@claude-flow/embeddings/dist/ruvector-onnx-embedding-service.d.ts

V3 Embedding Service - OpenAI, Transformers.js, Agentic-Flow (ONNX), Mock providers with hyperbolic embeddings, normalization, and chunking

/**
 * RuvectorOnnxEmbeddingService — ADR-121 Phase 1.
 *
 * Drop-in replacement for the legacy `TransformersEmbeddingService` that
 * uses `ruvector-onnx-embeddings-wasm` instead of the retired
 * `@xenova/transformers@2.x`. Matches the existing IEmbeddingService
 * contract so consumers don't migrate: opt in via
 * `provider: 'ruvector-onnx'` on `createEmbeddingService`.
 *
 * Why this exists:
 *  - `@xenova/transformers` was renamed upstream to
 *    `@huggingface/transformers@4.x` and the v2 line is unmaintained.
 *    Carrying it in the runtime tree of every embedding install is a
 *    growing liability.
 *  - `ruvector-onnx-embeddings-wasm` is pure WASM with SIMD + parallel
 *    workers, smaller install footprint (no native bindings, no
 *    Python-style model-conversion tooling shipped), and runs
 *    identically in Node, browsers, Workers, and Deno.
 *  - Same model catalog (all-MiniLM-L6-v2, BGE, E5, GTE) so output
 *    parity with the Xenova path is achievable to >0.999 cosine
 *    correlation on a fixed test corpus — that's the Phase 1
 *    acceptance gate.
 *
 * Optional peer dep: when `ruvector-onnx-embeddings-wasm` isn't
 * installed, construction throws a clear error naming the dep and the
 * single install command. Callers that don't opt into `ruvector-onnx`
 * are unaffected.
 */
import type { EmbeddingProvider, RuvectorOnnxEmbeddingConfig, EmbeddingResult, BatchEmbeddingResult } from './types.js';
import { BaseEmbeddingService } from './embedding-service-base.js';
export declare class RuvectorOnnxEmbeddingService extends BaseEmbeddingService {
    readonly provider: EmbeddingProvider;
    private embedder;
    private readonly modelName;
    private readonly normalizeOutput;
    private dimensionCache;
    constructor(config: RuvectorOnnxEmbeddingConfig);
    private initialize;
    embed(text: string): Promise<EmbeddingResult>;
    embedBatch(texts: string[]): Promise<BatchEmbeddingResult>;
    /**
     * L2-normalize a Float32Array view into a freshly-allocated
     * Float32Array. The WASM embedder can normalize internally via
     * WasmEmbedderConfig, but we do it here so the cached vector is
     * always normalized regardless of model defaults — keeps similarity
     * scoring predictable across providers.
     */
    private l2NormalizeView;
    /**
     * Release the WASM embedder. Idempotent; safe to call multiple times.
     */
    close(): Promise<void>;
}
/**
 * Factory helper — mirrors the createEmbeddingService convention so
 * callers can opt in without instantiating directly.
 */
export declare function createRuvectorOnnxEmbeddingService(config?: Omit<RuvectorOnnxEmbeddingConfig, 'provider'>): RuvectorOnnxEmbeddingService;
//# sourceMappingURL=ruvector-onnx-embedding-service.d.ts.map