🌿 Ayurveda Q/A Chatbot

An intelligent chatbot that allows users to upload text-based Ayurveda PDFs and ask questions based on the content using RAG (Retrieval-Augmented Generation) combining semantic search and LLM-based responses.

Features

• Upload Ayurveda PDFs (text-based only)
• Ask natural-language questions based on uploaded content
• Chunk text smartly using LangChain
• Semantic search with MiniLM embeddings
• Fast retrieval using FAISS
• Powered by LLaMA 3 via Groq API
• Based on the RAG architecture (Retrieval-Augmented Generation)
• Easy-to-use interface via Streamlit

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Tech Stack

Component	Technology
Frontend	Streamlit
Backend	FastAPI
Embeddings	HuggingFace MiniLM-L6-v2
Vector Search	FAISS
Language Model	LLaMA 3 via Groq API
PDF Processing	PyMuPDF + LangChain
Prompting	LangChain + Custom PromptTemplate
Environment Var	python-dotenv

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How It Works (RAG Workflow)

1. Upload: User uploads a text-based PDF
2. Text Extraction: PDF is read using PyMuPDF
3. Chunking: Text is broken into smaller pieces using RecursiveCharacterTextSplitter
4. Embedding: Chunks are embedded using HuggingFace MiniLM
5. Vector DB: Chunks are stored in a FAISS vector store
6. Q&A:Question → Similar chunks retrieved Chunks + Question → sent to LLaMA 3 LLM generates a final context-based answer This is a Retrieval-Augmented Generation (RAG) system.

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/yourusername/ayurveda-chatbot.git
cd ayurveda-chatbot

2. Create a Virtual Environment

python -m venv ayurveda_env
ayurveda_env\Scripts\activate  

3. Install Dependencies

pip install -r requirements.txt

4. Add Your .env File

Create a .env file with your Groq key:

GROQ_API_KEY=your_groq_api_key

5. Run the Backend

python app.py

6. Run the Streamlit Frontend

streamlit run streamlit_app.py