This PR correctly adds the response type json_schema as a {"response_format": {"type: "...", requires a value of the expected schema.

This change emphasizes an existing issue with json_object which currently functions the same as json_schema (requires a schema) but should be updated to produce JSON with no expected schema.

As this PR is non breaking an only adds the correct functionality. It may make sense to follow up with the changes to the json_object in another PR and include these changes on main

Issues related to enabling json_object vs json_schema

#3058
#2899
#2900

Thanks for adding the json_schema response type!

While this PR does implement it, I'm wondering if it might miss an opportunity to better align with OpenAI's specification, which was a key point in issue #3058. The key difference between OpenAI and TGI isn't just about response_format json_schema, but also the structure of the response_format itself. OpenAI uses a nested json_schema structure, while TGI currently uses json_object (and now json_schema in this PR) with a value field. Also, if we wanted to improve json_schema compatibility further down the line, changing it from your proposed implementation will introduce breaking changes.

Instead of making another alias, I'd propose to change the required response format for json_schema to the following, while keeping the structure of json_object/json for compatibility reasons. I'd agree that making the value field optional for json_object/json can be done in another PR.

  response_format: {
    "type": "json_schema",
    "json_schema": {
    "name": "some_name",
    "strict": true,
    "schema":  ... // the actual json schema
    }
  }

this PR has been updated to handle response_format.type == "json_schema" formats as shown in the example below.

# model id: meta-llama/Meta-Llama-3.1-8B-Instruct
import requests
import json

# simple person JSON schema
person_schema = {
    "type": "object",
    "properties": {
        "firstName": {
            "type": "string",
            "description": "The person's first name.",
            "minLength": 3,
        },
        "lastName": {
            "type": "string",
            "description": "The person's last name.",
            "minLength": 3,
        },
        "age": {"type": "integer", "minimum": 0},
    },
    "required": ["firstName", "lastName", "age"],
}

response = requests.post(
    "http://localhost:3000/v1/chat/completions",
    json={
        "model": "model-name",
        "messages": [
            {
                "role": "user",
                "content": "John Smith is a 32-year-old software engineer.",
            }
        ],
        "temperature": 0.0,
        "response_format": {
            "type": "json_schema",
            "value": {"name": "person", "strict": True, "schema": person_schema},
        },
    },
    headers={"Content-Type": "application/json"},
)

print(json.dumps(response.json(), indent=2))

with the response:

{
  "object": "chat.completion",
  "id": "",
  "created": 1742224552,
  "model": "model-name",
  "system_fingerprint": "3.1.1-dev0-native",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "{ \"firstName\": \"John\", \"lastName\": \"Smith\", \"age\": 32 }"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 46,
    "completion_tokens": 23,
    "total_tokens": 69
  }
}