diff --git a/.env.example b/.env.example index 89a6a46..89e41e0 100644 --- a/.env.example +++ b/.env.example @@ -1,6 +1,20 @@ +# —— 通义千问（推荐）：阿里云 DashScope 的 OpenAI 兼容地址 + 模型名 + API Key +# OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 +# OPENAI_API_KEY=sk-你的DashScopeKey +# OPENAI_MODEL=qwen3.5-plus OPENAI_API_KEY= OPENAI_BASE_URL= OPENAI_MODEL=gpt-4.1-mini +# 通义长文 JSON 常需 60~120s+，过短会 APITimeout 后走兜底 +OPENAI_TIMEOUT=120 +# SDK 自动重试次数。设为 0 可避免单次请求被隐式重试拖长（例如 30s 变 60s+） +OPENAI_MAX_RETRIES=0 +# 长文 JSON 建议 8192；通义等若正文仍偏短可适当再加大 +OPENAI_MAX_OUTPUT_TOKENS=8192 +OPENAI_SOURCE_MAX_CHARS=5000 +# 质检未通过时仍返回模型洗稿正文（quality_notes 记录问题）；设为 false 则严格退回保底稿 +AI_SOFT_ACCEPT=true +LOG_LEVEL=INFO WECHAT_APPID= WECHAT_SECRET= diff --git a/Dockerfile b/Dockerfile index d6caff3..acf247a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,9 +1,20 @@ -FROM python:3.11-slim +# syntax=docker/dockerfile:1 +# 国内拉基础镜像慢时：docker compose build --build-arg PY_BASE=docker.m.daocloud.io/library/python:3.11-slim +ARG PY_BASE=python:3.11-slim +FROM ${PY_BASE} WORKDIR /app +# 默认清华 PyPI；海外可：docker compose build --build-arg PIP_INDEX_URL=https://pypi.org/simple +ARG PIP_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple +ENV PIP_INDEX_URL=${PIP_INDEX_URL} \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + PIP_DEFAULT_TIMEOUT=120 + COPY requirements.txt ./ -RUN pip install --no-cache-dir -r requirements.txt +# BuildKit 缓存加速重复构建；需 Docker 20.10+（compose 默认开 BuildKit） +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install -r requirements.txt COPY . . diff --git a/app/config.py b/app/config.py index 8b2bc11..86d749f 100644 --- a/app/config.py +++ b/app/config.py @@ -9,8 +9,27 @@ class Settings(BaseSettings): openai_api_key: str | None = Field(default=None, alias="OPENAI_API_KEY") openai_base_url: str | None = Field(default=None, alias="OPENAI_BASE_URL") openai_model: str = Field(default="gpt-4.1-mini", alias="OPENAI_MODEL") - openai_timeout: float = Field(default=60.0, alias="OPENAI_TIMEOUT") + openai_timeout: float = Field( + default=120.0, + alias="OPENAI_TIMEOUT", + description="HTTP 等待模型单轮响应的最长时间（秒）。通义长文 JSON 建议 120~180", + ) + openai_max_retries: int = Field( + default=0, + alias="OPENAI_MAX_RETRIES", + description="OpenAI SDK 自动重试次数。为避免单次请求被重试拉长，默认 0。", + ) + openai_max_output_tokens: int = Field( + default=8192, + alias="OPENAI_MAX_OUTPUT_TOKENS", + description="单次模型输出 token 上限；通义等长文 JSON 需足够大", + ) openai_source_max_chars: int = Field(default=5000, alias="OPENAI_SOURCE_MAX_CHARS") + ai_soft_accept: bool = Field( + default=True, + alias="AI_SOFT_ACCEPT", + description="质检未通过时仍输出模型洗稿正文（mode=ai，quality_notes 记录问题），仅模型完全失败时用保底稿", + ) wechat_appid: str | None = Field(default=None, alias="WECHAT_APPID") wechat_secret: str | None = Field(default=None, alias="WECHAT_SECRET") diff --git a/app/logging_setup.py b/app/logging_setup.py new file mode 100644 index 0000000..6760144 --- /dev/null +++ b/app/logging_setup.py @@ -0,0 +1,25 @@ +"""统一日志格式，便于 grep / 日志平台解析。""" +from __future__ import annotations + +import logging +import os + + +def configure_logging() -> None: + level_name = os.getenv("LOG_LEVEL", "INFO").upper() + level = getattr(logging, level_name, logging.INFO) + + fmt = "%(asctime)s | %(levelname)s | %(name)s | %(message)s" + datefmt = "%Y-%m-%d %H:%M:%S" + + root = logging.getLogger() + if not root.handlers: + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt)) + root.addHandler(handler) + root.setLevel(level) + + # 降噪：第三方库默认 WARNING + logging.getLogger("httpx").setLevel(logging.WARNING) + logging.getLogger("httpcore").setLevel(logging.WARNING) + logging.getLogger("openai").setLevel(logging.WARNING) diff --git a/app/main.py b/app/main.py index ef2c537..5d4510c 100644 --- a/app/main.py +++ b/app/main.py @@ -1,17 +1,38 @@ from __future__ import annotations +import logging +from urllib.parse import urlparse + from fastapi import FastAPI, Request from fastapi.responses import HTMLResponse from fastapi.staticfiles import StaticFiles from fastapi.templating import Jinja2Templates from app.config import settings +from app.logging_setup import configure_logging +from app.middleware import RequestContextMiddleware from app.schemas import IMPublishRequest, RewriteRequest, WechatPublishRequest from app.services.ai_rewriter import AIRewriter from app.services.im import IMPublisher from app.services.wechat import WechatPublisher +configure_logging() +logger = logging.getLogger(__name__) + app = FastAPI(title=settings.app_name) + + +@app.on_event("startup") +async def _log_startup() -> None: + logger.info( + "app_start name=%s openai_configured=%s ai_soft_accept=%s", + settings.app_name, + bool(settings.openai_api_key), + settings.ai_soft_accept, + ) + + +app.add_middleware(RequestContextMiddleware) app.mount("/static", StaticFiles(directory="app/static"), name="static") templates = Jinja2Templates(directory="app/templates") @@ -25,16 +46,82 @@ async def index(request: Request): return templates.TemplateResponse("index.html", {"request": request, "app_name": settings.app_name}) +@app.get("/api/config") +async def api_config(): + """供页面展示：当前是否接入模型、模型名、提供方（不含密钥）。""" + base = settings.openai_base_url or "" + provider = "dashscope" if "dashscope.aliyuncs.com" in base else "openai_compatible" + host = urlparse(base).netloc if base else "" + return { + "openai_configured": bool(settings.openai_api_key), + "openai_model": settings.openai_model, + "provider": provider, + "base_url_host": host or None, + "openai_timeout_sec": settings.openai_timeout, + "openai_max_output_tokens": settings.openai_max_output_tokens, + } + + @app.post("/api/rewrite") -async def rewrite(req: RewriteRequest): - return rewriter.rewrite(req) +async def rewrite(req: RewriteRequest, request: Request): + rid = getattr(request.state, "request_id", "") + src = req.source_text or "" + logger.info( + "api_rewrite_in rid=%s source_chars=%d title_hint_chars=%d tone=%s audience=%s " + "keep_points_chars=%d avoid_words_chars=%d", + rid, + len(src), + len(req.title_hint or ""), + req.tone, + req.audience, + len(req.keep_points or ""), + len(req.avoid_words or ""), + ) + result = rewriter.rewrite(req, request_id=rid) + tr = result.trace or {} + logger.info( + "api_rewrite_out rid=%s mode=%s duration_ms=%s quality_notes=%d trace_steps=%s soft_accept=%s", + rid, + result.mode, + tr.get("duration_ms"), + len(result.quality_notes or []), + len((tr.get("steps") or [])), + tr.get("quality_soft_accept"), + ) + return result @app.post("/api/publish/wechat") -async def publish_wechat(req: WechatPublishRequest): - return await wechat.publish_draft(req) +async def publish_wechat(req: WechatPublishRequest, request: Request): + rid = getattr(request.state, "request_id", "") + logger.info( + "api_wechat_in rid=%s title_chars=%d summary_chars=%d body_md_chars=%d author_set=%s", + rid, + len(req.title or ""), + len(req.summary or ""), + len(req.body_markdown or ""), + bool((req.author or "").strip()), + ) + out = await wechat.publish_draft(req, request_id=rid) + logger.info( + "api_wechat_out rid=%s ok=%s detail=%s errcode=%s", + rid, + out.ok, + (out.detail or "")[:120], + (out.data or {}).get("errcode") if isinstance(out.data, dict) else None, + ) + return out @app.post("/api/publish/im") -async def publish_im(req: IMPublishRequest): - return await im.publish(req) +async def publish_im(req: IMPublishRequest, request: Request): + rid = getattr(request.state, "request_id", "") + logger.info( + "api_im_in rid=%s title_chars=%d body_md_chars=%d", + rid, + len(req.title or ""), + len(req.body_markdown or ""), + ) + out = await im.publish(req, request_id=rid) + logger.info("api_im_out rid=%s ok=%s detail=%s", rid, out.ok, (out.detail or "")[:120]) + return out diff --git a/app/middleware.py b/app/middleware.py new file mode 100644 index 0000000..bbf05da --- /dev/null +++ b/app/middleware.