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-output-dir=coverage-html -ignore-filename-regex='(tests|build|node_modules|gtest|gmock|\\.vcpkg|/usr)/' && llvm-cov export ./addon-test -instr-profile=coverage.profdata -format=lcov -ignore-filename-regex='(tests|build|node_modules|gtest|gmock|\\.vcpkg|/usr)/' > lcov.info && npm run coverage:cpp:summary","performance:install":"cd ./benchmarks/performance && npm install","test:types:consumer":"tsc -p test/types/tsconfig.cjs.json","coverage:cpp:summary":"cd build/test/unit && llvm-cov report ./addon-test --instr-profile=coverage.profdata -ignore-filename-regex='(tests|build|node_modules|gtest|gmock|\\.vcpkg|/usr)/' > coverage-summary.txt","test:mobile:generate":"npm run generate:benchmark-shards && bare ./scripts/generate-mobile-integration-tests.js","test:mobile:validate":"npm run generate:benchmark-shards && node scripts/validate-mobile-tests.js","mobile:copy-prebuilds":"cp -r prebuilds/android-arm64 prebuilds/android-ia32 || echo 'Warning: Failed to copy embed-llamacpp prebuilds to android-ia32'; cp -r prebuilds/android-arm64 prebuilds/android-arm || echo 'Warning: Failed to copy embed-llamacpp prebuilds to android-arm'; cp -r prebuilds/android-arm64 prebuilds/android-x64 || echo 'Warning: Failed to copy embed-llamacpp prebuilds to android-x64'; cp -r prebuilds/ios-arm64 prebuilds/ios-arm64-simulator 2>/dev/null || echo 'iOS prebuilds already present'; cp -r prebuilds/ios-arm64 prebuilds/ios-x64-simulator 2>/dev/null || echo 'iOS prebuilds already present'","benchmarks:param-sweep":"cd ./benchmarks/performance && npm run run:param-sweep --","verify:benchmark-shards":"node scripts/generate-benchmark-shards.js --check","generate:benchmark-shards":"node scripts/generate-benchmark-shards.js","test:integration:generate":"npm run generate:benchmark-shards && brittle -r test/integration/all.js test/integration/*.test.js && npm run test:mobile:generate","performance:prepare-models":"cd ./benchmarks/performance && npm run 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addon for qvac","maintainers":[{"name":"mafintosh","email":"mathiasbuus@gmail.com"},{"name":"prdn","email":"paolo.ardoino@gmail.com"}],"readme":"# embed-llamacpp\n\nThis native C++ addon, built using the `Bare` Runtime, simplifies running text embedding models to enable efficient generation of high-quality contextual text embeddings. It provides an easy interface to load, execute, and manage embedding model instances.\n\n## Table of Contents\n\n- [Supported platforms](#supported-platforms)\n- [Installation](#installation)\n- [Building from Source](#building-from-source)\n- [Usage](#usage)\n  - [1. Import the Model Class](#1-import-the-model-class)\n  - [2. Create the `args` obj](#2-create-the-args-obj)\n  - [3. Create `config`](#3-create-config)\n  - [4. Instanstiate the model](#4-instanstiate-the-model)\n  - [5. Load the model](#5-load-the-model)\n  - [6. Generate embeddings for input sequence](#6-generate-embeddings-for-input-sequence)\n  - [7. Release Resources](#7-release-resources)\n- [API behavior by state](#api-behavior-by-state)\n- [Assessing fit](#assessing-fit)\n- [IdMapIndex vector database](#idmapindex-vector-database)\n- [Quickstart Example](#quickstart-example)\n- [Other Examples](#other-examples)\n- [Benchmarking](#benchmarking)\n- [Tests](#tests)\n- [Glossary](#glossary)\n- [License](#license)\n\n## Supported platforms\n\n| Platform | Architecture | Min Version | Status | GPU Support |\n|----------|-------------|-------------|--------|-------------|\n| macOS | arm64, x64 | 14.0+ | ✅ Tier 1 | Metal |\n| iOS | arm64 | 17.0+ | ✅ Tier 1 | Metal |\n| Linux | arm64, x64 | Ubuntu-22+ | ✅ Tier 1 | Vulkan |\n| Android | arm64 | 12+ | ✅ Tier 1 | Vulkan, OpenCL (Adreno 700+) |\n| Windows | x64 | 10+ | ✅ Tier 1 | Vulkan |\n\n**Dependencies:**\n- inference-addon-cpp (≥1.1.2): C++ addon framework\n- @qvac/fabric (^0.18.0): Shared llama.cpp/ggml inference engine, including the vector-index API. The desktop runtime ships in the `@qvac/fabric-<host>` platform package that `@qvac/fabric` installs as an optional dependency, so install without `--omit=optional` and keep both present before `bare-make generate`/`build`. Android and iOS apps add `@qvac/fabric` and `@qvac/fabric-android-arm64` or `@qvac/fabric-ios` as direct dependencies at the same exact version.