieval-Augmented Generation) backend for the Teacher chat interface, featuring several production-grade optimizations:

Context-Aware Retrieval Architecture

sequenceDiagram
    participant U as User (Angular)
    participant F as Frontend
    participant B as FastAPI Backend
    participant V as Vector Store
    participant L as LLM (OpenAI/Claude)

    U->>F: "What is informed consent?"
    F->>B: POST /stream_chat<br/>{message, session_id, pages: [10-15]}
    B->>B: Load conversation history
    B->>L: Reformulate question with context
    L-->>B: "What is informed consent in business ethics?"
    B->>V: Search similarity (k=14, filter: pages 10-15)
    V-->>B: Return 14 relevant chunks
    B->>L: Generate response (streaming)
    loop Stream chunks
        L-->>B: Token chunk
        B-->>F: SSE chunk
        F-->>U: Display in real-time
    end
    B->>B: Save to session history

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Key Optimizations

1. Dynamic Page Filtering

Traditional RAG searches entire documents, leading to context noise. Our approach:

# Smart filtering by current section
retriever = vector_store.as_retriever(
    search_kwargs={
        "k": 14,  # Top-14 most relevant chunks
        "filter": {
            "source": current_pdf_path,
            "page": {"$in": current_section_pages}  # Section-specific
        }
    }
)

Impact: When a student is in "Section 3" (pages 15-20), the system only searches those specific pages instead of the entire 100-page document. This results in:

• ~70% reduction in irrelevant context retrieval
• Higher precision in answers
• Faster response times

2. History-Aware Question Reformulation

The system uses an LLM to reformulate vague follow-up questions before retrieval:

User: "What is informed consent?"
AI: [retrieves context and responds]

User: "Can you give me an example?"
System internally reformulates to: "Can you give an example of informed consent in business ethics?"
[Then retrieves with the full context]

Why this matters: Maintains conversation continuity across multi-turn interactions without losing context.

3. Streaming Response Architecture

• Real-time token-by-token response delivery via Server-Sent Events (SSE)
• Async FastAPI backend with LangChain streaming chains
• Better UX: Users see responses appear in real-time (ChatGPT-style)
• Optional Text-to-Speech: Toggle to enable voice playback of AI responses for accessibility and audio-based learning

4. Session Persistence with Redis

• Conversation history stored in Redis with ${userUID}_${teacherId} session keys
• Full message history (Human/AI pairs) persisted across sessions
• Survives page reloads, navigation, and server restarts
• Enables resuming learning sessions days later
• Fast retrieval (<10ms) for conversation context loading

Production Safeguards

Strict Context Adherence:

system_prompt = """
CRITICAL RULES:
1. ONLY use information from "Context:" section
2. If info not in context, respond: "I don't have that information..."
3. NEVER make up or infer information not in the documents
"""

Special Learning Modes:

• "Just ask me 2 serious questions..." → Quiz mode with 2 challenging questions
• "Can you explain [topic]..." → Detailed explanation with follow-up options
• Automatic difficulty adaptation based on student responses

Technical Stack (Backend)

• API Framework: FastAPI (async, high-performance)
• LLM Integration: LangChain with OpenAI/Anthropic models
• Vector Store: Pinecone/Chroma with semantic search
• Embeddings: OpenAI text-embedding-3-small
• Session Storage: Redis (in-memory, <10ms retrieval)
• Message History: LangChain's RedisChatMessageHistory for conversation persistence

📂 Backend Code Sample

A standalone, runnable example of the RAG endpoint is included in this repository:

👉 backend-sample/ - Complete FastAPI implementation with:

• Heavily commented code explaining each optimization
• Quick start guide for local testing
• All dependencies and environment setup

This sample demonstrates production-ready code that can be deployed immediately.

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🛠️ Tech Stack

• Framework: Angular 19 (standalone components, signals)
• Backend: Firebase Authentication + Cloud Firestore
• UI: Angular Material + Tailwind CSS
• i18n: Transloco (English, Spanish, French)
• Build: Angular CLI 19 + Vite

🚀 Quick Start

Prerequisites

# Node.js LTS (v22.x recommended)
node -v  # v22.20.0

# Angular CLI
npm install -g @angular/cli

Environment Setup

1. Create Firebase project at console.firebase.google.com
2. Create environment files:

// src/environments/environment.development.ts
export const environment = {
  BASEURL: 'your-firebase-api-url',
  BACK_CHAT_URL: 'your-backend-url',
  WEBSITE_NAME: 'Trainer Teacher',
  firebase: {
    apiKey: "your-api-key",
    authDomain: "your-project.firebaseapp.com",
    projectId: "your-project-id",
    storageBucket: "your-project.appspot.com",
    messagingSenderId: "your-sender-id",
    appId: "your-app-id"
  }
};

// src/environments/environment.ts (production)
export const environment = {
  // Same structure as development
};

