Back to directory

BiliSum

BiliSum generates AI summaries, transcripts and a searchable knowledge base from Bilibili, YouTube and local videos, usable from a desktop app, a command-line tool, or as a skill an AI coding agent can call.

BiliSum

BiliSum transcribes a video, then produces a text summary, an illustrated note that reads the transcript alongside video screenshots, a mind map, and an entry in a cross-video knowledge base that supports semantic search, keyword search and retrieval-augmented question answering. Transcription can use SiliconFlow ASR, other multimodal ASR services, or local Whisper and FunASR models, and the illustrated notes can use a configurable vision model to pull objective facts from candidate video frames and filter out low-quality ones.

A desktop app for Windows and macOS handles Bilibili sign-in by QR code and in-app updates; a command-line tool installed from npm can run independently or alongside the desktop app, sharing the same tasks, configuration and knowledge base, and a bundled skill lets an agent such as Codex, Claude or Cursor choose between a full summary, a quick brief, a transcript or a task-status check. A Docker image is also available.

The desktop client is built with Electron, React and TypeScript, the backend with FastAPI and SQLite, and all data, including transcripts and the knowledge base, is kept on the local machine.

More in RAG

See all 540
  • ai·rete·rag

    ai-rete-rag.com

    A platform that pairs a Rete rule engine with retrieval-augmented generation, so deterministic rules decide an outcome and grounded retrieval explains the decision in plain language.

  • Xtriever

    github.com

    A hybrid retrieval engine for retrieval-augmented generation, written in Rust to run on-device on a phone or a laptop, combining lexical BM25 search, dense embedding search, reciprocal-rank fusion and cross-encoder re-ranking with no server.

  • FileFlux

    github.com

    A .NET library that turns PDF, DOCX, HWP and other documents into optimized chunks for RAG systems, through a five-stage pipeline built on high-performance Rust FFI readers.