read.markets/app/jobs/indicator_summary_job.py

"""Hourly per-group indicator summaries — a short AI read at the top of each
Indicators tab. Costs ~$0.0003 per call on DeepSeek V4 Flash, so 10+ groups
hourly stays comfortably under the monthly cap."""
from __future__ import annotations

import asyncio
import re

import httpx
from sqlalchemy import desc, func, select

from app.config import get_settings, load_groups
from app.db import utcnow
from app.jobs._helpers import job_lifecycle, log
from app.jobs._market_context import latest_quotes_by_group, month_spend
from app.models import AICall, IndicatorSummary, JobRun
from app.services.cadence import DEFAULT_POLICY
from app.services.openrouter import (
    PROMPT_VERSION,
    active_model,
    build_aggregate_summary_system_prompt,
    build_aggregate_summary_user_prompt,
    build_summary_system_prompt,
    build_summary_user_prompt,
    call_llm,
    llm_configured,
    month_start,
)


AGGREGATE_GROUP_NAME = "__all__"


# Strip known meta-commentary openers the model sometimes leaks despite the
# prompt's hard constraints. Each pattern matches one leading sentence.
_LEAK_PATTERNS = [
    re.compile(p, re.IGNORECASE | re.DOTALL)
    for p in (
        # First-person meta — "I need to / I'll / I have to / I'm going to ..."
        r"^i\s+(?:need|have|must|should|am going|'ll|will|shall|can|am)[^.]*\.\s*",
        # "We need / we're / we are asked / we will ..."
        r"^we\s+(?:need|are|'re|will|shall|can|should|must|have)[^.]*\.\s*",
        r"^let\s+(?:me|us|'?s)[^.]*\.\s*",
        r"^here['’]s[^.]*\.\s*",
        r"^sure[,!]?\s[^.]*\.\s*",
        r"^looking at[^.]*\.\s*",
        r"^based on[^.]*\.\s*",
        r"^to (?:address|answer|write|summarise|summarize)[^.]*\.\s*",
        r"^first[,]?\s[^.]*\.\s*",
        r"^the (?:user|data shows|reader|task|request|reader sees|instructions?)[^.]*\.\s*",
        r"^summary[:.]\s*",
        r"^key\s*[:\-—]\s*",
        r"^must\s+(?:be|cite|explain|avoid|give|stay|provide)[^.]*\.\s*",
        r"^should\s+(?:be|give|cite|explain|avoid|provide)[^.]*\.\s*",
        r"^avoid[^.]*\.\s*",
        r"^cite\s+at\s+most[^.]*\.\s*",
        r"^be\s+(?:speculative|specific|concise|brief)[^.]*\.\s*",
        r"^stay\s+on[^.]*\.\s*",
        r"^okay[,]?\s+",
        r"^alright[,]?\s+",
        r"^thinking[^.]*\.\s*",
        # Prompt-leak prefixes — the model echoes example framing or rule
        # headers from the system prompt.
        r"^(?:good|bad|positive|negative)\s+example\s*[:\-—]\s*",
        r"^example\s+(?:good|bad)\s*[:\-—]\s*",
        r"^example\s*[:\-—]\s*",
        r"^reference\s+style\s*[:\-—]\s*",
        # Prompt label echoes (markdown-style or plain-text)
        r"^(?:hard\s+)?constraints?\s*[:\-—][^.\n]*[.\n]\s*",
        r"^key\s+observations?\s*[:\-—]\s*",
        r"^observations?\s*[:\-—]\s*",
        r"^focus\s+on[^.]*\.\s*",
        r"^output\s+the\s+read[^.]*\.\s*",
        r"^plain\s+prose[^.]*\.\s*",
        r"^the\s+indicators?[^.]*\.\s*",   # "The indicators include..." / "The indicators are..."
        r"^indicators?\s*[:\-—]\s*",
        r"^data\s*[:\-—]\s*",
        r"^analysis\s*[:\-—]\s*",
        r"^interpretation\s*[:\-—]\s*",
        r"^read\s*[:\-—]\s*",
        r"^note\s*[:\-—]\s*",
        # Sometimes the response gets wrapped in literal quotes
        r"^[\"“'`]+",
    )
]


