betto_inferencing 0.1.0-dev.2

ONNX Runtime inference and embedding models for dense text retrieval.

betto_inferencing

ONNX Runtime inference and embedding models for dense text retrieval. Part of the Bettongia open-source family.

Provides OnnxEmbeddingModel backed by BGE Small En v1.5 via betto_onnxrt, a BERT WordPiece tokenizer, SQ8 vector quantization helpers, and a validated model catalog with download-on-demand.

Features

• OnnxEmbeddingModel — loads a BGE embedding model and embeds text into L2-normalised float32 vectors. Supports download-on-demand (SHA-256 verified) or loading from an explicit file path. Accepts an optional tokenizer parameter to swap in a custom word segmentation backend (e.g. IcuTokenizer from package:betto_icu).
• EmbeddingModel — abstract interface, allowing the consuming application or database to depend on the interface without the FFI-heavy implementation.
• BertTokenizer — BERT WordPiece tokenizer loaded from a vocab.txt file. Normalises, segments, and assembles [CLS]/[SEP]-padded token ID sequences ready for ORT inference. Word segmentation delegates to a betto_lexical Tokenizer; defaults to RegExpTokenizer and accepts IcuTokenizer as a drop-in replacement.
• TokenizerOutput — value type returned by BertTokenizer.encode and XlmRobertaTokenizer.encode. Carries inputIds, attentionMask, and tokenTypeIds as parallel Int64List arrays, plus a truncated flag.
• XlmRobertaTokenizer — XLM-RoBERTa-family SentencePiece/Unigram tokenizer (e.g. multilingual-e5-small), loaded from a HuggingFace tokenizer.json file. See "Why not dart_sentencepiece_tokenizer alone" below for why this isn't a thin wrapper around that package.
• ModelCatalog — allowlist of validated embedding models with download-on-demand gating. ModelCatalog.lookup(id) returns the ModelSpec for a validated model; ModelCatalog.isKnown(id) checks registration without validating; ModelCatalog.defaultModelId is 'bge-small-en-v1.5'. Currently validated: bge-small-en-v1.5 (384-d). Registered but not yet validated: bge-m3-v1.0 (1024-d, multilingual).
• quantise / dequantise — SQ8 (scalar 8-bit) vector quantization helpers for compact storage of embedding indexes.
• Re-exports from betto_onnxrt: ModelDownloader, ModelSpec, ModelFile, ResolvedModel, DownloadProgress.

Platform support

This package is native-only. It must not be imported on the web platform.

Platform	Support	Notes
macOS	arm64 only	Intel (x86_64) is not supported
Linux	x64, aarch64
Windows	x64, arm64
Android	arm64-v8a, armeabi-v7a, x86_64, x86	Requires minSdkVersion 35
iOS	arm64	Requires the betto_onnxrt_ios companion plugin (iOS ≥ 16)
Web	Not supported

The ONNX Runtime shared library is staged at build time by the betto_onnxrt native-assets build hook — no manual download or bundling is required.

iOS setup

iOS ORT support requires the betto_onnxrt_ios Flutter plugin, which statically links the ORT XCFramework into the host app via SPM. No CocoaPods changes are needed. Add it alongside betto_inferencing:

dependencies:
  betto_inferencing: ^0.1.0-dev.2
  betto_onnxrt_ios: ^0.1.0-dev.1

Requires Flutter ≥ 3.27.0 and iOS ≥ 16.

Android

Set minSdkVersion to at least 35 in android/app/build.gradle:

android {
    defaultConfig {
        minSdk = 35
    }
}

Installation

dependencies:
  betto_inferencing: ^0.1.0-dev.2

Note: betto_inferencing uses native-assets build hooks via betto_onnxrt. Run dart test (or flutter test) from inside the package directory (not the workspace root) so the hook fires and the ORT binary is placed correctly.

Usage

Embed text (download-on-demand)

import 'package:betto_inferencing/betto_inferencing.dart';

final model = await OnnxEmbeddingModel.load(
  cacheDir: '/path/to/model/cache',
  onProgress: (received, total) {
    final pct = total > 0 ? (received * 100 ~/ total) : 0;
    print('Downloading: $pct% ($received / $total bytes)');
  },
);

try {
  final (embedding, truncated) = await model.embed('semantic search query');
  print('Dimensions: ${embedding.length}'); // 384
  print('Truncated: $truncated');
} finally {
  model.dispose();
}

On the first call ModelDownloader fetches and SHA-256-verifies the BGE Small En v1.5 model (~127 MB). Subsequent calls reuse the cached files.

Note: OnnxEmbeddingModel.load throws ArgumentError if neither modelPath nor cacheDir is supplied — there is no bundled model asset.

