https://github.com/niju-2004/rag-vector-db

Veterinary Chatbot with Multilingual Support This project is a Veterinary Chatbot that assists users with veterinary care questions in English and Tamil. It uses advanced NLP techniques, FAISS for efficient information retrieval, and Google Generative AI for accurate responses. The chatbot also generates visualizations , collects user feedback
https://github.com/niju-2004/rag-vector-db

chatbot flask gemini-api nlp-machine-learning python rag vector-database

Last synced: 3 months ago
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Veterinary Chatbot with Multilingual Support This project is a Veterinary Chatbot that assists users with veterinary care questions in English and Tamil. It uses advanced NLP techniques, FAISS for efficient information retrieval, and Google Generative AI for accurate responses. The chatbot also generates visualizations , collects user feedback

• Host: GitHub
• URL: https://github.com/niju-2004/rag-vector-db
• Owner: Niju-2004
• Created: 2025-01-02T06:08:14.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-05T10:35:34.000Z (over 1 year ago)
• Last Synced: 2025-02-05T10:36:33.369Z (over 1 year ago)
• Topics: chatbot, flask, gemini-api, nlp-machine-learning, python, rag, vector-database
• Language: Python
• Homepage:
• Size: 3.45 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
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.