_TRAILING_QUOTE = re.compile(r"[\"”'`]+\s*$")

# Tell-tale phrases that mean the model regurgitated the prompt as its
# "answer" — we'd rather show nothing than show this.
_LEAKAGE_FLAGS = (
    "≤60 words", "60 words", "must be under", "must cite", "must explain",
    "no meta-commentary", "no buy/sell", "horizon. ", "1-day moves",
    "the instructions are", "instructions:", "constraints:", "hard constraints",
    "good example", "bad example", "reference style",
)


def looks_like_leakage(text: str) -> bool:
    """Heuristic: after cleaning, if these phrases still appear, the output
    is contaminated prompt-regurgitation and shouldn't be shown."""
    low = text.lower()
    return any(flag in low for flag in _LEAKAGE_FLAGS)


def clean_summary(text: str) -> str:
    """Strip leading meta-commentary. If cleaning removes nearly everything
    (suggesting the model emitted reasoning then ran out of tokens), fall
    back to the last non-empty paragraph of the raw output — that's usually
    where the actual answer ended up."""
    raw = text.strip()
    out = raw
    # Up to 6 passes: handles compound leakage like
    # "Constraints: <...>. The indicators are: <...>. <actual answer>"
    for _ in range(6):
        before = out
        for pat in _LEAK_PATTERNS:
            out = pat.sub("", out, count=1).lstrip()
        if out == before:
            break
    if len(out) < 60 and len(raw) > 120:
        # Cleaning ate too much; take the last non-empty paragraph of raw.
        paragraphs = [p.strip() for p in re.split(r"\n\s*\n", raw) if p.strip()]
        if paragraphs:
            out = paragraphs[-1]
            # Re-strip leaders from the recovered paragraph too.
            for _ in range(2):
                before = out
                for pat in _LEAK_PATTERNS:
                    out = pat.sub("", out, count=1).lstrip()
                if out == before:
                    break
    # Trim any orphan closing quote/backtick from the wrap-strip above.
    out = _TRAILING_QUOTE.sub("", out).rstrip()
    return out



async def _generate_one(
    session, client: httpx.AsyncClient, group: str, quotes: list[dict],
    system_prompt: str, model: str, tone: str, analysis: str,
) -> bool:
    """Generate + persist one group's summary. Returns True on success.
    `model` is retained for ledger labelling but call_llm now picks the
    active-provider model itself."""
    user_prompt = build_summary_user_prompt(group, quotes)
    try:
        result = await call_llm(
            client,
            [{"role": "system", "content": system_prompt},
             {"role": "user",   "content": user_prompt}],
            max_tokens=800,  # DeepSeek sometimes spends 300+ on internal reasoning
        )
    except Exception as e:
        session.add(AICall(model=active_model(), status="error", error=str(e)[:500]))
        log.warning("ind_summary.failed", group=group, error=str(e)[:120])
        return False

    cleaned = clean_summary(result.content)
    if looks_like_leakage(cleaned) or len(cleaned) < 40:
        # Model regurgitated the prompt or produced nothing usable.
        # Don't persist — keep the last good summary visible. Log it so
        # we can see the rate of failures over time.
        log.warning("ind_summary.leakage_detected",
                    group=group, preview=cleaned[:120])
        session.add(AICall(
            model=result.model,
            prompt_tokens=result.prompt_tokens,
            completion_tokens=result.completion_tokens,
            cost_usd=result.cost_usd,
            status="leaked",
        ))
        return False

    session.add(IndicatorSummary(
        group_name=group,
        generated_at=utcnow(),
        model=result.model,
        tone=tone,
        analysis=analysis,
        prompt_version=PROMPT_VERSION,
        content=cleaned,
        prompt_tokens=result.prompt_tokens,
        completion_tokens=result.completion_tokens,
        cost_usd=result.cost_usd,
    ))
    session.add(AICall(
        model=result.model,
        prompt_tokens=result.prompt_tokens,
        completion_tokens=result.completion_tokens,
        cost_usd=result.cost_usd,
        status="ok",
    ))
    return True


async def run() -> None:
    async with job_lifecycle("indicator_summary_job") as (session, jr):
        if jr.status == "skipped":
            return
        s = get_settings()
        if not llm_configured():
            log.warning("ind_summary.skipped_no_key", provider=s.LLM_PROVIDER)
            jr.status = "skipped"
            return

