diary-news/backend/app/services/llm/enrichment.py

"""LLM 智能增强服务(翻译后调)。

4 个独立任务(按顺序):
  1. classify — 分类 + 黑名单 gate(命中则删文章,后 3 步跳过)
  2. format   — 排版译文(写入 body_zh_formatted,容器用 .article-body + 段落 .diary-para)
  3. image    — 生成插图(写入 image_ai_url,prompt 用正文第一段)
  4. commentary — 写点评(写入 commentary)

排版容器 CSS(固定,不再让用户改):
- 字体: system-ui 字体栈
- 字号: 17px
- 行高: 1.7
- 颜色: #3e3e3e
- 段落: margin-bottom 1.5em(自动空一行);class 名固定为 diary-para

黑名单机制:
- classify 任务合并 llm_settings.blocklist_tags(全局) + source.blocklist_tags(per-source)
- 注入到 prompt 的 {blocklist} 占位符,LLM 返回 {"drop": true, "categories": [...]} 则删文章
- 合并去重后为空 → classify 任务只产出 categories 不产出 drop

设计:
- 任务入口: enrich_article(article_id)
- 任务间互不影响:每个任务独立 try/except + 写 status
- 全部任务共走 LlmClient 的全局限速
- 若设置 enabled=False,只跳过(不调 LLM)
- 用户提示词模板可能不包含全部占位符,用 _safe_format 容错
"""
from __future__ import annotations

import asyncio
import logging
from typing import Any, Mapping

from sqlalchemy import select

from app.database import AsyncSessionLocal
from app.models.article import Article
from app.models.llm_setting import LlmSetting
from app.models.source import Source
from app.schemas.llm import get_default_prompts
from app.services.llm.client import LlmClient

logger = logging.getLogger("news.llm.enrichment")

# === 排版容器固定 CSS(项目级固定,不再让用户改)===
# 同时内联到 body_zh_formatted 的容器 div 的 style 属性上,
# 保证分享/邮件/导出场景下样式不丢;前端全局 .article-body 类做兜底。
ARTICLE_BODY_FONT_FAMILY = (
    "system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, "
    "'Helvetica Neue', sans-serif"
)
ARTICLE_BODY_FONT_SIZE = "17px"
ARTICLE_BODY_LINE_HEIGHT = "1.7"
ARTICLE_BODY_COLOR = "#3e3e3e"
ARTICLE_BODY_P_MARGIN_BOTTOM = "1.5em"

# === 排版段落 class 名(项目级固定,前端 .diary-para 兜底)===
DIARY_PARA_CLASS = "diary-para"

# === 插图默认尺寸(适中,不再用 1024x768)===
# 写死到 enrichment 里,行为稳定;setting.image_size 仍可由用户在 UI 改,
# 但默认行为不依赖它,避免意外被改成很大。
DEFAULT_IMAGE_SIZE = "768x512"
DEFAULT_IMAGE_FIRST_PARA_CHARS = 400  # 提取第一段最多用这么多字
DEFAULT_IMAGE_MAX_TAGS = 5  # 分类标签上限(多标签)


class _SafeDict(dict):
    """missing 返回 {key} 本身(占位符原样保留),不抛 KeyError。"""

    def __missing__(self, key: str) -> str:  # type: ignore[override]
        return "{" + key + "}"


def _safe_format(template: str, vars_: Mapping[str, Any]) -> str:
    """用 _SafeDict 跑 str.format,缺失的占位符保留原样而不是 KeyError。

    用途:数据库里用户已存的 prompt 模板可能是旧版的(只支持部分占位符),
    新代码传了更多变量也不应崩。
    """
    try:
        return template.format_map(_SafeDict(vars_))
    except (KeyError, IndexError) as e:
        # 极端情况(比如 {} 这种非法占位符)兜底
        logger.warning("_safe_format 解析失败,按原文返回: %s", e)
        return template


