read.markets/app/services/output_review.py

"""Second-pass reviewer agent for AI-generated reads.

The per-group and aggregate indicator summaries are generated in JSON
mode and the publishable text comes out of a single "read" field, but a
misbehaving model can still slip chain-of-thought INSIDE the field
("Let's see…", "X? Actually Y?", multi-question parentheticals). This
module makes a small second LLM call that judges the candidate read as
clean / unclean. Cost is ~$0.0001 per check; latency ~1-2 s in the
hourly job. No user-facing latency.

The reviewer is deliberately a tiny, JSON-shaped classifier — same
JSON-mode mechanism as the generator, so the verdict can't be lost in
prose. If parsing fails or the call errors, the row is rejected
(fail-safe: the previously cached good summary stays visible).
"""
from __future__ import annotations

import json
from dataclasses import dataclass

import httpx

from app.config import get_settings
from app.logging import get_logger
from app.services.openrouter import call_llm

log = get_logger("output_review")


# The reviewer runs through OpenRouter against a small, non-thinking
# model. DeepSeek-V4-flash (our generator default) emits internal
# chain-of-thought before its JSON output even when the prompt forbids
# it, which truncates the JSON at any reasonable max_tokens cap and
# breaks the parser. Anthropic's Haiku family answers structured-output
# tasks tersely and deterministically — no chain-of-thought tax. Cost
# is ~$0.0001-$0.0003 per review depending on candidate length.
DEFAULT_REVIEWER_MODEL = "anthropic/claude-haiku-4.5"


_SYSTEM_PROMPT = """\
You are a strict editor for a financial-markets dashboard. The author
was asked to produce a short interpretive read for human readers.
You receive their proposed read and decide if it is publishable as-is.

Mark CLEAN only if the text reads like a finished interpretation a
reader could see on a public dashboard without confusion.

Mark UNCLEAN if the text contains ANY of:
- Chain-of-thought / scratchpad markers used as thinking — phrases like
  "Let me", "Let's see", "we need to", "actually" (correcting itself),
  "wait", "hmm", "or rather", "I should".
- Self-questioning parentheticals: "Q1 2026? Actually Q4 2025?",
  "is it X or Y?", any place where the author appears to be working
  out the answer in front of the reader.
- Multiple rhetorical questions or any question that interrupts the
  declarative voice. A clean interpretive read is assertive.
- Meta-commentary about the task, output format, word limits, or
  instructions — e.g. "as required by the constraints", "the prompt
  asks", "let me address each".
- Partial / truncated content. Starts mid-word, mid-number, mid-clause.
- Visible internal numbers without clear meaning ("change 1y +5.9%?"),
  raw column names ("as_of 2026-01-01"), or any debug-like fragments.
- Anything other than the finished, publishable interpretation.

Return ONLY a JSON object with this exact shape:
{"clean": true | false, "reason": "<≤20 words, plain text>"}
No preamble, no markdown fences, no other fields.
"""


@dataclass(frozen=True)
class Verdict:
    clean: bool
    reason: str
    cost_usd: float | None  # cost of the review call itself, for the ledger


async def review_read(client: httpx.AsyncClient, candidate: str) -> Verdict:
    """Ask the LLM whether `candidate` is a publishable read.

    Returns Verdict(clean, reason, cost). Any error — provider failure,
    JSON parse failure, missing field, wrong type — yields a CONSERVATIVE
    verdict (clean=False) so the caller drops the candidate. The
    previously cached good summary stays visible on the dashboard."""
    if not candidate or not candidate.strip():
        return Verdict(clean=False, reason="empty candidate", cost_usd=0.0)

    messages = [
        {"role": "system", "content": _SYSTEM_PROMPT},
        # Sent as a fenced user turn so the model can't confuse the
        # candidate with instructions, even if the candidate happens to
        # contain prompt-like prose.
        {"role": "user", "content": f"Candidate read:\n```\n{candidate}\n```"},
    ]
    settings = get_settings()
    reviewer_model = getattr(settings, "REVIEWER_MODEL", None) or DEFAULT_REVIEWER_MODEL
    try:
        result = await call_llm(
            client, messages,
            # Pin to OpenRouter so a non-DeepSeek model like Haiku is
            # actually reachable; the default provider chain would try
            # DeepSeek native first and 404 on the Anthropic model name.
            provider="openrouter",
            model=reviewer_model,
            # 300 tokens is well above the ~30-token JSON verdict.
            # Haiku doesn't pad with hidden reasoning the way DeepSeek
            # does, so we don't need the 800-token headroom required to
            # absorb the generator's chain-of-thought.
            max_tokens=300,
            response_format={"type": "json_object"},
        )
    except Exception as e:
        log.warning("review.call_failed", error=str(e)[:200])
        return Verdict(clean=False, reason=f"reviewer error: {str(e)[:80]}",
                       cost_usd=None)

    try:
        parsed = json.loads(result.content)
    except json.JSONDecodeError:
        log.warning("review.parse_failed", preview=result.content[:200])
        return Verdict(clean=False, reason="reviewer returned non-JSON",
                       cost_usd=result.cost_usd)

    clean = parsed.get("clean")
    reason = parsed.get("reason") or ""
    if not isinstance(clean, bool):
        return Verdict(clean=False, reason="reviewer omitted bool 'clean'",
                       cost_usd=result.cost_usd)
    return Verdict(clean=clean, reason=str(reason)[:200], cost_usd=result.cost_usd)