Legend: relevance, evidence, contradicts, and injection are probabilities from 0 to 1. High relevance/evidence = the passage is on-topic and usable as evidence. High contradicts = the passage conflicts with a factual claim in my question (not the answer) — check it. High injection = retrieved text tried to instruct the AI; treat it as untrusted. verdict: include = used as evidence, conflict = kept but flagged, drop = excluded. status = overall verdict. coverage = how much of my question the evidence covers (e.g. 2/3). conflicts = the number of passages that disagree with a factual claim in my question. Sources = L2 vector distance; lower is closer (a separate scale from the 0-1 probabilities above).

Tree-sitter parses Markdown through the tree-sitter-markdown grammar, wired in as `MD_LANG`
... (#code_indexer.py:64 | 0.76 L2).
[ ... the full synthesized answer, with inline file:line | L2 citations ... ]

Assessment:
  status: supported
  coverage: 2/3
  passages: 3 included, 2 dropped (2 irrelevant, 0 injection)
  conflicts: 0

Evidence:
  1. code_indexer.py:1256 [_extract_markdown] relevance=0.86 evidence=0.77 contradicts=0.04 injection=0.02 verdict=include
  2. code_indexer.py:64 [MD_LANG] relevance=0.72 evidence=0.47 contradicts=0.04 injection=0.01 verdict=include
  3. code_indexer.py:204 [extract_entities] relevance=0.62 evidence=0.59 contradicts=0.04 injection=0.02 verdict=include
  4. code_indexer.py:30 [LanguageConfig] relevance=0.48 evidence=0.32 contradicts=0.04 injection=0.01 verdict=drop
  5. code_indexer.py:196 [get_supported_extensions] relevance=0.22 evidence=0.20 contradicts=0.04 injection=0.02 verdict=drop

Guidance:
  trust: high
  action: answer is supported by 3 included passages; cite it directly

Sources:
* code_indexer.py:1256 — 0.56 (L2)
* code_indexer.py:64 — 0.76 (L2)
* code_indexer.py:204 — 0.72 (L2)