Embed text (explicit path)

final model = await OnnxEmbeddingModel.load(
  modelPath: '/path/to/model.onnx',
);

Implement the interface

EmbeddingModel decouples the consuming application from this package's FFI dependency:

import 'package:betto_inferencing/betto_inferencing.dart';

class MyRetriever {
  MyRetriever(this._model);

  final EmbeddingModel _model;

  Future<List<double>> queryVector(String text) async {
    final (embedding, _) = await _model.embed(text);
    return embedding.toList();
  }
}

Look up a catalog model

// Lookup throws if the model is unknown or not yet validated.
final spec = ModelCatalog.lookup('bge-small-en-v1.5');
print(spec.meta['dimensions']); // 384

// Check registration without validation gating.
print(ModelCatalog.isKnown('bge-m3-v1.0')); // true

// Iterate all registered models (validated and not yet validated).
for (final spec in ModelCatalog.all) {
  print('${spec.id} — ${spec.meta['dimensions']}d');
}
// bge-small-en-v1.5 — 384d
// bge-m3-v1.0 — 1024d

SQ8 quantization

import 'package:betto_inferencing/betto_inferencing.dart';

final (embedding, _) = await model.embed('hello world');
final quantised = quantise(embedding);   // Uint8List, 384 bytes
final restored = dequantise(quantised);  // Float32List

Examples

The example/ directory contains runnable examples that walk through each feature in the package. Set BETTO_CACHE to a persistent directory so the model (~127 MB) is downloaded only once across all examples:

export BETTO_CACHE=$HOME/.cache/betto_examples

File	What it shows	Download required
example.dart	Basic load + single embed call with download-on-demand	Yes
model_catalog.dart	List all registered models, inspect ModelSpec metadata, isKnown, and error handling for unknown / unvalidated IDs	No
tokenizer.dart	BertTokenizer standalone: encode, token ID inspection, decode, WordPiece sub-token splitting, truncation detection	Yes (vocab only)
embed_and_compare.dart	Embed a query and several documents, rank by cosine similarity (dot product of L2-normalised vectors)	Yes
sq8_quantisation.dart	quantise/dequantise round-trip: 4× storage reduction, per-element reconstruction error, similarity score preservation	Yes

Run any example with:

dart run example/<name>.dart

Models

Model ID	Dimensions	Language	Status
bge-small-en-v1.5	384	English	✅ Validated
bge-m3-v1.0	1024	Multilingual	Registered, not yet validated

Why not dart_sentencepiece_tokenizer alone

XlmRobertaTokenizer depends on dart_sentencepiece_tokenizer (MIT licensed — compatible with this project's Apache-2.0 license; no NOTICE entry is needed for using it as a normal dependency) for vocabulary loading, Unigram Viterbi decoding, and BOS/EOS post-processing, but cannot use it as-is for the normalization step. Two independent defects were found in its HuggingFace tokenizer.json-loading path while integrating multilingual-e5-small (full investigation: plan_0_06_wi11_xlmr_tokenizer.md in the kmdb repository):

1. It never applies the Precompiled charsmap normalizer on the JSON loading path. tokenizer.json's normalizer field is a Sequence whose first entry has "type": "Precompiled" and a precompiled_charsmap key holding a base64-encoded Darts double-array trie (the substitution table SentencePiece bakes into a trained model — fullwidth-to-ASCII folding, ellipsis expansion, etc.; not plain NFKC). dart_sentencepiece_tokenizer's huggingface_json.dart parses this section's other flags correctly but never passes a precompiledCharsmap value through — it is silently dropped. (The charsmap data itself is populated elsewhere, but only on the separate native .model protobuf loading path, which this project does not use — see the plan's Investigation section for the exact source lines.) No existing Dart implementation of this trie format could be found anywhere, so this package implements its own: CharsmapTrie (lib/src/charsmap_trie.dart), whose traversal algorithm structure is ported from the Rust spm_precompiled crate — see NOTICE for attribution.
2. Its HuggingFace-JSON metadata parser also mis-derives whitespace/dummy-prefix configuration for tokenizer.json files that put it in pre_tokenizer rather than normalizer. multilingual-e5-small's tokenizer.json declares its dummy-prefix and whitespace-escaping behaviour entirely via a pre_tokenizer.Metaspace entry ({"type": "Metaspace", "replacement": "▁", "add_prefix_space": true}), which the parser never reads (it only inspects normalizer) — so all three of its derived flags come back false, making its own SpNormalizer an unconditional pass-through for this file. This is a second, independent defect from the charsmap-drop above, found during this package's own investigation rather than documented upstream.

XlmRobertaTokenizer works around both by composing its own charsmap-substitution, whitespace-collapse, and Metaspace-escaping steps before handing already-normalized text to dart_sentencepiece_tokenizer's public encode() — composition, not a fork: no dart_sentencepiece_tokenizer source is modified. If either upstream defect is fixed in a future release, this package's manual steps become redundant but harmless (idempotent no-ops on already-correct input) — no urgency to remove them, though doing so is a reasonable future cleanup.

License

Apache 2.0 — see LICENSE. Third-party attribution for ported algorithm structure is recorded in NOTICE.