Giorgio Gilestro
40cfb50e37
Two changes that together cut OpenRouter spend ~50% and give the daily log temporal awareness. 1. CadencePolicy (app/services/cadence.py): expensive AI jobs only fire hourly during the EU/US active window (Mon-Fri 07-21 UTC). Off-hours weekdays throttle to every 4h; weekends to every 12h. ai_log_job and indicator_summary_job both consult the policy before doing real work; market/news/portfolio ingest jobs stay hourly (cheap, no API cost). Skipped runs land in job_runs with status 'skipped' and the throttle reason in error. 2. Update mode for ai_log_job: when an earlier log exists for the current UTC day, it's passed to the model as 'Earlier log from today (generated HH:MM UTC)'. The system prompt grows an Update mode section instructing the model to revise — not restart — and anchor on what has CHANGED since the earlier draft. The TL;DR leads with intra-day change when meaningful, the watch list evolves rather than restarts. PROMPT_VERSION bumped to 5. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
328 lines
13 KiB
Python
328 lines
13 KiB
Python
"""Hourly per-group indicator summaries — a short AI read at the top of each
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Indicators tab. Costs ~$0.0003 per call on DeepSeek V4 Flash, so 10+ groups
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hourly stays comfortably under the monthly cap."""
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from __future__ import annotations
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import asyncio
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import re
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from collections import defaultdict
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import httpx
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from sqlalchemy import desc, func, select
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from app.config import get_settings, load_groups
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from app.db import utcnow
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from app.jobs._helpers import job_lifecycle, log
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from app.models import AICall, IndicatorSummary, JobRun, Quote
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from app.services.cadence import DEFAULT_POLICY
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from app.services.openrouter import (
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PROMPT_VERSION,
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build_aggregate_summary_system_prompt,
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build_aggregate_summary_user_prompt,
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build_summary_system_prompt,
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build_summary_user_prompt,
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call_openrouter,
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month_start,
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)
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AGGREGATE_GROUP_NAME = "__all__"
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# Strip known meta-commentary openers the model sometimes leaks despite the
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# prompt's hard constraints. Each pattern matches one leading sentence.
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_LEAK_PATTERNS = [
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re.compile(p, re.IGNORECASE | re.DOTALL)
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for p in (
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# First-person meta — "I need to / I'll / I have to / I'm going to ..."
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r"^i\s+(?:need|have|must|should|am going|'ll|will|shall|can|am)[^.]*\.\s*",
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# "We need / we're / we are asked / we will ..."
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r"^we\s+(?:need|are|'re|will|shall|can|should|must|have)[^.]*\.\s*",
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r"^let\s+(?:me|us|'?s)[^.]*\.\s*",
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r"^here['’]s[^.]*\.\s*",
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r"^sure[,!]?\s[^.]*\.\s*",
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r"^looking at[^.]*\.\s*",
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r"^based on[^.]*\.\s*",
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r"^to (?:address|answer|write|summarise|summarize)[^.]*\.\s*",
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r"^first[,]?\s[^.]*\.\s*",
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r"^the (?:user|data shows|reader|task|request|reader sees|instructions?)[^.]*\.\s*",
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r"^summary[:.]\s*",
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r"^key\s*[:\-—]\s*",
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r"^must\s+(?:be|cite|explain|avoid|give|stay|provide)[^.]*\.\s*",
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r"^should\s+(?:be|give|cite|explain|avoid|provide)[^.]*\.\s*",
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r"^avoid[^.]*\.\s*",
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r"^cite\s+at\s+most[^.]*\.\s*",
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r"^be\s+(?:speculative|specific|concise|brief)[^.]*\.\s*",
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r"^stay\s+on[^.]*\.\s*",
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r"^okay[,]?\s+",
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r"^alright[,]?\s+",
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r"^thinking[^.]*\.\s*",
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# Prompt-leak prefixes — the model echoes example framing or rule
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# headers from the system prompt.
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r"^(?:good|bad|positive|negative)\s+example\s*[:\-—]\s*",
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r"^example\s+(?:good|bad)\s*[:\-—]\s*",
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r"^example\s*[:\-—]\s*",
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r"^reference\s+style\s*[:\-—]\s*",
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# Prompt label echoes (markdown-style or plain-text)
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r"^(?:hard\s+)?constraints?\s*[:\-—][^.\n]*[.\n]\s*",
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r"^key\s+observations?\s*[:\-—]\s*",
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r"^observations?\s*[:\-—]\s*",
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r"^focus\s+on[^.]*\.\s*",
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r"^output\s+the\s+read[^.]*\.\s*",
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r"^plain\s+prose[^.]*\.\s*",
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r"^the\s+indicators?[^.]*\.\s*", # "The indicators include..." / "The indicators are..."
