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Models run on-device; repository content never leaves the machine.","maintainers":[{"name":"illodev","email":"hello@illodev.com"}],"readme":"# @illodev/workfile-search-local\n\nLocal embeddings semantic search provider for [Workfile](https://github.com/illodev/workfile).\nModels run on-device via [onnxruntime-web](https://onnxruntime.ai/) and\n[@huggingface/tokenizers](https://github.com/huggingface/tokenizers) (ONNX on CPU, WASM) —\nrepository content never leaves the machine.\n\n## Usage\n\n```bash\npnpm add -D @illodev/workfile-search-local\n```\n\n```js\n// project.config.mjs — the import is GUARDED on purpose. A bare\n// `import ... from \"@illodev/workfile-search-local\"` resolves from this file,\n// so the config would only load with node_modules present — and the generated\n// CI job runs `npx --yes @illodev/workfile@X doctor` on a clean clone. With\n// the guard, no package means lexical search, not a red pipeline.\nexport const integrations = await (async () => {\n    try {\n        const { localSearchIntegration } = await import(\n            \"@illodev/workfile-search-local\"\n        );\n        return [localSearchIntegration()];\n    } catch {\n        return [];\n    }\n})();\n\nexport default {\n    schemaVersion: 2,\n    name: \"My project\",\n    search: { provider: \"local-embeddings\" }\n};\n```\n\nThat is all: `workfile search`, the HTTP API, the UI and the MCP server rank\nhybrid results automatically. `workfile search QUERY --mode lexical` opts out\nper call.\n\n## The first pass, honestly\n\nThe first hybrid search over a workspace embeds every candidate record that is\nnot yet cached. On a few thousand records that is **minutes of sustained CPU**,\nand it is triggered by whichever surface searches first — the CLI, the board\nUI, or the MCP server a coding agent loads. The provider is built so that pass\nis survivable:\n\n- **Half the cores by default** (`numThreads`): ONNX no longer takes the whole\n  machine. Raise it deliberately if the box is idle.\n- **Batched with per-batch persistence** (`batchSize`, default 32): killing the\n  process keeps every completed batch; the next search resumes where it died\n  instead of starting over.\n- **Progress on stderr** once 64+ records are missing, so a long pass looks\n  like work instead of a hang. `onProgress` replaces it with your own sink.\n\nAfterwards the cache makes searches fast (typically seconds), and editing a\nrecord re-embeds that record only.\n\n## Behavior\n\n- The model (default `Xenova/multilingual-e5-small`, quantized, multilingual)\n  is downloaded once on first use into `modelDir` — about 135 MB of tokenizer\n  and weights — and everything afterwards is offline. Point `model` at a\n  directory holding `tokenizer.json` and the ONNX file to skip the download\n  entirely, which is how this runs on a machine with no network.\n- Record embeddings are cached in `~/.cache/workfile/embeddings`, keyed by\n  content hash — editing a card re-embeds that card only. Only the first\n  `passageChars` characters of a body are embedded, so edits beyond that\n  boundary intentionally do not invalidate the vector.\n- Scores are spread over the candidate set (best 1, worst 0) before Workfile\n  blends them with the lexical ranking (`search.semanticWeight`, default 0.35).\n- Workfile picks semantic candidates by lexical relevance first, filling the\n  remainder in index order up to `search.maxProviderRecords` — size that cap\n  to your corpus if you want every record eligible.\n\n## Options\n\n```js\nlocalSearchIntegration({\n    id: \"local-embeddings\",             // integration id, referenced by search.provider\n    model: \"Xenova/multilingual-e5-small\",\n    dtype: \"q8\",                        // which exported ONNX weights to load\n    cacheDir: \"~/.cache/workfile/embeddings-or-null\",\n    modelDir: \"~/.cache/workfile/models\", // where the model and tokenizer are kept\n    passageChars: 2000,                 // body characters embedded per record\n    embedder: null,                     // inject your own (texts) => vectors\n    numThreads: 4,                      // ONNX WASM threads; default: half the cores\n    batchSize: 32,                      // records per model call; cache persists per batch\n    onProgress: ({ done, total }) => {} // default: stderr lines on large passes\n});\n```\n","readmeFilename":"README.md"}