Stage 5 (SummarySynthesizer) was only sending aggregate timeline stats to the
LLM (cluster count, burst count, gap count) — the actual sequenced cluster data
that Stage 1 reconstructed was never included. The LLM had no per-cluster
timestamps, severity, burst flags, silence gaps, or representative text to
write the TIMELINE section from.
Added _build_timeline_block() to emit a numbered per-cluster summary matching
the format Stage 3 uses for the hypothesizer, and included it in the user
message alongside the hypothesis block.
Also fixed _build_hypothesis_block() to include the 2-4 sentence description
each hypothesis carries — previously only the title and novelty score reached
the LLM.
11 new tests cover _build_timeline_block() directly (burst label, gap threshold,
pattern tags, text truncation at 200 chars, null start_iso, multi-cluster
numbering). 529 tests passing.
- #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.
