Training

Automatic Image Tagging Pipeline

Automatically tag images with objects, scenes, colors, and custom labels using computer vision models. Perfect for DAM systems and media libraries.

image

Single Tier

Run in Builder

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY", namespace="image-tags")

# 1. A bucket for the photos, and a collection whose Gemini description step
#    returns tags as structured JSON. There is no separate object detector or
#    color extractor; one structured answer per image carries all three.
bucket = client.buckets.create(
    bucket_name="product-photos",
    bucket_schema={"properties": {"photo": {"type": "image"}}},
)
collection = client.collections.create(
    collection_name="product-photos",
    source={"type": "bucket", "bucket_ids": [bucket["bucket_id"]]},
    feature_extractor={
        "feature_extractor_name": "multimodal_extractor",
        "version": "v1",
        "parameters": {
            "run_video_description": True,
            "description_prompt": "Tag this photo for a media library.",
            "response_shape": {
                "type": "object",
                "properties": {
                    "tags": {"type": "array", "items": {"type": "string"}},
                    "objects": {"type": "array", "items": {"type": "string"}},
                    "dominant_colors": {"type": "array", "items": {"type": "string"}},
                },
            },
        },
    },
)

# 2. Upload and process
client.buckets.upload(
    bucket["bucket_id"],
    blobs=[{"property": "photo", "type": "image", "data": "s3://your-bucket/photos/IMG_0001.jpg"}],
)
client.collections.trigger(collection["collection_id"])

# 3. Read the tags back
docs = client.documents.list(collection["collection_id"], page_size=50)
for doc in docs["results"]:
    print(doc["document_id"], doc.get("json_output"))