find-image

import { MultimodalEmbeddingStore, cosineSimilarity, type StoredMultimodalEmbedding } from "../embeddings/index.js";

export type MultimodalQueryMode = "text" | "image" | "mixed";

export interface MultimodalSearchHit {
  entry: StoredMultimodalEmbedding;
  similarity: number;
  score: number;
}

export function searchMultimodalEmbeddings(
  queryEmbedding: number[],
  store: MultimodalEmbeddingStore,
  model: string,
  dimension: number,
  options: { maxResults: number; minScore: number; maxScoreExclusive?: number }
): MultimodalSearchHit[] {
  const entries = store.getAllEmbeddings(model, dimension);
  const hits: MultimodalSearchHit[] = [];
  for (const entry of entries) {
    if (entry.embedding.length !== queryEmbedding.length) continue;
    const similarity = cosineSimilarity(queryEmbedding, entry.embedding);
    const score = Math.round(Math.max(0, Math.min(1, similarity)) * 100);
    if (score >= options.minScore) {
      if (options.maxScoreExclusive !== undefined && score >= options.maxScoreExclusive) continue;
      hits.push({ entry, similarity, score });
    }
  }

  hits.sort((a, b) => b.score - a.score || b.similarity - a.similarity);
  return hits.slice(0, options.maxResults);
}