Using machine-learned models from Vespa Cloud

Vespa Cloud provides a set of machine-learned models that you can use in your applications. These models will always be available on Vespa Cloud and are frozen models. You can also bring your own embedding model, by deploying it in the Vespa application package.

You specify to use a model provided by Vespa Cloud by setting the model-id attribute where you specify a model config. For example, when configuring the Huggingface embedder provided by Vespa, you can write:

<container id="default" version="1.0">
    <component id="e5" type="hugging-face-embedder">
        <transformer-model model-id="e5-small-v2"/>
    </component>
    ...
</container>

With this, your application will have support for text embedding inference for both queries and documents. Nodes that have been provisioned with GPU acceleration, will automatically use GPU for embedding inference.

Vespa Cloud Embedding Models

Models on Vespa model hub are selected open-source embedding models with great performance. See the Massive Text Embedding Benchmark (MTEB) Leaderboard for details. These embedding models are useful for retrieval (semantic search), re-ranking, clustering, classification, and more.

Huggingface Embedder

These models are available for the Huggingface Embedder type="hugging-face-embedder". All these models supports both mapping from string or array<string> to tensor representations. The output tensor cell-precision can be <float> or <bfloat16>.

Using E5 Models

The E5 family uses keywords with the input to differentiate query and document side embedding.

The query text should be prefixed with "query: ". In this example the original user query is how to format e5 queries.

{
  "yql": "select doc_id from doc where ({targetHits:10}nearestNeighbor(embeddings,e))",
  "input.query(e)": "embed(e5, \"query: how to format e5 queries\")"
}

The same technique also must be applied for document side embedding inference. The input text should be prefixed with "passage: "

field embeddings type tensor<float>(p{}, x[768]) {
  indexing {
    input chunks |
	  for_each {
	    "passage: " . (input title || "") . " " . ( _ || "")
	  } | embed e5 | attribute
  }
}

The above example reads a chunks field of type array<string>, and prefixes each item with "passage: ", followed by the concatenation of the title and the item chunk (_). See execution value example.

Bert Embedder

These models are available for the Bert Embedder type="bert-embedder":

<container id="default" version="1.0">
    <component id="mini" type="bert-embedder">
        <transformer-model model-id="minilm-l6-v2"/>
        <tokenizer-vocab model-id="bert-base-uncased"/>
    </component>
    ...
</container>

Note bert-embedder requires both transformer-model and tokenizer-vocab.

Tokenization Embedders

These are embedder implementations that tokenize text and embed string to the vocabulary identifiers. These are most useful for creating the tensor inputs to re-ranking models that takes both the the query and document token identifiers as input. See example in the transformer ranking sample app.

Significance models

These are global significance models that can be added to significance element in services.xml.

Creating applications working both self-hosted and on Vespa Cloud

You can also specify both a model-id, which will be used on Vespa Cloud, and a url/path, which will be used on self-hosted deployments:

<transformer-model model-id="minilm-l6-v2" path="myAppPackageModels/myModel.onnx"/>

This can be useful for example to create an application package which uses models from Vespa Cloud for production and a scaled-down or dummy model for self-hosted development.

Using Vespa Cloud models with any config

Specifying a model-id can be done for any config field of type model, whether the config is from Vespa or defined by you.