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508 lines
23 KiB
Python
508 lines
23 KiB
Python
"""
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提议者（Proposer）与审核者（Reviewer）两个 Agent，以及一个带 token 计量的 LLM 客户端。
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设计要点（对应书中“提议者-审核者”机制）：
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- Proposer 只处理**文本**：论文正文 + 累积的结构化文字反馈；从不接收渲染图片。
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- Reviewer 每一轮**只看最新一版的渲染截图**，且每轮都是一次全新的、无历史的调用。
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- 单 Agent 自审对照组则相反：同一段对话里不断累积历次渲染的图片，上下文迅速膨胀。
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所有对 OpenAI 的调用都经过 TokenMeter 统计 prompt / completion token，
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用于最后的“单 Agent vs 双 Agent 上下文消耗”对比。
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"""
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import base64
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import datetime as dt
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import hashlib
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import io
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import json
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import os
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import re
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import time
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from pathlib import Path
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from openai import OpenAI
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from PIL import Image
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# 文本生成用的模型（Proposer / 单 Agent 的文本部分）
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TEXT_MODEL = os.environ.get("TEXT_MODEL", "gpt-5.6-luna")
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# 视觉审查用的模型（Reviewer / 单 Agent 的看图部分），必须支持图像
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VISION_MODEL = os.environ.get("VISION_MODEL", "gpt-5.6-luna")
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OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
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PROVIDER = os.environ.get("PPT_PROVIDER", "auto")
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def configure_provider(provider: str) -> None:
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global PROVIDER
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PROVIDER = provider
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def map_model_to_openrouter(model: str) -> str:
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"""把直连模型名映射为 OpenRouter 上的 id（非可映射 id 统一兜底到当前廉价旗舰）。"""
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if not model or "/" in model:
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return model or "openai/gpt-5.6-luna"
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m = model.lower()
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if m.startswith(("gpt-", "o1", "o3", "o4")):
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return "openai/" + model
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if m.startswith("claude"):
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if "haiku" in m:
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return "anthropic/claude-haiku-4.5"
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if "sonnet" in m:
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return "anthropic/claude-sonnet-4.6"
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return "anthropic/claude-opus-4.8"
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if m.startswith("gemini"):
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return "google/" + model
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return "openai/gpt-5.6-luna"
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# 发送给 Vision 前把截图缩放到该宽度，兼顾“看得清文字溢出”与“控制 token 成本”
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VISION_IMAGE_WIDTH = 1280
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FIGURE_PAGE_TITLES = {
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"paper_figure_1_transformer.png": "Transformer Architecture (Figure 1)",
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"paper_figure_3_long_distance.png": "Long-Distance Attention (Figure 3)",
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"paper_figure_4_anaphora.png": "Anaphora Attention (Figure 4)",
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}
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class TokenMeter:
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"""累计一个“角色/模式”消耗的 token，并记录每次调用的 prompt token（用于看上下文峰值）。"""
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def __init__(self, name: str):
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self.name = name
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self.prompt_tokens = 0
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self.completion_tokens = 0
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self.calls = 0
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self.peak_prompt_tokens = 0 # 单次调用最大的 prompt token —— 决定是否“撑爆上下文”
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self.per_call_prompt = []
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self.receipts = []
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def add(self, response, request: dict, latency_s: float | None = None):
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usage = response.usage
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self.calls += 1
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pt = usage.prompt_tokens
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self.prompt_tokens += pt
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self.completion_tokens += usage.completion_tokens
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self.peak_prompt_tokens = max(self.peak_prompt_tokens, pt)
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self.per_call_prompt.append(pt)
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choice = response.choices[0]
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receipt = {
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"meter": self.name,
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"called_at_utc": dt.datetime.now(dt.timezone.utc).isoformat(),
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"latency_s": round(latency_s, 3) if latency_s is not None else None,
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"request": _sanitize_for_evidence(request),
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"response": {
