Formatting model responses

By default, the model returns a response formatted with Markdown. Use the prompt text to get a response with additional formatting, e.g., with emoji, or in a different format, e.g., JSON, XML, etc.

Here is an example:

{
  "modelUri": "gpt://<folder_ID>/yandexgpt/latest",
  "completionOptions": {
    "stream": false,
    "temperature": 0.6,
    "maxTokens": "2000",
    "reasoningOptions": {
      "mode": "DISABLED"
    }
  },
  "messages": [
    {
      "role": "system",
      "text": "You are a smart assistant."
    },
    {
      "role": "user",
      "text": "Name any three groups of grocery store products. For each group, give three subgroups. Present the result as a JSON object, where each group of products is represented by a key in the JSON object, and arrays from the relevant subgroups are the values. No introductory phrases or explanations needed, just data. Do not use Markdown."
    }
  ]
}

Result:

{
  "result": {
    "alternatives": [
      {
        "message": {
          "role": "assistant",
          "text": "{\n    \"meat\": [\"beef\", \"pork\", \"mutton\"],\n    \"dairy products\": [\"milk\", \"curd\", \"sour cream\"],\n    \"fruit\": [\"apples\", \"bananas\", \"oranges\"]\n}"
        },
        "status": "ALTERNATIVE_STATUS_FINAL"
      }
    ],
    "usage": {
      "inputTextTokens": "87",
      "completionTokens": "58",
      "totalTokens": "145"
    },
    "modelVersion": "07.03.2024"
  }
}

The model returned a response in JSON format with line breaks replaced with \n and quotation marks escaped.

If you do not get the result you expect using the prompt, try fine-tuning the model.

API parameters for saving the response structureAPI parameters for saving the response structure

For some text generation models, you can set the response format not only via a prompt but also using request parameters. For example, you can use the response formatting options to request a response in JSON format. There are two options to choose from:

1. JSON with any structure:

   SDK

   API

   #!/usr/bin/env python3
   
   from __future__ import annotations
   
   import json
   
   import pydantic
   
   from yandex_cloud_ml_sdk import YCloudML
   
   text = """
   Name any three groups of grocery store products. 
   For each group, give three subgroups. 
   Present the result in JSON format.
   """
   
   
   def main() -> None:
       sdk = YCloudML(
           folder_id="<folder_ID>",
           auth="<API_key>",
       )
   
       model = sdk.models.completions("yandexgpt", model_version="rc")
   
       model = model.configure(response_format="json")
       result = model.run(
           [
               {"role": "user", "text": text},
           ]
       )
       print("JSON result:", result[0].text)
   
   
   if __name__ == "__main__":
       main()
   

   {
     "modelUri": "gpt://<folder_ID>/yandexgpt/rc",
     "completionOptions": {
       "stream": false
     },
     "messages": [
       {
         "role": "user",
         "text": "Name any three groups of grocery store products. For each group, give three subgroups. Present the result in JSON format."
       }
     ],
     "json_object": true
   }
   

   Tip

   If you want to get a JSON with an arbitrary structure, explicitly mention this in your prompt. Otherwise, the model may add extra brackets, spaces, indents and generate excessive tokens.

2. JSON strictly following the specified schema:

   SDK

   API

   #!/usr/bin/env python3
   
   from __future__ import annotations
   
   import json
   
   import pydantic
   
   from yandex_cloud_ml_sdk import YCloudML
   
   text = "Name the date of Gagarin's first flight."
   
   
   def main() -> None:
       sdk = YCloudML(
           folder_id="<folder_ID>",
           auth="<API_key>",
       )
   
       model = sdk.models.completions("yandexgpt", model_version="rc")
   
       model = model.configure(
           response_format={
               "json_schema": {
                   "properties": {
                       "day": {
                           "title": "Day",
                           "description": "Day of the month",
                           "type": "integer",
                       },
                       "month": {
                           "title": "Month",
                           "description": "Month, in a word",
                           "type": "string",
                       },
                       "year": {
                           "title": "Year",
                           "description": "Year",
                           "type": "integer",
                       },
                   },
                   "required": ["day", "month", "year"],
                   "type": "object",
               }
           }
       )
       result = model.run(
           [
               {"role": "user", "text": text},
           ]
       )
       print("JSON result:", result[0].text)
   
   
   if __name__ == "__main__":
       main()
   

   {
     "modelUri": "gpt://<folder_ID>/yandexgpt/rc",
     "completionOptions": {
       "stream": false
     },
     "messages": [
       {
         "role": "user",
         "text": "Name the date of Gagarin's first flight."
       }
     ],
     "json_schema": {
       "schema": {
         "properties": {
           "day": {
             "title": "Day",
             "description": "Day of the month",
             "type": "integer"
           },
           "month": {
             "title": "Month",
             "description": "Month, in a word",
             "type": "string"
           },
           "year": {
             "title": "Year",
             "description": "Year",
             "type": "integer"
           }
         },
         "required": [
           "day",
           "month",
           "year"
         ],
         "type": "object"
       }
     }
   }
   

A strict response structure is required when working with external tools using function calls.