Perform non-streaming chat completion inference on the service

POST /_inference/chat_completion/{inference_id}

The chat completion inference API enables rich responses for chat completion tasks. It only works with the chat_completion task type.

Note

The chat_completion task type supports both streaming and non-streaming. The Chat completion inference API provides more comprehensive customization options through more fields and function calling support. To determine whether a given inference service supports this task type, please see the page for that service.

These services support non-streaming chat completion inference:

AI21
Azure OpenAI
Deepseek
Elastic
FireworksAI
Groq
Huggingface
IBMWatsonX
Llama
Mistral
NVIDIA
OpenAI
OpenShiftAI

Authentication

Api key

Authorization: <value>

Elasticsearch APIs support key-based authentication. You must create an API key and use the encoded value in the request header. For example:

		curl -X GET "${ES_URL}/_cat/indices?v=true" \
  -H "Authorization: ApiKey ${API_KEY}"

To get API keys, use the /_security/api_key APIs.

Basic

Authorization: Basic <credentials>

Bearer

Authorization: Bearer <token>

Elasticsearch APIs support the use of bearer tokens in the Authorization HTTP header to authenticate with the API. For examples, refer to Token-based authentication services

Path parameters

Query parameters

Request body

messages object[] required

A list of objects representing the conversation. Requests should generally only add new messages from the user (role user). The other message roles (assistant, system, or tool) should generally only be copied from the response to a previous completion request, such that the messages array is built up throughout a conversation.

content union

The content of the message.

String example:

		{
   "content": "Some string"
}
		
	

Text example:

		{
  "content": [
      {
       "text": "Some text",
       "type": "text"
      }
   ]
}
		
	

Image example:

		{
  "content": [
      {
       "image_url": {
         "url": "data:image/jpeg;base64,..."
       },
       "type": "image_url"
      }
   ]
}
		
	

File example:

		{
  "content": [
      {
       "file": {
         "file_data": "data:application/pdf;base64,...",
         "filename": "somePDF"
       },
       "type": "file"
      }
   ]
}
		
	

role string required

The role of the message author. Valid values are user, assistant, system, and tool.

tool_call_id string

Only for tool role messages. The tool call that this message is responding to.

tool_calls object[]

Only for assistant role messages. The tool calls generated by the model. If it's specified, the content field is optional. Example:

		{
  "tool_calls": [
      {
          "id": "call_KcAjWtAww20AihPHphUh46Gd",
          "type": "function",
          "function": {
              "name": "get_current_weather",
              "arguments": "{\"location\":\"Boston, MA\"}"
          }
      }
  ]
}
		
	

reasoning string

Only for assistant role messages. The reasoning details generated by the model as plaintext. Currently supported only for elastic provider.

reasoning_details union oneOf[]

Only for assistant role messages. The reasoning details generated by the model as structured data. Currently supported only for elastic provider.

One of:

The reasoning detail with encrypted reasoning data that may be redacted or protected.

type enum required: Values: reasoning.encrypted
format string: The format of the reasoning detail.
id string: The identifier of the reasoning detail.
index number: The index of the reasoning detail, which indicates its position in the sequence of reasoning details generated by the model.
data string required: The encrypted reasoning data generated by the model, which may be redacted or protected based on the model's configuration and the nature of the reasoning information.

model string

The ID of the model to use. By default, the model ID is set to the value included when creating the inference endpoint.

max_completion_tokens number

The upper bound limit for the number of tokens that can be generated for a completion request.

reasoning object

The reasoning configuration for the completion request. This controls the model's reasoning process in one of two ways:

By specifying the model’s reasoning effort level with the effort field.
By enabling reasoning with default settings by setting enabled field to true.

It also includes optional settings to control:

The level of detail in the summary returned in the response with the summary field.
Whether reasoning details are included in the response at all with the exclude field.

Example (effort):

		{
   "reasoning": {
       "effort": "high",
       "summary": "concise",
       "exclude": false
   }
}
		
	

Example (enabled):

		{
   "reasoning": {
       "enabled": true,
       "summary": "concise",
       "exclude": false
   }
}
		
	

Currently supported only for elastic provider.

stop string[]

A sequence of strings to control when the model should stop generating additional tokens.

temperature number

The sampling temperature to use.

tool_choice union

Controls which tool is called by the model. String representation: One of auto, none, or requrired. auto allows the model to choose between calling tools and generating a message. none causes the model to not call any tools. required forces the model to call one or more tools. Example (object representation):

		{
  "tool_choice": {
      "type": "function",
      "function": {
          "name": "get_current_weather"
      }
  }
}
		
	

tools object[]

A list of tools that the model can call. Example:

		{
  "tools": [
      {
          "type": "function",
          "function": {
              "name": "get_price_of_item",
              "description": "Get the current price of an item",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "item": {
                          "id": "12345"
                      },
                      "unit": {
                          "type": "currency"
                      }
                  }
              }
          }
      }
  ]
}
		
	

top_p number

Nucleus sampling, an alternative to sampling with temperature.

