Welcome to the SECE Chatbot project! This project is designed to provide an interactive chatbot experience for queries related to Sri Eshwar College of Engineering (SECE). It utilizes Pinecone for vector storage, Hugging Face for embeddings, and Groq for language processing.
- Interactive Chatbot: Engage with an AI-powered chatbot to get information about SECE.
- Pinecone Integration: Efficient vector storage and similarity search using Pinecone.
- Hugging Face Embeddings: High-quality embeddings using Hugging Face's models.
- Groq Language Model: Leverage the power of the Groq language model for natural language understanding.
- Create api key with huggingface, groq, pinecone.
- Create index with desired name and change it in the files.
- Ensure you have added you own api key to use it...
- Make sure to run sece_chat_load.py to load data to vector db
sece-doc/sece doc data 1.pdf: The content of the college to retireve information.sece_chat_load: To load the content to vectordb.app.py: Main application file containing Flask setup and routes.templates/index.html: HTML file for the frontend.static/: Directory for static files like CSS and JS.requirements.txt: List of required Python packages.