Embeddings.info

Datasets, pipelines, and an interactive explorer for large-scale image/text embeddings, built on ClickHouse. Companion in spirit to adsb.exposed and reversedns.space.

Explorer website (index.html)

A zoomable, tile-rendered map of an embedding "sphere" — the whole corpus projected to 2-D via a random Hadamard rotation, colored by density (OKLCH lightness) and a third projected axis (OKLCH hue). Pure static HTML + Leaflet; it queries a ClickHouse HTTP endpoint directly (read-only website user).

• Point-cloud mode — each tile is a RowBinary raster of sparse (px,py,r,g,b) pixels aggregated server-side (GROUP BY pixel, density→lightness, mean-z→hue), scattered into a canvas ImageData.
• Thumbnail mode — one representative image per 75×75 cell, rendered from mmcommons.image_thumbs2 (75×75 gamma-correct RGB thumbnails) as PNG straight from ClickHouse.
• Click → report — nearest visible point → top-100 by cosineDistance over the embedding → thumbnail grid → click through to the full image.
• Switchers — dataset (nomic / clip / siglip2) and vector representation (original / Hadamard-rotated; bf16 / int8 / QBit bits+strides to follow).

Coordinates live in a 2^32 × 2^32 plane (L.CRS.Simple, tile_size = 2^(32−z)), exactly like the adsb tile math. Tiles are fast thanks to a mortonEncode(x,y) projection.

Data (ClickHouse service, mmcommons and ann databases)

• mmcommons.emb_{clip,nomic,siglip2} — ~99.1M YFCC100M image embeddings, Array(BFloat16) (768 / 768 / 1152-d), keyed by stripped MD5.
• mmcommons.image_thumbs / image_thumbs2 — 99.1M gamma-correct 75×75 raw-RGB thumbnails (v2 = center-square-crop + Lanczos-sharpened).
• mmcommons.image_blob — 99.6M source JPEG blobs; yfcc_metadata — Flickr metadata.
• ann.* — billion-scale ANN benchmark datasets (SIFT/DEEP/MSTuring/MSSpaceV/Text2Image 1B, LAION-400M, DINO-1B, MSMARCO, Wikipedia, OpenAI, Caselaw, YFCC, sparse SPLADE) with base + query + groundtruth. See the big-ann-benchmarks loaders in scripts/.

The projection columns and the read-only user are defined in sql/schema.sql.

Scripts (scripts/)

Snapshot of the pipelines used to build and analyze the data (they assume a working dir and the ClickHouse client in $PATH, with the write password in $CLICKHOUSE_CLOUD_EMBEDDINGS_PASSWORD — no secrets are committed). Highlights:

• Thumbnails — thumbs_s3*.py, thumb_s3*_chunk.sh, thumbs_s3*_run.sh (streaming S3→resize→insert, gamma-correct in linear light, decoupled fetch/resize).
• ANN datasets — binvec.py (.fbin/.u8bin/.i8bin→RowBinary), npyvec.py (fp16 .npy→BFloat16), gtparse.py / rangegt.py (groundtruth), sparse_load.py (CSR), annrun.sh / laion*.sh runners.
• Embeddings / analysis — embed*.py, quant*.py, hadamard_quant.py, train_{sentiment,topic}.py, *_recall*.sh, viz.py, etc.

Not included

Raw data, Parquet, the clickhouse binary, logs, and progress markers are excluded (see .gitignore).