Create text embeddings with OpenAI
Generate vector embeddings for text data to enable semantic search and similarity matching.
Updated Open in Colab
Find similar images with CLIP
Build visual similarity search to find images that look alike using OpenAI's CLIP model.
Search & Embeddings · Notebook · Updated
Colab runs it in your browser. If it calls a model provider, it asks for that provider's API key in a cell.
How it works
You add an embedding index using CLIP, which understands both images and text. This enables finding similar images or searching images by text description.
What it covers
- Create image embeddings with CLIP
- Search by image similarity
- Search by text description (cross-modal)
What it uses
- Install
- terminalpip install pixeltable sentence-transformers torch
- Pixeltable calls
pxt.create_table()insert()add_embedding_index()similarity()
A look at the code
search-similar-images.ipynb
# Add CLIP embedding index (supports both image and text queries)images.add_embedding_index('image', embedding=clip.using(model_id='openai/clip-vit-base-patch32'))
One cell of the notebook, copied as it is. Open it in Colab for the rest.
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