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SDK** is the canonical entry point to develop AI applications with QVAC.","maintainers":[{"name":"mafintosh","email":"mathiasbuus@gmail.com"},{"name":"prdn","email":"paolo.ardoino@gmail.com"}],"readme":"# QVAC SDK\n\n**QVAC SDK** is the canonical entry point to develop AI applications with QVAC.\n\n> _Part of **QVAC** ecosystem_\n> <br>\n> <sup>\n> <a href=\"https://qvac.tether.io/\" >Home</a> &nbsp;•&nbsp;\n> <a href=\"https://docs.qvac.tether.io/\" >Docs</a> &nbsp;•&nbsp;\n> <a href=\"https://discord.com/channels/1425125849346216029/1445400675189264516\" >Support</a> &nbsp;•&nbsp;\n> <a href=\"https://discord.com/invite/tetherdev\" >Discord</a>\n\n**QVAC SDK** is the main entry point for developing applications with QVAC. It is type-safe and exposes all QVAC capabilities through a unified interface. It runs on Node.js and [Expo](https://expo.dev).\n\nSee [https://docs.qvac.tether.io/sdk/getting-started](https://docs.qvac.tether.io/sdk/getting-started) for the comprehensive QVAC documentation.\n\nFor AI/LLM tools, use [https://docs.qvac.tether.io/llms-full.txt](https://docs.qvac.tether.io/llms-full.txt) as the consolidated plaintext documentation export.\n\n> **In-process Bare:** use [`@qvac/inference`](../inference/README.md). `@qvac/bare-sdk` is deprecated; last release is 0.18.2.\n\n## Supported environments and installation\n\nSee https://docs.qvac.tether.io/sdk/getting-started/installation\n\nOn Node.js, the SDK installs and launches its own Bare worker through `bare-runtime`.\nIts dependency range must satisfy `@qvac/inference`'s `engines.bare` requirement.\nWhen upgrading the SDK, reinstall dependencies with the existing lockfile so the\npackage manager can replace an older, incompatible runtime. Remove any override\nor resolution that forces the worker below that requirement; installing a newer\nglobal `bare` executable does not change the SDK's worker dependency.\n\n## Quickstart\n\n1. Create the examples workspace:\n\n```bash\nmkdir qvac-examples\ncd qvac-examples\nnpm init -y && npm pkg set type=module\n```\n\n2. Install the SDK:\n\n```bash\nnpm install @qvac/sdk\n```\n\n3. Create the quickstart script:\n\n```js\nimport { loadModel, LLAMA_3_2_1B_INST_Q4_0, completion, unloadModel } from '@qvac/sdk'\ntry {\n  // Load a model into memory\n  const modelId = await loadModel({\n    modelSrc: LLAMA_3_2_1B_INST_Q4_0,\n    onProgress: (progress) => {\n      console.log(progress)\n    }\n  })\n  // You can use the loaded model multiple times\n  const history = [\n    {\n      role: 'user',\n      content: 'Explain quantum computing in one sentence'\n    }\n  ]\n  const result = completion({ modelId, history, stream: true })\n  for await (const token of result.tokenStream) {\n    process.stdout.write(token)\n  }\n  // Unload model to free up system resources\n  await unloadModel({ modelId })\n} catch (error) {\n  console.error('❌ Error:', error)\n  process.exit(1)\n}\n```\n\n4. Run the quickstart script:\n\n```bash\nnode quickstart.js\n```\n\n## System resource diagnostics\n\nUse `getSystemResources` to inspect locally observed CPU, system-memory, GPU, and\ndriver capabilities. Pass `sample: true` only when you also need a fresh usage\nsample:\n\n```ts\nimport { getSystemResources } from '@qvac/sdk'\n\nconst resources = await getSystemResources({ sample: true })\n\nif (resources.capabilities.memory.totalBytes.status === 'supported') {\n  console.log('System memory:', resources.capabilities.memory.totalBytes.value)\n}\n\nif (resources.sample?.cpu.status === 'supported') {\n  console.log('CPU utilization:', resources.sample.cpu.value)\n}\n```\n\nSee the [system resources support matrix](./docs/system-resources-support-matrix.md)\nfor metric-level evidence and platform limitations.\n\nEvery metric reports `supported`, `unavailable`, `unverified`, or `failed`.\nSupported values include provenance with a source and optional scope. These\nvalues are diagnostics; they do not reserve memory or guarantee that a model\ncan be loaded.\n\nGPU capabilities expose observed driver names, versions, and graphics APIs.\nThese observations do not prove that an inference backend is compatible.\n\nProfiled inference operation events may include `event.backend` with the\nselected backend and device, graphics API, driver, fallback reason, and probe\nresult. Addons attach backend metadata with `attachBackendDiagnostics`; the SDK\nvalidates it before recording the operation event. `gpuId`, when present,\nidentifies a GPU from the current worker resource collector and is stable only\nfor that collector's lifetime. The SDK does not infer compatibility from driver\ninventory or log text.