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Add Hugging Face Inference Endpoints as a rest embedder guide (#3053)
--------- Co-authored-by: gui machiavelli <[email protected]>
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| title: Semantic Search with Hugging Face Inference Endpoints - Meilisearch documentation | ||
| description: This guide will walk you through the process of setting up Meilisearch with Hugging Face Inference Endpoints. | ||
| --- | ||
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| # Semantic search with Hugging Face Inference Endpoints | ||
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| ## Introduction | ||
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| This guide will walk you through the process of setting up a Meilisearch REST embedder with [Hugging Face Inference Endpoints](https://ui.endpoints.huggingface.co/) to enable semantic search capabilities. | ||
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| <Capsule intent="note" title="`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`. | ||
| </Capsule> | ||
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| ## Requirements | ||
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| To follow this guide, you'll need: | ||
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| - A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version 1.10 or above with the Vector store activated | ||
| - A [Hugging Face account](https://huggingface.co/) with a deployed inference endpoint | ||
| - The endpoint URL and API key of the deployed model on your Hugging Face account | ||
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| ## Configure the embedder | ||
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| Set up an embedder using the update settings endpoint: | ||
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| ```json | ||
| { | ||
| "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}}", "{{..}}"] | ||
| } | ||
| } | ||
| ``` | ||
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| In this configuration: | ||
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| - `source`: declares Meilisearch should connect to this embedder via its REST API | ||
| - `url`: replace `ENDPOINT_URL` with the address of your Hugging Face model endpoint | ||
| - `apiKey`: replace `API_KEY` with your Hugging Face API key | ||
| - `dimensions`: specifies the dimensions of the embeddings, which are 384 for `baai/bge-small-en-v1.5` | ||
| - `documentTemplate`: an optional but recommended [template](/learn/ai_powered_search/getting_started_with_ai_search) for the data you will send the embedder | ||
| - `request`: defines the structure and parameters of the request Meilisearch will send to the embedder | ||
| - `response`: defines the structure of the embedder's response | ||
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| 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](/reference/api/tasks). | ||
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| <Capsule intent="note"> | ||
| This example uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as its model, but Hugging Face offers [other options that may fit your dataset better](https://ui.endpoints.huggingface.co/catalog?task=sentence-embeddings). | ||
| </Capsule> | ||
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| ## Perform a semantic search | ||
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| With the embedder set up, you can now perform semantic searches. Make a search request with the `hybrid` search parameter, setting `semanticRatio` to `1`: | ||
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| ```json | ||
| { | ||
| "q": "QUERY_TERMS", | ||
| "hybrid": { | ||
| "semanticRatio": 1, | ||
| "embedder": "hf-inference" | ||
| } | ||
| } | ||
| ``` | ||
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| In this request: | ||
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| - `q`: the search query | ||
| - `hybrid`: enables AI-powered search functionality | ||
| - `semanticRatio`: controls the balance between semantic search and full-text search. Setting it to `1` means you will only receive semantic search results | ||
| - `embedder`: the name of the embedder used for generating embeddings | ||
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| ## Conclusion | ||
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| You have set up with an embedder using Hugging Face Inference Endpoints. This allows you to use pure semantic search capabilities in your application. | ||
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| Consult the [embedder setting documentation](/reference/api/settings#embedders-experimental) for more information on other embedder configuration options. |