atlasbot: streamline quick answers

services/comms/scripts/atlasbot/bot.py

@@ -3562,14 +3562,10 @@ def _open_ended_multi(
     fact_lines: list[str],
     fact_meta: dict[str, dict[str, Any]],
     history_lines: list[str],
-    mode: str,
     state: ThoughtState | None = None,
 ) -> str:
-    model = _model_for_mode(mode)
+    model = _model_for_mode("deep")
-    if mode == "fast":
+    total_steps = _open_ended_total_steps("deep")
-        total_steps = 4
-    else:
-        total_steps = 9
     if state:
         state.total_steps = total_steps
 
@@ -3591,52 +3587,6 @@ def _open_ended_multi(
         focus_tags = set(_ALLOWED_INSIGHT_TAGS)
     avoid_tags = _history_focus_tags(history_lines) if (subjective or _is_followup_query(prompt)) else set()
 
-    if mode == "fast":
-        primary_ids = _open_ended_select_facts(
-            prompt,
-            fact_pack=fact_pack,
-            fact_meta=fact_meta,
-            history_lines=history_lines,
-            focus_tags=focus_tags,
-            avoid_tags=avoid_tags,
-            avoid_fact_ids=[],
-            count=3,
-            subjective=subjective,
-            state=state,
-            step=2,
-            model=model,
-        )
-        focus_label = interpretation.get("focus_label") or "primary angle"
-        candidate = _open_ended_candidate(
-            prompt,
-            focus=str(focus_label),
-            fact_pack=fact_pack,
-            history_lines=history_lines,
-            subjective=subjective,
-            tone=str(tone),
-            allow_list=allow_list,
-            state=state,
-            step=3,
-            fact_hints=primary_ids,
-            model=model,
-        )
-        reply = _open_ended_synthesize(
-            prompt,
-            fact_pack=fact_pack,
-            history_lines=history_lines,
-            candidates=[candidate],
-            subjective=subjective,
-            tone=str(tone),
-            allow_list=allow_list,
-            state=state,
-            step=4,
-            model=model,
-            critique=None,
-        )
-        if state:
-            state.update("done", step=total_steps)
-        return reply
-
     angles = _open_ended_plan(
         prompt,
         fact_pack=fact_pack,
@@ -3757,41 +3707,52 @@ def _open_ended_multi(
 
 def _open_ended_total_steps(mode: str) -> int:
     if mode == "fast":
-        return 4
+        return 2
     return 9
 
 
 def _fast_fact_lines(
     fact_lines: list[str],
     fact_meta: dict[str, dict[str, Any]],
-    fact_ids: list[str],
+    *,
+    focus_tags: set[str],
+    avoid_tags: set[str],
+    limit: int = 10,
 ) -> list[str]:
-    if not fact_ids:
+    if not fact_lines:
-        return fact_lines
+        return []
-    selected = [
+    selected: list[str] = []
-        line
+    for idx, line in enumerate(fact_lines):
-        for line in fact_lines
+        fid = f"F{idx + 1}"
-        if fact_meta.get(line, {}).get("id") in set(fact_ids)
+        tags = set(fact_meta.get(fid, {}).get("tags") or [])
-    ]
+        if focus_tags and not (focus_tags & tags):
-    return selected or fact_lines
+            continue
+        if avoid_tags and (avoid_tags & tags):
+            continue
+        selected.append(line)
+        if len(selected) >= limit:
+            break
+    if selected:
+        return selected
+    trimmed = fact_lines[:limit]
+    return trimmed or fact_lines
 
 
 def _open_ended_fast_single(
     prompt: str,
     *,
-    fact_pack: str,
+    context: str,
-    history_lines: list[str],
     state: ThoughtState | None = None,
     model: str,
 ) -> str:
     if state:
-        state.update("drafting", step=2, note="summarizing")
+        state.update("drafting", step=1, note="summarizing")
-    context = fact_pack
     reply = _ollama_call(
         ("atlasbot_fast", "atlasbot_fast"),
         prompt,
         context=context,
         use_history=False,
+        system_override=_open_ended_system(),
         model=model,
     )
     if state:
@@ -3808,14 +3769,28 @@ def _open_ended_fast(
     history_lines: list[str],
     state: ThoughtState | None = None,
 ) -> str:
-    return _open_ended_multi(
+    model = _model_for_mode("fast")
+    subjective = _is_subjective_query(prompt)
+    focus_tags = _preferred_tags_for_prompt(prompt)
+    if not focus_tags and subjective:
+        focus_tags = set(_ALLOWED_INSIGHT_TAGS)
+    avoid_tags = _history_focus_tags(history_lines) if (subjective or _is_followup_query(prompt)) else set()
+    selected_lines = _fast_fact_lines(
+        fact_lines,
+        fact_meta,
+        focus_tags=focus_tags,
+        avoid_tags=avoid_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,
-        fact_pack=fact_pack,
+        context=context,
-        fact_lines=fact_lines,
-        fact_meta=fact_meta,
-        history_lines=history_lines,
-        mode="fast",
         state=state,
+        model=model,
     )
 
 
@@ -3834,7 +3809,6 @@ def _open_ended_deep(
         fact_lines=fact_lines,
         fact_meta=fact_meta,
         history_lines=history_lines,
-        mode="deep",
         state=state,
     )