\n- Bare Runtime (≥1.24.0): JavaScript runtime\n- Linux requires Clang/LLVM 22 with libc++\n\n\n## Installation\n\n### Prerequisites\n\nEnsure that the `Bare` Runtime is installed globally on your system. If it's not already installed, you can install it using:\n\n```bash\nnpm install -g bare@latest\n```\n\n### Installing the Package\n\n```bash\nnpm install @qvac/embed-llamacpp@latest\n```\n\n## Building from Source\n\nSee [build.md](./build.md) for detailed instructions on how to build the addon from source.\n\n## Usage\n\n### 1. Import the Model Class\n\n```js\nconst GGMLBert = require('@qvac/embed-llamacpp')\n```\n\n### 2. Create the `args` obj\n\n```js\nconst path = require('bare-path')\n\nconst args = {\n  files: { model: [path.join(dirPath, modelName)] },\n  config: {\n    device: 'gpu',\n    gpu_layers: '99',\n    batch_size: '1024',\n    ctx_size: '512'\n  },\n  logger: console,\n  opts: { stats: true }\n}\n```\n\nThe `args` obj contains the following properties:\n\n* `files.model`: An array of absolute paths to the model file(s) on disk. For sharded models, provide all shard paths.\n* `config`: A dictionary of hyper-parameters used to tweak the behaviour of the model (see [Create `config`](#3-create-config) below).\n* `logger`: This property is used to create a [`QvacLogger`](../logging) instance, which handles all logging functionality.\n* `opts.stats`: This flag determines whether to calculate inference stats.\n\n#### Sharded model usage\n\nThe addon does not discover companion files on disk — the caller MUST pass every file the model needs, in order, via `files.model`. For sharded GGUF models this includes the `.tensors.txt` companion file followed by each `.gguf` shard in numerical order.\n\n```js\nconst path = require('bare-path')\n\nconst dir = '/path/to/models'\nconst model = new GGMLBert({\n  files: {\n    model: [\n      path.join(dir, 'gte-large.Q2_K.tensors.txt'),\n      path.join(dir, 'gte-large.Q2_K-00001-of-00005.gguf'),\n      path.join(dir, 'gte-large.Q2_K-00002-of-00005.gguf'),\n      path.join(dir, 'gte-large.Q2_K-00003-of-00005.gguf'),\n      path.join(dir, 'gte-large.Q2_K-00004-of-00005.gguf'),\n      path.join(dir, 'gte-large.Q2_K-00005-of-00005.gguf')\n    ]\n  },\n  config: { device: 'gpu', gpu_layers: '99' },\n  logger: console,\n  opts: { stats: true }\n})\n```\n\nRules for the `files.model` array:\n\n* **Order matters.** The `.tensors.txt` file must come first, then shards in ascending numerical order (`00001-of-00005`, `00002-of-00005`, ...).\n* **All shards are required.** Missing any shard or the `.tensors.txt` companion will fail loading.\n* **Non-sharded models** pass a single absolute path: `files: { model: [modelPath] }`.\n* **Absolute paths only.** The addon reads each file directly via `bare-fs` during `load()`.\n\n### 3. Create `config`\n\nThe `config` is a plain JS object whose keys are forwarded directly to the native backend. All values must be strings (the native layer parses them with `getSubmap`).\n\n| Key              | Range / Type                                  | Default       | Description                                                                              |\n|------------------|-----------------------------------------------|---------------|------------------------------------------------------------------------------------------|\n| `device`         | `\"gpu\"` \\| `\"cpu\"`                            | `\"gpu\"`       | Device to run inference on                                                               |\n| `gpu_layers`     | string of integer                             | `\"0\"`         | Number of model layers to offload to GPU                                                 |\n| `batch_size`     | string of integer                             | `\"2048\"`      | Tokens processed per batch (input throughput)                                            |\n| `ctx_size`       | string of integer                             | model's trained context size (`n_ctx_train`) | Runtime context window in tokens (llama.cpp `n_ctx`); oversized values are capped to the model's trained context |\n| `pooling`        | `\"none\"` \\| `\"mean\"` \\| `\"cls\"` \\| `\"last\"` \\| `\"rank\"` | model default | Pooling strategy used to collapse token embeddings into a single sequence vector        |\n| `attention`      | `\"causal\"` \\| `\"non-causal\"`                  | model default | Attention type                                                                            |\n| `embd_normalize` | string of integer                             | `\"2\"`         | Embedding normalization (`-1` = none, `0` = max abs int16, `1` = taxicab, `2` = euclidean, `>2` = p-norm) |\n| `flash_attn`     | `\"on\"` \\| `\"off\"` \\| `\"auto\"`                 | `\"auto\"`      | Enable / disable flash attention                                                         |\n| `main-gpu`       | string of integer \\| `\"integrated\"` \\| `\"dedicated\"` | —      | GPU selection for multi-GPU systems                                                      |\n| `verbosity`      | string of `\"0\"`–`\"3\"` (0=ERROR, 1=WARNING, 2=INFO, 3=DEBUG) | `\"0\"` | Native logging verbosity. The `addonLogging.setLogger` callback receives only messages at or above this threshold. Use `\"2\"` for llama.cpp INFO logs and `\"3\"` for DEBUG logs. The verbosity level is process-global and is updated each time a model is constructed, so the most recently constructed model's `config.verbosity` wins for all subsequent native log dispatch. |\n\n#### Native addon logging\n\n`@qvac/embed-llamacpp/addonLogging` exposes the native C++ logger:\n\n```js\nconst { setLogger, releaseLogger } = require('@qvac/embed-llamacpp/addonLogging')\n\nsetLogger((priority, message) => {\n  console.log(priority, message)\n})\n```\n\nThe callback is wired before model load, but it still follows `config.verbosity`.\nWith the default `\"0\"` setting, only native `ERROR` messages are delivered. Set\n`config.verbosity` to `\"2\"` to receive llama.cpp `INFO` diagnostics, or `\"3\"` for\n`DEBUG` messages. The verbosity level is process-global and is updated each\ntime a model is constructed, so when multiple models are loaded the most\nrecently constructed model's `config.verbosity` wins for all subsequent\nnative log dispatch. Some startup diagnostics printed directly by llama.cpp\nmay still appear on `stderr` before the addon installs its callback.\n\n#### IGPU/GPU  selection logic:\n\n| Scenario                       | main-gpu not specified                | main-gpu: `\"dedicated\"`             | main-gpu: `\"integrated\"`           |\n|---------------------------------|---------------------------------------|-------------------------------------|-------------------------------------|\n| Devices considered              | All GPUs (dedicated + integrated)     | Only dedicated GPUs                 | Only integrated GPUs                |\n| System with iGPU only           | ✅ Uses iGPU                          | ❌ Falls back to CPU                | ✅ Uses iGPU                        |\n| System with dedicated GPU only  | ✅ Uses dedicated GPU                 | ✅ Uses dedicated GPU               | ❌ Falls back to CPU                |\n| System with both                | ✅ Uses dedicated GPU (preferred)     | ✅ Uses dedicated GPU               | ✅ Uses integrated GPU              |\n\n\n### 4. Instantiate the model\n\n```js\nconst model = new GGMLBert(args)\n```\n\n### 5. Load the model\n\n```js\nawait model.load()\n```\n\n`load()` takes no arguments. The addon streams each file listed in `files.model` directly from disk via `bare-fs` and then activates the model. There is no data loader, no progress callback, and no download step — the caller is responsible for ensuring the files already exist at the paths passed to the constructor.\n\n### 6. Generate embeddings for input sequence\n\nThe model outputs a vector for the input sequence.\n\n```js\nconst query = 'Hello, can you suggest a game I can play with my 1 year old daughter?'\nconst response = await model.run(query)\nconst embeddings = await response.await()\n```\n\nWhen `opts.stats` is enabled, `response.stats` includes runtime metrics such as `total_tokens`, `total_time_ms`, `tokens_per_second`, `context_size`, `trained_context_size`, and `backendDevice` (`\"cpu\"` or `\"gpu\"`). `context_size` is the active runtime llama context size, while `trained_context_size` is the model's trained context size. `backendDevice` reflects the resolved device used at runtime after backend selection/fallback logic, not only the requested config.\n\n### 7. Release Resources\n\nUnload the model when finished:\n\n```javascript\ntry {\n  await model.unload()\n} catch (error) {\n  console.error('Failed to unload model:', error)\n}\n```\n\n### API behavior by state\n\nThe following table describes the expected behavior of `run` and `cancel` depending on the current state (idle vs a job running). `cancel` can be called on the model (`model.cancel()`) or on the response (`response.cancel()`); both target the same underlying job.