Installation & Run

# Install dependencies
npm install

# Run development server
ng serve
# Navigate to http://localhost:4200

# Build for production
ng build

Firebase Configuration

# Set default Firebase project
firebase use default

# Deploy to Firebase Hosting
firebase deploy

📁 Project Structure

src/app/
├── components/
│   ├── auth/                  # Login/register components
│   ├── chat/                  # Chat interface
│   ├── evaluation/            # Exam taking component
│   ├── left-menu/             # Navigation menu
│   ├── school/                # Dashboard components
│   │   ├── dashboard/
│   │   ├── exam-result-list/
│   │   ├── exams-list/
│   │   ├── student-list/
│   │   └── teacher-list/
│   ├── superadmin/            # CRUD components
│   │   ├── teachers-crud/
│   │   ├── course-crud/
│   │   └── exam-crud/
│   └── shared/                # Reusable components
├── guards/
│   ├── admin.guard.ts         # Admin access guard
├── models/
│   ├── exam.ts                # Exam, Question, Option
│   ├── result.ts              # Result, QuestionAndAnswer
│   ├── teacher.ts             # Teacher, Section
│   ├── course.ts
│   ├── user.ts
│   └── student.ts
├── pages/
│   ├── mainselector-page/     # Landing page
│   ├── school-main-page/      # Admin dashboard
│   └── teacher-main-page/     # Teacher content page
├── services/
│   ├── auth.service.ts        # Firebase authentication
│   ├── exam.service.ts        # Exam queries
│   ├── exam-crud.service.ts   # Exam CRUD operations
│   ├── result.service.ts      # Result tracking
│   ├── teacher-crud.service.ts
│   ├── course-crud.service.ts
│   └── user.service.ts
└── app.routes.ts              # Route configuration

🎮 Exam Flow

graph TD
    A[Student opens /exam/:id] --> B[View Exam Intro]
    B --> C{Check Eligibility}
    C -->|Already Passed| D[Redirect]
    C -->|Too Soon| E[Show Cooldown Timer]
    C -->|Can Take| F[Shuffle Questions & Answers]
    F --> G[Create Initial Result Entry]
    G --> H[Start Timer]
    H --> I[Answer Questions]
    I --> J[Auto-save to Firestore]
    J --> K{More Questions?}
    K -->|Yes| I
    K -->|No| L[Review Summary]
    L --> M[Submit Exam]
    M --> N[Calculate Score]
    N --> O[Show Results]

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🔐 Authentication & Guards

Admin Guard

// Protects /crud, /course-crud, /exam-crud routes
if (user && user.email === 'martin@martin.com') {
  return true; // Admin access
}

📊 Data Models

Exam Structure

interface Exam {
  id: string;
  title: string;
  questions: Question[];
  passingPercentage: number;     // e.g., 70
  timeToWait: number;             // Minutes before retry
  timeToDoTheExam: number;        // Exam duration in minutes
  recruiterId?: string;
}

interface Question {
  text: string;
  options: Option[];              // 2-6 options
}

interface Option {
  text: string;
  isCorrect: boolean;             // Only one correct per question
}

Result Tracking

interface Result {
  userUID: string;
  examId: string;
  doingTheExamNow: boolean;       // Resume flag
  momentStartExam: string;        // ISO timestamp
  questions_answered: number;
  correctAnswers: number;
  examPassed?: boolean;
  questions: QuestionAndAnswer[]; // Saved answers
}

🌐 i18n Support

Transloco configuration with 3 languages:

availableLangs: ['en', 'es', 'fr']
defaultLang: 'en'

Translation files in /public/i18n/:

• en.json
• es.json
• fr.json

🚢 Deployment

Firebase Hosting

# Build production bundle
ng build

# Deploy to Firebase
firebase deploy

Environment Configuration

• Development: Uses environment.development.ts
• Production: Uses environment.ts

Configured in angular.json:

{
  "configurations": {
    "production": {
      "outputHashing": "all"
    },
    "development": {
      "optimization": false,
      "sourceMap": true,
      "fileReplacements": [{
        "replace": "src/environments/environment.ts",
        "with": "src/environments/environment.development.ts"
      }]
    }
  }
}

🔑 Key Architectural Patterns

Standalone Components

@Component({
  selector: 'app-exam',
  imports: [CommonModule, MatIconModule],
  standalone: true
})

Reactive Signals

currentUserSig = signal<User | null>(null);
// Auto-updates components on change

Firebase Integration

// Real-time data streams
exams$ = collectionData(examCollection) as Observable<Exam[]>;

Guard-based Security

{
  path: 'crud',
  component: TeachersCRUDComponent,
  canActivate: [adminGuard]
}

📖 Learning Path

1. Setup: Configure Firebase and environment files
2. Explore: Navigate the main selector and dashboard
3. Take Exam: Experience the student workflow at /exam/:id
4. Admin Panel: Access CRUD operations (requires admin email)
5. Customize: Modify exam settings, questions, and content

🎯 Production Features

• ✅ Auto-save Progress: Never lose exam answers
• ✅ Resume Capability: Continue interrupted exams
• ✅ Time Management: Accurate timer with resume support
• ✅ Anti-cheat: Question/answer shuffling
• ✅ Cooldown System: Prevent immediate retakes
• ✅ Role-based Access: Secure admin operations
• ✅ Multi-language: English, Spanish, French

🤝 Contributing

This is an educational platform template. Feel free to fork and adapt for your use case.

📝 License

MIT License - see LICENSE file for details

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Built with ❤️ using Angular 19, Firebase, and modern web standards