Type to search.

Create a custom inference endpoint

Auth The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats. Availability Generally available since 8.19 This functionality is generally available and ready for production usage. Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments. This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.

The custom service gives more control over how to interact with external inference services that aren't explicitly supported through dedicated integrations. The custom service gives you the ability to define the headers, url, query parameters, request body, and secrets. The custom service supports the template replacement functionality, which enables you to define a template that can be replaced with the value associated with that key. Templates are portions of a string that start with ${ and end with }. The parameters secret_parameters and task_settings are checked for keys for template replacement. Template replacement is supported in the request, headers, url, and query_parameters. If the definition (key) is not found for a template, an error message is returned. In case of an endpoint definition like the following:

Text 
PUT _inference/text_embedding/test-text-embedding
{
  "service": "custom",
  "service_settings": {
     "secret_parameters": {
          "api_key": "<some api key>"
     },
     "url": "...endpoints.huggingface.cloud/v1/embeddings",
     "headers": {
         "Authorization": "Bearer ${api_key}",
         "Content-Type": "application/json"
     },
     "request": "{\"input\": ${input}}",
     "response": {
         "json_parser": {
             "text_embeddings":"$.data[*].embedding[*]"
         }
     }
  }
}

To replace ${api_key} the secret_parameters and task_settings are checked for a key named api_key.

Note

Templates should not be surrounded by quotes.

Pre-defined templates:

  • ${input} refers to the array of input strings that comes from the input field of the subsequent inference requests.
  • ${input_type} refers to the input type translation values.
  • ${query} refers to the query field used specifically for reranking tasks.
  • ${top_n} refers to the top_n field available when performing rerank requests.
  • ${return_documents} refers to the return_documents field available when performing rerank requests.

Required privileges

Cluster privileges manage_inference

Path parameters

task_type enum required

The type of the inference task that the model will perform.

Values: text_embedding sparse_embedding rerank completion
custom_inference_id string required

The unique identifier of the inference endpoint.

Request body

service enum required

The type of service supported for the specified task type. In this case, custom.

Values: custom
service_settings object required

Settings used to install the inference model. These settings are specific to the custom service.

batch_size number

Specifies the batch size used for the semantic_text field. If the field is not provided, the default is 10. The batch size is the maximum number of inputs in a single request to the upstream service. The chunk within the batch are controlled by the selected chunking strategy for the semantic_text field.

Reference
headers object

Specifies the HTTP header parameters – such as Authentication or Content-Type – that are required to access the custom service. For example:

Text 
"headers":{
  "Authorization": "Bearer ${api_key}",
  "Content-Type": "application/json;charset=utf-8"
}
input_type object

Specifies the input type translation values that are used to replace the ${input_type} template in the request body. For example:

Text 
"input_type": {
  "translation": {
    "ingest": "do_ingest",
    "search": "do_search"
  },
  "default": "a_default"
},

If the subsequent inference requests come from a search context, the search key will be used and the template will be replaced with do_search. If it comes from the ingest context do_ingest is used. If it's a different context that is not specified, the default value will be used. If no default is specified an empty string is used. translation can be:

  • classification
  • clustering
  • ingest
  • search
query_parameters object

Specifies the query parameters as a list of tuples. The arrays inside the query_parameters must have two items, a key and a value. For example:

Text 
"query_parameters":[
  ["param_key", "some_value"],
  ["param_key", "another_value"],
  ["other_key", "other_value"]
]

If the base url is https://www.elastic.co it results in: https://www.elastic.co?param_key=some_value&param_key=another_value&other_key=other_value.

request object required

The request configuration object.

content string required

The body structure of the request. It requires passing in the string-escaped result of the JSON format HTTP request body. For example:

Text 
"request": "{\"input\":${input}}"
Note

The content string needs to be a single line except when using the Kibana console.

response object required

The response configuration object.

json_parser object required

Specifies the JSON parser that is used to parse the response from the custom service. Different task types require different json_parser parameters. For example:

Text 
# text_embedding
# For a response like this:

{
 "object": "list",
 "data": [
     {
       "object": "embedding",
       "index": 0,
       "embedding": [
           0.014539449,
           -0.015288644
       ]
     }
 ],
 "model": "text-embedding-ada-002-v2",
 "usage": {
     "prompt_tokens": 8,
     "total_tokens": 8
 }
}

# the json_parser definition should look like this:

"response":{
  "json_parser":{
    "text_embeddings":"$.data[*].embedding[*]"
  }
}

# sparse_embedding
# For a response like this:

{
  "request_id": "75C50B5B-E79E-4930-****-F48DBB392231",
  "latency": 22,
  "usage": {
     "token_count": 11
  },
  "result": {
     "sparse_embeddings": [
        {
          "index": 0,
          "embedding": [
            {
              "token_id": 6,
              "weight": 0.101
            },
            {
              "token_id": 163040,
              "weight": 0.28417
            }
          ]
        }
     ]
  }
}

# the json_parser definition should look like this:

"response":{
  "json_parser":{
    "token_path":"$.result.sparse_embeddings[*].embedding[*].token_id",
    "weight_path":"$.result.sparse_embeddings[*].embedding[*].weight"
  }
}

# rerank
# For a response like this:

{
  "results": [
    {
      "index": 3,
      "relevance_score": 0.999071,
      "document": "abc"
    },
    {
      "index": 4,
      "relevance_score": 0.7867867,
      "document": "123"
    },
    {
      "index": 0,
      "relevance_score": 0.32713068,
      "document": "super"
    }
  ],
}

# the json_parser definition should look like this:

"response":{
  "json_parser":{
    "reranked_index":"$.result.scores[*].index",
    "relevance_score":"$.result.scores[*].score",
    "document_text":"xxx"
  }
}

# completion
# For a response like this:

{
 "id": "chatcmpl-B9MBs8CjcvOU2jLn4n570S5qMJKcT",
 "object": "chat.completion",
 "created": 1741569952,
 "model": "gpt-4.1-2025-04-14",
 "choices": [
   {
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Hello! How can I assist you today?",
      "refusal": null,
      "annotations": []
    },
    "logprobs": null,
    "finish_reason": "stop"
  }
 ]
}

# the json_parser definition should look like this:

"response":{
  "json_parser":{
    "completion_result":"$.choices[*].message.content"
  }
}
  1. optional
  2. optional
secret_parameters object required

Specifies secret parameters, like api_key or api_token, that are required to access the custom service. For example:

Text 
"secret_parameters":{
  "api_key":"<api_key>"
}
url string

The URL endpoint to use for the requests.

Response

application/json
service string required

The service type

service_settings object required

Settings specific to the service

task_settings object

Task settings specific to the service and task type

inference_id string required

The inference Id

task_type enum required

The task type

Values: text_embedding sparse_embedding rerank completion