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import logging +import time +import uuid + +from starlette.middleware.base import BaseHTTPMiddleware +from starlette.requests import Request +from starlette.responses import Response + +logger = logging.getLogger("app.http") + + +class RequestContextMiddleware(BaseHTTPMiddleware): + """注入 request_id，记录访问日志与耗时。""" + + async def dispatch(self, request: Request, call_next) -> Response: + rid = request.headers.get("X-Request-ID") or str(uuid.uuid4()) + request.state.request_id = rid + + path = request.url.path + if path.startswith("/static"): + response = await call_next(request) + response.headers["X-Request-ID"] = rid + return response + + client = request.client.host if request.client else "-" + if path.startswith("/api"): + logger.info( + "http_in method=%s path=%s rid=%s client=%s", + request.method, + path, + rid, + client, + ) + + started = time.perf_counter() + try: + response = await call_next(request) + except Exception: + duration_ms = (time.perf_counter() - started) * 1000 + logger.exception( + "http_error method=%s path=%s duration_ms=%.1f rid=%s", + request.method, + path, + duration_ms, + rid, + ) + raise + + duration_ms = (time.perf_counter() - started) * 1000 + response.headers["X-Request-ID"] = rid + logger.info( + "http_out method=%s path=%s status=%s duration_ms=%.1f rid=%s", + request.method, + path, + response.status_code, + duration_ms, + rid, + ) + return response diff --git a/app/schemas.py b/app/schemas.py index cc63cbb..ffbadbf 100644 --- a/app/schemas.py +++ b/app/schemas.py @@ -1,3 +1,5 @@ +from typing import Any + from pydantic import BaseModel, Field @@ -16,6 +18,10 @@ class RewriteResponse(BaseModel): body_markdown: str mode: str = "ai" quality_notes: list[str] = [] + trace: dict[str, Any] | None = Field( + default=None, + description="改写链路追踪：请求 ID、耗时、模型、质检与降级原因，便于监测与回溯", + ) class WechatPublishRequest(BaseModel): diff --git a/app/services/ai_rewriter.py b/app/services/ai_rewriter.py index 5d395fd..cead49b 100644 --- a/app/services/ai_rewriter.py +++ b/app/services/ai_rewriter.py @@ -5,7 +5,9 @@ import json import logging import re import time +from typing import Any from textwrap import shorten +from urllib.parse import urlparse from openai import OpenAI @@ -15,47 +17,72 @@ from app.schemas import RewriteRequest, RewriteResponse logger = logging.getLogger(__name__) +def _api_host(url: str | None) -> str: + if not url: + return "" + try: + return urlparse(url).netloc or "" + except Exception: + return "" + + +def _is_likely_timeout_error(exc: BaseException) -> bool: + n = type(exc).__name__.lower() + if "timeout" in n: + return True + s = str(exc).lower() + return "timed out" in s or "timeout" in s + + +# 短文洗稿：5 个自然段、正文总字数上限（含标点） +MAX_BODY_CHARS = 500 +MIN_BODY_CHARS = 80 + + +def _preview_for_log(text: str, limit: int = 400) -> str: + t = (text or "").replace("\r\n", "\n").replace("\n", " ").strip() + if len(t) <= limit: + return t + return t[: limit - 1] + "…" + + SYSTEM_PROMPT = """ -你是顶级中文公众号主编，擅长把 X/Twitter 的观点型内容改写成高质量公众号文章。 -你的目标不是“同义替换”，而是“重构表达”，保证可读性、逻辑性和可发布性。 +你是资深中文科普类公众号编辑，擅长把长文、线程贴改写成**极短、好读**的推送。 +目标：在**不偏离原意**的前提下，用最少字数讲清一件事；不要写成技术方案、长文大纲或带很多小标题的文章。 硬性规则： -1) 保留核心事实与关键观点，不编造数据，不夸大结论； -2) 文章结构必须完整：导语 -> 核心观点 -> 深度分析 -> 落地建议 -> 结语； -3) 风格自然，避免 AI 套话（如“首先其次最后”“赋能”“闭环”等空话）； -4) 每节都要有信息增量，不要重复原文句式； -5) 输出必须是合法 JSON，字段：title, summary, body_markdown。 +1) **忠实原意**：只概括、转述原文已有信息，不编造事实，不偷换主题； +2) 语气通俗、干脆，避免套话堆砌； +3) 只输出合法 JSON：title, summary, body_markdown； +4) **body_markdown 约束**：恰好 **5 个自然段**；段与段之间用一个空行分隔；**不要**使用 # / ## 标题符号；全文（正文）总字数 **不超过 500 字**（含标点）； +5) title、summary 也要短：标题约 8～18 字；摘要约 40～80 字； +6) JSON 字符串内引号请用「」或『』，勿用未转义的英文 "。 """.strip() REWRITE_SCHEMA_HINT = """ -请输出 JSON： +请输出 JSON（勿包在 ``` 里），例如： { - "title": "20字内中文标题，明确价值点", - "summary": "80-120字中文摘要，说明读者收获", - "body_markdown": "完整Markdown正文" + "title": "短标题，点明主题", + "summary": "一句话到两句话摘要", + "body_markdown": "第一段内容…\\n\\n第二段…\\n\\n第三段…\\n\\n第四段…\\n\\n第五段…" } -正文格式要求（必须遵循）： -## 导语 -2-3段，交代背景、冲突与阅读价值。 +body_markdown 写法： +- 必须且只能有 **5 段**：每段若干完整句子，段之间 **\\n\\n**（空一行）； +- **禁止** markdown 标题（不要用 #）； +- 正文总长 **≤500 字**，宁可短而清楚，不要写满废话； +- 内容顺序建议：第 1 段交代在说什么；中间 3 段展开关键信息；最后 1 段收束或提醒（均须紧扣原文，勿乱发挥）。 +""".strip() -## 核心观点 -- 3~5条要点，每条是完整信息句，不要口号。 +# 通义等模型若首次过短/结构不对，再要一次 +_JSON_BODY_TOO_SHORT_RETRY = """ -## 深度分析 -### 1) 现象背后的原因 -2-3段 -### 2) 对行业/团队的影响 -2-3段 -### 3) 关键风险与边界 -2-3段 - -## 落地建议 -1. 三到五条可执行动作，尽量包含“谁在什么场景做什么”。 - -## 结语 -1段，收束观点并给出下一步建议。 +【系统复检】上一次 body_markdown 不符合要求。请重输出**完整** JSON： +- 正文必须 **恰好 5 个自然段**（仅 \\n\\n 分段），无 # 标题，总字数 **≤500 字**； +- 忠实原稿、简短高效； +- 引号只用「」『』； +- 只输出 JSON。 """.strip() @@ -70,47 +97,198 @@ class AIRewriter: api_key=settings.openai_api_key, base_url=settings.openai_base_url, timeout=settings.openai_timeout, - max_retries=1, + max_retries=max(0, int(settings.openai_max_retries)), ) + logger.info( + "AIRewriter_init model=%s api_host=%s prefer_chat_first=%s timeout_s=%s max_retries=%s", + settings.openai_model, + _api_host(settings.openai_base_url) or "(default)", + self._prefer_chat_first, + settings.openai_timeout, + settings.openai_max_retries, + ) + else: + logger.warning("AIRewriter_init openai_key_missing=1 rewrite_will_use_fallback_only=1") - def rewrite(self, req: RewriteRequest) -> RewriteResponse: + def rewrite(self, req: RewriteRequest, request_id: str = "") -> RewriteResponse: cleaned_source = self._clean_source(req.source_text) started = time.monotonic() + trace: dict[str, Any] = { + "request_id": request_id or None, + "model": settings.openai_model, + "provider": "dashscope" if self._prefer_chat_first else "openai_compatible", + "source_chars_in": len(req.source_text or ""), + "cleaned_chars": len(cleaned_source), + "openai_timeout_env_sec": settings.openai_timeout, + "steps": [], + } + + def _step(name: str, **extra: Any) -> None: + elapsed_ms = round((time.monotonic() - started) * 1000, 1) + trace["steps"].append({"name": name, "elapsed_ms": elapsed_ms, **extra}) + extra_fmt = "" + if extra: + parts: list[str] = [] + for k, v in extra.items(): + s = repr(v) + if len(s) > 200: + s = s[:197] + "..." + parts.append(f"{k}={s}") + extra_fmt = " " + " ".join(parts) + logger.info( + "rewrite_step rid=%s step=%s elapsed_ms=%s%s", + request_id or "-", + name, + elapsed_ms, + extra_fmt, + ) + + raw_in = (req.source_text or "").replace("\r\n", "\n").strip() + _step("clean_source", truncated=len(cleaned_source) < len(raw_in)) + + logger.info( + "rewrite_enter rid=%s model=%s client_ok=%s prefer_chat_first=%s " + "source_chars=%d cleaned_chars=%d ai_soft_accept=%s", + request_id or "-", + settings.openai_model, + bool(self._client), + self._prefer_chat_first, + trace["source_chars_in"], + len(cleaned_source), + settings.ai_soft_accept, + ) # Primary: model rewrite + quality gate + optional second-pass polish. if self._client: - # DashScope/Qwen works better with a single stable call. + # 通义长文 JSON 常需 40~90s+。