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"id": response.id,
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"model": response.model,
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"finish_reason": choice.finish_reason,
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"content": choice.message.content,
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},
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"usage": {
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"prompt_tokens": usage.prompt_tokens,
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"completion_tokens": usage.completion_tokens,
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"total_tokens": usage.total_tokens,
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"cached_prompt_tokens": getattr(
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getattr(usage, "prompt_tokens_details", None), "cached_tokens", None
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),
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},
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}
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self.receipts.append(receipt)
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checkpoint = os.environ.get("PPT_RECEIPT_CHECKPOINT")
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if checkpoint:
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path = Path(checkpoint)
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path.parent.mkdir(parents=True, exist_ok=True)
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existing = []
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if path.is_file():
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existing = json.loads(path.read_text(encoding="utf-8"))
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existing.append(receipt)
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temporary = path.with_suffix(path.suffix + ".tmp")
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temporary.write_text(
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json.dumps(existing, ensure_ascii=False, indent=2), encoding="utf-8"
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)
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temporary.replace(path)
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@property
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def total_tokens(self):
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return self.prompt_tokens + self.completion_tokens
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def _client() -> OpenAI:
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# 通用 OpenRouter 兜底：无直连 key，或默认 gpt-5.x（直连需组织实名认证）时改走 OpenRouter。
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global TEXT_MODEL, VISION_MODEL
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if PROVIDER == "ark":
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api_key = os.environ.get("ARK_API_KEY")
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base_url = "https://ark.cn-beijing.volces.com/api/v3"
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elif PROVIDER == "moonshot":
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api_key = os.environ.get("MOONSHOT_API_KEY")
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base_url = "https://api.moonshot.cn/v1"
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elif PROVIDER == "openrouter":
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api_key = os.environ.get("OPENROUTER_API_KEY")
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base_url = OPENROUTER_BASE_URL
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else:
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api_key = os.environ.get("OPENAI_API_KEY")
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base_url = os.environ.get("OPENAI_BASE_URL")
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orkey = os.environ.get("OPENROUTER_API_KEY")
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prefer_or = PROVIDER == "auto" and bool(orkey) and (
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(TEXT_MODEL or "").lower().startswith("gpt-5") or (VISION_MODEL or "").lower().startswith("gpt-5")
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)
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if PROVIDER == "openrouter" or prefer_or or (PROVIDER == "auto" and not api_key and orkey):
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api_key, base_url = orkey, OPENROUTER_BASE_URL
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# 走 OpenRouter 时把模型名映射为其 id（幂等：已带前缀的 id 原样返回）。
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TEXT_MODEL = map_model_to_openrouter(TEXT_MODEL)
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VISION_MODEL = map_model_to_openrouter(VISION_MODEL)
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if not api_key:
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raise SystemExit(
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"❌ 未检测到 OPENAI_API_KEY（或 OPENROUTER_API_KEY 兜底）。请先 `cp env.example .env` 并填入有效的 "
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"OpenAI API Key（或 `export OPENAI_API_KEY=your-openai-api-key` / `export OPENROUTER_API_KEY=...`）后再运行。"
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)
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# timeout + max_retries：单次网络抖动/SSL 中断会自动重试，而不是让整条流水线崩溃。
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return OpenAI(
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api_key=api_key,
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base_url=base_url,
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# Large single-agent requests carry the extracted paper plus several
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# full slide revisions (and later image history). Ark can legitimately
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# take more than one minute to produce the complete Markdown deck.
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# Keep bounded retries, but give each real request enough time instead
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# of abandoning an otherwise healthy formal campaign mid-arm.
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timeout=300.0,
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max_retries=4,
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)
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def _sanitize_for_evidence(value):
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"""Retain raw public text while replacing large data URLs with hashes."""
