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Elastic 文档 ›Elasticsearch 指南 [8.17] ›REST API ›推理 API

执行推理 API

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执行推理 API

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通过使用推理端点对输入文本执行推理任务。

推理 API 使您能够使用某些服务，例如内置的机器学习模型（ELSER、E5）、通过 Eland 上传的模型、Cohere、OpenAI、Azure、Google AI Studio、Google Vertex AI、Anthropic、Watsonx.ai 或 Hugging Face。对于内置模型和通过 Eland 上传的模型，推理 API 提供了一种使用和管理训练模型的替代方法。但是，如果您不打算使用推理 API 来使用这些模型，或者您想使用非 NLP 模型，请使用机器学习训练模型 API。

请求

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POST /_inference/<inference_id>

POST /_inference/<task_type>/<inference_id>

先决条件

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  • 需要 monitor_inference 集群权限 (内置的 inference_admin 和 inference_user 角色授予此权限)

描述

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执行推理 API 使您能够使用机器学习模型对您作为输入提供的数据执行特定任务。该 API 返回一个包含任务结果的响应。您使用的推理端点可以执行在创建端点时使用创建推理 API定义的特定任务。

路径参数

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<inference_id>
(必需，字符串) 推理端点的唯一标识符。
<task_type>
(可选，字符串) 模型执行的推理任务类型。

查询参数

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timeout
(可选，超时) 控制等待推理完成的时间量。默认为 30 秒。

请求正文

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input

(必需，字符串或字符串数组) 要在其上执行推理任务的文本。input 可以是单个字符串或数组。

目前，对于 completion 任务类型的推理端点，仅支持将单个字符串作为输入。

query
(必需，字符串) 仅适用于 rerank 推理端点。搜索查询文本。
task_settings
(可选，对象) 单个推理请求的任务设置。这些设置特定于您指定的 <task_type>，并覆盖初始化服务时指定的任务设置。

示例

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补全示例

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以下示例对示例问题执行补全操作。

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?"
}
Copy as curlTry in Elastic 

该 API 返回以下响应

{
  "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."
    }
  ]
}

重排序示例

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以下示例对示例输入执行重排序操作。

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"
}
Copy as curlTry in Elastic 

该 API 返回以下响应

{
  "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"
    }
  ]
}

稀疏嵌入示例

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以下示例对示例句子执行稀疏嵌入操作。

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."
}
Copy as curlTry in Elastic 

该 API 返回以下响应

{
  "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,
      (...)
    },
    (...)
  ]
}

文本嵌入示例

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以下示例使用 Cohere 集成对示例句子执行文本嵌入操作。

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"
  }
}
Copy as curlTry in Elastic 

该 API 返回以下响应

{
  "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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