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atlasbot: streamline quick answers

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This commit is contained in:
Brad Stein 2026-01-28 02:53:43 -03:00
parent 349a46ceab
commit 08ac598181
1 changed files with 47 additions and 73 deletions
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120
services/comms/scripts/atlasbot/bot.py
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@ -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)
if mode == "fast":
total_steps = 4
else:
total_steps = 9
model = _model_for_mode("deep")
total_steps = _open_ended_total_steps("deep")
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:
return fact_lines
selected = [
line
for line in fact_lines
if fact_meta.get(line, {}).get("id") in set(fact_ids)
]
return selected or fact_lines
if not fact_lines:
return []
selected: list[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)
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,
history_lines: list[str],
context: str,
state: ThoughtState | None = None,
model: str,
) -> str:
if state:
state.update("drafting", step=2, note="summarizing")
context = fact_pack
state.update("drafting", step=1, note="summarizing")
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,
fact_lines=fact_lines,
fact_meta=fact_meta,
history_lines=history_lines,
mode="fast",
context=context,
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,
)