旧代码错误地将首轮 cap 在 30s → APITimeoutError → 仅走兜底。 if self._prefer_chat_first: - first_pass_timeout = max(18.0, min(30.0, settings.openai_timeout)) + first_pass_timeout = max(45.0, min(300.0, float(settings.openai_timeout))) else: - first_pass_timeout = max(20.0, min(50.0, settings.openai_timeout)) - draft = self._model_rewrite(req, cleaned_source, timeout_sec=first_pass_timeout) + first_pass_timeout = max(20.0, min(120.0, float(settings.openai_timeout))) + trace["first_pass_http_timeout_sec"] = round(first_pass_timeout, 1) + logger.info( + "rewrite_model_first_pass rid=%s first_pass_http_timeout_s=%.1f openai_timeout_env_s=%.1f " + "lenient_qa=%s note=dashscope_uses_full_openai_timeout_not_capped_30", + request_id or "-", + first_pass_timeout, + settings.openai_timeout, + self._prefer_chat_first, + ) + t0 = time.monotonic() + draft = self._model_rewrite(req, cleaned_source, timeout_sec=first_pass_timeout, request_id=request_id) + _step( + "model_first_pass", + duration_ms=round((time.monotonic() - t0) * 1000, 1), + ok=bool(draft), + timeout_sec=first_pass_timeout, + ) + if not draft: + trace["quality_issues_final"] = ["模型未返回有效 JSON 或请求超时"] + trace["model_unavailable_hint"] = ( + "排查：① 日志是否 APITimeoutError → 提高 OPENAI_TIMEOUT（通义建议 120~180）并确认 " + "first_pass_http_timeout_sec 与 trace.openai_timeout_env_sec 一致；② 网络到 " + "dashscope.aliyuncs.com；③ 见 model_call_fail 的 is_likely_timeout。" + ) + _step("model_first_pass_failed", detail="timeout_or_invalid_json") if draft: normalized = self._normalize_result(draft) - issues = self._quality_issues(req, cleaned_source, normalized) + issues = self._quality_issues( + req, cleaned_source, normalized, lenient=self._prefer_chat_first + ) + trace["quality_issues_first"] = issues + logger.info( + "rewrite quality check rid=%s first_issues=%s body_chars=%d", + request_id, + issues, + len(normalized.get("body_markdown", "") or ""), + ) elapsed = time.monotonic() - started - remaining_budget = max(0.0, (first_pass_timeout + 20.0) - elapsed) - if issues and (not self._prefer_chat_first) and remaining_budget >= 10.0: + remaining_budget = max(0.0, (first_pass_timeout + 25.0) - elapsed) + polish_budget = min(22.0, remaining_budget) if self._prefer_chat_first else min(30.0, remaining_budget) + if issues and not ( + remaining_budget >= 8.0 and polish_budget >= 6.0 + ): + logger.info( + "rewrite_polish_skipped rid=%s first_issues=%d remaining_budget_s=%.1f polish_budget_s=%.1f", + request_id or "-", + len(issues), + remaining_budget, + polish_budget, + ) + if issues and remaining_budget >= 8.0 and polish_budget >= 6.0: + t1 = time.monotonic() polished = self._model_polish( req, cleaned_source, normalized, issues, - timeout_sec=min(30.0, remaining_budget), + timeout_sec=polish_budget, + request_id=request_id, + ) + _step( + "model_polish", + duration_ms=round((time.monotonic() - t1) * 1000, 1), + ok=bool(polished), ) if polished: normalized = self._normalize_result(polished) - final_issues = self._quality_issues(req, cleaned_source, normalized) + final_issues = self._quality_issues( + req, cleaned_source, normalized, lenient=self._prefer_chat_first + ) + trace["quality_issues_final"] = final_issues if not final_issues: - return RewriteResponse(**normalized, mode="ai", quality_notes=[]) - logger.warning("rewrite quality gate fallback triggered: %s", final_issues) + trace["duration_ms"] = round((time.monotonic() - started) * 1000, 1) + trace["mode"] = "ai" + logger.info( + "rewrite success rid=%s duration_ms=%.1f mode=ai", + request_id, + trace["duration_ms"], + ) + return RewriteResponse(**normalized, mode="ai", quality_notes=[], trace=trace) + # 模型已返回有效 JSON：默认「软接受」——仍视为 AI 洗稿，质检问题写入 quality_notes，避免误用模板稿 + if settings.ai_soft_accept and self._model_output_usable(normalized): + trace["duration_ms"] = round((time.monotonic() - started) * 1000, 1) + trace["mode"] = "ai" + trace["quality_soft_accept"] = True + trace["quality_warnings"] = final_issues + logger.warning( + "rewrite soft-accept rid=%s warnings=%s body_chars=%d", + request_id, + final_issues, + len(normalized.get("body_markdown", "") or ""), + ) + return RewriteResponse( + **normalized, + mode="ai", + quality_notes=final_issues, + trace=trace, + ) + logger.warning( + "rewrite quality gate fallback rid=%s issues=%s", + request_id, + final_issues, + ) + _step("quality_gate_failed", issues=final_issues) + else: + _step("skip_model", reason="OPENAI_API_KEY 未配置") + trace["quality_issues_final"] = ["未配置 OPENAI_API_KEY，使用本地保底稿"] # Secondary: deterministic fallback with publishable structure. - return self._fallback_rewrite(req, cleaned_source, reason="模型超时或质量未达标，已使用结构化保底稿") + reason = "模型未返回有效 JSON、超时，或质量未达标，已使用结构化保底稿" + trace["duration_ms"] = round((time.monotonic() - started) * 1000, 1) + logger.info( + "rewrite fallback rid=%s duration_ms=%.1f last_issues=%s", + request_id, + trace["duration_ms"], + trace.get("quality_issues_final"), + ) + return self._fallback_rewrite(req, cleaned_source, reason=reason, trace=trace) - def _model_rewrite(self, req: RewriteRequest, cleaned_source: str, timeout_sec: float) -> dict | None: + def _model_rewrite( + self, req: RewriteRequest, cleaned_source: str, timeout_sec: float, request_id: str = "" + ) -> dict | None: user_prompt = self._build_user_prompt(req, cleaned_source) - return self._call_model_json(user_prompt, timeout_sec=timeout_sec) + return self._call_model_json(user_prompt, timeout_sec=timeout_sec, request_id=request_id) def _model_polish( self, @@ -119,10 +297,11 @@ class AIRewriter: normalized: dict, issues: list[str], timeout_sec: float, + request_id: str = "", ) -> dict | None: issue_text = "\n".join([f"- {i}" for i in issues]) user_prompt = f""" -你上一次的改写稿质量未达标，请基于下面问题做彻底重写，不要只改几个词： +你上一次的改写稿未通过质检，请针对下列问题重写；体裁仍为**科普介绍类公众号**，**忠实原稿**，不要写成技术方案或内部汇报。 {issue_text} 原始内容： @@ -141,9 +320,9 @@ class AIRewriter: - 必须保留观点：{req.keep_points or '无'} - 避免词汇：{req.avoid_words or '无'} -请输出一个全新且高质量版本。{REWRITE_SCHEMA_HINT} +请输出一版全新稿件。{REWRITE_SCHEMA_HINT} """.strip() - return self._call_model_json(user_prompt, timeout_sec=timeout_sec) + return self._call_model_json(user_prompt, timeout_sec=timeout_sec, request_id=request_id) def _build_user_prompt(self, req: RewriteRequest, cleaned_source: str) -> str: return f""" @@ -157,94 +336,95 @@ class AIRewriter: - 必须保留观点：{req.keep_points or '无'} - 避免词汇：{req.avoid_words or '无'} -任务：请输出可直接用于公众号发布的文章。{REWRITE_SCHEMA_HINT} +任务：在**不偏离原帖主题与事实**的前提下，改写成科普介绍风格的公众号正文（好读、讲清楚，而非技术实施方案）。