atlasbot: simplify insight guard inputs

atlasbot/engine/answerer.py

@@ -46,6 +46,17 @@ class AnswerResult:
     meta: dict[str, Any]
 
 
+@dataclass(frozen=True)
+class InsightGuardInput:
+    question: str
+    reply: str
+    classify: dict[str, Any]
+    context: str
+    plan: "ModePlan"
+    call_llm: Callable[..., Awaitable[str]]
+    facts: list[str]
+
+
 @dataclass
 class EvidenceItem:
     path: str
@@ -783,13 +794,15 @@ class AnswerEngine:
                 if observer:
                     observer("insight_guard", "checking for concrete signals")
                 reply = await _apply_insight_guard(
-                    normalized,
-                    reply,
-                    classify,
-                    context,
-                    plan,
-                    call_llm,
-                    metric_facts or key_facts,
+                    InsightGuardInput(
+                        question=normalized,
+                        reply=reply,
+                        classify=classify,
+                        context=context,
+                        plan=plan,
+                        call_llm=call_llm,
+                        facts=metric_facts or key_facts,
+                    )
                 )
 
             if plan.use_critic:
@@ -2447,36 +2460,28 @@ def _should_use_insight_guard(classify: dict[str, Any]) -> bool:
     return style == "insightful" or qtype in {"open_ended", "planning"}
 
 
-async def _apply_insight_guard(
-    question: str,
-    reply: str,
-    classify: dict[str, Any],
-    context: str,
-    plan: ModePlan,
-    call_llm: Callable[..., Awaitable[str]],
-    facts: list[str],
-) -> str:
-    if not reply or not _should_use_insight_guard(classify):
-        return reply
-    guard_prompt = prompts.INSIGHT_GUARD_PROMPT.format(question=question, answer=reply)
-    guard_raw = await call_llm(
+async def _apply_insight_guard(inputs: InsightGuardInput) -> str:
+    if not inputs.reply or not _should_use_insight_guard(inputs.classify):
+        return inputs.reply
+    guard_prompt = prompts.INSIGHT_GUARD_PROMPT.format(question=inputs.question, answer=inputs.reply)
+    guard_raw = await inputs.call_llm(
         prompts.INSIGHT_GUARD_SYSTEM,
         guard_prompt,
-        context=context,
-        model=plan.fast_model,
+        context=inputs.context,
+        model=inputs.plan.fast_model,
         tag="insight_guard",
     )
     guard = _parse_json_block(guard_raw, fallback={})
     if guard.get("ok") is True:
-        return reply
-    fix_prompt = prompts.INSIGHT_FIX_PROMPT.format(question=question, answer=reply)
-    if facts:
-        fix_prompt = fix_prompt + "\nFacts:\n" + "\n".join(facts[:6])
-    return await call_llm(
+        return inputs.reply
+    fix_prompt = prompts.INSIGHT_FIX_PROMPT.format(question=inputs.question, answer=inputs.reply)
+    if inputs.facts:
+        fix_prompt = fix_prompt + "\nFacts:\n" + "\n".join(inputs.facts[:6])
+    return await inputs.call_llm(
         prompts.INSIGHT_FIX_SYSTEM,
         fix_prompt,
-        context=context,
-        model=plan.model,
+        context=inputs.context,
+        model=inputs.plan.model,
         tag="insight_fix",
     )