Image Extractor

Built-in extractor names are a deprecated alias — collections are now created by picking features. This pipeline is selected with features: ["image_search"] (for PDF input, use document_search). Existing feature_extractor configs keep working; see the migration guide.

View on GitHub

Runnable reference for this extractor — inputs, parameters, output fields, embedding models, and copy-paste examples. Auto-generated from the live registry.

The image extractor generates dense vector embeddings from images using Google’s SigLIP model (768D). Optimized for visual similarity search, product matching, and cross-modal search with text queries. Fast (~50-100ms per image) and cost-effective.

View extractor details at api.mixpeek.com/v1/collections/features/extractors/image_extractor_v1 or fetch programmatically with GET /v1/collections/features/extractors/{feature_extractor_id}.

Pipeline Steps

Filter Dataset (if collection_id provided)
- Filter to specified collection
Detect Content Types
- Sample 100 rows to identify images vs PDFs
PDF Page Expansion (conditional: if PDF content detected)
- Render each PDF page at 72 DPI using PyMuPDF
- Create separate image for each page
SigLIP Image Embedding Generation
- Resize to 224×224 internally
- GPU-accelerated inference
- Generate 768D visual embeddings
Thumbnail Generation (conditional: if enable_thumbnails=true)
- Resize to 640px width at 85% quality
- Upload to S3 with optional CDN
Output
- Image/page documents with embeddings
- Optional thumbnail URLs

When to Use

Use Case	Description
Image search	Find visually similar images in large collections
Visual similarity	Match products, artwork, or content by appearance
Content discovery	Recommend similar visual content
Cross-modal search	Find images using text queries (via SigLIP text encoder)
E-commerce	Product image search and visual recommendations
Stock photo search	Media library search by visual content

When NOT to Use

Scenario	Recommended Alternative
Face recognition	`face_identity_extractor`
Video content	`multimodal_extractor`
Text-heavy images requiring OCR	`multimodal_extractor` with OCR enabled
Audio content	`audio_extractor`

Input Schema

Field	Type	Required	Description
`image`	string	Yes	URL or S3 path to image file. Formats: JPEG, PNG, WebP, BMP. Any resolution (resized to 224x224 internally).

{
  "image": "s3://my-bucket/products/laptop-pro.jpg"
}

Input Examples:

Type	Example
Product image	`s3://my-bucket/products/laptop-pro.jpg`
Stock photo	`https://cdn.example.com/photos/sunset-beach.jpg`
Catalog image	`s3://catalog/items/SKU-12345.png`

Supported Formats: JPEG, PNG, WebP, BMP, GIF (static) Recommended Resolution: 224x224 or larger (automatically resized) Max File Size: 10MB recommended

Output Schema

Field	Type	Description
`image_extractor_v1_embedding`	float[768]	SigLIP image embedding, L2 normalized
`processing_time_ms`	number	Processing time in milliseconds
`thumbnail_url`	string	S3 URL of the thumbnail image (if generated)

{
  "image_extractor_v1_embedding": [0.023, -0.041, 0.018, ...],
  "processing_time_ms": 85.2,
  "thumbnail_url": "s3://mixpeek-storage/ns_123/thumbnails/thumb_001.jpg"
}

Parameters

The image extractor uses sensible defaults and requires no additional parameters for basic usage.

Parameter	Type	Default	Description
None required	-	-	All parameters use optimized defaults

Configuration Examples

{
  "feature_extractor": {
    "feature_extractor_name": "image_extractor",
    "version": "v1",
    "input_mappings": {
      "image": "image_url"
    },
    "field_passthrough": [
      { "source_path": "metadata.product_id" }
    ],
    "parameters": {}
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "image_extractor",
    "version": "v1",
    "input_mappings": {
      "image": "product_image"
    },
    "field_passthrough": [
      { "source_path": "metadata.sku" },
      { "source_path": "metadata.category" },
      { "source_path": "metadata.brand" }
    ],
    "parameters": {}
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "image_extractor",
    "version": "v1",
    "input_mappings": {
      "image": "photo_url"
    },
    "field_passthrough": [
      { "source_path": "metadata.photographer" },
      { "source_path": "metadata.tags" },
      { "source_path": "metadata.license" }
    ],
    "parameters": {}
  }
}

{
  "feature_extractor": {
    "feature_extractor_name": "image_extractor",
    "version": "v1",
    "input_mappings": {
      "image": "artwork_url"
    },
    "field_passthrough": [
      { "source_path": "metadata.artist" },
      { "source_path": "metadata.title" },
      { "source_path": "metadata.year" },
      { "source_path": "metadata.medium" }
    ],
    "parameters": {}
  }
}

Performance & Costs

Metric	Value
Processing speed	~50-100ms per image
Batch processing	Up to 16 images per batch
GPU acceleration	Supported for faster inference
Cost	Billed per image — see Billing & Pricing; rates come from `GET /v1/billing/pricing`

Vector Index

Property	Value
Feature URI	`mixpeek://image_extractor@v1/google_siglip_base_v1`
Index name	`image_extractor_v1_embedding`
Dimensions	768
Type	Dense
Distance metric	Cosine
Datatype	float32
Inference model	`google_siglip_base_v1`

In retrievers, reference this feature by its Feature URI above (the output name is google_siglip_base_v1, not the index name image_extractor_v1_embedding).

The SigLIP embeddings are compatible with SigLIP text embeddings, enabling cross-modal search where you can:

Find images using natural language text queries
Match images to text descriptions
Build hybrid search combining visual and textual similarity

Comparison with Other Image Extractors

Feature	image_extractor	multimodal_extractor
Dimensions	768	1408
Model	SigLIP	Vertex AI Multimodal
Processing	Image only	Video, Image, Text, GIF
Cross-modal	SigLIP text encoder	Vertex text encoder
Best For	Fast image search	Unified multimodal search
Cost	See Billing & Pricing	Higher (includes more features)

Limitations

Image only: Does not process video, audio, or text content
No OCR: Cannot extract text from images; use multimodal_extractor with OCR
No face recognition: For face matching, use face_identity_extractor
Single image: Processes one image at a time (batch via API)
Resolution: Input is resized to 224x224 internally

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Resources

Image Extractor

View on GitHub

Pipeline Steps

When to Use

When NOT to Use

Input Schema

Output Schema

Parameters

Configuration Examples

Performance & Costs

Vector Index

Comparison with Other Image Extractors

Limitations

View on GitHub

​Pipeline Steps

​When to Use

​When NOT to Use

​Input Schema

​Output Schema

​Parameters

​Configuration Examples

​Performance & Costs

​Vector Index

​Cross-Modal Search

​Comparison with Other Image Extractors

​Limitations

​Related

Pipeline Steps

When to Use

When NOT to Use

Input Schema

Output Schema

Parameters

Configuration Examples

Performance & Costs

Vector Index

Cross-Modal Search

Comparison with Other Image Extractors

Limitations

Related