Rag-VetBot Empowering Farmers through a Bilingual Chatbot: Integrating NLP for Access to Traditional Herbal Treatment Practices for Livestock Management

Introduction: This project aims to develop a bilingual chatbot that provides veterinary solutions using Natural Language Processing (NLP) for farmers. It leverages Retrieval-Augmented Generation (RAG) models to dynamically retrieve relevant content and ensure accurate responses.

Features: 1.NLP-based query understanding and response generation. 2.Uses a database of ethnoveterinary practices for remedies. 3.Easy-to-use web interface for non-technical users.

Folder Structure: Rag-Vector-DB/ ├── assets/ # Datasets and reference materials │ └── RPP_Dataset - Copy.xlsx ├── DB_Storage/ # Vector storage and indices │ ├── content.json # Automatically generated while running "vectorDB.py" │ ├── vectors.hnsw # Automatically generated while running "vectorDB.py" │ ├── vectors.index # Automatically generated while running "vectorDB.py" │ └── vectors_hnswlib.bin # Automatically generated while running "vectorDB.py" ├── src/ # Source code │ ├── app.py # Main application file │ ├── model.py # RAG model logic │ ├── vectorDB.py # Vector database handler │ ├── chatbot_errors.log # Log file for errors │ └── templates/ # HTML templates for web interface │ └── index.html ├── .gitattributes # Git attributes file ├── README.md # Project documentation └── requirements.txt #requirements to be installed

Dataset: Location: assets/RPP_Dataset - Copy.xlsx Description: Contains information on various animal diseases, symptoms, herbal remedies, and treatments.

Key Files and Folders:

1. app.py: The main application script for running the chatbot server.
2. model.py: Core logic for query processing and response generation using embeddings.
3. vectorDB.py: Handles vector database operations like indexing and retrieval.
4. index.html: The web interface for user interaction.

Running the Project: There are two ways to run the project:

1. Hosting on a Website: Follow these steps to host the RAG-Veterinary chatbot on a website:

• Install the dependencies: pip install -r requirements.txt
• Run python src/vectorDB.py to initialize the vector database.
• Update the LLM API key in src/model.py.
• Run python src/app.py (this will automatically execute src/model.py).
• Access the chatbot at the generated localhost link.

2. Running in the Terminal: Follow these steps to run the RAG-Veterinary chatbot in the terminal:

• Install the dependencies: pip install -r requirements.txt
• Run python src/vectorDB.py to initialize the vector database.
• Update the LLM API key in src/model.py.
• Run python src/model.py and provide the command line input.
• Responses will be generated in the terminal.