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Calling Third-Party Inference Services

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Última atualização: 2026-08-12 18:13:52
Traduzido por IA
ES can integrate third-party inference services through its inference capabilities, enabling flexible and efficient calls of various required capabilities. For more details, refer to the related document.

Step 1: Enabling Node Outbound Access

To enable ES to access third-party services on the public network, you need to enable Node Outbound Access first.


Step 2: Creating an Inference Endpoint

The following is a reference template for creating a custom inference:
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[*]"
         }
     }
  }
}
For example, the following is an example of using the SiliconFlow embedding service. You can refer to the template and fill in your API key and model name:
PUT _inference/text_embedding/test-text-embedding
{
    "service": "custom",
    "service_settings": {
        "secret_parameters": {
            "api_key": "sk-your_actual_API_key"
        },
        "url": "https://api.siliconflow.cn/v1/embeddings",
        "headers": {
            "Authorization": "Bearer sk-your_actual_API_key",
            "Content-Type": "application/json;charset=utf-8"
        },
        "request": "{\\"input\\": ${input}, \\"model\\": \\"BAAI/bge-large-zh-v1.5\\"}",
        "response": {
            "json_parser": {
                "text_embeddings": "$.data[*].embedding[*]"
            }
        }
    }
}

After execution, it indicates that the creation is successful if the following information is returned:


Step 3: Verifying the Inference Service

You can verify the inference service using the following statement. If successful, the embedding result is returned.
POST _inference/text_embedding/test-text-embedding
{ 
    "input": "hello, this is an inference service" 
}


Anterior: Inferencing with Machine Learning NodesPróximo: Data Writing

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