{
  "docId": "019dd923-5eff-723e-9be4-6cd7e97a57b9",
  "docSlug": "9be49d7769a68b67",
  "documentTitle": "2023 Investor Session Product Update",
  "authorId": "MongoDB",
  "authorName": "MongoDB",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 62,
  "pageCount": 74,
  "prevPage": 61,
  "nextPage": 63,
  "slideType": "other",
  "function": "present_framework",
  "density": "sparse",
  "nDataPoints": 4,
  "notes": "The slide illustrates the concept of vector embeddings in machine learning.",
  "elementsJson": [
    "action_title",
    "scatter_plot"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5eff-723e-9be4-6cd7e97a57b9/62",
  "deckHref": "/decks/019dd923-5eff-723e-9be4-6cd7e97a57b9",
  "deckJsonHref": "/decks/019dd923-5eff-723e-9be4-6cd7e97a57b9.json",
  "deckAnchorHref": "/decks/019dd923-5eff-723e-9be4-6cd7e97a57b9#slide-62",
  "components": [
    {
      "bbox": {
        "h": 0.52,
        "w": 0.56,
        "x": 0.22,
        "y": 0.35
      },
      "kind": "chart",
      "text": "Vector space plot",
      "attrs": null,
      "subkind": "scatter",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "4047802b-607f-4dd9-9d10-43fabbe18c05",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.74,
        "x": 0.13,
        "y": 0.13
      },
      "kind": "title",
      "text": "Similar Vectors plotted in space will be near one another",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f1c1dc7d-3c5e-41d7-9014-2fbc33787f2a",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "Vector Embeddings",
      "slug": null,
      "matchId": "62faef76-a742-40cd-825e-b8daf7820d21",
      "evidence": "Visual representation of semantic proximity in vector space",
      "confidence": 0.9
    }
  ],
  "arcBeats": [],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}