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    Semantic search with Hugging Face Inference Endpoints

    Introduction

    This guide will walk you through the process of setting up a Meilisearch REST embedder with Hugging Face Inference Endpoints to enable semantic search capabilities.

    `rest` or `huggingface`?

    You can use Hugging Face and Meilisearch in two ways: running the model locally by setting the embedder source to huggingface, or remotely in Hugging Face's servers by setting the embeder source to rest.

    Requirements

    To follow this guide, you'll need:

    Configure the embedder

    Set up an embedder using the update settings endpoint:

    {
  "hf-inference": {
    "source": "rest",
    "url": "ENDPOINT_URL",
    "apiKey": "API_KEY",
    "dimensions": 384,
    "documentTemplate": "CUSTOM_LIQUID_TEMPLATE",
    "request": {
      "inputs": ["{{text}}", "{{..}}"],
      "model": "baai/bge-small-en-v1.5"
    },
    "response": ["{{embedding}}", "{{..}}"]
  }
}

    In this configuration:

    Once you've configured the embedder, Meilisearch will automatically generate embeddings for your documents. Monitor the task using the Cloud UI or the get task endpoint.

    NOTE

    This example uses BAAI/bge-small-en-v1.5 as its model, but Hugging Face offers other options that may fit your dataset better.

    With the embedder set up, you can now perform semantic searches. Make a search request with the hybrid search parameter, setting semanticRatio to 1:

    {
  "q": "QUERY_TERMS",
  "hybrid": {
    "semanticRatio": 1,
    "embedder": "hf-inference"
  }
}

    In this request:

    Conclusion

    You have set up with an embedder using Hugging Face Inference Endpoints. This allows you to use pure semantic search capabilities in your application.

    Consult the embedder setting documentation for more information on other embedder configuration options.