{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/multimodal-search",
  "headline": "Multimodal Search: Cross-Modal Retrieval, Product Search, and Multimodal Embeddings",
  "description": "Multimodal search enables \"find me products that look like this photo\" or \"find videos about this topic\" -- bridging the gap between different media types through a shared embedding space. From e-commerce product search to enterprise knowledge retrieval, multimodal embeddings let users search across text, images, video, and audio with a single query, in any modality.",
  "dateCreated": "2026-05-24T02:49:13.639Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "MMSearch: Benchmarking the Potential of Large Multimodal Models as Search Engines",
      "sameAs": "https://github.com/CaraJ7/MMSearch"
    },
    {
      "@type": "CreativeWork",
      "name": "Multimodal Search: Searching with Semantic and Visual Understanding Using Multimodal Embeddings",
      "sameAs": "https://opensearch.org/blog/multimodal-semantic-search/"
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}