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The bundled local MiniLM path uses vendored WASM with no native addon, runtime network, or transitive install scripts.","maintainers":[{"name":"possumtechcom","email":"possumtech@pm.me"}],"readme":"# @plurnk/plurnk-mimetypes-embeddings\n\nPortable local/hosted embedder for [`@plurnk/plurnk-mimetypes`](https://github.com/plurnk/plurnk-service/tree/main/plurnk-mimetypes)' embedding seam ({§mimetype-embedding}).\n\n| Concern      | Contract                                                                                                                                    |\n|--------------|---------------------------------------------------------------------------------------------------------------------------------------------|\n| Installation | The default service composition installs this artifact as a required dependency; direct framework consumers install it when wanted.        |\n| Resolution   | The framework lazily imports this fixed artifact; it does not scan for competing embedder packages.                                        |\n| Computation  | `embedQuery()` encodes retrieval queries; `embedDocuments()` encodes an ordered corpus. Nothing embeds implicitly.                        |\n| Configured   | `PLURNK_EMBEDDING_MODEL` selects any standard provider route or alias supported by `@plurnk/plurnk-providers`.                          |\n\n## Bundled local model\n\n- **Xenova/all-MiniLM-L6-v2**, q8 quantized onnx (`onnx/model_quantized.onnx`), **384 dimensions**.\n- Pinned revision: `751bff37182d3f1213fa05d7196b954e230abad9` (`.model-pin`).\n- Model files are **bundled in the package**. Local mode performs no runtime network: it reads only these files and has no fetcher to disable. Integrity manifest in `model/model.sha256` (`npm run verify:model`).\n- Local inference runs on a **portable WASM runtime** — [`onnxruntime-web`](https://www.npmjs.com/package/onnxruntime-web) (single-threaded, **vendored** — see below) for the onnx graph, [`@huggingface/tokenizers`](https://www.npmjs.com/package/@huggingface/tokenizers) for WordPiece. No native N-API addon: supported Node hosts receive the same package bytes, with no per-platform binary or leaked event-loop handles. Output is vector-identical to the prior native (`onnxruntime-node`) path — same `model` identity, no re-embed.\n\n## Vendored local runtime (clean install, no install scripts)\n\n`onnxruntime-web` is **vendored** into `vendor/onnxruntime-web/`, not pulled as an npm dependency. The reason: `onnxruntime-web` hard-depends on `protobufjs`, whose `postinstall` script trips dependency script-gates (lavamoat, pnpm `approve-builds`, hardened npm) — so a first install downstream would greet the user with a script-approval prompt. `protobufjs` is a **phantom**: the `.onnx` protobuf is parsed inside the wasm, never by the JS library (proven — `require.cache`/`moduleLoadList` report zero on a real `embed()`).\n\nVendoring ORT's own self-contained pre-built dist removes both `onnxruntime-web` and `protobufjs` from the install tree, and a consumer install runs **zero** install scripts. The package depends only on the tokenizer engine and the standard provider/AI SDK seam already used by the service. The committed bytes are reproducible from `.ort-pin` via `npm run vendor:ort` and gated by `npm run verify:ort` (checksum + phantom assertion, run in `pretest`). Full rationale, the bump runbook, and the `npm audit` blind-spot note: [`vendor/onnxruntime-web/PROVENANCE.md`](vendor/onnxruntime-web/PROVENANCE.md).