\n\n### Profiler resource gauges\n\nResource gauges are disabled by default. Enable them explicitly to attach one\nworker resource sample to each profiled operation:\n\n```ts\nimport { profiler } from '@qvac/sdk'\n\nprofiler.enable({ mode: 'verbose', includeResourceGauges: true })\n\n// Run SDK operations, then inspect recentEvents[].resources.\nconst profile = profiler.exportJSON()\n```\n\nThe sample uses the same status, provenance, and scope semantics as\n`getSystemResources({ sample: true })`. Its `sampledAt` uses the same monotonic\nclock as the profiling event's `ts`, so the two timestamps are comparable.\n`resources.origin` records that the sample was taken on the `local` worker.\nSamples are delivered to `profiler.onRecord`; they\nare retained in `exportJSON().recentEvents` only in `verbose` mode. Enabling\ngauges in `summary` mode still incurs the sampling cost without retaining them.\nDisabling profiling or omitting `includeResourceGauges` performs no resource\nsampling. Enabling gauges adds one CPU query and one query per GPU to each\nprofiled operation's response path. If the worker resource collector is not\ninitialized, the event omits the resource block.\n\n## Pre-download model fit assessment\n\nUse `assessModelFit` to check, before downloading anything, whether models are\nlikely to fit in this device's memory. Each candidate is described as `loadModel`\ntakes it. It reads generated catalog metadata plus a fresh memory sample, and for\na single candidate the registry's weightless description of the artifact, so the\nengine's own fitter can answer — no weights, no load:\n\n```ts\nimport { assessModelFit, QWEN3_8B_INST_Q4_K_M } from '@qvac/sdk'\n\nconst result = await assessModelFit({\n  models: [{ modelSrc: QWEN3_8B_INST_Q4_K_M, modelType: 'llm', modelConfig: { ctx_size: 8192 } }],\n  execution: 'sequential',\n  policy: 'interactive-v1'\n})\n\nconsole.log(result.verdict) // 'likely-fits' | 'likely-too-large' | 'unknown'\n```\n\nThe result is advisory: it does not block `loadModel`, reserve memory, or make a\nperformance claim. `unknown` is a real answer meaning the evidence does not\nsupport a call either way — show it as \"can't say\", not as \"no\".\n\nSee [pre-download model fit assessment](./docs/assess-model-fit.md) for the\nbudget arithmetic, why estimates are ranges, the supported engine and workload\nmatrix, and the current calibration status.\n\n## Streaming transcription statistics\n\nWhisper and Parakeet duplex transcription sessions expose terminal engine\nstatistics after their event iterator completes:\n\n```ts\nconst session = await transcribeStream({ modelId })\n\nfor await (const text of session) {\n  process.stdout.write(text)\n}\n\nconst stats = await session.stats\nconsole.log(stats?.audioDuration, stats?.realTimeFactor)\n```\n\n`session.stats` resolves to `undefined` when the engine does not report\nstatistics.\n\nBatch `transcribe()` calls also expose terminal statistics:\n\n```ts\nconst call = transcribe({ modelId, audioChunk })\nconst text = await call\nconst stats = await call.stats\nconsole.log(text, stats?.encoderUsedCoreml)\n```\n\n`call.stats` resolves to `undefined` when the engine reports no statistics and\nrejects if transcription fails.\n\n## Parakeet Core ML encoders on Apple devices\n\nLoad a supported Parakeet registry constant as usual. On macOS and iOS, the SDK\ndownloads any complete, published Core ML encoder bundle with its GGUF and\nplaces the `.mlmodelc` directory where the native addon discovers it. This is\navailable for TDT 0.6B v3, Unified English 0.6B, EOU 120M v1, and streaming\nSortformer v2.1. The download includes the sidecar weights, so first load takes\nmore time and disk space. Other platforms download only the GGUF. If the\nsidecar is unavailable or cannot load, inference uses the GGUF encoder.\nSortformer v2.1 also needs a GGUF with the\n`parakeet.model_variant=sortformer-streaming-v2.1-aosc` metadata. Caching both\nsidecars alone does not activate Core ML. See\n[`examples/asr/parakeet-unified-coreml.ts`](examples/asr/parakeet-unified-coreml.ts)\nfor a local check of a Unified GGUF and its encoder bundle.\n\n## Examples\n\nIn the `./examples` subdirectory, you will find scripts demonstrating how to use all SDK functionalities. To try any of them:\n\n1. Build the SDK from source (see [Build](#build) section).\n2. Run using Bare, Node.js, or Bun as the runtime:\n\n```bash\n# With Bare\nbun run bare:example dist/examples/path/to/example.js\n\n# With Node\nnode dist/examples/path/to/example.js\n\n# With bun, straight from source\nbun run examples/path/to/example.ts\n```\n\n`examples/abot-world.ts` has a companion guide covering the hardware\nrequirements, scene-pack lifecycle, cancellation semantics and concurrency rules\nof an interactive world session: see\n[ABot-World interactive world sessions](./docs/abot-world.md).