{REWRITE_SCHEMA_HINT} """.strip() - def _fallback_rewrite(self, req: RewriteRequest, cleaned_source: str, reason: str) -> RewriteResponse: + def _fallback_rewrite( + self, req: RewriteRequest, cleaned_source: str, reason: str, trace: dict[str, Any] | None = None + ) -> RewriteResponse: sentences = self._extract_sentences(cleaned_source) points = self._pick_key_points(sentences, limit=5) title = req.title_hint.strip() or self._build_fallback_title(sentences) summary = self._build_fallback_summary(points, cleaned_source) - intro = self._build_intro(points, cleaned_source) analysis = self._build_analysis(points) - actions = self._build_actions(points) - conclusion = "如果你准备把这类内容持续做成栏目，建议建立固定模板：观点来源、关键证据、执行建议、复盘结论。" + conclusion = "细节仍以原帖为准；若话题在更新，请对照出处核对。" - body = ( - "## 导语\n" - f"{intro}\n\n" - "## 核心观点\n" - + "\n".join([f"- {p}" for p in points]) - + "\n\n" - "## 深度分析\n" - "### 1) 现象背后的原因\n" - f"{analysis['cause']}\n\n" - "### 2) 对行业/团队的影响\n" - f"{analysis['impact']}\n\n" - "### 3) 关键风险与边界\n" - f"{analysis['risk']}\n\n" - "## 落地建议\n" - + "\n".join([f"{i + 1}. {a}" for i, a in enumerate(actions)]) - + "\n\n" - "## 结语\n" - f"{conclusion}" - ) + def _one_line(s: str, n: int) -> str: + t = re.sub(r"\s+", " ", (s or "").strip()) + return t if len(t) <= n else t[: n - 1] + "…" + + paras = [ + _one_line(self._build_intro(points, cleaned_source), 105), + _one_line(analysis["cause"], 105), + _one_line(analysis["impact"], 105), + _one_line(analysis["risk"], 105), + _one_line(conclusion, 105), + ] + body = "\n\n".join(paras) + if len(body) > MAX_BODY_CHARS: + body = body[: MAX_BODY_CHARS - 1] + "…" normalized = { "title": title, "summary": summary, "body_markdown": self._format_markdown(body), } - return RewriteResponse(**normalized, mode="fallback", quality_notes=[reason]) + if trace is not None: + trace["mode"] = "fallback" + trace["fallback_reason"] = reason + rid = (trace or {}).get("request_id") or "-" + logger.info( + "rewrite_fallback_compose rid=%s reason=%s title_chars=%d summary_chars=%d body_chars=%d points=%d", + rid, + reason[:120], + len(normalized["title"]), + len(normalized["summary"]), + len(normalized["body_markdown"]), + len(points), + ) + return RewriteResponse(**normalized, mode="fallback", quality_notes=[reason], trace=trace) def _build_fallback_title(self, sentences: list[str]) -> str: - seed = sentences[0] if sentences else "内容改写" + seed = sentences[0] if sentences else "内容导读" seed = shorten(seed, width=16, placeholder="") - return f"{seed}：给内容创作者的实战拆解" + return f"{seed}：一文读懂在说什么" def _build_fallback_summary(self, points: list[str], source: str) -> str: if len(points) >= 2: - return f"本文提炼了{points[0]}，并进一步分析{points[1]}，最后给出可直接执行的发布建议，帮助你把观点内容做成高质量公众号文章。" - return shorten(re.sub(r"\s+", " ", source), width=110, placeholder="...") + return shorten( + f"原帖在谈：{points[0]}；另一点：{points[1]}。", + width=85, + placeholder="…", + ) + return shorten(re.sub(r"\s+", " ", source), width=85, placeholder="…") def _build_intro(self, points: list[str], source: str) -> str: focus = points[0] if points else shorten(source, width=42, placeholder="...") return ( - f"这篇内容的价值不在“信息多”，而在于它点出了一个真正值得关注的问题：{focus}。\n\n" - "对公众号读者来说，最关心的是这件事会带来什么变化、现在能做什么。" - "因此本文不做逐句复述，而是按“观点-影响-动作”重组，方便直接落地。" + f"原帖主要在谈：{focus}。下面用更适合公众号阅读的方式，把脉络和重点捋清楚，方便你快速抓住作者在表达什么。\n\n" + "说明：这是基于原文的导读式整理，若需引用细节，请以原帖为准。" ) def _build_analysis(self, points: list[str]) -> dict[str, str]: - p1 = points[0] if points else "行业正在从信息堆叠转向结果导向" - p2 = points[1] if len(points) > 1 else "团队协作方式被自动化流程重塑" - p3 = points[2] if len(points) > 2 else "内容质量会成为真正分水岭" + p1 = points[0] if points else "原文讨论的核心现象" + p2 = points[1] if len(points) > 1 else "与读者日常能感知到的关联" + p3 = points[2] if len(points) > 2 else "原文可能提到的限制或尚未定论之处" return { "cause": ( - f"从表面看是工具迭代，实质是生产逻辑变化。{p1}，意味着过去依赖经验的环节，正在被标准化流程替代。" - "谁先完成流程化改造，谁就更容易稳定产出。" + f"先把事情放在原文的语境里理解：{p1}。" + "这里侧重讲清楚「作者在说什么」，而不是替原文下结论。" ), "impact": ( - f"短期影响体现在效率，中长期影响体现在品牌认知。{p2}。" - "如果只追求发布速度，内容会快速同质化；如果把洞察和表达打磨成体系，内容资产会持续增值。" + f"对大多数读者来说，更关心的是：这和自己有什么关系。{p2}。" + "若原帖偏专业，这里尽量用通俗说法转述，避免写成给决策层的公文。" ), "risk": ( - f"最大的风险不是‘不用 AI’，而是‘只用 AI’。{p3}。" - "没有事实校对与人工观点把关，文章容易出现空泛表达、错误引用和结论过度。" + f"任何公开讨论都有边界：{p3}。" + "若话题仍在变化，结论可能更新，阅读时建议保留一点审慎，必要时回看原始出处。" ), } - def _build_actions(self, points: list[str]) -> list[str]: - anchor = points[0] if points else "核心观点" - return [ - f"先确定本篇唯一主线：围绕“{anchor}”展开，删除与主线无关的段落。", - "按“导语-观点-分析-建议-结语”五段式重排正文，每段只解决一个问题。", - "为每个核心观点补一条可验证依据（数据、案例或公开来源），提升可信度。", - "发布前做一次反 AI 味检查：删掉空话，替换为具体动作和明确对象。", - "将高表现文章沉淀为模板，下次复用同样结构提高稳定性。", - ] - def _clean_source(self, text: str) -> str: src = (text or "").replace("\r\n", "\n").strip() src = re.sub(r"https?://\S+", "", src) @@ -266,11 +446,11 @@ class AIRewriter: def _pick_key_points(self, sentences: list[str], limit: int) -> list[str]: points: list[str] = [] templates = [ - "核心变化：{}", - "关键问题：{}", - "方法调整：{}", - "结果反馈：{}", - "结论启示：{}", + "值得关注：{}", + "背景要点：{}", + "原文强调：{}", + "延伸信息：{}", + "阅读提示：{}", ] for s in sentences: if len(s) < 12: @@ -309,18 +489,153 @@ class AIRewriter: raise ValueError("model output is not valid JSON") - def _call_model_json(self, user_prompt: str, timeout_sec: float) -> dict | None: + def _chat_completions_json(self, user_prompt: str, timeout_sec: float, request_id: str) -> dict | None: + """chat.completions：通义兼容层在 json_object 下易产出极短 JSON，故 DashScope 不传 response_format，并支持短文自动重试。""" + max_attempts = 2 if self._prefer_chat_first else 1 + deadline = time.monotonic() + max(0.0, timeout_sec) + pe = user_prompt + for attempt in range(max_attempts): + if attempt == 1: + pe = user_prompt + _JSON_BODY_TOO_SHORT_RETRY + remaining = deadline - time.monotonic() + if remaining <= 0: + logger.warning( + "model_call_budget_exhausted rid=%s api=chat.completions attempt=%d/%d", + request_id or "-", + attempt + 1, + max_attempts, + ) + return None + try: + logger.info( + "model_call_try rid=%s api=chat.completions.create attempt=%d/%d max_tokens=%d json_object=%s timeout_s=%.1f", + request_id or "-", + attempt + 1, + max_attempts, + settings.openai_max_output_tokens, + not self._prefer_chat_first, + remaining, + ) + t0 = time.monotonic() + create_kwargs: dict[str, Any] = { + "model": settings.openai_model, + "messages": [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": pe}, + ], + "max_tokens": settings.openai_max_output_tokens, + "temperature": 0.4, + "extra_body": {"enable_thinking": False}, + "timeout": remaining, + } + # OpenAI 官方 API 在 json_object 下表现稳定；通义兼容模式若开启则常出现正文被压成一两百字。 + if not self._prefer_chat_first: + create_kwargs["response_format"] = {"type": "json_object"} + completion = self._client.chat.completions.create(**create_kwargs) + except Exception as exc: + is_to = _is_likely_timeout_error(exc) + logger.warning( + "model_call_fail rid=%s api=chat.completions attempt=%d/%d exc_type=%s exc=%s " + "is_likely_timeout=%s http_timeout_budget_s=%.1f openai_timeout_env_s=%.1f max_tokens=%d " + "hint=%s", + request_id or "-", + attempt + 1, + max_attempts, + type(exc).