Summary: SnapCatalog is the merchant loop: a SKU photo becomes category, colors, and a title, then a shopper can drop a reference shot or type “navy linen shirt” and land on the same index. You do not run a tagging queue and a vector database as two products. You declare pxt.Image. Gemini writes the attributes. CLIP sits on the image. pip install pixeltable.
The Product#
- Ingest a product image with
sku - Auto-fill category, colors, title from the pixels
- Shop-the-look: upload a photo, return nearest SKUs
- The same index answers a text query
Image→video is find a video from an image. Library search as a hosted UI is PixelSearch. This post is a catalog row: one image, tags, reverse image.
The Stitch You Delete#
A folder of JPEGs. A batch job that calls a VLM. A CSV of tags you paste into Shopify. CLIP embeddings in Pinecone keyed by SKU. A second path for text. Pixeltable is the row those joints were syncing by hand. Same CLIP-on-image idea as the Photos clone — here the entity is a SKU, not a family photo.
The Receipt#
One huggingface.clip embedder on the image serves the shopper photo (similarity(ref_image)) and a text query (similarity(string='navy linen shirt')). Do not stack a second CLIP index with different kwargs on the same column. Insert a SKU; tags and the index follow. Same file on Cloud: pxt db update → pxt schema update → pxt service update.
What This Is Not#
Not a marketplace. Not Shopify. Auth and checkout stay where they are. Not PixelSearch as a hosted consumer app — that UI already exists. This is the table behind shop-the-look.
People Also Ask#
Why embedding= not image_embed=? Same CLIP, one kwarg, so text and image queries share the index — the Photos-post form. Frame search on video uses image_embed= on the iterator column; do not mix both styles on one snippet.
Do I need a tag taxonomy table? Gemini fills JSON. If you already have a taxonomy, constrain the prompt. Do not stand up a second store for it.
How do I go to Cloud? Same app.py. PIXELTABLE_API_KEY, pxt://org:db, then db → schema → service.
Is this an AI automation workflow? Yes. Insert a JPEG; Gemini tags and CLIP run as columns. The category is AI automation workflow.
Tag the Pixels, Search the Same Vector#
Declare the product. Apply the file. Insert the JPEG. Call shop_the_look. Keep the catalog. Delete the tagging farm.
The same CLIP-on-image index with campaign spend on the row is AdRadar.
- Pixeltable on GitHub:
pip install pixeltable - ClipFinder — NL video moments
- CallSense — sales-call auditor
- DocuVision — PDF Q&A
- SafeStream — UGC gate
- ClaimBot — claims triage
- Pixeltable documentation
