67 lines
1.5 KiB
Markdown
67 lines
1.5 KiB
Markdown
# Ayurveda Chatbot using LLaMA and RAG
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This project is an interactive Ayurveda chatbot that uses a **Retrieval-Augmented Generation (RAG)** pipeline powered by the **LLaMA language model via Groq**. The chatbot provides Ayurvedic knowledge and answers user queries based on pre-trained PDF content.
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---
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## Features
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- **PDF Knowledge Base**: Pretrained on Ayurvedic texts for domain-specific answers.
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- **RAG Pipeline**: Combines FAISS vector retrieval and LLaMA for context-aware responses.
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- **Streamlit Interface**: Easy-to-use frontend for interacting with the chatbot.
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---
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## Requirements
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- Python 3.8+
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- GPU support (optional but recommended for faster LLM inference)
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- LLaMA model via Groq
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---
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## Installation
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### 1. Clone the Repository
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```bash
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git clone https://git.digimantra.com/SHREY/AyurBot.git
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cd AyurBot
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```
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### 2. Create and Activate a Virtual Environment
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On Linux/macOS:
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```bash
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python3 -m venv env
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source env/bin/activate
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```
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On Windows:
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```
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python -m venv env
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env\Scripts\activate
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```
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3. Install Dependencies
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```bash
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pip install -r requirements.txt
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```
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4. Set .env file
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Usage
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1. Preprocess PDF and Create FAISS Index
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Ensure the PDF file (e.g., ayurveda_text.pdf) is placed in the project directory.
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Run the backend script to preprocess the data and create a FAISS index:
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```bash
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python3 backend.py Book1.pdf Book2.pdf --index-path faiss_index
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```
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2. Start the Chatbot
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Launch the Streamlit interface:
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```bash
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streamlit run frontend.py
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```
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Access the chatbot in your browser at http://localhost:8501.
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