Add support for fireworks

llama_stack/providers/adapters/inference/fireworks/fireworks.py

@@ -67,10 +67,10 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
         model: str,
         messages: List[Message],
         sampling_params: Optional[SamplingParams] = SamplingParams(),
-        response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
@@ -81,6 +81,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
             tools=tools or [],
             tool_choice=tool_choice,
             tool_prompt_format=tool_prompt_format,
+            response_format=response_format,
             stream=stream,
             logprobs=logprobs,
         )
@@ -117,6 +118,20 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
 
         options = get_sampling_options(request)
         options.setdefault("max_tokens", 512)
+
+        if fmt := request.response_format:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                options["response_format"] = {
+                    "type": "grammar",
+                    "grammar": fmt.bnf,
+                }
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
         return {
             "model": self.map_to_provider_model(request.model),
             "prompt": prompt,

llama_stack/providers/tests/inference/test_inference.py

@@ -10,7 +10,7 @@ import os
 import pytest
 import pytest_asyncio
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
@@ -191,7 +191,10 @@ async def test_structured_output(inference_settings):
     params = inference_settings["common_params"]
 
     provider = inference_impl.routing_table.get_provider_impl(params["model"])
-    if provider.__provider_id__ != "meta-reference":
+    if provider.__provider_spec__.provider_type not in (
+        "meta-reference",
+        "remote::fireworks",
+    ):
         pytest.skip("Other inference providers don't support structured output yet")
 
     class AnswerFormat(BaseModel):
@@ -207,7 +210,7 @@ async def test_structured_output(inference_settings):
         ],
         stream=False,
         response_format=JsonResponseFormat(
-            schema=AnswerFormat.schema(),
+            schema=AnswerFormat.model_json_schema(),
         ),
         **inference_settings["common_params"],
     )
@@ -222,6 +225,21 @@ async def test_structured_output(inference_settings):
     assert answer.year_of_birth == 1963
     assert answer.num_seasons_in_nba == 15
 
+    response = await inference_impl.chat_completion(
+        messages=[
+            SystemMessage(content="You are a helpful assistant."),
+            UserMessage(content="Please give me information about Michael Jordan."),
+        ],
+        stream=False,
+        **inference_settings["common_params"],
+    )
+
+    assert isinstance(response, ChatCompletionResponse)
+    assert isinstance(response.completion_message.content, str)
+
+    with pytest.raises(ValidationError):
+        AnswerFormat.parse_raw(response.completion_message.content)
+
 
 @pytest.mark.asyncio
 async def test_chat_completion_streaming(inference_settings, sample_messages):