feat: Support JSON Schema in OpenAI-Compatible API

tensorrt_llm/serve/openai_protocol.py

@@ -54,8 +54,9 @@ class StructuralTag(OpenAIBaseModel):
 
 
 class ResponseFormat(OpenAIBaseModel):
-    # type must be "json_object" or "text" or "structural_tag"
-    type: Literal["text", "json_object", "structural_tag"]
+    # type must be one of "text", "json", "json_object", or "structural_tag"
+    type: Literal["text", "json", "json_object", "structural_tag"]
+    schema: Optional[dict] = None
     structures: Optional[List[StructuralTag]] = None
     triggers: Optional[List[str]] = None
 
@@ -144,6 +145,12 @@ def _response_format_to_guided_decoding_params(
         return None
     elif response_format.type == "text":
         return None
+    elif response_format.type == "json":
+        if response_format.schema is None:
+            raise ValueError(
+                "The 'schema' field is required when response_format.type is 'json'."
+            )
+        return GuidedDecodingParams(json=response_format.schema)
     elif response_format.type == "json_object":
         return GuidedDecodingParams(json_object=True)
     elif response_format.type == "structural_tag":
@@ -207,7 +214,7 @@ class CompletionRequest(OpenAIBaseModel):
         default=None,
         description=
         ("Similar to chat completion, this parameter specifies the format of "
-         "output. {'type': 'json_object'}, {'type': 'text' }, {'type': 'structural_tag'} are "
+         "output. {'type': 'json_object'}, {'type': 'text' }, {'type': 'structural_tag'}, {'type': 'json'} are "
          "supported."),
     )
 

tests/integration/defs/test_e2e.py

@@ -1443,6 +1443,14 @@ def test_openai_chat_structural_tag_example(llm_venv):
     ])
 
 
+def test_openai_chat_json_example(llm_venv):
+    test_root = unittest_path() / "llmapi" / "apps"
+
+    llm_venv.run_cmd(
+        ["-m", "pytest",
+         str(test_root / "_test_openai_chat_json.py")])
+
+
 @pytest.mark.skip_less_device(2)
 @pytest.mark.skip_less_device_memory(40000)
 def test_openai_multi_chat_example(llm_root, llm_venv):

tests/integration/test_lists/test-db/l0_a10.yml

@@ -22,6 +22,7 @@ l0_a10:
   - disaggregated/test_disaggregated.py::test_disaggregated_mixed[TinyLlama-1.1B-Chat-v1.0]
   - disaggregated/test_disaggregated.py::test_disaggregated_overlap[TinyLlama-1.1B-Chat-v1.0]
   - test_e2e.py::test_openai_chat_structural_tag_example
+  - test_e2e.py::test_openai_chat_json_example
   - test_e2e.py::test_openai_chat_multimodal_example
   - test_e2e.py::test_openai_lora
   - test_e2e.py::test_trtllm_serve_multimodal_example

tests/unittest/llmapi/apps/_test_openai_chat_json.py

@@ -0,0 +1,145 @@
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/aae6927be06dedbda39c6b0c30f6aa3242b84388/tests/entrypoints/openai/test_chat.py
+import json
+import os
+import tempfile
+from typing import Any
+
+import jsonschema
+import openai
+import pytest
+import yaml
+
+from ..test_llm import get_model_path
+from .openai_server import RemoteOpenAIServer
+
+pytestmark = pytest.mark.threadleak(enabled=False)
+
+
+@pytest.fixture(scope="module", ids=["TinyLlama-1.1B-Chat"])
+def model_name():
+    return "llama-models-v2/TinyLlama-1.1B-Chat-v1.0"
+
+
+@pytest.fixture(scope="module")
+def temp_extra_llm_api_options_file(request):
+    temp_dir = tempfile.gettempdir()
+    temp_file_path = os.path.join(temp_dir, "extra_llm_api_options.yaml")
+    try:
+        extra_llm_api_options_dict = {
+            "guided_decoding_backend": "xgrammar",
+            "disable_overlap_scheduler":
+            True,  # Guided decoding is not supported with overlap scheduler
+        }
+
+        with open(temp_file_path, "w") as f:
+            yaml.dump(extra_llm_api_options_dict, f)
+
+        yield temp_file_path
+    finally:
+        if os.path.exists(temp_file_path):
+            os.remove(temp_file_path)
+
+
+@pytest.fixture(scope="module")
+def server(model_name: str, temp_extra_llm_api_options_file: str):
+    model_path = get_model_path(model_name)
+    args = [
+        "--backend", "pytorch", "--extra_llm_api_options",
+        temp_extra_llm_api_options_file
+    ]
+    with RemoteOpenAIServer(model_path, args) as remote_server:
+        yield remote_server
+
+
+@pytest.fixture(scope="module")
+def client(server: RemoteOpenAIServer):
+    return server.get_client()
+
+
+@pytest.fixture(scope="module")
+def async_client(server: RemoteOpenAIServer):
+    return server.get_async_client()
+
+
+@pytest.fixture(scope="module")
+def user_profile_schema():
+    """Provides a sample JSON schema for a user profile."""
+    return {
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": "string",
+                "description": "The full name of the user."
+            },
+            "age": {
+                "type": "integer",
+                "description": "The age of the user, in years."
+            },
+        },
+        "required": ["name", "age"],
+    }
+
+
+def test_chat_json_schema(client: openai.OpenAI, model_name: str,
+                          user_profile_schema):
+    """
+    Tests the `json` response format in a multi-turn synchronous conversation.
+    Adapted from https://github.com/vllm-project/vllm/blob/aae6927be06dedbda39c6b0c30f6aa3242b84388/tests/entrypoints/openai/test_chat.py#L413
+    """
+
+    def _create_and_validate_response(
+            messages: list[dict[str, Any]]) -> dict[str, Any]:
+        chat_completion = client.chat.completions.create(
+            model=model_name,
+            messages=messages,
+            max_tokens=1000,
+            temperature=0.0,
+            response_format={
+                "type": "json",
+                "schema": user_profile_schema
+            },
+        )
+        message = chat_completion.choices[0].message
+        assert message.content is not None
+        try:
+            message_json = json.loads(message.content)
+        except json.JSONDecodeError:
+            pytest.fail(
+                f"The output was not a valid JSON string. Output: {message.content}"
+            )
+
+        jsonschema.validate(instance=message_json, schema=user_profile_schema)
+        return message_json, message.content
+
+    messages = [
+        {
+            "role": "system",
+            "content": "you are a helpful assistant"
+        },
+        {
+            "role":
+            "user",
+            "content":
+            f"Give an example JSON for an employee profile that fits this schema: {user_profile_schema}",
+        },
+    ]
+    first_json, first_content = _create_and_validate_response(messages)
+    messages.extend([
+        {
+            "role": "assistant",
+            "content": first_content,
+        },
+        {
+            "role": "user",
+            "content": "Give me another one with a different name and age.",
+        },
+    ])
+    second_json, second_content = _create_and_validate_response(messages)
+
+    assert (
+        first_json["name"] != second_json["name"]
+    ), "The model should have generated a different name in the second turn."
+    assert (
+        first_json["age"] != second_json["age"]
+    ), "The model should have generated a different age in the second turn."