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if isinstance(value, dict):
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return {key: _sanitize_for_evidence(item) for key, item in value.items()}
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if isinstance(value, list):
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return [_sanitize_for_evidence(item) for item in value]
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if isinstance(value, str) and value.startswith("data:"):
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return {
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"data_url_sha256": hashlib.sha256(value.encode("utf-8")).hexdigest(),
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"characters": len(value),
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"media_type": value.split(";", 1)[0][5:],
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}
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return value
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def encode_image(path: str) -> str:
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"""读取 PNG，缩放到统一宽度，编码为 data URL（base64）。"""
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img = Image.open(path).convert("RGB")
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if img.width > VISION_IMAGE_WIDTH:
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h = int(img.height * VISION_IMAGE_WIDTH / img.width)
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img = img.resize((VISION_IMAGE_WIDTH, h), Image.LANCZOS)
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buf = io.BytesIO()
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img.save(buf, format="PNG")
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b64 = base64.b64encode(buf.getvalue()).decode()
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return f"data:image/png;base64,{b64}"
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def _extract_json(text: str):
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"""从模型回复里稳健地抽取 JSON（容忍 ```json 代码块或前后多余文字）。"""
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text = text.strip()
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m = re.search(r"```(?:json)?\s*(.*?)```", text, re.DOTALL)
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if m:
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text = m.group(1).strip()
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# 找到第一个 { 到最后一个 }
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start = text.find("{")
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end = text.rfind("}")
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if start != -1 and end != -1:
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text = text[start:end + 1]
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return json.loads(text)
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def _extract_slides_md(text: str) -> str:
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"""从模型回复里抽取 slides.md 内容（容忍 ```markdown 包裹）。"""
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m = re.search(r"```(?:markdown|md)?\s*(.*?)```", text, re.DOTALL)
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if m:
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return m.group(1).strip()
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return text.strip()
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def _slide_count(slides: str) -> int:
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"""Count Slidev pages when the required frontmatter is present."""
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# The closing frontmatter fence plus every inter-page fence yields exactly
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# one ``\n---\n`` occurrence per rendered page.
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return slides.replace("\r\n", "\n").count("\n---\n")
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def _slide_contract_issues(slides: str) -> list[str]:
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"""Return deterministic density/page-count failures before rendering.
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Vision remains the authority for pixel-level quality. This inexpensive
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source gate prevents repeatedly paying to render drafts that already
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violate the Proposer's explicit 18--20 page / four-bullet instructions.
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"""
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normalized = slides.replace("\r\n", "\n")
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pages = normalized.split("\n---\n")[1:]
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issues = []
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if not 18 <= len(pages) <= 20:
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issues.append(f"page count is {len(pages)}; required 18-20")