# === 获取当前设置(行锁 + 缓存刷新)===
async def get_setting() -> LlmSetting:
    """读 llm_settings 单行;不存在则用默认值插入。"""
    async with AsyncSessionLocal() as session:
        row = (await session.execute(select(LlmSetting).where(LlmSetting.id == 1))).scalar_one_or_none()
        if row is None:
            defaults = get_default_prompts()
            row = LlmSetting(
                id=1,
                format_prompt=defaults["format_prompt"],
                classify_prompt=defaults["classify_prompt"],
                commentary_prompt=defaults["commentary_prompt"],
                image_prompt_template=defaults["image_prompt_template"],
            )
            session.add(row)
            await session.commit()
            await session.refresh(row)
        return row


# === 单任务:format ===
async def _enrich_format(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    template = setting.format_prompt or get_default_prompts()["format_prompt"]
    prompt = _safe_format(template, {"body": (article.body_zh_text or "")[:6000]})
    text = await client.chat(
        system="你是中文新闻排版助手,只输出排版后的纯文本。",
        user=prompt,
        temperature=0.3,
        max_tokens=2000,
    )
    # 段落 class 名固定为 diary-para(项目级固定,前端 .diary-para 兜底);
    # CSS 仍内联到 style,保证分享/导出场景不丢。
    parts = [
        f'<p class="{DIARY_PARA_CLASS}">{p.strip()}</p>'
        for p in text.split("\n\n")
        if p.strip()
    ]
    if not parts:
        article.body_zh_formatted = None
    else:
        article.body_zh_formatted = _wrap_article_body("\n".join(parts))
    article.format_status = "ok"


def _wrap_article_body(inner_html: str) -> str:
    """把排版好的段落包到带固定 CSS 的 <div class='article-body'> 里。

    CSS 同时内联到 style 属性(分享/导出样式不丢)+ class 名(前端全局类可覆盖)。
    """
    inline_style = (
        f"font-family:{ARTICLE_BODY_FONT_FAMILY};"
        f"font-size:{ARTICLE_BODY_FONT_SIZE};"
        f"line-height:{ARTICLE_BODY_LINE_HEIGHT};"
        f"color:{ARTICLE_BODY_COLOR};"
    )
    # 段落样式也内联,保证 v-html 渲染时一定生效
    p_style = f"margin:0 0 {ARTICLE_BODY_P_MARGIN_BOTTOM} 0;"
    # 内层 HTML 的 <p class="diary-para" ...> 形式;把 style 插到 class 后面
    inner_with_p_style = inner_html.replace(
        f'<p class="{DIARY_PARA_CLASS}">',
        f'<p class="{DIARY_PARA_CLASS}" style="{p_style}">',
    )
    return f'<div class="article-body" style="{inline_style}">{inner_with_p_style}</div>'


# === 单任务:classify (含黑名单 drop gate) ===
async def _enrich_classify(
    article: Article,
    setting: LlmSetting,
    client: LlmClient,
    blocklist: list[str],
) -> tuple[bool, list[str]]:
    """分类 + 黑名单判断。

    返回 (drop, categories):
      - drop=True  → 整篇文章应删除(分类命中 blocklist)
      - categories → 写入 article.category 的多标签列表
    """
    template = setting.classify_prompt or get_default_prompts()["classify_prompt"]
    # 老 prompt 可能只支持 {title}/{summary},不支持 {body} / {blocklist} —— _safe_format 兜底
    vars_ = {
        "title": (article.title_zh or article.title)[:200],
        "summary": (article.summary_zh or "")[:400],
        "body": (article.body_zh_text or "")[:1500],
        "blocklist": "、".join(blocklist) if blocklist else "(无)",
    }
    prompt = _safe_format(template, vars_)
    result = await client.classify_json(
        system="你是新闻分类助手,只返回 JSON。",
        user=prompt,
    )
    cats_raw = result.get("categories") or result.get("tags") or []
    cats: list[str] = []
    if isinstance(cats_raw, list):
        cats = [str(c).strip() for c in cats_raw[:DEFAULT_IMAGE_MAX_TAGS] if str(c).strip()]
    # 兼容两种来源:LLM 自己判断的 drop 字段,或后端兜底检查命中
    drop_flag = bool(result.get("drop"))
    if not drop_flag and blocklist:
        # 兜底:即使 LLM 没正确返回 drop 字段,我们也用本地匹配兜底
        bl_set = {b.strip() for b in blocklist if b and b.strip()}
        drop_flag = any(c in bl_set for c in cats)
    return drop_flag, cats