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r"^indicators?\s*[:\-—]\s*",
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r"^data\s*[:\-—]\s*",
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r"^analysis\s*[:\-—]\s*",
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r"^interpretation\s*[:\-—]\s*",
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r"^read\s*[:\-—]\s*",
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r"^note\s*[:\-—]\s*",
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# Sometimes the response gets wrapped in literal quotes
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r"^[\"“'`]+",
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)
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]
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_TRAILING_QUOTE = re.compile(r"[\"”'`]+\s*$")
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# Tell-tale phrases that mean the model regurgitated the prompt as its
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# "answer" — we'd rather show nothing than show this.
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_LEAKAGE_FLAGS = (
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"≤60 words", "60 words", "must be under", "must cite", "must explain",
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"no meta-commentary", "no buy/sell", "horizon. ", "1-day moves",
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"the instructions are", "instructions:", "constraints:", "hard constraints",
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"good example", "bad example", "reference style",
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)
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def looks_like_leakage(text: str) -> bool:
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"""Heuristic: after cleaning, if these phrases still appear, the output
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is contaminated prompt-regurgitation and shouldn't be shown."""
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low = text.lower()
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return any(flag in low for flag in _LEAKAGE_FLAGS)
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def clean_summary(text: str) -> str:
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"""Strip leading meta-commentary. If cleaning removes nearly everything
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(suggesting the model emitted reasoning then ran out of tokens), fall
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back to the last non-empty paragraph of the raw output — that's usually
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where the actual answer ended up."""
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raw = text.strip()
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out = raw
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# Up to 6 passes: handles compound leakage like
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# "Constraints: <...>. The indicators are: <...>. <actual answer>"
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for _ in range(6):
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before = out
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for pat in _LEAK_PATTERNS:
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out = pat.sub("", out, count=1).lstrip()
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if out == before:
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break
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if len(out) < 60 and len(raw) > 120:
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# Cleaning ate too much; take the last non-empty paragraph of raw.
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paragraphs = [p.strip() for p in re.split(r"\n\s*\n", raw) if p.strip()]
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if paragraphs:
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out = paragraphs[-1]
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# Re-strip leaders from the recovered paragraph too.
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for _ in range(2):
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before = out
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for pat in _LEAK_PATTERNS:
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out = pat.sub("", out, count=1).lstrip()
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if out == before:
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break
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# Trim any orphan closing quote/backtick from the wrap-strip above.
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out = _TRAILING_QUOTE.sub("", out).rstrip()
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return out
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async def _latest_quotes_by_group(session) -> dict[str, list[dict]]:
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"""Latest non-null quote per (group, symbol). Drops error rows."""
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sub = (
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select(Quote.group_name, Quote.symbol,
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func.max(Quote.fetched_at).label("mx"))
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.group_by(Quote.group_name, Quote.symbol)
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.subquery()
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)
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rows = (await session.execute(
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select(Quote).join(
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sub,
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(Quote.group_name == sub.c.group_name)
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& (Quote.symbol == sub.c.symbol)
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& (Quote.fetched_at == sub.c.mx),
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).where(Quote.price.is_not(None))
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.order_by(Quote.group_name, Quote.symbol)
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)).scalars().all()
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by_group: dict[str, list[dict]] = defaultdict(list)
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for q in rows:
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by_group[q.group_name].append({
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"symbol": q.symbol, "label": q.label,
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"price": q.price, "currency": q.currency,
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"as_of": q.as_of, "changes": q.changes,
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})
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return by_group
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async def _month_spend(session) -> float:
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total = (await session.execute(
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select(func.coalesce(func.sum(AICall.cost_usd), 0.0))
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.where(AICall.called_at >= month_start())
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)).scalar()
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return float(total or 0.0)
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async def _generate_one(
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session, client: httpx.AsyncClient, group: str, quotes: list[dict],
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system_prompt: str, model: str, tone: str, analysis: str,
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) -> bool:
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"""Generate + persist one group's summary. Returns True on success."""
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user_prompt = build_summary_user_prompt(group, quotes)
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try:
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result = await call_openrouter(
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client,
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[{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}],
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model=model,
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max_tokens=800, # DeepSeek sometimes spends 300+ on internal reasoning
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)
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except Exception as e:
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session.add(AICall(model=model, status="error", error=str(e)[:500]))
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log.warning("ind_summary.failed", group=group, error=str(e)[:120])
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return False
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cleaned = clean_summary(result.content)
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if looks_like_leakage(cleaned) or len(cleaned) < 40:
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# Model regurgitated the prompt or produced nothing usable.
