feat: migrate embeddings to Candle WASM + remove semantic type inference

Major architectural changes:

1. EMBEDDINGS ENGINE (ONNX → Candle WASM):
   - Replace ONNX Runtime with Rust Candle compiled to WASM
   - Embedded model in WASM binary (no external downloads)
   - Quantized Q8 precision with <50MB memory footprint
   - Zero-download, offline-first operation
   - Same embedding quality (all-MiniLM-L6-v2)

2. REMOVE SEMANTIC TYPE INFERENCE:
   - Delete embeddedKeywordEmbeddings.ts (14MB of pre-computed embeddings)
   - Remove typeAwareQueryPlanner.ts and semanticTypeInference.ts
   - Remove VerbExactMatchSignal (uses keyword embeddings)
   - Update SmartRelationshipExtractor to 3 signals (55%/30%/15% weights)

API CHANGES (requires v7.0.0):
- Removed: inferTypes(), inferNouns(), inferVerbs(), inferIntent()
- Removed: getSemanticTypeInference(), SemanticTypeInference class
- Removed: TypeInference, SemanticTypeInferenceOptions types

Users can still use natural language queries in find() - they just
need to specify type explicitly for type-optimized searches.

PACKAGE SIZE IMPACT:
- Compressed: 90.1 MB → 86.2 MB (-4.3%)
- Uncompressed: 114.4 MB → 100.3 MB (-12%)
- ~448K lines of code removed

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
David Snelling 2026-01-06 12:52:34 -08:00
parent 81cd16e41b
commit da7d2ed29d
60 changed files with 3887 additions and 448557 deletions

View file

@ -1,13 +1,11 @@
/**
* Asset Loader
*
* Resolves paths to model files (ONNX model, vocabulary) across environments.
* Handles Node.js, Bun, and bundled scenarios.
* @deprecated This class is no longer used. Model weights are now embedded
* in the Candle WASM binary at compile time. Kept for backward compatibility.
*
* Asset Resolution Order:
* 1. Environment variable: BRAINY_MODEL_PATH
* 2. Package-relative: node_modules/@soulcraft/brainy/assets/models/
* 3. Project-relative: ./assets/models/
* Previously: Resolved paths to ONNX model files across environments.
* Now: Use CandleEmbeddingEngine which loads embedded model automatically.
*/
import { MODEL_CONSTANTS } from './types.js'