On-device emoji semantic lookup CLI powered by vector search

It's a simple CLI that uses sentence-transformers/all-MiniLM-L6-v2 quantized ONNX model and a vector search powered by sqlite-vec to help you to find emoji you are looking for

• Python 3.12 or higher

uv pip install -e .

The CLI will automatically download the emoji data from this gist when needed. You can also use your own emoji data by placing it in shortnames_override.json.

The CLI will automatically download the quantized ONNX model from sentence-transformers/all-MiniLM-L6-v2 when needed. You can also use any other bi-encoder of your choice that fits your device by replacing the ONNX file. If you are using a model that requires different prompts to encode a query and a searched entity (emojis in our case) then you will need to modify the source code.

The tiny dedicated finetuned MiniLM is coming soon.

1. Search for emojis (the CLI will automatically download dependencies and build the index if needed):

emji happy birthday
emji coffee break
emji celebration party

2. Select and copy: Choose from the interactive list, and the emoji will be copied to your clipboard!

3. Force rebuild index (if needed):

emji --build-index

• emji <text> - Search for emojis matching your description
• emji --build-index - Force rebuild the semantic search index

Options:

• --n <number>: Number of results to return (default: 3)

# Find celebration emojis
emji party celebration

# Find food-related emojis
emji delicious food

# Find weather emojis
emji sunny day

# Get more results
emji animals --n 5

# Force rebuild the index
emji --build-index

1. Automatic Setup: On first run, the CLI automatically downloads the required model and emoji data
2. Embedding Generation: Uses a quantized version of sentence-transformers/all-MiniLM-L6-v2 to convert emoji names and descriptions into 384-dimensional vectors
3. Vector Database: Stores embeddings in SQLite with sqlite-vec extension for fast similarity search
4. Semantic Matching: When you search, your query is converted to an embedding and compared against all emoji embeddings
5. Interactive Selection: The best matches are presented in an interactive menu for you to choose from

# create virtual env
uv venv
# activate
source .venv/bin/activate
# sync dependencies
uv sync --all-groups
# Set up pre-commit hooks
pre-commit install

Licensed under the Apache License 2.0. See for details.

Contributions are welcome! Please feel free to submit a Pull Request.