\n\n## Build\n\nUse the [Bun](https://bun.sh/) package manager:\n\n```bash\nbun i\n```\n\n`@qvac/inference` resolves to its published release by default. To build and test against the in-repo engine at the same commit, link it first:\n\n```bash\nbun run sdk-source:workspace\n```\n\n```bash\nbun run build  # or `watch` for hotreload\n```\n\n```bash\nbun run build:pack\n```\n\nThis outputs a tarball under `dist/sdk-{version}.tgz` that you can install in your project, e.g.:\n\n```bash\nnpm i path/to/sdk-0.3.0.tgz\n```\n\n## Testing\n\nThe SDK test suite is organized into two buckets by runtime:\n\n| Bucket            | Runtime    | Location | Command                                |\n| ----------------- | ---------- | -------- | -------------------------------------- |\n| Unit              | Bun / Node | `test/`  | `bun run test:unit`                    |\n| Client (consumer) | Node / RN  | `e2e/`   | See [`e2e/README.md`](./e2e/README.md) |\n\nSee [`TESTING.md`](./TESTING.md) for the full decision tree on where new tests should land.\n\n## Contributing\n\n### Commit Message and PR Title Format\n\nThis repository enforces structured commit messages and PR titles to maintain consistency and generate changelogs automatically.\n\n#### Format\n\n**Commit messages:**\n\n```\nprefix[tags]?: subject\n```\n\n**PR titles:**\n\n```\nTICKET prefix[tags]: subject\n```\n\n#### Allowed Prefixes\n\n- `feat` - New features or capabilities\n- `fix` - Bug fixes\n- `doc` - Documentation changes\n- `test` - Test additions or modifications\n- `mod` - Model-related changes\n- `chore` - Maintenance tasks\n- `infra` - CI/CD, tooling, infrastructure\n\n#### Allowed Tags\n\nTags are optional:\n\n- `[api]` - API changes (non-breaking)\n- `[bc]` - Breaking changes (including breaking API changes)\n\n#### Examples\n\n**Valid commit messages:**\n\n```bash\nfeat: add RAG support for LanceDB\nfix[api]: fix completion stream error handling\ndoc: update installation instructions\nfeat[bc]: redesign loadModel signature\nchore: update dependencies\n```\n\n**Valid PR titles:**\n\n```bash\nQVAC-123 feat: add RAG support for LanceDB\nQVAC-456 fix[api]: fix completion stream error handling\nQVAC-789 doc: update installation instructions\nQVAC-101 feat[bc]: redesign loadModel signature\n```\n\n#### Code Examples Requirements\n\nWhen creating PRs with specific tags, you must include code examples in the PR description:\n\n**`[bc]` tag requirements:**\n\nMust include BEFORE/AFTER code examples showing the migration path:\n\n````markdown\n## BC Changes\n\n**BEFORE:**\n\n```typescript\nconst model = await loadModel('model-path')\n```\n\n**AFTER:**\n\n```typescript\nconst modelId = await loadModel('model-path', { modelType: 'llm' })\n```\n````\n\nOr using inline comments:\n\n````markdown\n```typescript\n// old\nconst model = await loadModel('model-path')\n\n// new\nconst modelId = await loadModel('model-path', { modelType: 'llm' })\n```\n````\n\n**`[api]` tag requirements (non-breaking):**\n\nMust include at least one fenced code block showing the new API usage:\n\n````markdown\n## New API\n\n```typescript\n// New completion API with streaming support\nfor await (const token of completion({\n  modelId,\n  history: [{ role: 'user', content: 'Hello!' }]\n}).tokenStream) {\n  process.stdout.write(token)\n}\n```\n````\n\n#### Validation\n\n- **Commit messages** are validated automatically via Husky commit-msg hook\n- **PR titles and descriptions** are validated via GitHub Actions on PR creation/update\n- Invalid commits or PRs will be rejected with helpful error messages\n- **Auto-skipped commits:** The following Git-generated commits bypass validation:\n  - Merge commits (e.g., `Merge pull request #123`)\n  - Version bumps (e.g., `1.0.0`, `v1.0.0`)\n  - Revert commits (e.g., `Revert \"feat: add feature\"`)\n  - Squash commits (e.g., `squash! fix: bug fix`)\n\n#### Generating Changelogs\n\nOnce your PRs are merged into `dev`, you can generate a changelog:\n\n```bash\nnpm run changelog:generate\n```\n\nThis will:\n\n1. Compare versions between `dev` and `main` branches\n2. Collect all merged PRs\n3. Parse and classify each PR by prefix\n4. Generate `changelog/<version>/CHANGELOG.md`\n5. Generate `changelog/<version>/breaking.md` for BC changes (with code examples)\n6. Generate `changelog/<version>/api.md` for API changes (with code examples)\n","readmeFilename":"README.md"}