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for page_number, page in enumerate(pages, 1):
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bullet_count = len(re.findall(r"(?m)^\s*(?:[-*+]|[■▪•])\s+", page))
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if bullet_count > 4:
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issues.append(
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f"page {page_number} has {bullet_count} bullets; maximum is 4 total"
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)
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figure_match = re.search(r"/((?:paper_figure_)[^\s\"')>]+)", page)
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if figure_match:
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required_title = FIGURE_PAGE_TITLES.get(figure_match.group(1))
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if required_title and not re.search(
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rf"(?m)^#{{1,3}}\s+{re.escape(required_title)}\s*$", page
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):
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issues.append(
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f"page {page_number} source figure must use one-line title: "
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f"{required_title}"
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)
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style_match = re.search(r"style=[\"']([^\"']*)[\"']", page)
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style = style_match.group(1) if style_match else ""
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if not all(re.search(pattern, style) for pattern in (
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r"(?:^|;)\s*max-height:\s*460px\s*(?:;|$)",
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r"(?:^|;)\s*width:\s*100%\s*(?:;|$)",
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r"(?:^|;)\s*object-fit:\s*contain\s*(?:;|$)",
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)):
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issues.append(
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f"page {page_number} source figure must use inline style "
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"max-height: 460px; width: 100%; object-fit: contain;"
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)
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prose_lines = [
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line.strip()
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for line in page.splitlines()
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if line.strip()
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and not line.lstrip().startswith("#")
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and "<img" not in line
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]
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if bullet_count or len(prose_lines) > 1:
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issues.append(
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f"page {page_number} source figure must have only a title "
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"and at most one caption"
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)
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return issues
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# --------------------------------------------------------------------------- #
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# Reviewer 的审查评分标准（Proposer / 单 Agent / 独立评委共用同一套 rubric）
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# --------------------------------------------------------------------------- #
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REVIEW_RUBRIC = """你是一名严格的演示文稿质量审核员（Reviewer）。你会看到一份由 Slidev 渲染出的 PPT，
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每张图对应一页幻灯片（按顺序编号，从第 1 页开始）。请逐页检查以下问题：
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- text_overflow（文字溢出/被裁切超出页面边界）
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- overcrowded（内容过多/过于拥挤/留白不足）
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- image_size（图片过大顶出布局，或过小看不清）
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- readability（字号过小、对比度差、代码块难读）
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- layout（对齐混乱、标题与正文比例失衡、空页）
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请以**目标用户是听众**的严格标准审查——一页幻灯片若要点超过约 5 条、或正文文字块偏长、
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或图片挤压了文字空间，都应视为 overcrowded/image_size 问题。五个或更少的精炼要点本身
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是可接受的，不得仅因页面恰好有五个要点而报错。报告真实存在的问题，
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但不要放过"塞得太满"。对每个问题给出：page（页码，整数）、
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issue_type（上面之一）、severity（high/medium/low）、suggestion（具体、可执行的修改建议，中文）。
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同时给出：
