📚 TalkBookAI

TalkBookAI lets readers chat with books—literally. Log in, pick a book from the list, and start a conversation with an AI that responds in the voice of the author, drawing from the book’s content and your ongoing chat history. It’s like having a personal dialogue with literary legends.

---

🧠 Tech Stack

Backend (/backend)

• FastAPI – blazing-fast Python API framework
• MongoDB Atlas – document storage + vector search
• LangChain – orchestrates RAG (Retrieval-Augmented Generation) pipelines
• Gemini API – powers LLM responses and embeddings
• JWT Auth – secure session management with HTTP-only cookies
• Docker + Render – containerized deployment

Frontend (/frontend)

• React Router v7 (Framework Mode) – SPA routing & nested layouts
• Tailwind CSS + ShadCN UI – modern styling and accessible components
• Vercel – lightning-fast global deployment

---

✨ Features

• 🔐 Authentication – JWT stored in HTTP-only cookies
• 📖 Book Selection – choose from a curated library
• 🧑‍💬 Conversational AI – responds in the voice of the author
• 🧠 Memory – contextual replies based on previous chat turns
• 📄 PDF Ingestion – onboard new books with an ingestion script
• 🌍 CORS-Enabled – secure frontend-backend communication

---

🚀 Getting Started

Prerequisites

• Python 3.11+
• Node.js 22+
• Docker (optional, for containerized setup)
• MongoDB Atlas account
• Gemini API key

---

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt

# .env file variables:
# MONGODB_URI=
# GOOGLE_API_KEY=
# SECRET_KEY=
# DB_NAME=
# FRONTEND_URL=

fastapi dev

(Optional) Docker Workflow

cd backend
docker build -t talkbookai-backend .
docker run -p 8000:8000 --env-file .env talkbookai-backend

Uses a minimal python:3.11-slim base and launches uvicorn main:app --host 0.0.0.0 --port 8000.

---

Frontend Setup

cd frontend
npm install

# .env file variables:
# VITE_BACKENDURL=

npm run dev

---

🧪 PDF Ingestion

To ingest a new book:

cd backend
python ingestion_handler.py   # or: py -m ingestion_handler

The script:

• Extracts and chunks the text
• Generates embeddings via Gemini
• Stores metadata and vectors in MongoDB

---

🛡️ Security Notes

• JWTs are stored in HTTP-only cookies to mitigate XSS attacks
• CORS configured for safe cross-origin requests (Vercel ↔ Render)
• Secrets managed in .env files (never committed to version control)

---

Screenshot

---

📈 Roadmap

• Chapter-specific context retrieval
• Multi-author personality modes

---

📄 License

MIT License