ai-functions/dist/embeddings.d.ts

Core AI primitives for building intelligent applications

/**
 * Embedding utilities from AI SDK
 *
 * Re-exports embed, embedMany, and cosineSimilarity from the Vercel AI SDK
 * with additional convenience wrappers.
 *
 * Default model: Cloudflare Workers AI @cf/baai/bge-m3
 *
 * @packageDocumentation
 */
export { embed, embedMany, cosineSimilarity } from 'ai';
export type { EmbeddingModel, Embedding } from 'ai';
export { cloudflare, cloudflareEmbedding, DEFAULT_CF_EMBEDDING_MODEL } from 'ai-providers/cloudflare';
/**
 * Get the default embedding model (Cloudflare @cf/baai/bge-m3)
 */
export declare function getDefaultEmbeddingModel(): import("ai").EmbeddingModel<string>;
/**
 * Embed a single value using the default Cloudflare model
 *
 * @example
 * ```ts
 * import { embedText } from 'ai-functions'
 *
 * const { embedding } = await embedText('hello world')
 * ```
 */
export declare function embedText(value: string): Promise<import("ai").EmbedResult>;
/**
 * Embed multiple values using the default Cloudflare model
 *
 * @example
 * ```ts
 * import { embedTexts } from 'ai-functions'
 *
 * const { embeddings } = await embedTexts(['doc1', 'doc2', 'doc3'])
 * ```
 */
export declare function embedTexts(values: string[]): Promise<import("ai").EmbedManyResult>;
/**
 * Result of an embed operation
 */
export interface EmbedResult<T = string> {
    /** The original input value */
    value: T;
    /** The generated embedding vector */
    embedding: number[];
    /** Token usage */
    usage: {
        tokens: number;
    };
}
/**
 * Result of an embedMany operation
 */
export interface EmbedManyResult<T = string> {
    /** The original input values */
    values: T[];
    /** The generated embedding vectors */
    embeddings: number[][];
    /** Token usage */
    usage: {
        tokens: number;
    };
}
/**
 * Find the most similar items to a query embedding
 *
 * @example
 * ```ts
 * import { embed, embedMany, findSimilar } from 'ai-functions'
 *
 * const documents = ['doc1', 'doc2', 'doc3']
 * const { embeddings } = await embedMany({ model, values: documents })
 * const { embedding: queryEmbedding } = await embed({ model, value: 'search query' })
 *
 * const results = findSimilar(queryEmbedding, embeddings, documents, { topK: 2 })
 * // [{ item: 'doc1', score: 0.95, index: 0 }, { item: 'doc2', score: 0.82, index: 1 }]
 * ```
 */
export declare function findSimilar<T>(queryEmbedding: number[], embeddings: number[][], items: T[], options?: {
    /** Number of results to return (default: 10) */
    topK?: number;
    /** Minimum similarity score (default: 0) */
    minScore?: number;
}): Array<{
    item: T;
    score: number;
    index: number;
}>;
/**
 * Calculate pairwise similarities between all embeddings
 *
 * @example
 * ```ts
 * const matrix = pairwiseSimilarity(embeddings)
 * // matrix[i][j] = similarity between embeddings[i] and embeddings[j]
 * ```
 */
export declare function pairwiseSimilarity(embeddings: number[][]): number[][];
/**
 * Cluster embeddings by similarity using a simple threshold-based approach
 *
 * @example
 * ```ts
 * const clusters = clusterBySimilarity(embeddings, items, { threshold: 0.8 })
 * // [[item1, item2], [item3], [item4, item5, item6]]
 * ```
 */
export declare function clusterBySimilarity<T>(embeddings: number[][], items: T[], options?: {
    /** Similarity threshold for clustering (default: 0.8) */
    threshold?: number;
}): T[][];
/**
 * Average multiple embeddings into a single embedding
 * Useful for creating document embeddings from chunk embeddings
 */
export declare function averageEmbeddings(embeddings: number[][]): number[];
/**
 * Normalize an embedding to unit length
 */
export declare function normalizeEmbedding(embedding: number[]): number[];
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