Add support for Structured Output / Guided decoding (#281)

Added support for structured output in the API and added a reference implementation for meta-reference.

A few notes:

* Two formats are specified in the API: Json schema and EBNF based grammar
* Implementation only supports Json for now
We use lm-format-enhancer to provide the implementation right now but may change this especially because BNF grammars aren't supported by that library.
Fireworks has support for structured output and Together has limited supported for it too. Subsequent PRs will add these changes. We would like all our inference providers to provide structured output for llama models since it is an extremely important and highly sought-after need by the developers.

llama_stack/providers/tests/inference/test_inference.py

@@ -10,6 +10,8 @@ import os
 import pytest
 import pytest_asyncio
 
+from pydantic import BaseModel, ValidationError
+
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
 
@@ -183,6 +185,63 @@ async def test_chat_completion_non_streaming(inference_settings, sample_messages
     assert len(response.completion_message.content) > 0
 
 
+@pytest.mark.asyncio
+async def test_structured_output(inference_settings):
+    inference_impl = inference_settings["impl"]
+    params = inference_settings["common_params"]
+
+    provider = inference_impl.routing_table.get_provider_impl(params["model"])
+    if provider.__provider_spec__.provider_type not in (
+        "meta-reference",
+        "remote::fireworks",
+        "remote::tgi",
+    ):
+        pytest.skip("Other inference providers don't support structured output yet")
+
+    class AnswerFormat(BaseModel):
+        first_name: str
+        last_name: str
+        year_of_birth: int
+        num_seasons_in_nba: int
+
+    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,
+        response_format=JsonResponseFormat(
+            schema=AnswerFormat.model_json_schema(),
+        ),
+        **inference_settings["common_params"],
+    )
+
+    assert isinstance(response, ChatCompletionResponse)
+    assert response.completion_message.role == "assistant"
+    assert isinstance(response.completion_message.content, str)
+
+    answer = AnswerFormat.parse_raw(response.completion_message.content)
+    assert answer.first_name == "Michael"
+    assert answer.last_name == "Jordan"
+    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):
     inference_impl = inference_settings["impl"]