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.