pyr0ball
7816ceba61
- #33: Wrap ClassifiedTimeline.cluster_severities in MappingProxyType for true immutability (frozen=True only blocks field reassignment, not dict mutation). - #34: Remove dead suppression branch in synthesizer._build_hypothesis_block. active[] is already filtered to not rh.suppress, so the 'Yes — suppressed' branch was unreachable. Now shows novelty score only. - #35: Extract shared _llm_client.py with call_llm() + extract_content() + strip_json_fences(). Both RootCauseHypothesizer and SummarySynthesizer now import from one source. Also strips JSON fences from LLM output before parsing in hypothesizer._parse_response. - #36: Add per-stage try/except in pipeline.run_pipeline(). Unhandled stage exceptions now emit {type: 'error'} + {type: 'done'} SSE events instead of silently closing the stream. - #37: Move format_context_block() call inside the legacy LLM branch in diagnose/__init__.py — it was being computed unconditionally but only used in the non-pipeline path. - #38: Coerce supporting_cluster_ids items to str() in hypothesizer _parse_response to guard against LLMs returning integers instead of string cluster IDs.
77 lines
2 KiB
Python
77 lines
2 KiB
Python
"""Pipeline data types for the multi-agent diagnose pipeline."""
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from __future__ import annotations
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from dataclasses import dataclass
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from types import MappingProxyType
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from typing import Literal
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SeverityLabel = Literal["CRITICAL", "ERROR", "WARN", "INFO", "DEBUG", "UNKNOWN"]
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@dataclass(frozen=True)
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class EventCluster:
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"""A time-correlated group of log entries within the timeline."""
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cluster_id: str
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entries: tuple[str, ...] # entry_id refs
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start_iso: str | None
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end_iso: str | None
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duration_seconds: float
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source_ids: tuple[str, ...]
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pattern_tags: tuple[str, ...]
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severity: SeverityLabel
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burst: bool
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gap_before_seconds: float
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representative_text: str
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@dataclass(frozen=True)
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class TimelineResult:
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"""Structured timeline of event clusters built from log entries."""
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clusters: tuple[EventCluster, ...]
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total_entries: int
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window_start: str | None
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window_end: str | None
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gap_count: int
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burst_count: int
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dominant_sources: tuple[str, ...]
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@dataclass(frozen=True)
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class ClassifiedTimeline:
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"""Timeline annotated with ML-assigned severity per cluster.
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``cluster_severities`` is a ``MappingProxyType`` so the mapping is
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fully immutable — consistent with the ``frozen=True`` intent.
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"""
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timeline: TimelineResult
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cluster_severities: MappingProxyType # MappingProxyType[str, SeverityLabel]
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classifier_used: Literal["ml", "pattern_tags", "regex"]
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model_id: str | None
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@dataclass(frozen=True)
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class Hypothesis:
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"""A root-cause hypothesis generated by Stage 3."""
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hypothesis_id: str
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title: str
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description: str
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confidence: float
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supporting_cluster_ids: tuple[str, ...]
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runbook_refs: tuple[str, ...]
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severity: SeverityLabel
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@dataclass(frozen=True)
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class RankedHypothesis:
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"""A hypothesis enriched by Stage 4 false-positive suppression."""
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hypothesis: Hypothesis
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novelty_score: float
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similarity_to_known: float
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suppress: bool
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suppression_reason: str | None
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