__name__, + exc, + is_to, + remaining, + settings.openai_timeout, + settings.openai_max_output_tokens, + ( + "典型原因：单轮 HTTP 等待短于模型生成长文 JSON 所需时间；已取消错误的 30s 上限，" + "请确认 OPENAI_TIMEOUT>=120 并重启进程。" + ) + if is_to and self._prefer_chat_first + else ( + "若为超时：增大 OPENAI_TIMEOUT；否则检查 Key/模型名/网络。" + if is_to + else "" + ), + ) + if self._prefer_chat_first: + return None + raise + + choice = completion.choices[0] if completion.choices else None + msg = (choice.message.content if choice else "") or "" + fr = getattr(choice, "finish_reason", None) if choice else None + usage = getattr(completion, "usage", None) + udump = ( + usage.model_dump() + if usage is not None and hasattr(usage, "model_dump") + else usage + ) + ms = (time.monotonic() - t0) * 1000 + logger.info( + "model_call_ok rid=%s api=chat.completions attempt=%d duration_ms=%.1f output_chars=%d " + "finish_reason=%s usage=%s preview=%s", + request_id or "-", + attempt + 1, + ms, + len(msg), + fr, + udump, + _preview_for_log(msg, 380), + ) + logger.debug( + "model_call_raw rid=%s api=chat.completions attempt=%d body=%s", + request_id or "-", + attempt + 1, + msg, + ) + + try: + parsed = self._parse_response_json(msg) + except Exception as exc: + logger.warning( + "model_json_parse_fail rid=%s attempt=%d err=%s", + request_id or "-", + attempt + 1, + exc, + ) + if not self._prefer_chat_first: + raise + if attempt == max_attempts - 1: + return None + continue + + raw_body = str(parsed.get("body_markdown", "")).strip() + bl = len(raw_body) + pc = len([p for p in re.split(r"\n\s*\n", raw_body) if p.strip()]) + if self._prefer_chat_first and attempt == 0 and (bl < 40 or pc < 3): + logger.warning( + "model_body_retry rid=%s body_chars=%d paragraphs=%d reason=too_thin_or_not_segmented", + request_id or "-", + bl, + pc, + ) + continue + return parsed + return None + + def _call_model_json(self, user_prompt: str, timeout_sec: float, request_id: str = "") -> dict | None: + methods = ["chat", "responses"] if self._prefer_chat_first else ["responses", "chat"] logger.info( - "AI request start model=%s timeout=%.1fs prefer_chat_first=%s prompt_chars=%d", + "model_call_begin rid=%s model=%s timeout_s=%.1f prefer_chat_first=%s prompt_chars=%d " + "try_order=%s", + request_id or "-", settings.openai_model, timeout_sec, self._prefer_chat_first, len(user_prompt), + methods, ) - methods = ["chat", "responses"] if self._prefer_chat_first else ["responses", "chat"] for method in methods: + t0 = time.monotonic() if method == "responses": try: + logger.info("model_call_try rid=%s api=OpenAI.responses.create", request_id or "-") completion = self._client.responses.create( model=settings.openai_model, input=[ @@ -331,35 +646,59 @@ class AIRewriter: timeout=timeout_sec, ) output_text = completion.output_text or "" - logger.info("AI raw output (responses): %s", output_text) + ms = (time.monotonic() - t0) * 1000 + logger.info( + "model_call_ok rid=%s api=responses duration_ms=%.1f output_chars=%d preview=%s", + request_id or "-", + ms, + len(output_text), + _preview_for_log(output_text, 380), + ) + logger.debug("model_call_raw rid=%s api=responses body=%s", request_id or "-", output_text) return self._parse_response_json(output_text) except Exception as exc: - logger.warning("responses API failed: %s", exc) + logger.warning( + "model_call_fail rid=%s api=responses duration_ms=%.1f exc_type=%s exc=%s", + request_id or "-", + (time.monotonic() - t0) * 1000, + type(exc).__name__, + exc, + ) continue if method == "chat": try: - completion = self._client.chat.completions.create( - model=settings.openai_model, - messages=[ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": user_prompt}, - ], - response_format={"type": "json_object"}, - max_tokens=1800, - temperature=0.4, - extra_body={"enable_thinking": False}, - timeout=timeout_sec, - ) - msg = completion.choices[0].message.content if completion.choices else "" - logger.info("AI raw output (chat.completions): %s", msg or "") - return self._parse_response_json(msg or "") - except Exception as exc: - logger.warning("chat.completions API failed: %s", exc) - # DashScope compatibility path: don't spend extra time on responses fallback. + t_chat = time.monotonic() + out = self._chat_completions_json(user_prompt, timeout_sec, request_id) + if out is not None: + return out if self._prefer_chat_first: + logger.info( + "model_call_stop rid=%s reason=dashscope_chat_no_valid_json duration_ms=%.1f", + request_id or "-", + (time.monotonic() - t_chat) * 1000, + ) + return None + except Exception as exc: + logger.warning( + "model_call_fail rid=%s api=chat.completions duration_ms=%.1f exc_type=%s exc=%s", + request_id or "-", + (time.monotonic() - t0) * 1000, + type(exc).__name__, + exc, + ) + if self._prefer_chat_first: + logger.info( + "model_call_stop rid=%s reason=dashscope_chat_exception", + request_id or "-", + ) return None continue + logger.error( + "model_call_exhausted rid=%s methods_tried=%s result=none", + request_id or "-", + methods, + ) return None def _normalize_result(self, data: dict) -> dict: @@ -370,49 +709,57 @@ class AIRewriter: if not title: title = "公众号改写稿" if not summary: - summary = shorten(re.sub(r"\s+", " ", body), width=110, placeholder="...") + summary = shorten(re.sub(r"\s+", " ", body), width=90, placeholder="...") - body = self._ensure_sections(body) + body = re.sub(r"(?m)^#{1,6}\s+[^\n]*\n?", "", body).strip() + body = self._normalize_body_length(body) body = self._format_markdown(body) return {"title": title, "summary": summary, "body_markdown": body} - def _ensure_sections(self, body: str) -> str: + def _normalize_body_length(self, body: str) -> str: text = (body or "").strip() - required = ["## 导语", "## 核心观点", "## 深度分析", "## 落地建议", "## 结语"] - missing = [h for h in required if h not in text] if not text: - text = "## 导语\n\n内容生成失败，请重试。\n" - if missing: - # Light touch: append missing sections to keep publish structure stable. - pads = "\n\n".join([f"{h}\n\n（待补充）" for h in missing]) - text = f"{text}\n\n{pads}" + text = "（正文生成失败，请重试。）" + if len(text) > MAX_BODY_CHARS: + text = text[: MAX_BODY_CHARS - 1] + "…" return text - def _quality_issues(self, req: RewriteRequest, source: str, normalized: dict) -> list[str]: + def _quality_issues( + self, req: RewriteRequest, source: str, normalized: dict, lenient: bool = False + ) -> list[str]: issues: list[str] = [] title = normalized.get("title", "") summary = normalized.get("summary", "") body = normalized.get("body_markdown", "") - if len(title) < 8 or len(title) > 34: - issues.append("标题长度不理想（建议 8-34 字）") + min_title, max_title = (4, 30) if lenient else (6, 24) + if len(title) < min_title or len(title) > max_title: + issues.append(f"标题长度不理想（建议 {min_title}-{max_title} 字，短标题即可）") - if len(summary) < 60: - issues.append("摘要过短，信息量不足") + min_summary, max_summary = (20, 100) if lenient else (25, 90) + if len(summary) < min_summary: + issues.append("摘要过短") + elif len(summary) > max_summary: + issues.append(f"摘要过长（建议 ≤{max_summary} 字）") - headings = re.findall(r"(?m)^##\s+.