feat(briefing): wire pre-processing pipeline; run_compilation returns (text, metadata)

- Task change detection via snapshot diff
- RSS scoring/filtering via briefing_preferences
- Weather card via parse_weather_card_data (staleness-gated)
- News card markdown format with ordering constraint
- Metadata stored on Message record via post_message()

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-03-25 10:38:28 -04:00
parent e3c1e97cfa
commit dc93e0d39f
3 changed files with 86 additions and 23 deletions
+2 -2
View File
@@ -232,6 +232,6 @@ async def manual_trigger():
model = await get_setting(g.user.id, "default_model", "")
conv = await get_or_create_today_conversation(g.user.id, model)
text = await run_compilation(g.user.id, slot, model)
msg = await post_message(conv.id, "assistant", text)
text, metadata = await run_compilation(g.user.id, slot, model)
msg = await post_message(conv.id, "assistant", text, metadata=metadata)
return jsonify({"conversation_id": conv.id, "message_id": msg.id, "slot": slot})
@@ -248,24 +248,36 @@ def _internal_system_prompt(profile_body: str) -> str:
def _external_system_prompt() -> str:
return (
"You are a briefing assistant for external information. Your job is to summarise "
"the user's RSS feed digest and weather forecast into a concise, engaging update. "
"Group related news items. Note any significant weather changes. "
"Be informative but brief. Do not discuss tasks, calendar, or work items."
"You are a briefing assistant for external information. Your job is to present "
"selected news items and summarise any remaining RSS content. "
"IMPORTANT: Weather is handled separately — do NOT include any weather section.\n\n"
"Format each news item EXACTLY as:\n"
"**[Headline text](source_url)**\n"
"*Outlet Name · Day Month*\n"
"One or two sentence summary.\n\n"
"Present news items in the EXACT ORDER they are provided. Do not reorder them. "
"After the news cards, add a brief paragraph for any remaining context."
)
def _internal_user_prompt(data: dict, slot: str) -> str:
lines = [f"Briefing slot: {slot}", f"Date: {data['date']}", ""]
if data["overdue_tasks"]:
lines.append(f"OVERDUE ({len(data['overdue_tasks'])}):")
lines.extend(f" - {t}" for t in data["overdue_tasks"])
if data.get("unchanged_task_count", 0) > 0:
lines.append(
f"({data['unchanged_task_count']} tasks are unchanged since the last briefing "
"— acknowledge briefly, do not list them.)"
)
lines.append("")
if data["due_today"]:
changed = data.get("changed_tasks") or data.get("overdue_tasks", [])
if changed:
lines.append(f"CHANGED/NEW TASKS ({len(changed)}):")
lines.extend(f" - {t}" for t in changed)
lines.append("")
if data.get("due_today"):
lines.append(f"DUE TODAY ({len(data['due_today'])}):")
lines.extend(f" - {t}" for t in data["due_today"])
lines.append("")
if data["high_priority"]:
if data.get("high_priority"):
lines.append("HIGH PRIORITY (in progress):")
lines.extend(f" - {t}" for t in data["high_priority"])
lines.append("")
@@ -326,53 +338,104 @@ async def _get_temp_unit(user_id: int) -> str:
return "C"
async def run_compilation(user_id: int, slot: str, model: str | None = None) -> str:
async def run_compilation(
user_id: int,
slot: str,
model: str | None = None,
) -> tuple[str, dict]:
"""
Run the full two-lane briefing pipeline for a user and slot.
Returns the combined briefing text to be posted as the opening assistant message.
Returns (briefing_text, metadata_dict) where metadata contains
weather card data and rss_item_ids for frontend rendering.
"""
if model is None:
model = await get_setting(user_id, "default_model", Config.OLLAMA_MODEL)
from fabledassistant.services.briefing_profile import get_profile_body
from fabledassistant.services.briefing_preferences import (
load_topic_preferences,
load_topic_reaction_scores,
score_and_filter_items,
)
from fabledassistant.services.weather import parse_weather_card_data, get_cached_weather_rows
profile_body, temp_unit = await asyncio.gather(
get_profile_body(user_id),
_get_temp_unit(user_id),
)
# Parallel gather
internal_data, external_data = await asyncio.gather(
# ── Pre-processing ──────────────────────────────────────────────────────────
include_topics, exclude_topics = await load_topic_preferences(user_id)
topic_scores = await load_topic_reaction_scores(user_id)
# Parallel raw gather — weather rows fetched in same gather to avoid extra DB round-trip
internal_data, external_data, weather_rows = await asyncio.gather(
_gather_internal(user_id),
_gather_external(user_id),
get_cached_weather_rows(user_id),
)
# Two-lane LLM synthesis (both calls run concurrently)
# Task change detection
all_tasks = internal_data.get("all_tasks_raw", [])
changed_tasks, unchanged_count = await split_changed_tasks(user_id, all_tasks)
# RSS filtering
raw_rss = external_data.get("rss_items") or []
filtered_rss = score_and_filter_items(
raw_rss,
include_topics=include_topics,
exclude_topics=exclude_topics,
topic_scores=topic_scores,
max_items=10,
)
rss_item_ids = [item["id"] for item in filtered_rss if item.get("id")]
# Weather staleness gate — returns None if data is >24h old
weather_card = parse_weather_card_data(weather_rows[0], temp_unit) if weather_rows else None
# ── LLM Synthesis ──────────────────────────────────────────────────────────
# Build filtered internal data with only changed tasks
today = internal_data["date"]
internal_data_filtered = dict(internal_data)
internal_data_filtered["unchanged_task_count"] = unchanged_count
internal_data_filtered["changed_tasks"] = [format_task(t) for t in changed_tasks]
# Build filtered external data (suppress weather prose — card handles it)
external_data_filtered = {
"rss_items": filtered_rss,
"weather": [],
}
internal_text, external_text = await asyncio.gather(
_llm_synthesise(
_internal_system_prompt(profile_body),
_internal_user_prompt(internal_data, slot),
_internal_user_prompt(internal_data_filtered, slot),
model,
),
_llm_synthesise(
_external_system_prompt(),
_external_user_prompt(external_data, slot, temp_unit),
_external_user_prompt(external_data_filtered, slot, temp_unit),
model,
),
)
# ── Post-processing ─────────────────────────────────────────────────────────
await upsert_task_snapshots(user_id, all_tasks)
metadata: dict = {"rss_item_ids": rss_item_ids, "weather": weather_card}
if not internal_text and not external_text:
logger.warning("Briefing compilation produced no content for user %d slot %s", user_id, slot)
return ""
return "", metadata
greeting = slot_greeting(slot)
today = internal_data["date"]
parts = [f"**{greeting}{today}**", ""]
if internal_text:
parts += ["## Your Day", "", internal_text, ""]
if external_text:
parts += ["## The World", "", external_text]
return "\n".join(parts).strip()
return "\n".join(parts).strip(), metadata
async def run_slot_injection(user_id: int, slot: str, model: str | None = None) -> str:
@@ -153,9 +153,9 @@ async def _run_slot_for_user(user_id: int, slot: str) -> None:
await _run_profile_closeout(user_id, model)
conv = await get_or_create_today_conversation(user_id, model)
text = await run_compilation(user_id, slot, model)
text, metadata = await run_compilation(user_id, slot, model)
if text:
await post_message(conv.id, "assistant", text)
await post_message(conv.id, "assistant", text, metadata=metadata)
else:
conv = await get_or_create_today_conversation(user_id, model)