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    media

    Image
    Description
    Converter

    Generate rich, multi-sentence descriptions of images covering composition, subjects, colors, mood, and context. Ideal for detailed content cataloging, creative writing prompts, and advanced search indexing.

    Max file size: 50 MB
    Estimated: 2-6 sec per image
    6 input formats

    How It Works

    1

    Upload an image or provide a URL.

    2

    A high-resolution vision model analyzes all visual elements.

    3

    Spatial relationships, colors, textures, and subjects are identified.

    4

    An LLM synthesizes findings into a coherent multi-sentence description.

    5

    The description is returned with structural metadata.

    Code Examples

    import os, requests
    
    API = "https://api.mixpeek.com"
    H = {"Authorization": f"Bearer {os.environ['MIXPEEK_API_KEY']}",
         "X-Namespace": os.environ["NAMESPACE_ID"]}
    
    # 1. a bucket, with a schema that declares the field you will send
    bucket = requests.post(f"{API}/v1/buckets", headers=H, json={
        "bucket_name": "image-inputs",
        "bucket_schema": {"properties": {"image": {"type": "image"}}},
    }).json()
    
    # 2. land the file as an object. the URL goes in data, on the blob
    requests.post(f"{API}/v1/buckets/{bucket['bucket_id']}/objects", headers=H, json={
        "key_prefix": "run-1",
        "blobs": [{"property": "image", "type": "image",
                   "data": "https://example.com/photo.jpg"}],
    })
    
    # 3. a collection over that bucket, running the extractor
    collection = requests.post(f"{API}/v1/collections", headers=H, json={
        "collection_name": "image-to-description",
        "source": {"type": "bucket", "bucket_ids": [bucket["bucket_id"]]},
        "feature_extractor": {"feature_extractor_name": "image_extractor", "version": "v1"},
    }).json()
    
    # 4. run extraction over the bucket
    requests.post(f"{API}/v1/buckets/{bucket['bucket_id']}/batches", headers=H, json={
        "collection_ids": [collection["collection_id"]],
        "auto_submit": True,
    })
    
    # 5. read the output
    docs = requests.get(
        f"{API}/v1/collections/{collection['collection_id']}/documents", headers=H
    ).json()
    print(docs)

    Use Cases

    Create detailed catalog entries for artwork and photography
    Generate rich product descriptions from product images
    Build detailed indexes for stock photo libraries
    Produce descriptive metadata for museum and archive collections

    Supported Input Formats

    JPEG
    PNG
    WebP
    TIFF
    BMP
    GIF

    Quick Info

    Categorymedia
    Max File Size50 MB
    Est. Time2-6 sec per image

    Processing millions of files?

    Run this as a managed pipeline over your whole library, no infrastructure to build or maintain. Talk to us about processing at scale.

    Run it over a library

    Mixpeek runs this conversion as a pipeline over a whole library in your object storage, with the output landing as queryable documents. It is not a single-file converter.

    Frequently Asked Questions

    Ready to convert image to description?

    Start using the Mixpeek Image to Description in minutes. Sign up for a free API key and follow the documentation to get started.