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chore: import upstream snapshot with attribution
2026-07-13 13:10:45 +08:00

134 lines
4.8 KiB
Python

"""Paper-format ``top_3_predictions`` predictor — package split.
Originally a single ``predictor.py`` file; split on 2026-06-09 into four
focused modules so the upcoming structured-outputs experiment can land in
``llm_call_structured.py`` without bloating an already-overloaded file.
Module layout:
- ``vocabulary.py`` — closed-vocabulary constants (taxonomies, root_causes,
fault_object services / nodes / namespaces). Single source of truth for
the scorer enum surfaces and the structured-output schema enums.
- ``snapping.py`` — Lever A: controlled-vocabulary snapping with the
cross-concept blocklist guard.
- ``rerank.py`` — Lever D: conservative evidence-weighted top-3 rescue.
- ``llm_call.py`` — the default text-emit predictor LLM call + prompt
construction + response parsing.
- ``llm_call_structured_openai.py`` — OpenAI structured-outputs predictor
variant. Same prompts as ``llm_call.py``, but grammar-constrained
sampling at the API layer via ``response_format`` + Pydantic Literal
enums from ``vocabulary.py``. Selected via
``predictor_variant: "structured"`` in the bench config. Future
multi-provider peers: ``llm_call_structured_anthropic.py``,
``llm_call_structured_deepseek.py``.
Backward-compat re-exports: existing
``from tests.benchmarks.cloudopsbench.predictor import X`` callers keep
working because every public (and underscore-private) name from the four
modules above is re-exported here.
"""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from tests.benchmarks.cloudopsbench.predictor.llm_call import (
_FENCED_JSON,
_build_system_prompt,
_build_user_prompt,
_parse_predictions,
emit_paper_predictions,
)
from tests.benchmarks.cloudopsbench.predictor.llm_call_structured_openai import (
emit_paper_predictions_structured,
)
from tests.benchmarks.cloudopsbench.predictor.rerank import (
_RERANK_MIN_TOKEN_LEN,
_RERANK_STOPWORDS,
_prediction_tokens,
rerank_predictions_by_evidence,
)
from tests.benchmarks.cloudopsbench.predictor.snapping import (
_BLOCKED_CONCEPT_PAIRS,
_KNOWN_NAMESPACES_BY_NORM,
_KNOWN_NODES_BY_NORM,
_KNOWN_SERVICES_BY_NORM,
_ROOT_CAUSE_BY_NORM,
_ROOT_CAUSE_SNAP_CUTOFF,
_crosses_blocked_concept_boundary,
_snap_fault_object,
_snap_root_cause,
)
from tests.benchmarks.cloudopsbench.predictor.vocabulary import (
_FAULT_OBJECT_NAMESPACES,
_FAULT_OBJECT_NODES,
_FAULT_OBJECT_SERVICES,
_ROOT_CAUSES,
_TAXONOMY_CATEGORIES,
)
if TYPE_CHECKING:
# ``align_predictions_to_investigation`` and ``apply_investigation_handoff`` are
# PEP 562 lazy module attributes resolved by ``__getattr__`` below (see the
# docstring there). Declaring them here makes them visible to static analyzers
# that can't follow ``__getattr__`` (CodeQL ``py/undefined-export``, ruff F822)
# without eagerly importing ``investigation_handoff`` and its scoring deps.
from tests.benchmarks.cloudopsbench.predictor.investigation_handoff import (
align_predictions_to_investigation,
apply_investigation_handoff,
)
__all__ = [
# vocabulary
"_FAULT_OBJECT_NAMESPACES",
"_FAULT_OBJECT_NODES",
"_FAULT_OBJECT_SERVICES",
"_ROOT_CAUSES",
"_TAXONOMY_CATEGORIES",
# snapping
"_BLOCKED_CONCEPT_PAIRS",
"_KNOWN_NAMESPACES_BY_NORM",
"_KNOWN_NODES_BY_NORM",
"_KNOWN_SERVICES_BY_NORM",
"_ROOT_CAUSE_BY_NORM",
"_ROOT_CAUSE_SNAP_CUTOFF",
"_crosses_blocked_concept_boundary",
"_snap_fault_object",
"_snap_root_cause",
# investigation handoff (B1)
"align_predictions_to_investigation",
"apply_investigation_handoff",
# rerank
"_RERANK_MIN_TOKEN_LEN",
"_RERANK_STOPWORDS",
"_prediction_tokens",
"rerank_predictions_by_evidence",
# llm_call
"_FENCED_JSON",
"_build_system_prompt",
"_build_user_prompt",
"_parse_predictions",
"emit_paper_predictions",
# llm_call_structured_openai
"emit_paper_predictions_structured",
]
_INVESTIGATION_HANDOFF_EXPORTS = frozenset(
{"align_predictions_to_investigation", "apply_investigation_handoff"}
)
def __getattr__(name: str) -> Any:
"""Lazy-load investigation handoff so importing ``vocabulary`` from this
package does not pull in handoff (and its scoring dependencies) at init."""
if name in _INVESTIGATION_HANDOFF_EXPORTS:
from tests.benchmarks.cloudopsbench.predictor.investigation_handoff import (
align_predictions_to_investigation,
apply_investigation_handoff,
)
return {
"align_predictions_to_investigation": align_predictions_to_investigation,
"apply_investigation_handoff": apply_investigation_handoff,
}[name]
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")