> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Image Extractor
> Dense vector embeddings for images using Google SigLIP (768D) for visual similarity search
Built-in extractor names are a **deprecated alias** — collections are now created by picking [features](/docs/processing/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](/docs/processing/extractor-migration).
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](https://api.mixpeek.com/v1/collections/features/extractors/image_extractor_v1) or fetch programmatically with `GET /v1/collections/features/extractors/{feature_extractor_id}`.
## Pipeline Steps
1. **Filter Dataset** (if collection\_id provided)
* Filter to specified collection
2. **Detect Content Types**
* Sample 100 rows to identify images vs PDFs
3. **PDF Page Expansion** (conditional: if PDF content detected)
* Render each PDF page at 72 DPI using PyMuPDF
* Create separate image for each page
4. **SigLIP Image Embedding Generation**
* Resize to 224×224 internally
* GPU-accelerated inference
* Generate 768D visual embeddings
5. **Thumbnail Generation** (conditional: if `enable_thumbnails=true`)
* Resize to 640px width at 85% quality
* Upload to S3 with optional CDN
6. **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). |
```json theme={null}
{
"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) |
```json theme={null}
{
"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
```json Basic Image Embedding theme={null}
{
"feature_extractor": {
"feature_extractor_name": "image_extractor",
"version": "v1",
"input_mappings": {
"image": "image_url"
},
"field_passthrough": [
{ "source_path": "metadata.product_id" }
],
"parameters": {}
}
}
```
```json E-commerce Product Images theme={null}
{
"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": {}
}
}
```
```json Stock Photo Library theme={null}
{
"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": {}
}
}
```
```json Art Collection theme={null}
{
"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](/docs/platform/billing); 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`).
### Cross-Modal Search
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](/docs/platform/billing) | 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
## Related
* [Feature Extractors Overview](/docs/processing/feature-extractors)
* [Multimodal Extractor](/docs/processing/extractors/multimodal)
* [Face Identity Extractor](/docs/processing/extractors/face-identity)
* [Text Extractor](/docs/processing/extractors/text)
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