rom1504/clip-retrieval

Health v2 · maintenance only

Adoption not part of the score

2795 stars · 238 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python toolkit for computing CLIP embeddings for images and text and building a semantic search/retrieval system on top of them. It includes components for inference, indexing, filtering, a Flask backend, and a web frontend UI.

Use cases

• build a semantic search system over image datasets
• compute clip embeddings for millions of images
• index and query image+text embeddings with knn
• filter large image datasets by semantic similarity
• host a clip retrieval backend with a web ui
• search images by text query
• build multimodal retrieval at billion scale

When to choose

• you need to compute CLIP embeddings at scale and search over them
• you want an end-to-end pipeline from image dataset to searchable index
• you need to filter image-text datasets semantically like LAION did

When to avoid

• you need general-purpose vector search without CLIP models
• you want a fully managed hosted service rather than self-run tooling
• your data is text-only and you don't need multimodal embeddings

Facets

library · maturity active

machine-learning search-engine vector-database image-processing rag machine-learning computer-vision artificial-intelligence data-science python cli clip-embeddings semantic-search multimodal knn-index image-retrieval flask-backend search gpu web-server

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