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/scoring/__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 .scoring import *

src/llama_stack/apis/scoring/scoring.py

@@ -0,0 +1,93 @@
+# 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 typing import Any, Protocol, runtime_checkable
+
+from pydantic import BaseModel
+
+from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
+from llama_stack.apis.version import LLAMA_STACK_API_V1
+from llama_stack.schema_utils import json_schema_type, webmethod
+
+# mapping of metric to value
+ScoringResultRow = dict[str, Any]
+
+
+@json_schema_type
+class ScoringResult(BaseModel):
+    """
+    A scoring result for a single row.
+
+    :param score_rows: The scoring result for each row. Each row is a map of column name to value.
+    :param aggregated_results: Map of metric name to aggregated value
+    """
+
+    score_rows: list[ScoringResultRow]
+    # aggregated metrics to value
+    aggregated_results: dict[str, Any]
+
+
+@json_schema_type
+class ScoreBatchResponse(BaseModel):
+    """Response from batch scoring operations on datasets.
+
+    :param dataset_id: (Optional) The identifier of the dataset that was scored
+    :param results: A map of scoring function name to ScoringResult
+    """
+
+    dataset_id: str | None = None
+    results: dict[str, ScoringResult]
+
+
+@json_schema_type
+class ScoreResponse(BaseModel):
+    """
+    The response from scoring.
+
+    :param results: A map of scoring function name to ScoringResult.
+    """
+
+    # each key in the dict is a scoring function name
+    results: dict[str, ScoringResult]
+
+
+class ScoringFunctionStore(Protocol):
+    def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn: ...
+
+
+@runtime_checkable
+class Scoring(Protocol):
+    scoring_function_store: ScoringFunctionStore
+
+    @webmethod(route="/scoring/score-batch", method="POST", level=LLAMA_STACK_API_V1)
+    async def score_batch(
+        self,
+        dataset_id: str,
+        scoring_functions: dict[str, ScoringFnParams | None],
+        save_results_dataset: bool = False,
+    ) -> ScoreBatchResponse:
+        """Score a batch of rows.
+
+        :param dataset_id: The ID of the dataset to score.
+        :param scoring_functions: The scoring functions to use for the scoring.
+        :param save_results_dataset: Whether to save the results to a dataset.
+        :returns: A ScoreBatchResponse.
+        """
+        ...
+
+    @webmethod(route="/scoring/score", method="POST", level=LLAMA_STACK_API_V1)
+    async def score(
+        self,
+        input_rows: list[dict[str, Any]],
+        scoring_functions: dict[str, ScoringFnParams | None],
+    ) -> ScoreResponse:
+        """Score a list of rows.
+
+        :param input_rows: The rows to score.
+        :param scoring_functions: The scoring functions to use for the scoring.
+        :returns: A ScoreResponse object containing rows and aggregated results.
+        """
+        ...