chore(package): migrate to src/ layout (#3920)

Migrates package structure to src/ layout following Python packaging
best practices.

All code moved from `llama_stack/` to `src/llama_stack/`. Public API
unchanged - imports remain `import llama_stack.*`.

Updated build configs, pre-commit hooks, scripts, and GitHub workflows
accordingly. All hooks pass, package builds cleanly.

**Developer note**: Reinstall after pulling: `pip install -e .`

src/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 *

src/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py

@@ -0,0 +1,77 @@
+# 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, Protocol
+
+from pydantic import BaseModel
+
+from llama_stack.apis.inference import Message
+from llama_stack.apis.version import LLAMA_STACK_API_V1
+from llama_stack.schema_utils import json_schema_type, webmethod
+
+
+class FilteringFunction(Enum):
+    """The type of filtering function.
+
+    :cvar none: No filtering applied, accept all generated synthetic data
+    :cvar random: Random sampling of generated data points
+    :cvar top_k: Keep only the top-k highest scoring synthetic data samples
+    :cvar top_p: Nucleus-style filtering, keep samples exceeding cumulative score threshold
+    :cvar top_k_top_p: Combined top-k and top-p filtering strategy
+    :cvar sigmoid: Apply sigmoid function for probability-based filtering
+    """
+
+    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
+
+    :param dialogs: List of conversation messages to use as input for synthetic data generation
+    :param filtering_function: Type of filtering to apply to generated synthetic data samples
+    :param model: (Optional) The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint
+    """
+
+    dialogs: list[Message]
+    filtering_function: FilteringFunction = FilteringFunction.none
+    model: str | None = None
+
+
+@json_schema_type
+class SyntheticDataGenerationResponse(BaseModel):
+    """Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold.
+
+    :param synthetic_data: List of generated synthetic data samples that passed the filtering criteria
+    :param statistics: (Optional) Statistical information about the generation process and filtering results
+    """
+
+    synthetic_data: list[dict[str, Any]]
+    statistics: dict[str, Any] | None = None
+
+
+class SyntheticDataGeneration(Protocol):
+    @webmethod(route="/synthetic-data-generation/generate", level=LLAMA_STACK_API_V1)
+    def synthetic_data_generate(
+        self,
+        dialogs: list[Message],
+        filtering_function: FilteringFunction = FilteringFunction.none,
+        model: str | None = None,
+    ) -> SyntheticDataGenerationResponse:
+        """Generate synthetic data based on input dialogs and apply filtering.
+
+        :param dialogs: List of conversation messages to use as input for synthetic data generation
+        :param filtering_function: Type of filtering to apply to generated synthetic data samples
+        :param model: (Optional) The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint
+        :returns: Response containing filtered synthetic data samples and optional statistics
+        """
+        ...