Response

application/json

id string required: The unique identifier for the completion.
choices object[]: The list of completion choices the model generated for the input message.
model string required: The model used to generate the completion.
object string required: The object type.
usage object: The token usage statistics for the completion request. Omitted when usage information is not available.

Examples

Request Console 1 / 6 languages

Run POST _inference/chat_completion/openai-completion to perform a chat completion on the example question with non-streaming.

Console Kibana Dev Tools

POST _inference/chat_completion/openai-completion
{
  "model": "gpt-4o",
  "messages": [
      {
          "role": "user",
          "content": "What is Elastic?"
      }
  ]
}

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.nonStreamingChatCompletion({
  inference_id: "openai-completion",
  chat_completion_request: {
    model: "gpt-4o",
    messages: [
      {
        role: "user",
        content: "What is Elastic?",
      },
    ],
  },
});

Python Elasticsearch Python Client

resp = client.inference.non_streaming_chat_completion(
    inference_id="openai-completion",
    chat_completion_request={
        "model": "gpt-4o",
        "messages": [
            {
                "role": "user",
                "content": "What is Elastic?"
            }
        ]
    },
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/chat_completion/openai-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": "What is Elastic?"
      }
    ]
  }'

PHP Elasticsearch PHP Client

$resp = $client->inference()->nonStreamingChatCompletion([
    "inference_id" => "openai-completion",
    "body" => [
        "model" => "gpt-4o",
        "messages" => array(
            [
                "role" => "user",
                "content" => "What is Elastic?",
            ],
        ),
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.non_streaming_chat_completion(
  inference_id: "openai-completion",
  body: {
    "model": "gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": "What is Elastic?"
      }
    ]
  }
)

Response

A successful response from POST _inference/chat_completion/openai-completion for a plain user message. The object field is chat.completion (as opposed to chat.completion.chunk in streaming responses). The message wrapper contains the model-generated text. Usage statistics include per-category token breakdowns when available.

{
  "id": "chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3",
  "choices": [
    {
      "message": {
        "content": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics, built around the open-source Elasticsearch engine.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

Request Console 1 / 2 languages

Run POST _inference/chat_completion/openai-completion to perform a non-streaming chat completion using an Assistant message with tool_calls.

Console Kibana Dev Tools

POST _inference/chat_completion/openai-completion
{
  "messages": [
      {
          "role": "assistant",
          "content": "Let's find out what the weather is",
          "tool_calls": [ 
              {
                  "id": "call_KcAjWtAww20AihPHphUh46Gd",
                  "type": "function",
                  "function": {
                      "name": "get_current_weather",
                      "arguments": "{\"location\":\"Boston, MA\"}"
                  }
              }
          ]
      },
      { 
          "role": "tool",
          "content": "The weather is cold",
          "tool_call_id": "call_KcAjWtAww20AihPHphUh46Gd"
      }
  ]
}

Generated from the API spec

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/chat_completion/openai-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
        {
            "role": "assistant",
            "content": "Let'\''s find out what the weather is",
            "tool_calls": [ 
                {
                    "id": "call_KcAjWtAww20AihPHphUh46Gd",
                    "type": "function",
                    "function": {
                        "name": "get_current_weather",
                        "arguments": "{\"location\":\"Boston, MA\"}"
                    }
                }
            ]
        },
        { 
            "role": "tool",
            "content": "The weather is cold",
            "tool_call_id": "call_KcAjWtAww20AihPHphUh46Gd"
        }
    ]
  }'

Generated from the API spec

Response

{
  "id": "chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3",
  "choices": [
    {
      "message": {
        "content": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics, built around the open-source Elasticsearch engine.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

Request Console 1 / 2 languages

Run POST _inference/chat_completion/openai-completion to perform a non-streaming chat completion using a User message with tools and tool_choice.

Console Kibana Dev Tools

POST _inference/chat_completion/openai-completion
{
  "messages": [
      {
          "role": "user",
          "content": [
              {
                  "type": "text",
                  "text": "What's the price of a scarf?"
              }
          ]
      }
  ],
  "tools": [
      {
          "type": "function",
          "function": {
              "name": "get_current_price",
              "description": "Get the current price of a item",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "item": {
                          "id": "123"
                      }
                  }
              }
          }
      }
  ],
  "tool_choice": {
      "type": "function",
      "function": {
          "name": "get_current_price"
      }
  }
}

Generated from the API spec

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/chat_completion/openai-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What'\''s the price of a scarf?"
                }
            ]
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_current_price",
                "description": "Get the current price of a item",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "item": {
                            "id": "123"
                        }
                    }
                }
            }
        }
    ],
    "tool_choice": {
        "type": "function",
        "function": {
            "name": "get_current_price"
        }
    }
  }'

Generated from the API spec

Response

{
  "id": "chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3",
  "choices": [
    {
      "message": {
        "content": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics, built around the open-source Elasticsearch engine.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

Request Console 1 / 2 languages

Run POST _inference/chat_completion/reasoning-chat-completion to perform a non-streaming chat completion task, using both effort parameter based reasoning configuration and including reasoning generated by the model on previous step.

