Visual Product Search

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY", namespace="product-search")

# 1. A bucket for product photos with their catalog fields as object metadata, and a collection that embeds each photo
bucket = client.buckets.create(
    bucket_name="products",
    bucket_schema={
        "properties": {
            "photo": {
                "type": "image",
            },
        },
    },
)
collection = client.collections.create(
    collection_name="product_catalog",
    source={"type": "bucket", "bucket_ids": [bucket["bucket_id"]]},
    feature_extractor={
        "feature_extractor_name": "multimodal_extractor",
        "version": "v1",
    },
)

# 2. Upload and process
client.buckets.upload(
    bucket["bucket_id"],
    blobs=[{"property": "photo", "type": "image", "data": "s3://your-bucket/product-images/trail-runner.jpg"}],
    metadata={
        "name": "Trail runner",
        "price": 89,
        "in_stock": True,
    },
)
client.collections.trigger(collection["collection_id"])

# 3. A customer photo searched against the product embeddings
retriever = client.retrievers.create(
    retriever_name="product-search",
    collection_identifiers=["product_catalog"],
    input_schema={
        "photo": {
            "type": "image",
            "required": True,
        },
    },
    stages=[
        {
            "stage_name": "search",
            "stage_id": "feature_search",
            "parameters": {
                "searches": [
                    {
                        "feature_uri": "mixpeek://multimodal_extractor@v1/vertex_multimodal_embedding",
                        "query": {
                            "input_mode": "content",
                            "value": "{{INPUT.photo}}",
                        },
                        "top_k": 50,
                    },
                ],
                "final_top_k": 20,
            },
        },
    ],
)

# 4. Search
results = client.retrievers.execute(
    retriever["retriever_id"],
    inputs={
    "photo": "https://example.com/customer-photo.jpg",
},
)

for doc in results["documents"]:
    print(doc["document_id"], doc["score"])