read.markets/app/jobs/ai_log_job.py
Giorgio Gilestro 4adc8dfe82 openrouter: split into llm_prompts (prompt engineering) + transport
openrouter.py was 790 lines mixing two orthogonal concerns:
- Prompt engineering (build_system_prompt, build_summary_*,
  build_chat_*, build_daily_digest_*, etc.) — ~400 lines, changes
  weekly as PROMPT_VERSION bumps
- LLM transport (call_llm, _provider_chain, _call_provider, retry
  + fallback machinery) — ~250 lines, rarely changes

Extracted the prompt-engineering surface to app/services/llm_prompts.py.
Transport stays in openrouter.py (consistent with the filename — the
OpenRouter URL is the transport's anchor).

All import sites (jobs, routers, services, tests) split their
multi-import lines into two: prompt-things from llm_prompts, transport
from openrouter. PROMPT_VERSION constant, _TONE_ALIASES, _resolve_tone,
and SYSTEM_PROMPT moved with the prompt functions.

No behaviour change — pure relocation. Function signatures, body, and
naming all preserved.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-27 21:27:23 +02:00

219 lines
8.2 KiB
Python

"""Hourly AI strategic-log generator. Pulls already-persisted market data and
headlines from the DB (no live fetches), calls OpenRouter, persists the log
and a row in the cost ledger."""
from __future__ import annotations
import asyncio
import httpx
from sqlalchemy import desc, func, select
from app.config import get_settings
from app.db import utcnow
from app.jobs._helpers import job_lifecycle, log
from app.jobs._market_context import (
REFERENCE_LINE,
latest_quotes_by_group,
month_spend,
recent_headlines_by_bucket,
)
from app.models import AICall, JobRun, StrategicLog, StrategicLogTranslation, User
from app.services.cadence import DEFAULT_POLICY
from app.services.i18n import ACTIVE_LANGUAGES
from app.services.llm_prompts import (
PROMPT_VERSION,
build_system_prompt,
build_user_prompt,
)
from app.services.openrouter import (
active_model,
call_llm,
llm_configured,
)
from app.services.translation import translate
async def translate_log_for_active_languages(session, log_id: int) -> None:
"""Fan out per-language translations for the strategic log identified
by ``log_id``.
Reads ``users.lang`` (deduplicated, restricted to ACTIVE_LANGUAGES
minus English), one translation call per language in parallel via
``asyncio.gather``, persists each successful result as a
``StrategicLogTranslation`` row. Per-language failures are logged
but never raise — the strategic log itself is already committed at
this point and translation is a best-effort enhancement.
The job orchestrator calls this AFTER the English ``StrategicLog``
row is committed; pass the row's ``id`` in.
"""
target_langs = sorted({l for l in ACTIVE_LANGUAGES if l != "en"})
if not target_langs:
return
active_langs = (await session.execute(
select(User.lang).distinct().where(User.lang.in_(target_langs))
)).scalars().all()
if not active_langs:
return
log_row = await session.get(StrategicLog, log_id)
if log_row is None:
log.warning("log.translate.missing_log", log_id=log_id)
return
async with httpx.AsyncClient(follow_redirects=True, timeout=60) as client:
results = await asyncio.gather(*[
translate(client, log_row.content, lang)
for lang in active_langs
], return_exceptions=True)
for lang, result in zip(active_langs, results):
if isinstance(result, Exception):
log.warning("log.translate.failed", lang=lang, log_id=log_id,
error=str(result)[:200])
continue
translated_md, llm_result = result
session.add(StrategicLogTranslation(
log_id=log_id, lang=lang,
content=translated_md,
generated_at=utcnow(),
model=llm_result.model,
prompt_tokens=llm_result.prompt_tokens,
completion_tokens=llm_result.completion_tokens,
cost_usd=llm_result.cost_usd,
