NEWVectors or files. Pick a path.Start →
    Text

    Web Scraper

    Extract structured data from webpages while maintaining semantic context and relationships

    v1 · 5 cr/page, 1 cr/extraction, 2 cr/image
    Note: This playground provides simulated output to showcase functionality. No input data is processed or stored on our servers. Use this demo to explore the feature extractor's capabilities before integrating it into your application.

    Input

    Enter the text you want to process

    How many levels of nested content to extract. Default: 3

    Minimum confidence score to include a field (0.0-1.0). Default: 0.7

    How strictly to enforce the target schema. Default: flexible

    Whether to preserve HTML structure in output. Default: false

    Whether to extract page metadata (title, description, etc.). Default: true

    Output

    {
      "url": "https://example.com/article",
      "extracted_at": "2024-01-20T10:30:00Z",
      "metadata": {
        "title": "Sample Article Title",
        "description": "Article description from meta tags",
        "author": "John Doe",
        "published_date": "2024-01-15",
        "language": "en"
      },
      "content": {
        "main_heading": "Article Main Heading",
        "body_text": "Full article text content...",
        "sections": [
          {
            "heading": "Section 1",
            "content": "Section 1 content...",
            "confidence": 0.95
          }
        ]
      },
      "structured_data": {
        "products": [],
        "prices": [],
        "ratings": [],
        "reviews": []
      },
      "semantic_relationships": [
        {
          "type": "parent-child",
          "from": "main_heading",
          "to": "sections",
          "confidence": 0.98
        }
      ],
      "confidence_scores": {
        "overall": 0.92,
        "field_scores": {
          "title": 0.98,
          "body_text": 0.95,
          "metadata": 0.88
        }
      }
    }

    Ready to run Web Scraper on your data? Spin it up in Studio: no infra to host.

    Run this in Studio

    Web Scraper: common questions

    What is the Web Scraper + Multimodal Embeddings extractor used for?
    Documentation freshness detection, knowledge base building, job board ingestion, API example indexing, curriculum validation.
    How much does the Web Scraper + Multimodal Embeddings extractor cost?
    5 cr/page, 1 cr/extraction, 2 cr/image. Credits are billed per unit processed; see mixpeek.com/pricing for the credit rate.
    What file types does Web Scraper + Multimodal Embeddings support?
    Text. It produces embeddings from intfloat__multilingual_e5_large_instruct, jinaai__jina_embeddings_v2_base_code, google__siglip_base_patch16_224, facebook__dinov2_base.

    Already have embeddings? Skip extraction: search your own vectors with MVS, from $25/mo for up to 1M vectors.

    Try MVS →

    Recent updates

    Full changelog