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- overall_score：0-100 的整体质量分（越高越好）
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- pass：布尔值，仅当整份 PPT **既无 high 也无 medium 级问题**、排版干净可读时才为 true
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严格输出如下 JSON（不要输出任何多余文字）：
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{
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"overall_score": <int>,
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"pass": <bool>,
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"issues": [
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{"page": <int>, "issue_type": "<type>", "severity": "<high|medium|low>", "suggestion": "<中文建议>"}
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]
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}"""
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class Reviewer:
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"""审核者 Agent：看最新一版渲染截图，输出结构化 JSON 建议。每轮独立调用、无历史。"""
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def __init__(self, meter: TokenMeter):
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self.client = _client()
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self.meter = meter
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def review(self, png_paths: list[str]) -> dict:
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content = [{"type": "text",
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"text": f"这份 PPT 共 {len(png_paths)} 页，下面按页码顺序给出每一页的渲染截图。请审查。"}]
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for i, p in enumerate(png_paths, 1):
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content.append({"type": "text", "text": f"第 {i} 页："})
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content.append({"type": "image_url",
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"image_url": {"url": encode_image(p), "detail": "high"}})
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request = {
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"model": VISION_MODEL,
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"messages": [
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{"role": "system", "content": REVIEW_RUBRIC},
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{"role": "user", "content": content},
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],
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"temperature": 0.2,
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}
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started = time.monotonic()
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resp = self.client.chat.completions.create(**request)
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self.meter.add(resp, request, time.monotonic() - started)
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return _extract_json(resp.choices[0].message.content)
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PROPOSER_SYSTEM = """你是一名擅长把学术论文转化为演示文稿的 Proposer Agent。
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你用 Slidev 框架（Markdown + HTML）编写 PPT 源码 slides.md。
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Slidev 语法要点：
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- 文件开头是 YAML frontmatter（--- 包裹），设置 theme: default。
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- 用单独一行的 `---`（前后空行）分隔每一页幻灯片。
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- 首页通常放标题、作者、会议。
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- 引用图片用 markdown：，可用 HTML 控制尺寸，
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例如 <img src="/speedup_bar.png" class="h-60 mx-auto" />。
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- 可用 Windi/Uno CSS 工具类控制排版（如 text-sm、grid grid-cols-2 gap-4）。
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要求：
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- 最终生成 18-20 页，覆盖论文的标题、背景/动机、方法、实验结果、局限与结论。
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- 至少在 3 页中使用提供的图表/表格，且图文匹配。
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- 每页最多 4 个要点，不得粘贴长段原文，宁可精简也不要溢出。
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- 三张原论文图必须各占一张专用页面；为避免 UnoCSS 动态类漏编译，图片必须使用行内属性
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`style="max-height: 460px; width: 100%; object-fit: contain;"`，并且页面除标题和一行
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短图注外不得放正文，以保证整张图和图注都在页面边界内且标签可读。
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- 三张原图页面必须分别使用不会换行的精确标题：`Transformer Architecture (Figure 1)`、
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`Long-Distance Attention (Figure 3)`、`Anaphora Attention (Figure 4)`。
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- 要点统一用 Markdown `- `，不要用 `■` 等字符伪装项目符号；多个小节合计仍不得超过 4 条。
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- 只输出 slides.md 的完整内容，用 ```markdown 代码块包裹，不要额外解释。"""
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|
||
|
||
class Proposer:
|
||
"""提议者 Agent：只吃文本（论文 + 累积文字反馈），产出 slides.md。"""
|
||
|
||
def __init__(self, meter: TokenMeter, paper_md: str, figures: dict):