))
await session.commit()
async def run() -> None:
async with job_lifecycle("ai_log_job") as (session, jr):
if jr.status == "skipped":
return
s = get_settings()
if not llm_configured():
log.warning("ai_log.skipped_no_key", provider=s.LLM_PROVIDER)
jr.status = "skipped"
return
# Cadence: hourly during EU/US active hours; throttled off-hours.
last_success = (await session.execute(
select(func.max(JobRun.finished_at)).where(
JobRun.name == "ai_log_job",
JobRun.status == "success",
)
)).scalar()
should_run, reason = DEFAULT_POLICY.should_run(last_success)
if not should_run:
log.info("ai_log.cadence_skip", reason=reason)
jr.status = "skipped"
jr.error = reason
return
spent = await month_spend(session)
if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
log.warning("ai_log.cap_reached", spent=spent,
cap=s.OPENROUTER_MONTHLY_CAP_USD)
jr.status = "skipped"
jr.error = f"monthly cost cap reached (${spent:.2f})"
return
quotes = await latest_quotes_by_group(session)
news = await recent_headlines_by_bucket(session)
if not quotes and not news:
log.warning("ai_log.no_data_yet")
jr.status = "skipped"
return
# Look up the most recent log generated today (UTC) so the model can
# update it rather than start from scratch. This gives the model
# temporal awareness — "since this morning's read, X has changed".
today_start = utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
previous_log = (await session.execute(
select(StrategicLog)
.where(StrategicLog.generated_at >= today_start)
.order_by(desc(StrategicLog.generated_at))
.limit(1)
)).scalar_one_or_none()
anchor = s.CASSANDRA_ANCHOR_DATE or None
user_prompt = build_user_prompt(
today=utcnow(),
anchor=anchor,
quotes_by_group=quotes,
headlines_by_bucket=news,
reference_line=REFERENCE_LINE,
previous_log=previous_log,
)
# Phase 2 voice pivot (PROMPT_VERSION 6): generate both tones per
# run so the dashboard toggle is instant. Analysis stays on the
# operator-configured default (DRY|SPECULATIVE is a system-wide
# preference, not a per-user toggle). PRO was dropped.
analysis = (s.CASSANDRA_ANALYSIS or "SPECULATIVE").upper()
variants = [
("NOVICE", analysis),
("INTERMEDIATE", analysis),
]
written = 0
async with httpx.AsyncClient(follow_redirects=True) as client:
for tone, analysis in variants:
# Re-check cost cap between variants so a runaway run is
# bounded.
spent = await month_spend(session)
if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
log.warning("ai_log.cap_reached_midrun",
spent=spent, completed=written)
break
system_prompt = build_system_prompt(tone, analysis)
try:
result = await call_llm(
client,
[{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}],
)
except Exception as e:
session.add(AICall(
model=active_model(), status="error",
error=f"{tone}/{analysis}: {str(e)[:480]}",
))
await session.commit()
log.error("ai_log.variant_failed",
tone=tone, analysis=analysis, error=str(e)[:200])
continue
slog = StrategicLog(
generated_at=utcnow(),
model=result.model,
anchor_date=anchor,
prompt_version=PROMPT_VERSION,
tone=tone,
analysis=analysis,
content=result.content,
prompt_tokens=result.prompt_tokens,
completion_tokens=result.completion_tokens,
cost_usd=result.cost_usd,
)
session.add(slog)
session.add(AICall(
model=result.model,
prompt_tokens=result.prompt_tokens,
completion_tokens=result.completion_tokens,
cost_usd=result.cost_usd,
status="ok",
))
await session.commit()
await translate_log_for_active_languages(session, slog.id)
written += 1
log.info("ai_log.variant_done",
tone=tone, analysis=analysis,
prompt_tokens=result.prompt_tokens,
completion_tokens=result.completion_tokens)
jr.items_written = written
log.info("ai_log.done", variants=written, total=len(variants))
if __name__ == "__main__":
asyncio.run(run())