\n\n| Current state | Action called | What happens |\n|---------------|----------------|----------------------------------------------------------------|\n| idle          | run            | **Allowed** — starts inference, returns `QvacResponse`        |\n| idle          | cancel         | **Allowed** — no-op (no job to cancel); Promise resolves       |\n| run           | run            | **Throw** — second `run()` throws `\"Cannot set new job: a job is already set or being processed\"` once it reaches the head of the queue; previous response must settle first. |\n| run           | cancel         | **Allowed** — cancels current job; Promise resolves when job has stopped      |\n\nA second `run()` while a job is active is serialized by `exclusiveRunQueue` — it waits in the queue until the previous `_runInternal` returns, then enters the busy guard. Because the busy flag (`_hasActiveResponse`) is only cleared when the previous `response.await()` settles, the second call rejects with `\"Cannot set new job: a job is already set or being processed\"`. The queue eliminates race conditions but does not retry or buffer results; callers must wait for the previous `response.await()` to settle (or call `model.cancel()`) before issuing the next request.\n\n**Cancellation API:** Prefer cancelling from the model: `await model.cancel()`. This cancels the current job and the Promise resolves when the job has actually stopped (future-based in C++). You can also call `await response.cancel()` on the value returned by `run()`; it is equivalent and targets the same job. Both are no-op when idle.\n\n## Assessing fit\n\n`assessFit` projects a load against the memory free right now. It reads GGUF metadata and never weight data, so the registry's weightless copy of a model answers the same as the model itself and the projection can run before anything is downloaded.\n\n```js\nconst EmbedLlamacpp = require('@qvac/embed-llamacpp')\n\nconst fit = EmbedLlamacpp.assessFit({\n  modelPath: '/models/embed.gguf',\n  params: { 'batch-size': '1024' }\n})\n\nfit.status // 'fits' | 'does-not-fit' | 'error'\nfit.reason // 'fits', 'does-not-fit', 'model-unreadable', 'no-backend-device' or 'unsupported-config'\nfit.gpuLayers // what fits, which is not always what the request asked for\nfit.ctxSize\nfit.devices // one row per device the model was assigned to, then a `host` row\nfit.deviceBytes // model, context and compute summed across the devices, host excluded\nfit.hostBytes // the same, for the trailing host row\n```\n\n`params` takes the load in llama's own CLI spelling without the leading `--`, exactly as the loader takes it: `gpu-layers`, `tensor-split`, `batch-size` and the rest. Each is dispatched through llama's argument table, so a placement pinned there reaches the projection. A setting llama does not recognise, or a flag asked to be off that can only assert itself, is `status: \"error\"` with `unsupported-config`.\n\nPinning `gpu-layers` fixes the placement: the fitter refuses to move layers off a device the load claimed, so the answer is whether that exact placement fits, never a reduced one. Leave it unset for a projection that can place the model itself.\n\nThe projection applies the embedding load's own normalization: the micro-batch follows `batch-size`, a single sequence uses a unified KV cache, and the context is pinned to the model's trained context, or capped at it.\n\n`minCtxSize` sets a floor the fitter may not reduce the context below, defaulting to llama's own `fit-ctx` of 4096. `marginBytes` is the memory to leave free on every device, defaulting to llama's `fit-target` of 1 GiB per device. `backendsDir` is where the dynamically-loaded ggml backends live.\n\nA model the fitter cannot read is `status: \"error\"`; only a broken request throws.\n\n## IdMapIndex vector database\n\n`IdMapIndex` is a synchronous CPU vector index with stable unsigned 64-bit IDs. Vector-only consumers should use the subpath export so importing the index does not load the embedding-model runtime:\n\n```javascript\nconst IdMapIndex = require('@qvac/embed-llamacpp/idMapIndex')\n\nconst index = new IdMapIndex({ dim: 768, storage: 'turbovec-q4' })\ntry {\n  index.addWithIds(vectors, new BigUint64Array([1n, 2n]))\n  index.prepare()\n  const { ids, scores } = index.search(query, 2)\n} finally {\n  index.dispose()\n}\n```\n\n`vectors` contains row-major `Float32Array` data with one `dim`-sized row per ID. Queries use the same layout and may contain multiple rows. Search uses dot-product similarity; L2-normalize indexed vectors and queries first when cosine similarity is required. Results are ordered by descending score and then ascending ID.