atlasbot: prioritize fact selection for quick answers

services/comms/scripts/atlasbot/bot.py

@@ -3169,6 +3169,23 @@ def _preferred_tags_for_prompt(prompt: str) -> set[str]:
     return tags & _ALLOWED_INSIGHT_TAGS
 
 
+def _primary_tags_for_prompt(prompt: str) -> set[str]:
+    q = normalize_query(prompt)
+    if any(word in q for word in ("cpu", "ram", "memory", "net", "network", "io", "disk", "hottest", "busy", "usage", "utilization", "load")):
+        return {"utilization"}
+    if any(word in q for word in ("postgres", "database", "db", "connections")):
+        return {"database"}
+    if any(word in q for word in ("pod", "pods", "deployment", "job", "cronjob")):
+        return {"pods"}
+    if any(word in q for word in ("workload", "service", "namespace")):
+        return {"workloads"}
+    if any(word in q for word in ("ready", "not ready", "down", "unreachable", "availability")):
+        return {"availability"}
+    if any(word in q for word in ("node", "nodes", "hardware", "arch", "architecture", "rpi", "jetson", "amd64", "arm64", "worker", "control-plane")):
+        return {"hardware", "inventory", "architecture"}
+    return set()
+
+
 _TAG_KEYWORDS: dict[str, tuple[str, ...]] = {
     "utilization": ("cpu", "ram", "memory", "net", "network", "io", "disk", "usage", "utilization", "hottest", "busy"),
     "database": ("postgres", "db", "database", "connections"),
@@ -3745,25 +3762,43 @@ def _fast_fact_lines(
     *,
     focus_tags: set[str],
     avoid_tags: set[str],
+    primary_tags: set[str] | None = None,
     limit: int = 10,
 ) -> list[str]:
     if not fact_lines:
         return []
-    selected: list[str] = []
+    primary_tags = primary_tags or set()
+    scored: list[tuple[int, int, str]] = []
     for idx, line in enumerate(fact_lines):
         fid = f"F{idx + 1}"
         tags = set(fact_meta.get(fid, {}).get("tags") or [])
-        if focus_tags and not (focus_tags & tags):
-            continue
         if avoid_tags and (avoid_tags & tags):
             continue
-        selected.append(line)
+        score = 0
+        if primary_tags:
+            score += 4 * len(tags & primary_tags)
+        if focus_tags:
+            score += 2 * len(tags & focus_tags)
+        scored.append((score, idx, line))
+    scored.sort(key=lambda item: (-item[0], item[1]))
+    selected: list[str] = []
+    for score, _, line in scored:
+        if score <= 0 and selected:
+            break
+        if score > 0:
+            selected.append(line)
         if len(selected) >= limit:
             break
-    if selected:
+    if not selected:
-        return selected
+        selected = [line for _, _, line in scored[:limit]]
-    trimmed = fact_lines[:limit]
+    elif len(selected) < limit:
-    return trimmed or fact_lines
+        for _, _, line in scored:
+            if line in selected:
+                continue
+            selected.append(line)
+            if len(selected) >= limit:
+                break
+    return selected
 
 
 def _open_ended_fast_single(
@@ -3799,6 +3834,7 @@ def _open_ended_fast(
 ) -> str:
     model = _model_for_mode("fast")
     subjective = _is_subjective_query(prompt)
+    primary_tags = _primary_tags_for_prompt(prompt)
     focus_tags = _preferred_tags_for_prompt(prompt)
     if not focus_tags and subjective:
         focus_tags = set(_ALLOWED_INSIGHT_TAGS)
@@ -3808,15 +3844,15 @@ def _open_ended_fast(
         fact_meta,
         focus_tags=focus_tags,
         avoid_tags=avoid_tags,
+        primary_tags=primary_tags,
     )
     selected_meta = _fact_pack_meta(selected_lines)
     selected_pack = _fact_pack_text(selected_lines, selected_meta)
-    context = _append_history_context(selected_pack, history_lines)
     if state:
         state.total_steps = _open_ended_total_steps("fast")
     return _open_ended_fast_single(
         prompt,
-        context=context,
+        context=selected_pack,
         state=state,
         model=model,
     )