語義搜尋（裝置端）
使用裝置端嵌入按語義搜尋您自己的文件：貼上多段文本，提出問題，即可獲得排序結果。
First use downloads the all-MiniLM-L6-v2 embedding model (roughly 25 MB) from Hugging Face. After that, everything runs on your device: your documents and query are never uploaded.
Important: this embedding model is English-centric. It ranks English documents well; Japanese and other non-English text usually matches poorly, because the model was trained mostly on English.
使用方法
- Paste documents separated by blank lines (2–20). The first line of each block is shown as its title.
- Type a query and press Search. The first run downloads the embedding model.
- Documents are ranked by meaning, with cosine similarity scores.
注意事項
Embeddings are computed on-device with Xenova/all-MiniLM-L6-v2 via Transformers.js; neither your documents nor your query are uploaded. The model was trained mostly on English, so it is English-centric: non-English documents, including Japanese, often rank poorly. That limitation is stated inside the tool as well.
常見問題
- How is this different from keyword search?
- Keyword search only checks whether the same words appear. Semantic search converts text into vectors and compares meaning, so relevant documents can rank highly without sharing words. For exact-match needs, keyword search is still more reliable.
- Do the scores mean anything absolute?
- Treat scores as relative ranking signals within this document set. They change with the set, and 0.5 does not mean 'half correct'.