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--- |
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license: gpl-3.0 |
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tags: |
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- typesense |
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- semantic search |
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- vector search |
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--- |
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# Typesense Built-in Embedding Models |
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This repository holds all the built-in ML models supported by [Typesense](https://typesense.org) for semantic search currently. |
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If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it. (See below for instructions). |
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## Usage |
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Here's an example of how to specify the model to use for auto-embedding generation when creating a collection in Typesense: |
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```bash |
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curl -X POST \ |
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'http://localhost:8108/collections' \ |
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-H 'Content-Type: application/json' \ |
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-H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \ |
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-d '{ |
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"name": "products", |
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"fields": [ |
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{ |
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"name": "product_name", |
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"type": "string" |
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}, |
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{ |
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"name": "embedding", |
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"type": "float[]", |
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"embed": { |
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"from": [ |
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"product_name" |
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], |
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"model_config": { |
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"model_name": "ts/all-MiniLM-L12-v2" |
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} |
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} |
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} |
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] |
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}' |
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``` |
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Replace `all-MiniLM-L12-v2` with any model name from this repository. |
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Here's a detailed step-by-step article with more information: https://typesense.org/docs/guide/semantic-search.html |
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## Contributing |
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If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it. (See below for instructions). |
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### Convert a model to ONNX format |
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#### Converting a Hugging Face Transformers Model |
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To convert any model from Hugging Face to ONNX format, you can follow the instructions in [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) using the ```optimum-cli```. |
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#### Converting a PyTorch Model |
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If you have a PyTorch model, you can use the ```torch.onnx``` APIs to convert it to the ONNX format. More information on the conversion process can be found [here](https://pytorch.org/docs/stable/onnx.html). |
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#### Converting a Tensorflow Model |
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For Tensorflow models, you can utilize the tf2onnx tool to convert them to the ONNX format. Detailed guidance on this conversion can be found [here](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx). |
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#### Creating model config |
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Before submitting your ONNX model through a PR, you need to organize the necessary files under a folder with the model's name. Ensure that your model configuration adheres to the following structure: |
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- **Model File**: The ONNX model file. |
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- **Vocab File**: The vocabulary file required for the model. |
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- **Model Config File**: Named as config.json, this file should contain the following keys: |
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| Key | Description | Optional | |
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|-----|-------------|----------| |
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|model_md5| MD5 checksum of model file as string| No | |
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|vocab_md5| MD5 checksum of vocab file as string| No | |
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|model_type| Model type (currently only ```bert``` and ```xlm_roberta``` supported)| No | |
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|vocab_file_name| File name of vocab file| No | |
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|indexing_prefix| Prefix to be added before embedding documents| Yes | |
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|query_prefix| Prefix to be added before embedding queries | Yes | |
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Please make sure that the information in the configuration file is accurate and complete before submitting your PR. |
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We appreciate your contributions to expand our collection of supported embedding models! |
