📄 PDF Q&A Chatbot (FastAPI + Mistral + Next.js)

A lightweight AI system that lets you upload PDF files and ask questions about them in natural language using Mistral via OpenRouter API.

Screencast.from.2025-07-31.09-41-12.webm

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🔧 Tech Stack

• Backend: FastAPI, PyPDF2, SentenceTransformers, ChromaDB, OpenRouter (Mistral)
• Frontend: Next.js + Tailwind + Lucide Icons
• LLM: Mistral (via OpenRouter)
• Embedding Model: all-MiniLM-L6-v2 (via SentenceTransformers)

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Overview

🧠 Features

• 📤 Upload PDF
• ✂️ Extract and chunk text
• 🧠 Generate vector embeddings and store in ChromaDB
• 💬 Ask natural language questions
• 🤖 Answer generated using Mistral LLM with relevant chunks
• 🗑️ Clear/reset vector DB anytime

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🚀 Backend (FastAPI)

Install dependencies:

pip install fastapi uvicorn python-dotenv requests PyPDF2 \
            sentence-transformers chromadb

Create .env file:

OPENROUTER_API_KEY=your_openrouter_key_here

Run server:

uvicorn main:app --reload

API Endpoints:

• POST /upload_pdf/ → Upload and process PDF
• POST /ask/ → Ask a question using Mistral
• POST /reset_all_db/ → Reset vector store

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💻 Frontend (Next.js UI)

Create app:

npx create-next-app@latest pdf-chat-ui
cd pdf-chat-ui
npm install lucide-react

Add ChatBox.tsx in /components/

Handles:

• PDF Upload
• API Calls
• Chat history UI
• Question box and reset logic

Run frontend:

npm run dev

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🔄 Reset Vector DB

Use the following endpoint to delete all stored embeddings:

POST /reset_all_db/

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📌 Notes

• CORS middleware is enabled
• Embeddings are generated once per upload
• All conversations are stateless for now (no persistent memory)
• ChromaDB stores are automatically created and queried

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🧪 Sample Flow

# 1. Upload a PDF
curl -X POST http://localhost:8000/upload_pdf/ \
  -F "file=@example.pdf"

# 2. Ask a question
curl -X POST http://localhost:8000/ask/ \
  -F "query=What is the summary of this file?"

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🔮 Future Improvements

• 🔁 Add chat memory
• 👥 Multi-user support
• 🌍 Multi-language translation
• 📊 PDF analytics and metrics