\n\n## Install\n\nService users already receive this package. Direct framework consumers install\nit to enable the embedding seam, or install it independently for its exported\nsurface:\n\n```sh\nnpm install @plurnk/plurnk-mimetypes-embeddings\n```\n\n## Usage\n\nThe framework resolves this artifact lazily. Its `embedding` process channel is\na query embedding; semantic-index derivation calls the explicit document\nsurface:\n\n```js\nconst query = await mimetypes.process(\n    { content: \"database connection error\", hint: \"text/plain\" },\n    { channels: [\"embedding\"] },\n);\nconst corpus = await mimetypes.embedDocuments([\n    \"The connection pool rejected a stale socket.\",\n    \"The migration completed successfully.\",\n]);\n```\n\nDirect consumers use the same role-separated contract:\n\n```js\nimport { EmbeddingVector } from \"@plurnk/plurnk-mimetypes\";\nimport {\n    dimension,\n    embedQuery,\n    embedDocuments,\n} from \"@plurnk/plurnk-mimetypes-embeddings\";\n\nconst { vector, metadata } = await embedQuery(\"database connection error\");\nconst values = EmbeddingVector.decode(vector, dimension);\nconst { vectors } = await embedDocuments([\"A database troubleshooting guide.\"]);\n```\n\n## Exports\n\n- `embedQuery(text, { signal }) → Promise<{ vector, metadata }>` — encodes one retrieval query.\n- `embedDocuments(texts, { onProgress, signal }) → Promise<{ vectors, metadata }>` — encodes an ordered corpus. The AI SDK partitions hosted calls against the adapter and exact-route profile envelopes while preserving input order; local mode uses a bounded worker pool. Progress and cancellation span the composed call.\n- `metadata` — `{ inputTokens, warnings, accounting, providerMetadata?, responses? }`. Hosted usage, ordered physical-request accounting, provider evidence, and bounded response headers are preserved when supplied; raw response bodies are not retained. Local usage is explicitly `null` with an empty physical-request list, never estimated.\n- `dimension`, `contextWindow`, and `tokenizerModel` — exact profile facts. Hosted construction never performs a paid inference to discover them.\n- `model` — a stable vector-space identity derived from provider, model, dimensions, window, exact tokenizer bytes, pooling, normalization, and query/document policy. Store it beside every vector; vectors with different identities are incomparable.\n- `countTokens(text, { signal })` — exact counting in the bundled local model or a built-in hosted profile vocabulary. Profile tokenizer engines and compressed bytes inflate lazily on first use.\n- `dispose()` — releases the local runtime and every worker, attempting all teardown paths and aggregating failures. Concurrent calls join one attempt; later use resolves a fresh generation. Hosted mode is a no-op.\n\nThe bundled model is symmetric: query and document encodings of identical text\nmatch. Qwen retrieval models are intentionally asymmetric: queries receive the\nupstream retrieval instruction and documents remain unchanged. That role\npolicy is part of the vector-space identity.\n\n## Provider routes\n\nUnset `PLURNK_EMBEDDING_MODEL` selects the bundled hermetic MiniLM runtime.\nOtherwise it accepts the same exact `<provider>/<model>` route or\n`PLURNK_MODEL_<alias>` selector as generation. Endpoint overrides, catalog\ncredentials, custom provider declarations, retries, and alias scoping remain\nowned by `@plurnk/plurnk-providers`; this package does not define a second HTTP\nor authentication stack. Built-in profiles carry only their required exact\ntokenizer bytes, compressed at rest. An unknown custom profile can name a vocabulary from the\noptional general catalog:\n\n```sh\nnpm install @plurnk/plurnk-mimetypes-tokenizers\n```\n\nSelection occurs before adapter import: local mode does not load provider\nconstructors, and configured mode does not load the bundled local ONNX/model\nruntime. A configured profile does not parse its tokenizer vocabulary until\nchunk planning requests an exact count.\n\n```dotenv\n# Hosted catalog route\nPLURNK_EMBEDDING_MODEL=cloudflare-workers-ai/@cf/qwen/qwen3-embedding-0.6b\nCLOUDFLARE_ACCOUNT_ID=...