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Perform inference API

This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

Performs an inference task on an input text by using an inference endpoint.

The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the Machine learning trained model APIs.

Request

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POST /_inference/<inference_id>

POST /_inference/<task_type>/<inference_id>

Prerequisites

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Requires the monitor_inference cluster privilege (the built-in inference_admin and inference_user roles grant this privilege)

Description

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The perform inference API enables you to use machine learning models to perform specific tasks on data that you provide as an input. The API returns a response with the results of the tasks. The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the Create inference API.

Path parameters

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<inference_id>: (Required, string) The unique identifier of the inference endpoint.
<task_type>: (Optional, string) The type of inference task that the model performs.

Query parameters

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timeout: (Optional, timeout) Controls the amount of time to wait for the inference to complete. Defaults to 30 seconds.

Request body

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input: (Required, string or array of strings) The text on which you want to perform the inference task. input can be a single string or an array.

Inference endpoints for the completion task type currently only support a single string as input.
query: (Required, string) Only for rerank inference endpoints. The search query text.
task_settings: (Optional, object) Task settings for the individual inference request. These settings are specific to the <task_type> you specified and override the task settings specified when initializing the service.

Examples

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Completion example

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The following example performs a completion on the example question.

resp = client.inference.inference(
    task_type="completion",
    inference_id="openai_chat_completions",
    input="What is Elastic?",
)
print(resp)

response = client.inference.inference(
  task_type: 'completion',
  inference_id: 'openai_chat_completions',
  body: {
    input: 'What is Elastic?'
  }
)
puts response

const response = await client.inference.inference({
  task_type: "completion",
  inference_id: "openai_chat_completions",
  input: "What is Elastic?",
});
console.log(response);

POST _inference/completion/openai_chat_completions
{
  "input": "What is Elastic?"
}

The API returns the following response:

{
  "completion": [
    {
      "result": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics. Their flagship product is Elasticsearch, an open-source, distributed search engine that allows users to search, analyze, and visualize large volumes of data in real-time. Elastic also offers products such as Kibana, a data visualization tool, and Logstash, a log management and pipeline tool, as well as various other tools and solutions for data analysis and management."
    }
  ]
}

Rerank example

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The following example performs reranking on the example input.

resp = client.inference.inference(
    task_type="rerank",
    inference_id="cohere_rerank",
    input=[
        "luke",
        "like",
        "leia",
        "chewy",
        "r2d2",
        "star",
        "wars"
    ],
    query="star wars main character",
)
print(resp)

response = client.inference.inference(
  task_type: 'rerank',
  inference_id: 'cohere_rerank',
  body: {
    input: [
      'luke',
      'like',
      'leia',
      'chewy',
      'r2d2',
      'star',
      'wars'
    ],
    query: 'star wars main character'
  }
)
puts response

const response = await client.inference.inference({
  task_type: "rerank",
  inference_id: "cohere_rerank",
  input: ["luke", "like", "leia", "chewy", "r2d2", "star", "wars"],
  query: "star wars main character",
});
console.log(response);

POST _inference/rerank/cohere_rerank
{
  "input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
  "query": "star wars main character"
}

The API returns the following response:

{
  "rerank": [
    {
      "index": "2",
      "relevance_score": "0.011597361",
      "text": "leia"
    },
    {
      "index": "0",
      "relevance_score": "0.006338922",
      "text": "luke"
    },
    {
      "index": "5",
      "relevance_score": "0.0016166499",
      "text": "star"
    },
    {
      "index": "4",
      "relevance_score": "0.0011695103",
      "text": "r2d2"
    },
    {
      "index": "1",
      "relevance_score": "5.614787E-4",
      "text": "like"
    },
    {
      "index": "6",
      "relevance_score": "3.7850367E-4",
      "text": "wars"
    },
    {
      "index": "3",
      "relevance_score": "1.2508839E-5",
      "text": "chewy"
    }
  ]
}

Sparse embedding example

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The following example performs sparse embedding on the example sentence.

resp = client.inference.inference(
    task_type="sparse_embedding",
    inference_id="my-elser-model",
    input="The sky above the port was the color of television tuned to a dead channel.",
)
print(resp)

response = client.inference.inference(
  task_type: 'sparse_embedding',
  inference_id: 'my-elser-model',
  body: {
    input: 'The sky above the port was the color of television tuned to a dead channel.'
  }
)
puts response

const response = await client.inference.inference({
  task_type: "sparse_embedding",
  inference_id: "my-elser-model",
  input:
    "The sky above the port was the color of television tuned to a dead channel.",
});
console.log(response);

POST _inference/sparse_embedding/my-elser-model
{
  "input": "The sky above the port was the color of television tuned to a dead channel."
}

The API returns the following response:

{
  "sparse_embedding": [
    {
      "port": 2.1259406,
      "sky": 1.7073475,
      "color": 1.6922266,
      "dead": 1.6247464,
      "television": 1.3525393,
      "above": 1.2425821,
      "tuned": 1.1440028,
      "colors": 1.1218185,
      "tv": 1.0111054,
      "ports": 1.0067928,
      "poem": 1.0042328,
      "channel": 0.99471164,
      "tune": 0.96235967,
      "scene": 0.9020516,
      (...)
    },
    (...)
  ]
}

Text embedding example

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The following example performs text embedding on the example sentence using the Cohere integration.

resp = client.inference.inference(
    task_type="text_embedding",
    inference_id="my-cohere-endpoint",
    input="The sky above the port was the color of television tuned to a dead channel.",
    task_settings={
        "input_type": "ingest"
    },
)
print(resp)

const response = await client.inference.inference({
  task_type: "text_embedding",
  inference_id: "my-cohere-endpoint",
  input:
    "The sky above the port was the color of television tuned to a dead channel.",
  task_settings: {
    input_type: "ingest",
  },
});
console.log(response);

POST _inference/text_embedding/my-cohere-endpoint
{
  "input": "The sky above the port was the color of television tuned to a dead channel.",
  "task_settings": {
    "input_type": "ingest"
  }
}

The API returns the following response:

{
  "text_embedding": [
    {
      "embedding": [
        {
          0.018569946,
          -0.036895752,
          0.01486969,
          -0.0045204163,
          -0.04385376,
          0.0075950623,
          0.04260254,
          -0.004005432,
          0.007865906,
          0.030792236,
          -0.050476074,
          0.011795044,
          -0.011642456,
          -0.010070801,
          (...)
        },
        (...)
      ]
    }
  ]
}

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