Truncation fix: call_llm() in _llm_client.py now accepts max_tokens (default
2048) and passes it in both the cf-orch task payload and the OpenAI-compat
fallback body. Hypothesizer uses max_tokens=1024 (JSON array output);
synthesizer and legacy summarize use 2048 (structured 5-section narrative).
Without this, backends use their own default (often 512 tokens), causing
mid-sentence truncation of the diagnosis output.
UI fix: reasoning card changed from bg-accent/5 border-accent/30 (opacity
modifiers on CSS variables don't compose reliably across themes) to the
callout pattern: bg-surface-raised with a solid border-l-4 border-accent.
Header label changed from text-text-dim to text-accent for visual anchoring.
Text remains text-text-primary for guaranteed contrast on both light and dark
themes.
Tracks: #56 (technical-level post-processor, filed as follow-on feature)
Reasoning models (e.g. foundation-sec-8b) emit valid JSON then repeat it
inside a markdown fence block. json.loads() fails on the combined text.
extract_first_json_array() scans for the first '[' and walks to its
matching ']' with proper string/escape/nesting handling, then returns
just that slice. Combined with strip_json_fences(), this handles all
observed output patterns:
- bare JSON array (standard models)
- fenced JSON array (fence-wrapping models)
- bare array followed by fenced repeat (reasoning models)
- #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.
- Add _coerce_float() module-level helper: catches TypeError/ValueError from
non-numeric LLM output (e.g. 'high', 'N/A') and returns a caller-supplied
default instead of raising.
- Replace float(item.get('confidence', 0.5)) with
_coerce_float(item.get('confidence'), 0.5) in _parse_response.
- Guard supporting_cluster_ids: tuple(item.get(...) or []) so a JSON null
from the LLM does not cause TypeError('NoneType is not iterable').
- runbook_refs is hardcoded as () and not sourced from LLM output; no change
needed there.
- Add test_non_numeric_confidence_uses_default (Test 10) to cover the 'high'
string case: asserts no exception and confidence == 0.5.
- 341 tests passing (+1).
Closes: #29
