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Danny WhiteandJoshen Lim
d914b81f47
* feat: move storage settings * feat: redirect * feat: database settings in service area * feat: move data api settings * fix: revert data API placement * feat: minor UX touches * fix: simplify configuration group * feat: references to database settings * feat: references to storage settings * fix: redirects and formatting * fix: Import StorageMenu dynamically to avoid SSR issues with useLocalStorage * fix: move Data API closer to semantic siblings * fix: revert smart comma * Shift bucket sort logic into storage explorer store --------- Co-authored-by: Joshen Lim <joshenlimek@gmail.com>
85 lines
3.1 KiB
Markdown
85 lines
3.1 KiB
Markdown
# Image Search with Supabase Vector
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In this example we're implementing image search using the [OpenAI CLIP Model](https://github.com/openai/CLIP), which was trained on a variety of (image, text)-pairs.
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We're implementing two methods in the [`/image_search/main.py` file](/image_search/main.py):
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1. The `seed` method generates embeddings for the images in the `images` folder and upserts them into a collection in Supabase Vector.
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2. The `search` method generates an embedding from the search query and performs a vector similarity search query.
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## Prerequisites
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Before running this example, ensure you have:
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- Python 3.8 or higher installed
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- A Supabase account (sign up at https://supabase.com)
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- Poetry package manager
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- Basic familiarity with vector databases (helpful but not required)
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## Setup
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- Create a new project in your [Supabase dashboard](https://supabase.com/dashboard)
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- Go to Settings > Database and copy your connection string
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- Ensure the Vector extension is enabled in your project
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- Install poetry: `pip install poetry`
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- Activate the virtual environment: `poetry shell`
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- (to leave the venv just run `exit`)
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- Install app dependencies: `poetry install`
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## Run locally
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### Generate the embeddings and seed the collection
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- `supabase start`
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- `poetry run seed`
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- Check the embeddings stored in the local Supabase Dashboard: http://localhost:54323/project/default/editor > schema: vecs
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**What to expect:** The seed command will process all images in the `images` folder and generate vector embeddings for each one.
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### Perform a search
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- `poetry run search "bike in front of red brick wall"`
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**What to expect:** The search will return a list of images ranked by similarity to your search query, along with similarity scores.
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## Run on hosted Supabase project
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- Set `DB_CONNECTION` with the connection string from your hosted Supabase Dashboard: https://supabase.com/dashboard/project/_/database/settings > Connection string > URI
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## Example Search Queries
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Try these search queries to test the image search functionality:
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- `"bike in front of red brick wall"`
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- `"person walking in park"`
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- `"blue sky with clouds"`
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- `"city street at night"`
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## Troubleshooting
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**Common Issues:**
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- **Poetry not found:** Make sure Poetry is installed with `pip install poetry`
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- **Connection errors:** Verify your Supabase connection string is correct
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- **No search results:** Ensure you've run the seed command first to populate the database
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- **Python version errors:** This example requires Python 3.8 or higher
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## How It Works
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This example uses the CLIP (Contrastive Language-Image Pre-training) model to:
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1. Convert images into high-dimensional vector representations (embeddings)
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2. Convert text search queries into similar vector representations
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3. Find images with embeddings most similar to the search query embedding
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4. Return ranked results based on vector similarity scores
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## Attributions
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### Models
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[clip-ViT-B-32](https://www.sbert.net/examples/applications/image-search/README.html) via [Hugging Face](https://huggingface.co/sentence-transformers/clip-ViT-B-32)
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### Images
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Images from https://unsplash.com/license via https://picsum.photos/
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