|
||
self.client = _client()
|
||
self.meter = meter
|
||
fig_desc = "\n".join(f"- {name}：{desc}" for name, desc in figures.items())
|
||
first_user = (
|
||
f"以下是论文全文（Markdown）：\n\n{paper_md}\n\n"
|
||
f"可直接引用的图表文件（放在 Slidev public 目录，用 /文件名 引用）：\n{fig_desc}\n\n"
|
||
f"请生成一版 18-20 页的完整初稿。内容必须忠于论文，至少引用上面列出的三张原论文图，"
|
||
f"并覆盖问题、Transformer 架构、注意力机制、训练、主要实验、局限和结论。"
|
||
f"不要复制长段原文；每页保持听众可读的信息密度。生成完整的 slides.md。"
|
||
)
|
||
# Proposer 的对话历史——只累积文本，永不加入图片
|
||
self.messages = [
|
||
{"role": "system", "content": PROPOSER_SYSTEM},
|
||
{"role": "user", "content": first_user},
|
||
]
|
||
|
||
def _generate(self) -> str:
|
||
for attempt in range(3):
|
||
request = {"model": TEXT_MODEL, "messages": self.messages, "temperature": 0.3}
|
||
started = time.monotonic()
|
||
resp = self.client.chat.completions.create(**request)
|
||
self.meter.add(resp, request, time.monotonic() - started)
|
||
reply = resp.choices[0].message.content
|
||
self.messages.append({"role": "assistant", "content": reply})
|
||
slides = _extract_slides_md(reply)
|
||
issues = _slide_contract_issues(slides)
|
||
if not issues:
|
||
return slides
|
||
if attempt < 2:
|
||
self.messages.append({
|
||
"role": "user",
|
||
"content": (
|
||
"硬性源码验收失败：" + "; ".join(issues) + "。"
|
||
"请在不删除三张原论文图、不丢失主要贡献的前提下精简、合并或拆分内容，"
|
||
"重新输出完整 slides.md；必须保持 18–20 页且每页最多 4 个 Markdown 要点。"
|
||
),
|
||
})
|
||
raise RuntimeError(
|
||
"Proposer failed the source density contract after 3 attempts: "
|
||
+ "; ".join(issues)
|
||
)
|
||
|
||
def propose(self) -> str:
|
||
"""首轮生成。"""
|
||
return self._generate()
|
||
|
||
def revise(self, review: dict) -> str:
|
||
"""根据 Reviewer 的结构化文字反馈修订（只把 JSON 文本加入上下文）。"""
|
||
feedback = json.dumps(review, ensure_ascii=False, indent=2)
|
||
self.messages.append({
|
||
"role": "user",
|
||
"content": (
|
||
"审核者（Reviewer）渲染了你上一版 slides.md 的每一页截图，"
|
||
"给出如下结构化改进建议（JSON）：\n\n"
|
||
f"{feedback}\n\n"
|
||
"请理解这些问题并修订 slides.md（可拆页、精简文字、调整图片尺寸等），"
|
||
"重新输出完整的 slides.md。修订后仍必须保持 18–20 页；解决拥挤时优先精简与合并，"
|
||
"不得用无限拆页规避版面问题。"
|
||
),
|
||
})
|
||
return self._generate()
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# 单 Agent 自审对照组：同一段对话里既生成、又看自己的渲染图、又修订。
|
||
# 关键区别：历次渲染的图片会**留在**同一上下文里，导致上下文随迭代快速膨胀。
|
||
# --------------------------------------------------------------------------- #
|
||
SELF_REVIEW_SYSTEM = PROPOSER_SYSTEM + """
|
||
|
||
此外，你还要**自我审查**：当收到自己 PPT 的渲染截图时，先在心里按下列标准找出问题
|
||
（文字溢出、内容拥挤、图片尺寸、可读性、布局），再据此输出修订后的完整 slides.md。"""
|
||
|
||
|
||
class SelfReviewAgent:
|
||
"""单 Agent 自审：一条不断增长的对话，图片累积在上下文中。"""
|
||
|
||
def __init__(self, meter: TokenMeter, paper_md: str, figures: dict):
|
||
self.client = _client()
|
||
self.meter = meter
|
||
fig_desc = "\n".join(f"- {name}：{desc}" for name, desc in figures.items())
|
||
first_user = (
|
||
f"以下是论文全文（Markdown）：\n\n{paper_md}\n\n"
|
||
f"可直接引用的图表文件：\n{fig_desc}\n\n"
|
||
f"请生成一版 18-20 页的完整初稿。内容必须忠于论文，至少引用上面列出的三张原论文图，"
|
||
f"并覆盖问题、Transformer 架构、注意力机制、训练、主要实验、局限和结论。"
|
||
f"不要复制长段原文；每页保持听众可读的信息密度。生成完整的 slides.md。"
|
||
)
|
||
self.messages = [
|
||
{"role": "system", "content": SELF_REVIEW_SYSTEM},
|
||
{"role": "user", "content": first_user},
|
||
]
|
||
|
||
def propose(self) -> str:
|
||
return self._generate_with_page_gate()
|
||
|
||
def _generate_with_page_gate(self) -> str:
|
||
for attempt in range(3):
|
||
request = {"model": VISION_MODEL, "messages": self.messages, "temperature": 0.3}
|
||
started = time.monotonic()
|
||
resp = self.client.chat.completions.create(**request)
|
||
self.meter.add(resp, request, time.monotonic() - started)
|
||
reply = resp.choices[0].message.content
|
||
self.messages.append({"role": "assistant", "content": reply})
|
||
slides = _extract_slides_md(reply)
|
||
issues = _slide_contract_issues(slides)
|
||
if not issues:
|
||
return slides
|
||
if attempt < 2:
|
||
self.messages.append({
|
||
"role": "user",
|
||
"content": (
|
||
"硬性源码验收失败：" + "; ".join(issues) + "。"
|
||
"请保留三张原论文图和全部核心章节，压缩或重组后重新输出完整 slides.md；"
|
||
"必须保持 18–20 页且每页最多 4 个 Markdown 要点。"
|
||
),
|
||
})
|
||
raise RuntimeError(
|
||
"Single Agent failed the source density contract after 3 attempts: "
|
||
+ "; ".join(issues)
|
||
)
|
||
|
||
def self_review_and_revise(self, png_paths: list[str]) -> str:
|
||
"""把最新渲染截图加入**同一**上下文，让模型自审并修订。图片会一直留在历史里。"""
|
||
content = [{"type": "text",
|
||
"text": (f"这是你上一版 slides.md 渲染出的 {len(png_paths)} 页截图。"
|
||
"请自我审查（文字溢出/拥挤/图片尺寸/可读性/布局），"
|
||
"然后输出修订后的完整 slides.md。修订后硬性保持 18–20 页，"
|
||
"并保留三张原论文图。")}]
|
||
for i, p in enumerate(png_paths, 1):
|
||
content.append({"type": "text", "text": f"第 {i} 页："})
|
||
content.append({"type": "image_url",
|
||
"image_url": {"url": encode_image(p), "detail": "high"}})
|
||
self.messages.append({"role": "user", "content": content})
|
||
return self._generate_with_page_gate()
|
||
|
||
|
||
def independent_judge(png_paths: list[str], meter: TokenMeter) -> dict:
|
||
"""用同一套 rubric、独立地给某一版最终 PPT 打分，用于公平比较两种方案的质量。"""
|
||
reviewer = Reviewer(meter)
|
||
return reviewer.review(png_paths)
|