chore!: remove SDG API (#4035)

# What does this PR do?

This API hasn't received any traction and close to zero interest from
the community. Let's revisit in the future if things change.

Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

src/llama_stack/apis/synthetic_data_generation/__init__.py

@@ -1,7 +0,0 @@
-# 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

@@ -1,77 +0,0 @@
-# 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
-        """
-        ...

src/llama_stack/core/stack.py

@@ -31,7 +31,6 @@ from llama_stack.apis.safety import Safety
 from llama_stack.apis.scoring import Scoring
 from llama_stack.apis.scoring_functions import ScoringFunctions
 from llama_stack.apis.shields import Shields
-from llama_stack.apis.synthetic_data_generation import SyntheticDataGeneration
 from llama_stack.apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.core.conversations.conversations import ConversationServiceConfig, ConversationServiceImpl
@@ -66,7 +65,6 @@ class LlamaStack(
     Agents,
     Batches,
     Safety,
-    SyntheticDataGeneration,
     Datasets,
     PostTraining,
     VectorIO,