Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

examples/edge-full/pkg/onnx/ruvector_onnx_embeddings_wasm.d.ts

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+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * Strategy for pooling token embeddings into a single sentence embedding
+ */
+export enum PoolingStrategy {
+  /**
+   * Average all token embeddings (most common)
+   */
+  Mean = 0,
+  /**
+   * Use only the [CLS] token embedding
+   */
+  Cls = 1,
+  /**
+   * Take the maximum value across all tokens for each dimension
+   */
+  Max = 2,
+  /**
+   * Mean pooling normalized by sqrt of sequence length
+   */
+  MeanSqrtLen = 3,
+  /**
+   * Use the last token embedding (for decoder models)
+   */
+  LastToken = 4,
+}
+
+export class WasmEmbedder {
+  free(): void;
+  [Symbol.dispose](): void;
+  /**
+   * Get maximum sequence length
+   */
+  maxLength(): number;
+  /**
+   * Compute similarity between two texts
+   */
+  similarity(text1: string, text2: string): number;
+  /**
+   * Generate embeddings for multiple texts
+   */
+  embedBatch(texts: string[]): Float32Array;
+  /**
+   * Create embedder with custom configuration
+   */
+  static withConfig(model_bytes: Uint8Array, tokenizer_json: string, config: WasmEmbedderConfig): WasmEmbedder;
+  /**
+   * Create a new embedder from model and tokenizer bytes
+   *
+   * # Arguments
+   * * `model_bytes` - ONNX model file bytes
+   * * `tokenizer_json` - Tokenizer JSON configuration
+   */
+  constructor(model_bytes: Uint8Array, tokenizer_json: string);
+  /**
+   * Get the embedding dimension
+   */
+  dimension(): number;
+  /**
+   * Generate embedding for a single text
+   */
+  embedOne(text: string): Float32Array;
+}
+
+export class WasmEmbedderConfig {
+  free(): void;
+  [Symbol.dispose](): void;
+  /**
+   * Set pooling strategy (0=Mean, 1=Cls, 2=Max, 3=MeanSqrtLen, 4=LastToken)
+   */
+  setPooling(pooling: number): WasmEmbedderConfig;
+  /**
+   * Set whether to normalize embeddings
+   */
+  setNormalize(normalize: boolean): WasmEmbedderConfig;
+  /**
+   * Set maximum sequence length
+   */
+  setMaxLength(max_length: number): WasmEmbedderConfig;
+  /**
+   * Create a new configuration
+   */
+  constructor();
+}
+
+/**
+ * Compute cosine similarity between two embedding vectors (JS-friendly)
+ */
+export function cosineSimilarity(a: Float32Array, b: Float32Array): number;
+
+/**
+ * Initialize panic hook for better error messages in WASM
+ */
+export function init(): void;
+
+/**
+ * L2 normalize an embedding vector (JS-friendly)
+ */
+export function normalizeL2(embedding: Float32Array): Float32Array;
+
+/**
+ * Check if SIMD is available (for performance info)
+ * Returns true if compiled with WASM SIMD128 support
+ */
+export function simd_available(): boolean;
+
+/**
+ * Get the library version
+ */
+export function version(): string;