Show HN: Turn Bilibili favorites into a personal RAG knowledge base (github.com)

A new tool has been introduced that transforms user-favorite videos from Bilibili into a personal retrievable knowledge base, leveraging advanced AI/Machine Learning techniques. This project is tailored for organizing interviews, lectures, and educational content, making it significant for educators, researchers, and learners who seek to streamline their video-based knowledge acquisition. The system features automated content retrieval, voice transcription (ASR), semantic search through vector retrieval, and responsive Q&A capabilities based on Retrieval-Augmented Generation (RAG). Key technical details include the use of a back-end built with FastAPI and a LLM powered by LangChain combined with DashScope, while data is managed in ChromaDB and SQLite. Users can easily log in via Bilibili, where the tool will fetch their favorite videos, convert speech to text, create embeddings, and build an accessible knowledge library. This innovation includes practical setup instructions with dependencies like ffmpeg, enhancing its accessibility. Despite potential challenges with some Bilibili URLs due to authentication issues, the tool has intelligent fallback processes. Overall, this project represents a significant step towards integrating AI with educational resources, allowing for better content interaction and knowledge management.