atlasbot: emphasize key facts and validate runbooks

atlasbot/engine/answerer.py

@@ -4,6 +4,7 @@ import logging
 import math
 import re
 import time
+import difflib
 from dataclasses import dataclass
 from typing import Any, Callable
 
@@ -254,6 +255,7 @@ class AnswerEngine:
                 chunks = _chunk_lines(summary_lines, plan.chunk_lines)
                 scored = await _score_chunks(call_llm, chunks, normalized, sub_questions, plan)
                 selected = _select_chunks(chunks, scored, plan, keyword_tokens)
+                key_facts = _key_fact_lines(summary_lines, keyword_tokens)
                 if self._settings.debug_pipeline:
                     scored_preview = sorted(
                         [{"id": c["id"], "score": scored.get(c["id"], 0.0), "summary": c["summary"]} for c in chunks],
@@ -268,6 +270,8 @@ class AnswerEngine:
                         },
                     )
                 snapshot_context = "ClusterSnapshot:\n" + "\n".join([chunk["text"] for chunk in selected])
+                if key_facts:
+                    snapshot_context = "KeyFacts:\n" + "\n".join(key_facts) + "\n\n" + snapshot_context
 
             context = _join_context(
                 [kb_summary, _format_runbooks(runbooks), snapshot_context, history_ctx if classify.get("follow_up") else ""]
@@ -372,6 +376,8 @@ class AnswerEngine:
                         )
                         resolver = _parse_json_block(resolver_raw, fallback={})
                         candidate = resolver.get("path") if isinstance(resolver.get("path"), str) else None
+                        if not (candidate and candidate in runbook_paths):
+                            candidate = _best_runbook_match(invalid[0], runbook_paths)
                         if candidate and candidate in runbook_paths:
                             enforce_prompt = prompts.RUNBOOK_ENFORCE_PROMPT.format(path=candidate)
                             reply = await call_llm(
@@ -845,6 +851,22 @@ def _summary_lines(snapshot: dict[str, Any] | None) -> list[str]:
     return [line for line in text.splitlines() if line.strip()]
 
 
+def _key_fact_lines(lines: list[str], keywords: list[str] | None, limit: int = 6) -> list[str]:
+    if not lines or not keywords:
+        return []
+    lowered = [kw.lower() for kw in keywords if kw]
+    if not lowered:
+        return []
+    matches: list[str] = []
+    for line in lines:
+        line_lower = line.lower()
+        if any(kw in line_lower for kw in lowered):
+            matches.append(line)
+        if len(matches) >= limit:
+            break
+    return matches
+
+
 def _lexicon_context(summary: dict[str, Any]) -> str:
     if not isinstance(summary, dict):
         return ""
@@ -1086,6 +1108,19 @@ def _needs_runbook_reference(question: str, allowed: list[str], reply: str) -> b
     return True
 
 
+def _best_runbook_match(candidate: str, allowed: list[str]) -> str | None:
+    if not candidate or not allowed:
+        return None
+    best = None
+    best_score = 0.0
+    for path in allowed:
+        score = difflib.SequenceMatcher(a=candidate.lower(), b=path.lower()).ratio()
+        if score > best_score:
+            best_score = score
+            best = path
+    return best if best_score >= 0.4 else None
+
+
 def _resolve_path(data: Any, path: str) -> Any | None:
     cursor = data
     for part in re.split(r"\.(?![^\[]*\])", path):