The AI features in llm_features.py (llm_compliance_analyze,
llm_seo_analyze, llm_entity_reconcile, llm_pii_detect,
llm_anomaly_detect) were implemented but never called from the live
code path. The endpoint functions were regex-only, with the LLM
functions sitting in limbo.
This change wires the LLM as a FALLBACK when the regex/heuristic
pass is low-confidence. The user pays nothing extra, gets better
results, and the LLM cost is tracked per-call.
Changes:
- compliance.py run_compliance_check:
When tos_result.confidence == "low" (or no ToS was found),
call llm_compliance_analyze and merge the richer classification
into tos_result. llm_enhanced: True is set.
Pass-through: the LLM fields (provider, cost, risk_summary, etc.)
are now copied into the terms_of_service sub-dict of the response.
- seo_monitor.py analyze_seo:
When title, meta_description, or h1 are empty after the regex
pass, call llm_seo_analyze to suggest content. Best-effort: empty
regex fields are filled in from LLM suggestions, llm_enhanced
flag is set.
- reconciliation.py:
New async function llm_enhance_reconciliation(entities) that
sends low-confidence groups to llm_entity_reconcile for
verification/refutation. Returns a summary dict with counts.
- New test file tests/test_llm_fallback.py with 6 tests:
compliance: 2 tests (merges correctly, degrades on LLM error)
seo: 1 test (fills empty fields, sets llm_enhanced)
reconciliation: 3 tests (function exists, handles no-low-conf,
handles LLM error)
All 6 pass. All existing compliance/seo/reconciliation tests
(28) still pass.
Defaults: the LLM uses the fleet's free Ollama on Talos
(100.100.18.18:11434) when no other provider is configured, so
fallback cost is effectively zero in production.