+$", body) - if len(headings) < 5: - issues.append("二级标题不足，结构不完整") + paragraphs = [p.strip() for p in re.split(r"\n\s*\n", body) if p.strip()] + pc = len(paragraphs) + need_p = 4 if lenient else 5 + if pc < need_p: + issues.append(f"正文需约 5 个自然段、空行分隔（当前 {pc} 段）") + elif not lenient and pc > 6: + issues.append(f"正文段落过多（当前 {pc} 段），请合并为 5 段左右") - paragraphs = [p.strip() for p in body.split("\n\n") if p.strip()] - if len(paragraphs) < 10: - issues.append("正文段落偏少，展开不充分") + if len(body) > MAX_BODY_CHARS: + issues.append(f"正文超过 {MAX_BODY_CHARS} 字（当前 {len(body)} 字），请压缩") + elif len(body) < MIN_BODY_CHARS: + issues.append(f"正文过短（当前阈值 ≥{MIN_BODY_CHARS} 字）") - if len(body) < 900: - issues.append("正文过短，无法支撑公众号发布") + if re.search(r"(?m)^#+\s", body): + issues.append("正文请勿使用 # 标题符号，只用自然段") - if self._looks_like_raw_copy(source, body): + if self._looks_like_raw_copy(source, body, lenient=lenient): issues.append("改写与原文相似度过高，疑似未充分重写") if req.avoid_words: @@ -428,7 +775,7 @@ class AIRewriter: return issues - def _looks_like_raw_copy(self, source: str, rewritten: str) -> bool: + def _looks_like_raw_copy(self, source: str, rewritten: str, lenient: bool = False) -> bool: src = re.sub(r"\s+", "", source or "") dst = re.sub(r"\s+", "", rewritten or "") if not src or not dst: @@ -436,10 +783,20 @@ class AIRewriter: if dst in src or src in dst: return True ratio = difflib.SequenceMatcher(a=src[:3500], b=dst[:3500]).ratio() - return ratio >= 0.80 + threshold = 0.88 if lenient else 0.80 + return ratio >= threshold + + def _model_output_usable(self, normalized: dict) -> bool: + """模型 JSON 可解析且正文有实质内容时，允许软接受（不走模板保底）。""" + body = (normalized.get("body_markdown") or "").strip() + title = (normalized.get("title") or "").strip() + if len(title) < 4 or len(body) < 40: + return False + if len(body) > MAX_BODY_CHARS + 80: + return False + return True def _format_markdown(self, text: str) -> str: body = text.replace("\r\n", "\n").strip() body = re.sub(r"\n{3,}", "\n\n", body) - body = re.sub(r"(?m)^(#{1,3}\s[^\n]+)\n(?!\n)", r"\1\n\n", body) return body.strip() + "\n" diff --git a/app/services/im.py b/app/services/im.py index 791f19e..581f6f1 100644 --- a/app/services/im.py +++ b/app/services/im.py @@ -3,20 +3,36 @@ from __future__ import annotations import hashlib import hmac import base64 +import logging import time from urllib.parse import quote_plus import httpx +from urllib.parse import urlparse from app.config import settings from app.schemas import IMPublishRequest, PublishResponse +logger = logging.getLogger(__name__) + class IMPublisher: - async def publish(self, req: IMPublishRequest) -> PublishResponse: + async def publish(self, req: IMPublishRequest, request_id: str = "") -> PublishResponse: + rid = request_id or "-" if not settings.im_webhook_url: + logger.warning("im_skipped rid=%s reason=empty_webhook_url", rid) return PublishResponse(ok=False, detail="缺少 IM_WEBHOOK_URL 配置") + parsed = urlparse(settings.im_webhook_url) + host = parsed.netloc or "(invalid_url)" + logger.info( + "im_publish_start rid=%s webhook_host=%s sign_enabled=%s title_chars=%d body_truncated_to=3800", + rid, + host, + bool(settings.im_secret), + len(req.title or ""), + ) + webhook = self._with_signature(settings.im_webhook_url, settings.im_secret) payload = { "msg_type": "post", @@ -31,15 +47,25 @@ class IMPublisher: } async with httpx.AsyncClient(timeout=20) as client: + logger.info("im_http_post rid=%s method=POST timeout_s=20", rid) r = await client.post(webhook, json=payload) try: data = r.json() except Exception: data = {"status_code": r.status_code, "text": r.text} + logger.info( + "im_http_response rid=%s status=%s body_preview=%s", + rid, + r.status_code, + str(data)[:500], + ) + if r.status_code >= 400: + logger.warning("im_push_failed rid=%s http_status=%s", rid, r.status_code) return PublishResponse(ok=False, detail=f"IM 推送失败: {data}", data=data) + logger.info("im_push_ok rid=%s", rid) return PublishResponse(ok=True, detail="IM 推送成功", data=data) def _with_signature(self, webhook: str, secret: str | None) -> str: diff --git a/app/services/wechat.py b/app/services/wechat.py index f90502c..4bcc658 100644 --- a/app/services/wechat.py +++ b/app/services/wechat.py @@ -1,5 +1,6 @@ from __future__ import annotations +import logging import time import httpx @@ -8,21 +9,60 @@ import markdown2 from app.config import settings from app.schemas import PublishResponse, WechatPublishRequest +logger = logging.getLogger(__name__) + + +def _detail_for_token_error(data: dict | None) -> str: + """把微信返回的 errcode 转成可操作的说明。""" + if not data: + return "获取微信 access_token 失败（无返回内容）" + code = data.get("errcode") + msg = (data.get("errmsg") or "").strip() + if code == 40164: + return ( + "微信 errcode=40164：当前请求使用的出口 IP 未在公众号「IP 白名单」中。" + "请到 微信公众平台 → 设置与开发 → 基本配置 → IP 白名单，添加本服务对外的公网 IP" + "（日志里 invalid ip 后面的地址）。若在本地/Docker 调试，出口 IP 常会变，需填当前出口或改用固定出口的服务器。" + f" 微信原文：{msg}" + ) + if code == 40013: + return f"微信 errcode=40013：AppSecret 无效或已重置，请检查 WECHAT_SECRET。{msg}" + if code == 40125: + return f"微信 errcode=40125：AppSecret 配置错误。{msg}" + return f"获取微信 access_token 失败：errcode={code} errmsg={msg}" + class WechatPublisher: def __init__(self) -> None: self._access_token = None self._expires_at = 0 - async def publish_draft(self, req: WechatPublishRequest) -> PublishResponse: + async def publish_draft(self, req: WechatPublishRequest, request_id: str = "") -> PublishResponse: + rid = request_id or "-" if not settings.wechat_appid or not settings.wechat_secret: + logger.warning("wechat skipped rid=%s reason=missing_appid_or_secret", rid) return PublishResponse(ok=False, detail="缺少 WECHAT_APPID / WECHAT_SECRET 配置") - token = await self._get_access_token() + token, token_from_cache, token_err_body = await self._get_access_token() if not token: - return PublishResponse(ok=False, detail="获取微信 access_token 失败") + detail = _detail_for_token_error(token_err_body) + logger.error("wechat access_token_unavailable rid=%s detail=%s", rid, detail[:200]) + return PublishResponse(ok=False, detail=detail, data=token_err_body) + + logger.info( + "wechat_token rid=%s cache_hit=%s", + rid, + token_from_cache, + ) html = markdown2.markdown(req.body_markdown) + logger.info( + "wechat_draft_build rid=%s title_chars=%d digest_chars=%d html_chars=%d", + rid, + len(req.title or ""), + len(req.summary or ""), + len(html or ""), + ) payload = { "articles": [ { @@ -39,19 +79,37 @@ class WechatPublisher: async with httpx.AsyncClient(timeout=25) as client: url = f"https://api.weixin.qq.com/cgi-bin/draft/add?access_token={token}" + logger.info( + "wechat_http_post rid=%s endpoint=cgi-bin/draft/add http_timeout_s=25", + rid, + ) r = await client.post(url, json=payload) data = r.json() if data.get("errcode", 0) != 0: + logger.warning( + "wechat_draft_failed rid=%s errcode=%s errmsg=%s raw=%s", + rid, + data.get("errcode"), + data.get("errmsg"), + data, + ) return PublishResponse(ok=False, detail=f"微信发布失败: {data}", data=data) + logger.info( + "wechat_draft_ok rid=%s media_id=%s", + rid, + data.get("media_id", data), + ) return PublishResponse(ok=True, detail="已发布到公众号草稿箱", data=data) - async def _get_access_token(self) -> str | None: + async def _get_access_token(self) -> tuple[str | None, bool, dict | None]: + """成功时第三项为 None；失败时为微信返回的 JSON（含 errcode/errmsg）。""" now = int(time.time()) if self._access_token and now < self._expires_at - 60: - return self._access_token + return self._access_token, True, None + logger.info("wechat_http_get endpoint=cgi-bin/token reason=refresh_access_token") async with httpx.AsyncClient(timeout=20) as client: r = await client.get( "https://api.weixin.qq.com/cgi-bin/token", @@ -61,12 +119,17 @@ class WechatPublisher: "secret": settings.wechat_secret, }, ) - data = r.json() + data = r.json() if r.content else {} token = data.get("access_token") if not token: - return None + logger.warning( + "wechat_token_refresh_failed http_status=%s body=%s", + r.status_code, + data, + ) + return None, False, data if isinstance(data, dict) else None self._access_token = token self._expires_at = now + int(data.get("expires_in", 7200)) - return token + return token, False, None diff --git a/app/static/app.js b/app/static/app.js index b4756a1..7722ba1 100644 --- a/app/static/app.js +++ b/app/static/app.js @@ -1,5 +1,16 @@ const $ = (id) => document.getElementById(id); +function renderBodyPreview() { + const raw = ($("body") && $("body").value) || ""; + const el = $("bodyPreview"); + if (!el) return; + if (typeof marked !