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# Don't persist — keep the last good summary visible. Log it so
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# we can see the rate of failures over time.
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log.warning("ind_summary.leakage_detected",
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group=group, preview=cleaned[:120])
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session.add(AICall(
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model=result.model,
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prompt_tokens=result.prompt_tokens,
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completion_tokens=result.completion_tokens,
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cost_usd=result.cost_usd,
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status="leaked",
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))
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return False
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session.add(IndicatorSummary(
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group_name=group,
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generated_at=utcnow(),
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model=result.model,
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tone=tone,
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analysis=analysis,
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prompt_version=PROMPT_VERSION,
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content=cleaned,
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prompt_tokens=result.prompt_tokens,
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completion_tokens=result.completion_tokens,
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cost_usd=result.cost_usd,
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))
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session.add(AICall(
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model=result.model,
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prompt_tokens=result.prompt_tokens,
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completion_tokens=result.completion_tokens,
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cost_usd=result.cost_usd,
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status="ok",
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))
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return True
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async def run() -> None:
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async with job_lifecycle("indicator_summary_job") as (session, jr):
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if jr.status == "skipped":
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return
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s = get_settings()
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if not s.OPENROUTER_API_KEY:
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jr.status = "skipped"
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return
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# Cadence — same policy as ai_log_job: hourly during EU/US active,
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# throttled off-hours and weekends.
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last_success = (await session.execute(
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select(func.max(JobRun.finished_at)).where(
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JobRun.name == "indicator_summary_job",
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JobRun.status == "success",
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)
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)).scalar()
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should_run, reason = DEFAULT_POLICY.should_run(last_success)
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if not should_run:
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log.info("ind_summary.cadence_skip", reason=reason)
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jr.status = "skipped"
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jr.error = reason
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return
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spent = await _month_spend(session)
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if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
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jr.status = "skipped"
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jr.error = f"monthly cap reached (${spent:.2f})"
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return
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groups = await _latest_quotes_by_group(session)
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# Only summarise groups currently configured in TOML — drops stale
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# group names (e.g. an old "pie" before T212 sourcing) that still have
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# quotes in the table but no UI presence.
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configured = set(load_groups(s.BASELINE_TOML, s.PORTFOLIO_TOML).keys())
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groups = {g: q for g, q in groups.items() if g in configured}
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if not groups:
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jr.status = "skipped"
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return
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tone = s.CASSANDRA_TONE.upper()
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analysis = s.CASSANDRA_ANALYSIS.upper()
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system_prompt = build_summary_system_prompt(tone, analysis)
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written = 0
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async with httpx.AsyncClient(follow_redirects=True) as client:
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# Sequential rather than parallel — OpenRouter free tiers can
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# throttle bursts; total work is small (~12 calls × ~5s each).
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for group, quotes in groups.items():
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ok = await _generate_one(
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session, client, group, quotes,
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system_prompt, s.OPENROUTER_MODEL, tone, analysis,
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)
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if ok:
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written += 1
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await session.commit() # partial progress survives mid-job error
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# One aggregate read across all groups, stored under __all__.
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agg_system = build_aggregate_summary_system_prompt(tone, analysis)
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agg_user = build_aggregate_summary_user_prompt(groups)
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try:
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result = await call_openrouter(
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client,
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[{"role": "system", "content": agg_system},
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{"role": "user", "content": agg_user}],
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model=s.OPENROUTER_MODEL,
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max_tokens=1500, # room for reasoning + 80-word output
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)
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session.add(IndicatorSummary(
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group_name=AGGREGATE_GROUP_NAME,
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generated_at=utcnow(),
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model=result.model,
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tone=tone,
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analysis=analysis,
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prompt_version=PROMPT_VERSION,
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content=clean_summary(result.content),
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prompt_tokens=result.prompt_tokens,
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completion_tokens=result.completion_tokens,
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cost_usd=result.cost_usd,
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))
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session.add(AICall(
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model=result.model,
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prompt_tokens=result.prompt_tokens,
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completion_tokens=result.completion_tokens,
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cost_usd=result.cost_usd, status="ok",
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))
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written += 1
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except Exception as e:
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session.add(AICall(
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model=s.OPENROUTER_MODEL, status="error", error=str(e)[:500],
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))
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log.warning("ind_summary.agg_failed", error=str(e)[:120])
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await session.commit()
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jr.items_written = written
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log.info("ind_summary.done", groups=len(groups), written=written)
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if __name__ == "__main__":
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asyncio.run(run())
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