def _merge_blocklist(setting: LlmSetting, source: Source | None) -> list[str]:
    """合并全局 + per-source blocklist,去重保序。"""
    out: list[str] = []
    seen: set[str] = set()
    sources: list[list[str]] = [setting.blocklist_tags or []]
    if source is not None:
        sources.append(source.blocklist_tags or [])
    for src in sources:
        for t in src:
            t = (t or "").strip()
            if t and t not in seen:
                seen.add(t)
                out.append(t)
    return out


# === 单任务:image ===
async def _enrich_image(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    template = setting.image_prompt_template or get_default_prompts()["image_prompt_template"]
    # 用正文第一段作为 prompt(英文 prompt 走 title 仍可工作,所以 title 也带上作 fallback)
    first_para = _first_paragraph(article.body_zh_text or "", max_chars=DEFAULT_IMAGE_FIRST_PARA_CHARS)
    if not first_para:
        first_para = (article.title_zh or article.title or "")[:200]
    title_for_prompt = (article.title_zh or article.title or "")[:200]
    # template 同时支持 {body} 和 {title} 两种占位符;老的只支持 {title} 也能跑
    prompt = _safe_format(template, {"body": first_para, "title": title_for_prompt})
    url = await client.generate_image(prompt, size=DEFAULT_IMAGE_SIZE)
    article.image_ai_url = url
    article.image_ai_status = "ok"


def _first_paragraph(text: str, max_chars: int) -> str:
    """取正文第一段(按 \\n\\n 切)。如果首段超长就截断。"""
    if not text:
        return ""
    for p in text.split("\n\n"):
        p = p.strip()
        if p:
            return p[:max_chars]
    return ""


# === 单任务:commentary ===
async def _enrich_commentary(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    template = setting.commentary_prompt or get_default_prompts()["commentary_prompt"]
    prompt = _safe_format(
        template,
        {
            "title": (article.title_zh or article.title)[:200],
            "body": (article.body_zh_text or "")[:3000],
        },
    )
    text = await client.chat(
        system="你是资深新闻评论员。",
        user=prompt,
        temperature=0.6,
        max_tokens=600,
    )
    article.commentary = text or None
    article.commentary_status = "ok"


# === 总编排:enrich_article ===
async def enrich_article(article_id: int) -> dict[str, str]:
    """对单篇文章做 4 项 LLM 增强。

    顺序:classify(黑名单 gate) → format → image → commentary
    - classify 命中 blocklist → 整篇文章 DELETE,后续任务直接 return
    - 任一任务失败,只标 status 不影响其他任务

    返回 {task: status} 字典(用于日志)。
    """
    async with AsyncSessionLocal() as session:
        art = (
            await session.execute(select(Article).where(Article.id == article_id))
        ).scalar_one_or_none()
        if not art:
            logger.warning("enrich_article: id=%s not found", article_id)
            return {}
        if not (art.title_zh or art.body_zh_text):
            logger.info("enrich_article: id=%s no translation yet, skip", article_id)
            return {}

    # 拉取设置
    setting = await get_setting()
    if not setting.enabled:
        logger.info("enrich_article: llm disabled, skip id=%s", article_id)
        return {"format": "skipped", "classify": "skipped", "image": "skipped", "commentary": "skipped"}

    # 用配置生成 client(允许热改设置)
    client = LlmClient(
        chat_model=setting.chat_model,
        image_model=setting.image_model,
        interval_sec=setting.interval_sec,
    )

    results: dict[str, str] = {}

    async with AsyncSessionLocal() as session:
        art = (
            await session.execute(select(Article).where(Article.id == article_id))
        ).scalar_one_or_none()
        if not art:
            return {}

