🧠 A/L Biology Chatbot

Live: https://33f9bbefa8d176a3bd.gradio.live/

RAG Architecture

An AI-powered chatbot that answers Advanced Level Biology questions by extracting knowledge from uploaded PDF textbooks.
It uses OCR for scanned PDFs, LangChain for text processing, FAISS for vector search, and Groq's LLaMA 3 model for accurate answers.
The chatbot runs on Google Colab and stores all data in Google Drive.

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🚀 Features

• 📄 PDF Upload & OCR – Extracts text from normal or scanned PDFs.
• 🗄 Google Drive Integration – Stores PDFs, extracted text, and vector index permanently.
• 📚 Text Chunking – Splits large documents into manageable parts for LLM processing.
• 🧩 Semantic Search – Uses FAISS vector store for fast retrieval.
• 🤖 AI Model – Powered by Groq's LLaMA3-70B via LangChain.
• 💬 Gradio Interface – Chat in a user-friendly web UI.
• 🔍 Domain-Specific Knowledge – Trained only on your provided A/L Biology materials.

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🛠 Tech Stack

• Python
• LangChain – Text processing & LLM integration
• Groq API – LLaMA 3 large language model
• HuggingFace Embeddings – Sentence Transformers for vectorization
• FAISS – Fast similarity search
• pytesseract & pdf2image – OCR
• Google Colab + Google Drive – Storage & execution
• Gradio – Chat UI

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📌 Usage

Upload one or more A/L Biology textbooks/notes PDFs.

The system extracts all content and stores it.

Ask any question related to the uploaded content in the chat interface.

Get precise, context-aware answers instantly.

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🔐 Environment Variables

Create a .env file or set in Colab:

GROQ_API_KEY=your_groq_api_key_here