        # Cadence — same policy as ai_log_job: hourly during EU/US active,
        # throttled off-hours and weekends.
        last_success = (await session.execute(
            select(func.max(JobRun.finished_at)).where(
                JobRun.name == "indicator_summary_job",
                JobRun.status == "success",
            )
        )).scalar()
        should_run, reason = DEFAULT_POLICY.should_run(last_success)
        if not should_run:
            log.info("ind_summary.cadence_skip", reason=reason)
            jr.status = "skipped"
            jr.error = reason
            return

        spent = await month_spend(session)
        if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
            jr.status = "skipped"
            jr.error = f"monthly cap reached (${spent:.2f})"
            return

        groups = await latest_quotes_by_group(session)
        # Only summarise groups currently configured in TOML — drops stale
        # group names (e.g. an old "pie" before T212 sourcing) that still have
        # quotes in the table but no UI presence.
        configured = set(load_groups(s.BASELINE_TOML, s.PORTFOLIO_TOML).keys())
        groups = {g: q for g, q in groups.items() if g in configured}
        if not groups:
            jr.status = "skipped"
            return

        # Phase 2 voice pivot (PROMPT_VERSION 6): generate both tones each
        # run so the dashboard toggle is instant. ANALYSIS stays on the
        # operator-configured default.
        analysis = (s.CASSANDRA_ANALYSIS or "SPECULATIVE").upper()
        tones = ("NOVICE", "INTERMEDIATE")

        written = 0
        async with httpx.AsyncClient(follow_redirects=True) as client:
            # Sequential rather than parallel — OpenRouter free tiers can
            # throttle bursts; total work is small (~14-16 calls × ~5s each).
            for tone in tones:
                system_prompt = build_summary_system_prompt(tone, analysis)
                for group, quotes in groups.items():
                    ok = await _generate_one(
                        session, client, group, quotes,
                        system_prompt, active_model(), tone, analysis,
                    )
                    if ok:
                        written += 1
                    await session.commit()  # partial progress survives mid-job error

                # One aggregate read across all groups, stored under __all__.
                agg_system = build_aggregate_summary_system_prompt(tone, analysis)
                agg_user = build_aggregate_summary_user_prompt(groups)
                try:
                    result = await call_llm(
                        client,
                        [{"role": "system", "content": agg_system},
                         {"role": "user", "content": agg_user}],
                        max_tokens=1500,  # room for reasoning + 80-word output
                    )
                    session.add(IndicatorSummary(
                        group_name=AGGREGATE_GROUP_NAME,
                        generated_at=utcnow(),
                        model=result.model,
                        tone=tone,
                        analysis=analysis,
                        prompt_version=PROMPT_VERSION,
                        content=clean_summary(result.content),
                        prompt_tokens=result.prompt_tokens,
                        completion_tokens=result.completion_tokens,
                        cost_usd=result.cost_usd,
                    ))
                    session.add(AICall(
                        model=result.model,
                        prompt_tokens=result.prompt_tokens,
                        completion_tokens=result.completion_tokens,
                        cost_usd=result.cost_usd, status="ok",
                    ))
                    written += 1
                except Exception as e:
                    session.add(AICall(
                        model=active_model(), status="error",
                        error=f"{tone}/agg: {str(e)[:480]}",
                    ))
                    log.warning("ind_summary.agg_failed",
                                tone=tone, error=str(e)[:120])
                await session.commit()

        jr.items_written = written
        log.info("ind_summary.done",
                 groups=len(groups), tones=len(tones), written=written)


if __name__ == "__main__":
    asyncio.run(run())