        # === 1) classify(黑名单 gate,优先执行)===
        blocklist = _merge_blocklist(setting, art.source if art.source_id else None)
        try:
            drop, cats = await _enrich_classify(art, setting, client, blocklist)
            art.classify_status = "ok"
            if cats:
                art.category = ",".join(cats)[:64] or None
            if drop:
                # 命中 blocklist → 删文章,后续 3 步全跳
                logger.info(
                    "enrich_article id=%s dropped (blocklist hit, cats=%s, blocklist=%s)",
                    article_id, cats, blocklist,
                )
                await session.delete(art)
                await session.commit()
                return {"classify": "dropped", "format": "skipped", "image": "skipped", "commentary": "skipped"}
        except Exception as e:
            logger.exception("enrich classify failed for article %s: %s", article_id, e)
            art.classify_status = "failed"
            results["classify"] = f"failed:{type(e).__name__}"
            # classify 失败也继续(format/image/commentary 还能跑)

        # === 2) format ===
        try:
            await _enrich_format(art, setting, client)
            results["format"] = "ok"
        except Exception as e:
            logger.exception("enrich format failed for article %s: %s", article_id, e)
            art.format_status = "failed"
            results["format"] = f"failed:{type(e).__name__}"

        # === 3) image ===
        try:
            await _enrich_image(art, setting, client)
            results["image"] = "ok"
        except Exception as e:
            logger.exception("enrich image failed for article %s: %s", article_id, e)
            art.image_ai_status = "failed"
            results["image"] = f"failed:{type(e).__name__}"

        # === 4) commentary ===
        try:
            await _enrich_commentary(art, setting, client)
            results["commentary"] = "ok"
        except Exception as e:
            logger.exception("enrich commentary failed for article %s: %s", article_id, e)
            art.commentary_status = "failed"
            results["commentary"] = f"failed:{type(e).__name__}"

        await session.commit()
    logger.info("enrich_article id=%s: %s", article_id, results)
    return results


# === 后台循环 ===
# 与 translation_loop 一样,常驻从队列里取文章
ENRICHMENT_INTERVAL_SEC = 5.0  # 没活时等待
ENRICHMENT_BATCH_SIZE = 1


async def enrichment_loop() -> None:
    """扫描已翻译但未 enrich 的文章(任一 *_status 为 pending/n/a 且 translation_status=ok)。

    跟 translation_loop 一样常驻。
    """
    logger.info("enrichment_loop started")
    # 等一下让翻译先跑
    await asyncio.sleep(10)
    while True:
        try:
            async with AsyncSessionLocal() as session:
                # 已翻译完成 + 4 个状态中至少有一个是 pending
                rows = (
                    await session.execute(
                        select(Article)
                        .where(
                            Article.translation_status == "ok",
                            Article.title_zh.is_not(None),
                        )
                        .order_by(Article.translated_at.asc().nullslast(), Article.id.asc())
                        .limit(ENRICHMENT_BATCH_SIZE * 5)  # 多取几个找需要 enrich 的
                    )
                ).scalars()
                candidates = list(rows)

            # 过滤:任一 *_status 是 pending
            todo_ids: list[int] = []
            for a in candidates:
                statuses = [
                    a.format_status or "pending",
                    a.classify_status or "pending",
                    a.image_ai_status or "pending",
                    a.commentary_status or "pending",
                ]
                if any(s in ("pending", "failed", "n/a") for s in statuses):
                    todo_ids.append(a.id)
                if len(todo_ids) >= ENRICHMENT_BATCH_SIZE:
                    break

            if not todo_ids:
                await asyncio.sleep(ENRICHMENT_INTERVAL_SEC)
                continue

            for aid in todo_ids:
                try:
                    await enrich_article(aid)
                except Exception as e:
                    logger.exception("enrich_article %s in loop failed: %s", aid, e)
                await asyncio.sleep(0.5)  # 文章间轻节流
        except Exception as e:
            logger.exception("enrichment_loop error: %s", e)
            await asyncio.sleep(ENRICHMENT_INTERVAL_SEC)