\nCLOUDFLARE_API_KEY=...\n\n# Operator-run OpenAI-compatible endpoint\nPLURNK_PROVIDERS_PROVIDER_LOCAL_NPM=@ai-sdk/openai-compatible\nPLURNK_MODEL_embeddings=local/Qwen/Qwen3-Embedding-0.6B\nPLURNK_BASEURL_embeddings=http://127.0.0.1:8080/v1\nPLURNK_EMBEDDING_MODEL=embeddings\n\n# The bundled model served by a llama-server (GGUF) — a known model on any provider\nPLURNK_PROVIDERS_PROVIDER_LOCAL_EMBED_NPM=@ai-sdk/openai-compatible\nPLURNK_PROVIDERS_PROVIDER_LOCAL_EMBED_BASE_URL=http://127.0.0.1:11436/v1\nPLURNK_EMBEDDING_MODEL=local-embed/sentence-transformers/all-MiniLM-L6-v2\n```\n\nBuilt-in profiles currently cover:\n\n| Route | Dimensions | Input window | Inputs / request | Exact tokenizer |\n|-------|-----------:|-------------:|-----------------:|-----------------|\n| `cloudflare-workers-ai/@cf/qwen/qwen3-embedding-0.6b` | 1024 | 8192 | 1 | `Qwen/Qwen3-Embedding-0.6B` |\n| `fireworks-ai/accounts/fireworks/models/qwen3-embedding-0p6b` | 1024 | 32768 | 1 | `Qwen/Qwen3-Embedding-0.6B` |\n| `fireworks-ai/accounts/fireworks/models/qwen3-embedding-8b` | 4096 | 40960 | 1 | `Qwen/Qwen3-Embedding-8B` |\n| `openrouter/qwen/qwen3-embedding-8b` | 4096 | 32768 | 1 | `Qwen/Qwen3-Embedding-8B` |\n| `openai/text-embedding-3-small` | 1536 | 8191 | 36 | `cl100k` |\n| `<any provider>/sentence-transformers/all-MiniLM-L6-v2` | 384 | 510 | 32 | bundled `Xenova/all-MiniLM-L6-v2` |\n\nThe Cloudflare row describes the current direct Workers AI model contract; the\nseparate AI Search integration publishes a smaller ingestion limit. Routes\nwithout a published aggregate request envelope use one input per physical\nrequest and recover throughput through bounded concurrency. OpenAI's 36-input\ncap keeps the worst-case aggregate beneath its documented 300,000-token\nrequest ceiling.\n\nThe MiniLM row is the bundled runtime's own model served over `/v1/embeddings`\n(a llama-server on the GGUF): its profile counts with the bundled vocabulary,\nleaves `[CLS]`/`[SEP]` to the server, and takes pooling and normalization from\nthe endpoint, so a served copy needs a provider declaration and nothing else.\n\nAn unknown route is supported without guessing: declare all four exact facts\nwith `PLURNK_EMBEDDING_DIMENSIONS`, `PLURNK_EMBEDDING_CONTEXT_WINDOW`,\n`PLURNK_EMBEDDING_TOKENIZER`, and\n`PLURNK_EMBEDDING_MAX_INPUTS_PER_REQUEST`. Such a profile is symmetric. A\nbuilt-in profile rejects these duplicate declarations so one fact retains one\nowner.\n\n`PLURNK_EMBEDDING_MAX_INPUTS_PER_REQUEST` is the unknown route's standard\npartition cardinality; it is transport policy, not vector-space identity.\n`PLURNK_EMBEDDING_CONCURRENCY` independently bounds simultaneous hosted\nrequests after partitioning. `PLURNK_EMBEDDING_WORKERS` sizes only the local\ndocument pool: empty leaves one core free on hosts larger than four cores, a\npositive integer is exact, and `-1` claims every core. Each local worker owns a\nmodel copy.\n\n## Scripts\n\n- `npm run build:model` — re-download the pinned revision into `model/` and regenerate `model/model.sha256`.\n- `npm run verify:model` — check the committed model bytes against the manifest.\n- `npm run fetch:profile-tokenizers` — reproduce the exact tokenizer bytes owned by built-in hosted profiles from their pinned revisions.\n- `npm run verify:profile-tokenizers` — verify every hosted-profile tokenizer byte and identity against its manifest.\n- `npm run vendor:ort` — re-copy the onnxruntime-web runtime from `.ort-pin` into `vendor/` and regenerate `ort.sha256` (re-asserts the protobufjs-phantom invariant).\n- `npm run verify:ort` — check the vendored runtime against its manifest and the phantom invariant (runs before `test:unit`).\n- `npm test` — unit (duck surface, determinism, normalization, cosine sanity, vendoring phantom guard) + integration (real framework loader path).\n","readmeFilename":"README.md"}