\n\n### Storage modes\n\n| Storage | Effective bits | Snapshot | Mmap | Delta log |\n|---------|---------------:|----------|------|-----------|\n| `f32` | 32 | v2 | Yes | Yes |\n| `q8` | 8 | v2 | Yes | Yes |\n| `q4` | 4 | v2 | Yes | Yes |\n| `turbovec-q4` | 4 | v3 | No | No |\n| `turbovec-q2` | 2 | v3 | No | No |\n\nThe default storage is `q8`. `bitWidth: 2` selects `turbovec-q2`, while `bitWidth: 4` selects generic `q4`; use `storage: 'turbovec-q4'` explicitly for TurboVec Q4.\n\nTurboVec requires a 64-bit target and dimensions divisible by 8 and no greater than 1,024. It provides approximate rotated/quantized dot-product search. TurboVec snapshots support `write()` and `load()`, but reject mmap loading, delta-log loading, logged mutations, and delta compaction.\n\n### Operations and lifecycle\n\n- `addWithIds()`, `remove()`, `contains()`, and `compact()` mutate or inspect the index.\n- `search()`, `searchFiltered()`, and `prepareFilter()` perform full-scan retrieval.\n- `buildIvf()` enables approximate `searchIvf()` retrieval. IVF state is in-memory only and must be rebuilt after a mutation or snapshot load.\n- `write()` and `load()` persist all storage modes. Generic `f32`, `q8`, and `q4` additionally support `loadMmap()`, `loadWithDelta()`, `addLogged()`, `removeLogged()`, and `compactDelta()`.\n- Mutations invalidate prepared filters and IVF state.\n- Call `dispose()` on indexes and prepared filters when finished. Disposal is idempotent.\n\nSee [RAG with TurboVec](./examples/ragWithTurboVec.js) for an end-to-end embedding and retrieval example.\n\n## Quickstart Example\n\nClone the repository and navigate to it:\n```bash\ncd embed-llamacpp\n```\n\nInstall dependencies:\n```bash\nnpm install\n```\n\nRun the quickstart example (uses examples/quickstart.js):\n```bash\nnpm run quickstart\n```\n\n## Other Examples\n\n- [Batch Inference](./examples/batchInference.js) – Demonstrates running multiple prompts at once using batch inference.\n- [Native Logging](./examples/nativelog.js) – Demonstrates C++ addon logging integration.\n- [RAG with TurboVec](./examples/ragWithTurboVec.js) – Embeds document chunks, retrieves relevant context with `IdMapIndex`, and prepares it for an LLM.\n\nRun the TurboVec RAG retrieval example on a 64-bit desktop:\n```bash\nbare examples/ragWithTurboVec.js\n```\n\n## Benchmarking\n\nWe conduct rigorous benchmarking of our embedding models to evaluate their retrieval effectiveness and computational efficiency across diverse tasks and datasets. Our evaluation framework incorporates standard information retrieval metrics and system performance indicators to provide a holistic view of model quality.\n\n### Running Benchmarks\n\nFor instructions on running benchmarks yourself, see the [Benchmark Runner Documentation](./benchmarks/README.md).\n\nThe benchmarking covers:\n\n* **Retrieval Quality**:\n\n  * **nDCG\\@k**: Quality of ranked results based on relevance and position\n  * **MRR\\@k**: Position of the first relevant result per query\n  * **Recall\\@k**: Coverage of relevant results in the top *k*\n  * **Precision\\@k**: Proportion of top *k* results that are relevant\n\nResults are continuously updated with new releases to ensure up-to-date performance insights.\n\n## Tests\n\nIntegration tests are located in [`test/integration/`](./test/integration/) and cover core embed functionality: single-file model load → embed → unload, multi-instance concurrency (two embed instances running simultaneously, repeated load/unload cycles, unloading one instance while another processes), and the public `run()` / `cancel()` lifecycle. These tests help prevent regressions and ensure the library remains stable as contributions are made to the project.\n\nC++ unit tests live under [`addon/test/`](./addon/test/) and exercise the native components at a lower level, including backend selection, single-step inference, end-to-end embedding generation, and pooling. These tests validate the native implementation and help catch issues early in development.\n\n> **Note:** This package is *embeddings only*. There is no tool-calling, multimodal, KV-cache, or chat-template support — those features belong to the LLM addon ([`@qvac/llm-llamacpp`](../llm-llamacpp/)).\n\n## Glossary\n\n* **Bare Runtime** - Small and modular JavaScript runtime for desktop and mobile. [Learn more](https://docs.pears.com/reference/bare-overview).\n\n## License\n\nThis project is licensed under the Apache-2.0 [License](./LICENSE) – see the LICENSE file for details.\n\n_For questions or issues, please open an issue on the GitHub repository._\n","readmeFilename":"README.md"}