{"id":26257912,"url":"https://github.com/niju-2004/rag-vector-db","last_synced_at":"2026-04-17T10:02:31.260Z","repository":{"id":270651510,"uuid":"911050313","full_name":"Niju-2004/Rag-Vector-DB","owner":"Niju-2004","description":"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. 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It leverages Retrieval-Augmented Generation (RAG) models to dynamically retrieve relevant content and ensure accurate responses.\n\nFeatures:\n1.NLP-based query understanding and response generation.\n2.Uses a database of ethnoveterinary practices for remedies.\n3.Easy-to-use web interface for non-technical users.\n\nFolder Structure:\nRag-Vector-DB/\n├── assets/                  # Datasets and reference materials\n│   └── RPP_Dataset - Copy.xlsx\n├── DB_Storage/              # Vector storage and indices\n│   ├── content.json         # Automatically generated while running \"vectorDB.py\"\n│   ├── vectors.hnsw         # Automatically generated while running \"vectorDB.py\"\n│   ├── vectors.index        # Automatically generated while running \"vectorDB.py\"\n│   └── vectors_hnswlib.bin  # Automatically generated while running \"vectorDB.py\"\n├── src/                     # Source code\n│   ├── app.py               # Main application file\n│   ├── model.py             # RAG model logic\n│   ├── vectorDB.py          # Vector database handler\n│   ├── chatbot_errors.log   # Log file for errors\n│   └── templates/           # HTML templates for web interface\n│       └── index.html\n├── .gitattributes           # Git attributes file\n├── README.md                # Project documentation\n└── requirements.txt         #requirements to be installed\n\n\nDataset:\nLocation: assets/RPP_Dataset - Copy.xlsx\nDescription: Contains information on various animal diseases, symptoms, herbal remedies, and treatments.\n\nKey Files and Folders:\n1) app.py: The main application script for running the chatbot server.\n2) model.py: Core logic for query processing and response generation using embeddings.\n3) vectorDB.py: Handles vector database operations like indexing and retrieval.\n4) index.html: The web interface for user interaction.\n\nRunning the Project:\nThere are two ways to run the project:\n\n1) Hosting on a Website:\nFollow these steps to host the RAG-Veterinary chatbot on a website:\n* Install the dependencies: pip install -r requirements.txt\n* Run python src/vectorDB.py to initialize the vector database.\n* Update the LLM API key in src/model.py.\n* Run python src/app.py (this will automatically execute src/model.py).\n* Access the chatbot at the generated localhost link.\n\n2) Running in the Terminal:\nFollow these steps to run the RAG-Veterinary chatbot in the terminal:\n* Install the dependencies: pip install -r requirements.txt\n* Run python src/vectorDB.py to initialize the vector database.\n* Update the LLM API key in src/model.py.\n* Run python src/model.py and provide the command line input.\n* Responses will be generated in the terminal.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniju-2004%2Frag-vector-db","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fniju-2004%2Frag-vector-db","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniju-2004%2Frag-vector-db/lists"}