AI-Driven Book Recommendation App with Natural Language User Input
This project is a book recommendation service that suggests books based on a user's inputted genre and book titles. It's built upon a database of 7000 books retrieved from Kaggle. Using openn AI as the large language model, vector embeddings were created with the Kaggle dataset to allow for quick vector search to find semantically similar books through natural language input.
Tool/Framework/Service
Software Dev:
- NextJS, ReactJS
- Tailwindcss
- Vercel Server
Large Language Model:
- Vector Similar Search
- Text Gneration
- Open AI API, Cohere AI API
- Weaviate Vectorization Database
Key Points
Input genre and book titles to get book recommendations with AI
The OpenAI text embedding model vectorizes book descriptions and user preferences, enabling accurate searches for matching books and improving user experience over traditional book recommendations ystems
LLM related Service and Data Pipeline Building
Not only implement pipeline in web dev environment, but also create Python workflow to configure, access and manage vector embeddings in Vectorization Database
Minimalistic experimental interface
This project is experimental and technology-focused, so I streamlined the interface to prioritize and deliver the core functionalities.