Semantic search with the inference API

Semantic search helps you find data based on the intent and contextual meaning of a search query, instead of a match on query terms (lexical search).

In this tutorial, learn how to use the inference API workflow with various services to perform semantic search on your data.

Amazon Bedrock <amazon-bedrock.html>
Azure AI Studio <azure-ai-studio.html>
Azure OpenAI <azure-openai.html>
Cohere <cohere.html>
ELSER <elser.html>
HuggingFace <#>
Mistral <#>
OpenAI <#>
Service Alpha <#>
Service Bravo <#>
Service Charlie <#>
Service Delta <#>
Service Echo <#>
Service Foxtrot <#>

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Cohere

The examples in this tutorial use Cohere's embed-english-v3.0 model.
You can use any Cohere model as they are all supported by the inference API.

Requirements

A Cohere account is required to use the inference API with the Cohere service.

Create an inference endpoint

Create an inference endpoint by using the Create inference API:

Create inference example for `Cohere`

PUT _inference/text_embedding/cohere_embeddings
{
    "service": "cohere",
    "service_settings": {
        "api_key": "<api_key>",
        "model_id": "embed-english-v3.0",
        "embedding_type": "byte"
    }
}

• The task type is text_embedding in the path and the inference_id which is the unique identifier of the inference endpoint is cohere_embeddings.
• The API key of your Cohere account. You can find your API keys in your Cohere dashboard under the API keys section. You need to provide your API key only once. The Get inference API does not return your API key.
• The name of the embedding model to use. You can find the list of Cohere embedding models here.

Note

When using this model the recommended similarity measure to use in the dense_vector field mapping is dot_product. In the case of Cohere models, the embeddings are normalized to unit length in which case the dot_product and the cosine measures are equivalent.

Create the index mapping

The mapping of the destination index—the index that contains the embeddings that the model will create based on your input text—must be created. The destination index must have a field with the dense_vector field type for most models and the sparse_vector field type for the sparse vector models like in the case of the elser service to index the output of the used model.

Create index mapping for `Cohere`

PUT cohere-embeddings
{
  "mappings": {
    "properties": {
      "content_embedding": {
        "type": "dense_vector",
        "dims": 1024,
        "element_type": "byte"
      },
      "content": {
        "type": "text"
      }
    }
  }
}

• The name of the field to contain the generated tokens. It must be refrenced in the inference pipeline configuration in the next step.
• The field to contain the tokens is a dense_vector field.
• The output dimensions of the model. Find this value in the Cohere documentation of the model you use.
• The name of the field from which to create the dense vector representation. In this example, the name of the field is content. It must be referenced in the inference pipeline configuration in the next step.
• The field type which is text in this example.