Console Kibana Dev Tools

POST _inference/chat_completion/reasoning-chat-completion
{
  "messages": [{
      "role": "user",
      "content": [{
          "type": "text",
          "text": "Barber shaves all those, who do not shave themselves. Who shaves the barber?"
        }
      ]
    }, {
      "role": "assistant",
      "content": [{
          "type": "text",
          "text": "This is the barber paradox. Such a barber cannot logically exist."
        }
      ],
      "reasoning": "If the barber shaves himself, he should not; if he does not, he should.",
      "reasoning_details": [{
          "type": "reasoning.encrypted",
          "data": "[REDACTED]"
        }, {
          "type": "reasoning.summary",
          "summary": "Barber shaving himself creates contradiction"
        }, {
          "type": "reasoning.text",
          "text": "If the barber shaves himself, he should not; if he does not, he should.",
          "signature": "sig_123"
        }
      ]
    }, {
      "role": "user",
      "content": [{
          "type": "text",
          "text": "What if there are 2 barbers?"
        }
      ]
    }
  ],
  "reasoning": {
    "effort": "high",
    "summary": "detailed",
    "exclude": false
  }
}

Generated from the API spec

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/chat_completion/reasoning-chat-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{
        "role": "user",
        "content": [{
            "type": "text",
            "text": "Barber shaves all those, who do not shave themselves. Who shaves the barber?"
          }
        ]
      }, {
        "role": "assistant",
        "content": [{
            "type": "text",
            "text": "This is the barber paradox. Such a barber cannot logically exist."
          }
        ],
        "reasoning": "If the barber shaves himself, he should not; if he does not, he should.",
        "reasoning_details": [{
            "type": "reasoning.encrypted",
            "data": "[REDACTED]"
          }, {
            "type": "reasoning.summary",
            "summary": "Barber shaving himself creates contradiction"
          }, {
            "type": "reasoning.text",
            "text": "If the barber shaves himself, he should not; if he does not, he should.",
            "signature": "sig_123"
          }
        ]
      }, {
        "role": "user",
        "content": [{
            "type": "text",
            "text": "What if there are 2 barbers?"
          }
        ]
      }
    ],
    "reasoning": {
      "effort": "high",
      "summary": "detailed",
      "exclude": false
    }
  }'

Generated from the API spec

Response

{
  "id": "chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3",
  "choices": [
    {
      "message": {
        "content": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics, built around the open-source Elasticsearch engine.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

Request Console 1 / 2 languages

Run POST _inference/chat_completion/reasoning-chat-completion to perform a non-streaming chat completion task, using both enabled parameter based reasoning configuration and including reasoning generated by the model on previous step.

Console Kibana Dev Tools

POST _inference/chat_completion/reasoning-chat-completion
{
  "messages": [{
      "role": "user",
      "content": [{
          "type": "text",
          "text": "Barber shaves all those, who do not shave themselves. Who shaves the barber?"
        }
      ]
    }, {
      "role": "assistant",
      "content": [{
          "type": "text",
          "text": "This is the barber paradox. Such a barber cannot logically exist."
        }
      ],
      "reasoning": "If the barber shaves himself, he should not; if he does not, he should.",
      "reasoning_details": [{
          "type": "reasoning.encrypted",
          "data": "[REDACTED]"
        }, {
          "type": "reasoning.summary",
          "summary": "Barber shaving himself creates contradiction"
        }, {
          "type": "reasoning.text",
          "text": "If the barber shaves himself, he should not; if he does not, he should.",
          "signature": "sig_123"
        }
      ]
    }, {
      "role": "user",
      "content": [{
          "type": "text",
          "text": "What if there are 2 barbers?"
        }
      ]
    }
  ],
  "reasoning": {
    "enabled": true,
    "summary": "detailed",
    "exclude": false
  }
}

Generated from the API spec

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/chat_completion/reasoning-chat-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{
        "role": "user",
        "content": [{
            "type": "text",
            "text": "Barber shaves all those, who do not shave themselves. Who shaves the barber?"
          }
        ]
      }, {
        "role": "assistant",
        "content": [{
            "type": "text",
            "text": "This is the barber paradox. Such a barber cannot logically exist."
          }
        ],
        "reasoning": "If the barber shaves himself, he should not; if he does not, he should.",
        "reasoning_details": [{
            "type": "reasoning.encrypted",
            "data": "[REDACTED]"
          }, {
            "type": "reasoning.summary",
            "summary": "Barber shaving himself creates contradiction"
          }, {
            "type": "reasoning.text",
            "text": "If the barber shaves himself, he should not; if he does not, he should.",
            "signature": "sig_123"
          }
        ]
      }, {
        "role": "user",
        "content": [{
            "type": "text",
            "text": "What if there are 2 barbers?"
          }
        ]
      }
    ],
    "reasoning": {
      "enabled": true,
      "summary": "detailed",
      "exclude": false
    }
  }'

Generated from the API spec

Response

{
  "id": "chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3",
  "choices": [
    {
      "message": {
        "content": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics, built around the open-source Elasticsearch engine.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

Authentication

Path parameters

Query parameters

Request body { }

Response

Request body