Codemod from llama_toolchain -> llama_stack

- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent

llama_stack/apis/synthetic_data_generation/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .synthetic_data_generation import *  # noqa: F401 F403

llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py

@@ -0,0 +1,54 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+
+from typing import Any, Dict, List, Optional, Protocol
+
+from llama_models.schema_utils import json_schema_type, webmethod
+
+from pydantic import BaseModel
+
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.apis.reward_scoring import *  # noqa: F403
+
+
+class FilteringFunction(Enum):
+    """The type of filtering function."""
+
+    none = "none"
+    random = "random"
+    top_k = "top_k"
+    top_p = "top_p"
+    top_k_top_p = "top_k_top_p"
+    sigmoid = "sigmoid"
+
+
+@json_schema_type
+class SyntheticDataGenerationRequest(BaseModel):
+    """Request to generate synthetic data. A small batch of prompts and a filtering function"""
+
+    dialogs: List[Message]
+    filtering_function: FilteringFunction = FilteringFunction.none
+    model: Optional[str] = None
+
+
+@json_schema_type
+class SyntheticDataGenerationResponse(BaseModel):
+    """Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
+
+    synthetic_data: List[ScoredDialogGenerations]
+    statistics: Optional[Dict[str, Any]] = None
+
+
+class SyntheticDataGeneration(Protocol):
+    @webmethod(route="/synthetic_data_generation/generate")
+    def synthetic_data_generate(
+        self,
+        dialogs: List[Message],
+        filtering_function: FilteringFunction = FilteringFunction.none,
+        model: Optional[str] = None,
+    ) -> Union[SyntheticDataGenerationResponse]: ...