> ## 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 Runnable reference for this extractor — inputs, parameters, output fields, embedding models, and copy-paste examples. Auto-generated from the live registry. Image extractor pipeline showing SigLIP processing and embedding generation 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** | 2 credits per image | ## 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** | 2 credits/image | 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](/processing/feature-extractors) * [Multimodal Extractor](/processing/extractors/multimodal) * [Face Identity Extractor](/processing/extractors/face-identity) * [Text Extractor](/processing/extractors/text)