[Frontend] add json_schema support from OpenAI protocol

tests/entrypoints/openai/test_chat.py

@@ -755,6 +755,39 @@ async def test_response_format_json_object(client: openai.AsyncOpenAI):
         assert loaded == {"result": 2}, loaded
 
 
+@pytest.mark.asyncio
+async def test_response_format_json_schema(client: openai.AsyncOpenAI):
+    for _ in range(2):
+        resp = await client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[{
+                "role":
+                "user",
+                "content": ('what is 1+1? please respond with a JSON object, '
+                            'the format is {"result": 2}')
+            }],
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "foo_test",
+                    "schema": {
+                        "type": "object",
+                        "properties": {
+                            "result": {
+                                "type": "integer"
+                            },
+                        },
+                    },
+                }
+            })
+
+        content = resp.choices[0].message.content
+        assert content is not None
+
+        loaded = json.loads(content)
+        assert loaded == {"result": 2}, loaded
+
+
 @pytest.mark.asyncio
 async def test_extra_fields(client: openai.AsyncOpenAI):
     with pytest.raises(BadRequestError) as exc_info:

vllm/entrypoints/openai/protocol.py

@@ -84,9 +84,19 @@ class UsageInfo(OpenAIBaseModel):
     completion_tokens: Optional[int] = 0
 
 
+class JsonSchemaResponseFormat(OpenAIBaseModel):
+    name: str
+    description: Optional[str] = None
+    # schema is the field in openai but that causes conflicts with pydantic so
+    # instead use json_schema with an alias
+    json_schema: Optional[Dict[str, Any]] = Field(default=None, alias='schema')
+    strict: Optional[bool] = None
+
+
 class ResponseFormat(OpenAIBaseModel):
-    # type must be "json_object" or "text"
-    type: Literal["text", "json_object"]
+    # type must be "json_schema", "json_object" or "text"
+    type: Literal["text", "json_object", "json_schema"]
+    json_schema: Optional[JsonSchemaResponseFormat] = None
 
 
 class StreamOptions(OpenAIBaseModel):

vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py

@@ -49,6 +49,13 @@ async def get_lm_format_enforcer_guided_decoding_logits_processor(
           and request.response_format.type == "json_object"):
         character_level_parser = JsonSchemaParser(
             None)  # None means any json object
+    elif (request.response_format is not None
+          and request.response_format.type == "json_schema"
+          and request.response_format.json_schema is not None
+          and request.response_format.json_schema.json_schema is not None):
+        schema = _normalize_json_schema_object(
+            request.response_format.json_schema.json_schema)
+        character_level_parser = JsonSchemaParser(schema)
     else:
         return None
 

vllm/model_executor/guided_decoding/outlines_decoding.py

@@ -127,6 +127,13 @@ def _get_guide_and_mode(
           and request.response_format is not None
           and request.response_format.type == "json_object"):
         return JSON_GRAMMAR, GuidedDecodingMode.GRAMMAR
+    elif (not isinstance(request, GuidedDecodingRequest)
+          and request.response_format is not None
+          and request.response_format.type == "json_schema"
+          and request.response_format.json_schema is not None
+          and request.response_format.json_schema.json_schema is not None):
+        json = json_dumps(request.response_format.json_schema.json_schema)
+        return json, GuidedDecodingMode.JSON
     else:
         return None, None