| # PrepAI – Intelligent Interview & Notes Assistant | |
| PrepAI is a comprehensive AI-powered platform designed to help users prepare for interviews, generate personalized quizzes, manage study notes, and even participate in real-time mock interviews using conversational AI. It blends multiple modern technologies such as FastAPI, React, ChromaDB, PostgreSQL, Vapi, Groq, and LlamaIndex into a cohesive, production-ready system. | |
| --- | |
| ## 🚀 Live Demo | |
| ▶ **Try the project here:** [https://huggingface.co/spaces/aki-008/prepAI](https://huggingface.co/spaces/aki-008/prepAI) | |
| --- | |
| ## 🚀 Overview | |
| PrepAI enables users to: | |
| - Conduct **AI-powered mock interviews** with real-time voice interaction. | |
| - Upload PDFs and **chat with notes** using vector search + RAG. | |
| - Generate **MCQ quizzes** from resumes or study material. | |
| - Maintain persistent **chat sessions**, interview transcripts, and summaries. | |
| - Work inside a fully containerized architecture with isolated services. | |
| This system is ideal for interview preparation platforms, ed-tech tools, or personal study automation. | |
| --- | |
| ## 🧩 Architecture | |
| PrepAI is built with a modular full-stack architecture: | |
| ### **Backend (FastAPI)** | |
| - JWT Authentication | |
| - Upload & process PDFs | |
| - ChromaDB vector store integration | |
| - Streaming LLM responses | |
| - Vapi-powered live interview assistant | |
| - Quiz generation using Groq/OpenAI | |
| ### **Frontend (React + TypeScript + Vite)** | |
| - User onboarding (Sign In / Sign Up) | |
| - PDF upload, preview, rename, deletion | |
| - Interactive note-chat interface | |
| - Dashboard with metrics | |
| - Seamless Vapi interview client | |
| ### **Database Layer** | |
| - **PostgreSQL** stores: | |
| - Users | |
| - PDFs | |
| - Chat sessions | |
| - Messages | |
| - Metadata | |
| ### **Vector Search Layer** | |
| - **ChromaDB** stores: | |
| - Chunked PDF embeddings | |
| - User-ingested knowledge | |
| ### **AI Services** | |
| - **Groq/OpenAI** for LLM responses | |
| - **SentenceTransformers** for embeddings | |
| - **Vapi** for real-time voice conversations | |
| - **LlamaIndex** for PDF parsing & chunking | |
| --- | |
| ## 📦 Directory Structure | |
| ``` | |
| aki-008-prepai/ | |
| ├── Backend/ | |
| │ ├── app/ | |
| │ ├── Dockerfile | |
| │ └── requirements.txt | |
| │ | |
| ├── Frontend/ | |
| │ ├── src/ | |
| │ ├── Dockerfile | |
| │ └── vite.config.ts | |
| │ | |
| ├── docker-compose.yml | |
| └── RUN.md | |
| ``` | |
| --- | |
| ## ⚙️ Installation & Setup | |
| ### **1. Clone the repository** | |
| ``` | |
| git clone <repo-url> | |
| cd aki-008-prepai | |
| ``` | |
| ### **2. Create your `.env` file** | |
| Provide: | |
| - PostgreSQL credentials | |
| - Groq API key | |
| - OpenAI key (if needed) | |
| - VAPI_PRIVATE_KEY | |
| - VAPI_PUBLIC_KEY | |
| - VAPI_ASSISTANT_ID | |
| Example: | |
| ``` | |
| DATABASE_URL=postgresql+asyncpg://postgres:password@db:5432/studentdb | |
| GROQ_API_KEY=your-key | |
| VAPI_PRIVATE_KEY=your-key | |
| VAPI_PUBLIC_KEY=your-key | |
| VAPI_ASSISTANT_ID=your-assistant-id | |
| ``` | |
| ### **3. Run the system (Docker)** | |
| ``` | |
| docker-compose up --build | |
| ``` | |
| Services started: | |
| - Frontend → [http://localhost:5173](http://localhost:5173) | |
| - Backend → [http://localhost:8000](http://localhost:8000) | |
| - ChromaDB → [http://localhost:8080](http://localhost:8080) | |
| - PostgreSQL → [http://localhost:5432](http://localhost:5432) | |
| ### **4. Dev mode (manual)** | |
| Backend: | |
| ``` | |
| cd Backend | |
| python run.py | |
| ``` | |
| Frontend: | |
| ``` | |
| cd Frontend | |
| npm install | |
| npm run dev | |
| ``` | |
| ChromaDB: | |
| ``` | |
| chroma run --host 0.0.0.0 --port 8080 --path ./chroma_store | |
| ``` | |
| --- | |
| ## 🧪 Key Features in Detail | |
| ### **1. PDF Upload & Notes Chat** | |
| - PDFs are chunked using PyMuPDF + LlamaIndex. | |
| - Embeddings generated via MiniLM. | |
| - Chunks stored in ChromaDB. | |
| - Users can open chats tied to each PDF with full chat history. | |
| ### **2. AI Interview System** | |
| - Dynamic prompt generation based on job-role, experience, difficulty. | |
| - Real-time Vapi-based interview with: | |
| - Emotion recognition | |
| - Adjustable voice | |
| - Adaptive follow-ups | |
| - Strict 5-minute flow | |
| - Transcripts saved automatically. | |
| ### **3. Quiz Generation** | |
| From resumes or notes: | |
| - Strict rules enforced by the SYSTEM_PROMPT | |
| - Always 10 MCQs with 4 options | |
| - JSON-structured output | |
| - Options + explanations | |
| ### **4. Authentication** | |
| - Secure hashing using Argon2 | |
| - JWT tokens | |
| - Protected routes for all user-specific actions | |
| --- | |
| ## 🛠️ Development Notes | |
| ### **Backend** | |
| - Powered by **FastAPI** with async SQLAlchemy. | |
| - Auto-table creation on startup. | |
| - Organized into clear routers: Auth, Notes, Interview, Quiz. | |
| - Streaming responses for chat. | |
| ### **Frontend** | |
| - Built on **React + TypeScript**. | |
| - Modern UI with Tailwind. | |
| - Routes include: Home, Dashboard, Notes, Interview. | |
| - ProtectedRoute ensures authentication. | |
| ### **Docker Setup** | |
| `docker-compose.yml` orchestrates: | |
| - PostgreSQL database | |
| - ChromaDB vector server | |
| - Backend (Python) | |
| - Frontend (Nginx) | |
| --- | |
| ## 📄 Interview Transcript Storage | |
| During every interview: | |
| - All real-time transcripts are appended in `Backend/transcripts/<call_id>.txt`. | |
| - Summary is appended at end of call. | |
| --- | |
| ## 🚧 Roadmap | |
| Future improvements: | |
| - User analytics dashboard | |
| - Multi-file knowledge merging | |
| - Advanced scoring for interview responses | |
| - Multi-voice model selector | |
| - Mobile-friendly front-end layout | |
| --- | |
| ## 🧑💻 Contributing | |
| Feel free to open issues or submit pull requests. Contributions are welcome for both frontend and backend. | |
| --- | |
| ## 📜 License | |
| This project is licensed under your chosen license (MIT recommended). | |
| --- | |
| ### ❤️ Thank you for using PrepAI! | |
| If you'd like additional documentation (API reference, UML diagrams, onboarding guide), just ask! | |