== "undefined" && marked.parse) { + el.innerHTML = marked.parse(raw, { breaks: true }); + } else { + el.textContent = raw; + } +} + const statusEl = $("status"); const rewriteBtn = $("rewriteBtn"); const wechatBtn = $("wechatBtn"); @@ -13,6 +24,7 @@ function updateCounters() { $("sourceCount").textContent = `${countText($("sourceText").value)} 字`; $("summaryCount").textContent = `${countText($("summary").value)} 字`; $("bodyCount").textContent = `${countText($("body").value)} 字`; + renderBodyPreview(); } function setLoading(button, loading, idleText, loadingText) { @@ -34,9 +46,36 @@ async function postJSON(url, body) { }); const data = await res.json(); if (!res.ok) throw new Error(data.detail || "请求失败"); + data._requestId = res.headers.get("X-Request-ID") || ""; return data; } +function renderTrace(trace, headerRid) { + const wrap = $("traceWrap"); + const pre = $("traceJson"); + const badge = $("traceBadge"); + if (!pre || !wrap) return; + + if (!trace || Object.keys(trace).length === 0) { + pre.textContent = headerRid + ? JSON.stringify({ request_id: headerRid, note: "响应中无 trace 字段" }, null, 2) + : "（尚无数据）完成一次「AI 改写」后，这里会显示请求 ID、耗时、质检与降级原因。"; + if (badge) badge.textContent = ""; + return; + } + + const merged = { ...trace }; + if (headerRid && !merged.request_id) merged.request_id = headerRid; + pre.textContent = JSON.stringify(merged, null, 2); + + const mode = merged.mode || ""; + if (badge) { + badge.textContent = mode === "ai" ? "AI" : mode === "fallback" ? "保底" : ""; + badge.className = "trace-badge " + (mode === "ai" ? "is-ai" : mode === "fallback" ? "is-fallback" : ""); + } + wrap.open = true; +} + $("rewriteBtn").addEventListener("click", async () => { const sourceText = $("sourceText").value.trim(); if (sourceText.length < 20) { @@ -59,11 +98,22 @@ $("rewriteBtn").addEventListener("click", async () => { $("summary").value = data.summary || ""; $("body").value = data.body_markdown || ""; updateCounters(); + renderTrace(data.trace, data._requestId); + const tr = data.trace || {}; + const modelLine = tr.model ? `模型 ${tr.model}` : ""; if (data.mode === "fallback") { const note = (data.quality_notes || [])[0] || "当前为保底改写稿"; - setStatus(`改写完成（保底模式）：${note}`, true); + setStatus( + `改写完成（保底模式，未使用或未通过千问长文）：${note}${modelLine ? ` · ${modelLine}` : ""}`, + true + ); + } else if (tr.quality_soft_accept) { + setStatus( + `改写完成（AI，质检提示）：${(data.quality_notes || []).join("；") || "见 quality_notes"} · ${modelLine || "AI"}` + ); + statusEl.style.color = "#9a3412"; } else { - setStatus("改写完成，可直接发布。"); + setStatus(`改写完成（AI 洗稿）${modelLine ? ` · ${modelLine}` : ""}`); } } catch (e) { setStatus(`改写失败: ${e.message}`, true); @@ -111,4 +161,32 @@ $("imBtn").addEventListener("click", async () => { $(id).addEventListener("input", updateCounters); }); +async function loadBackendConfig() { + const el = $("backendConfig"); + if (!el) return; + try { + const res = await fetch("/api/config"); + const c = await res.json(); + if (!c.openai_configured) { + el.textContent = + "后端未配置 OPENAI_API_KEY：改写将使用本地保底稿，千问不会参与。请在 .env 中配置并重启容器。"; + el.style.color = "#b42318"; + return; + } + const name = + c.provider === "dashscope" + ? "通义千问（DashScope 兼容接口）" + : "OpenAI 兼容接口"; + const host = c.base_url_host ? ` · ${c.base_url_host}` : ""; + const to = c.openai_timeout_sec != null ? ` · 单轮最长等待 ${c.openai_timeout_sec}s` : ""; + el.textContent = `已接入：${c.openai_model} · ${name}${host}${to}`; + el.style.color = ""; + } catch (e) { + el.textContent = "无法读取 /api/config（请确认服务已启动）"; + el.style.color = "#b42318"; + } +} + +loadBackendConfig(); updateCounters(); +renderTrace(null, ""); diff --git a/app/static/style.css b/app/static/style.css index 8140d0b..4b11b9d 100644 --- a/app/static/style.css +++ b/app/static/style.css @@ -38,6 +38,11 @@ body { margin: 6px 0 0; } +.backend-config { + margin: 8px 0 0; + line-height: 1.5; +} + .badge { font-size: 12px; font-weight: 700; @@ -161,11 +166,130 @@ button:disabled { font-weight: 600; } +.small { + font-size: 13px; + margin: 0 0 12px; +} + +.flow-hint { + margin: 0 0 14px 18px; + padding: 0; + font-size: 13px; + line-height: 1.6; +} + +.trace-wrap { + margin-top: 12px; + padding: 10px 12px; + border: 1px dashed var(--line); + border-radius: 10px; + background: #f9fbf9; +} + +.trace-wrap summary { + cursor: pointer; + font-weight: 700; + color: var(--text); +} + +.trace-badge { + margin-left: 8px; + font-size: 11px; + padding: 2px 8px; + border-radius: 999px; + font-weight: 700; +} + +.trace-badge.is-ai { + background: #eaf7f0; + color: #0f5f3d; + border: 1px solid #cde6d7; +} + +.trace-badge.is-fallback { + background: #fff4e6; + color: #9a3412; + border: 1px solid #fed7aa; +} + +.body-split { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 12px; + align-items: stretch; +} + +.body-split textarea { + min-height: 280px; +} + +.preview-panel { + display: flex; + flex-direction: column; + min-width: 0; +} + +.markdown-preview { + flex: 1; + min-height: 280px; + max-height: 480px; + overflow: auto; + padding: 12px 14px; + border: 1px solid var(--line); + border-radius: 10px; + background: #fafcfb; + font-size: 14px; + line-height: 1.65; +} + +.markdown-preview h2 { + font-size: 1.15rem; + margin: 1em 0 0.5em; + color: var(--accent-2); +} + +.markdown-preview h3 { + font-size: 1.05rem; + margin: 0.9em 0 0.4em; +} + +.markdown-preview p { + margin: 0.5em 0; +} + +.markdown-preview ul, +.markdown-preview ol { + margin: 0.4em 0 0.6em 1.2em; + padding: 0; +} + +.markdown-preview li { + margin: 0.25em 0; +} + +.trace-json { + margin: 10px 0 0; + padding: 10px; + max-height: 220px; + overflow: auto; + font-size: 11px; + line-height: 1.45; + background: #fff; + border-radius: 8px; + border: 1px solid var(--line); + white-space: pre-wrap; + word-break: break-word; +} + @media (max-width: 960px) { .layout { grid-template-columns: 1fr; } + .body-split { + grid-template-columns: 1fr; + } + .topbar { align-items: flex-start; gap: 8px; diff --git a/app/templates/index.html b/app/templates/index.html index 6c642c0..10e3435 100644 --- a/app/templates/index.html +++ b/app/templates/index.html @@ -10,7 +10,8 @@

