text2vec is a Python library for converting text (words, sentences, paragraphs) into dense vector embeddings, with a strong focus on Chinese semantic similarity. It provides multiple embedding backends: Word2Vec (Tencent embeddings), Sentence-BERT, CoSENT, BGE, and BM25 for lexical ranking. The library offers a unified API via `SentenceModel` and `Similarity` classes, a command-line interface for batch embedding, and training/fine-tuning scripts for supervised and unsupervised text matching. It also includes deployment examples (FastAPI, Jina, Gradio) and evaluation benchmarks (C-MTEB, MTEB). While primarily an application/library, it has strong research components with detailed model comparison reports and dataset building scripts. The codebase is modular and well-documented, though some training code contains boilerplate repetition and dependency version ranges are broad, introducing potential drift.
StoryToolkitAI is a video editing toolkit that leverages AI to enhance post-production workflows. It operates in dual modes (GUI and CLI) with core functionality centered around transcription, semantic search, speaker diarization, and integration with DaVinci Resolve Studio. The application enables users to transcribe media files, search content using natural language, generate stories via LLM assistance, and export to professional editing formats (EDL, XML, Fountain). Key architectural elements include a plugin-based system for LLM providers, a persistent task queue with dependency tracking for CPU/GPU workloads, and project-based file linking for unified search across media assets. The tool emphasizes local processing with optional external API usage for advanced features.