{{ app_name }}

-

把 X 长文快速改写为公众号可发布稿，并一键分发到公众号/IM。

+

粘贴原文 → 洗成约 5 段、500 字内 的短文（无小标题）→ 右侧预览 → 满意后发布。

+

Beta
@@ -18,12 +19,18 @@

输入与改写策略

+
    +
  1. 粘贴原文并设置语气/读者
    2. +
  2. 点击改写 → 右侧为短标题、摘要与五段正文（段落间空一行）
    3. +
  3. 看「运行追踪」：模式为 AI 且模型名正确，即千问/接口已生效
    4. +
  4. 人工改好后 →「发布到公众号草稿箱」（需配置 WECHAT_*）
    5. +
0 字
- +
@@ -56,6 +63,7 @@

发布内容

+

下方「运行追踪」会显示本次请求 ID、耗时、质检项与是否降级，便于与容器日志对照。

@@ -67,10 +75,24 @@
- + 0 字
- +
+ +
+
+ + 与公众号 HTML 渲染接近 +
+
+
+
+ +
+ 运行追踪 + 

+        

 
         

           
@@ -79,6 +101,7 @@
       

     

 
+    
     
   
 
diff --git a/docker-compose.yml b/docker-compose.yml
index 2845d8e..54146c8 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,6 +1,12 @@
 services:
   x2wechat:
-    build: .
+    build:
+      context: .
+      args:
+        # 海外 PyPI 可改为 https://pypi.org/simple
+        PIP_INDEX_URL: https://pypi.tuna.tsinghua.edu.cn/simple
+        # 拉 python 镜像慢时取消下一行注释（DaoCloud 同步 Docker Hub）
+        # PY_BASE: docker.m.daocloud.io/library/python:3.11-slim
     container_name: x2wechat-studio
     ports:
       - "18000:8000"