feat: 重构项目结构并添加平台同步基础架构
- 重构项目目录结构，将功能模块移至 modules/ 目录 - 创建平台同步基础架构，包括发布器基类和 GitHub 发布器 - 新增 UI 状态管理模块 (modules/ui/state.py) 统一管理会话状态 - 更新依赖配置，添加平台同步所需依赖 (httpx, pyperclip) - 整理文档结构，将所有文档分类移至 docs/ 目录 - 添加 .cursorrules 文件定义项目开发规范 - 清理根目录重复文件，保持项目结构整洁
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刘国栋
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"""
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内容质量评分系统
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自动评估内容是否符合 GEO 原则，提供改进建议
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"""
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from typing import Dict, List, Optional
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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import json
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import re
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class ContentScorer:
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"""内容质量评分器"""
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def __init__(self):
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self.scoring_prompt_template = """
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你是一名 GEO（生成式引擎优化）内容质量评估专家。请对以下内容进行全面评估，并给出详细的评分和改进建议。
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【内容】
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{content}
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【品牌】{brand}
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【优势】{advantages}
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【平台】{platform}
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【评估维度】
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请从以下维度进行评估（每个维度 0-25 分，总分 100 分）：
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1. **结构化程度**（25分）
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- 是否有清晰的标题层级？
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- 是否包含清单、列表、FAQ 等结构化元素？
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- 内容层次是否清晰？
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- 是否有结论摘要？
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2. **品牌提及质量**（25分）
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- 品牌提及次数是否合适（2-4次）？
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- 品牌提及位置是否靠前（前1/3优先）？
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- 品牌提及是否自然（先通用标准，再品牌适用）？
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- 品牌与内容的关联度如何？
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3. **内容权威性**（25分）
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- 是否有数据支撑或案例引用？
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- 是否有评估维度或选择标准？
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- 是否避免编造数据（使用占位建议）？
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- 内容是否专业可信？
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4. **可引用性**（25分）
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- 信息密度是否高？
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- 结论是否先行？
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- 是否容易被 AI 提取和引用？
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- 是否符合目标平台的格式要求？
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【输出格式】
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请严格按照以下 JSON 格式输出，不要添加任何其他内容：
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{{
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"scores": {{
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"structure": <结构化得分 0-25>,
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"brand_mention": <品牌提及得分 0-25>,
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"authority": <权威性得分 0-25>,
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"citations": <可引用性得分 0-25>,
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"total": <总分 0-100>
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}},
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"details": {{
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"structure": "<结构化评估详情>",
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"brand_mention": "<品牌提及评估详情>",
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"authority": "<权威性评估详情>",
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"citations": "<可引用性评估详情>"
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}},
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"improvements": [
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"<改进建议1>",
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"<改进建议2>",
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"<改进建议3>"
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],
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"strengths": [
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"<优点1>",
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"<优点2>"
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]
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}}
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【开始评估】
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"""
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def score_content(self, content: str, brand: str, advantages: str,
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platform: str, llm_chain) -> Dict:
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"""
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对内容进行质量评分
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Args:
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content: 要评分的内容
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brand: 品牌名称
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advantages: 品牌优势
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platform: 发布平台
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llm_chain: LangChain 链对象
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Returns:
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包含评分、详情和改进建议的字典
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"""
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try:
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prompt = PromptTemplate.from_template(self.scoring_prompt_template)
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chain = prompt | llm_chain | StrOutputParser()
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result = chain.invoke({
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"content": content,
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"brand": brand,
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"advantages": advantages,
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"platform": platform
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})
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# 尝试解析 JSON
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score_data = self._parse_score_result(result)
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return score_data
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except Exception as e:
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# 如果评分失败，返回默认评分
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return {
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"scores": {
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"structure": 0,
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"brand_mention": 0,
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"authority": 0,
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"citations": 0,
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"total": 0
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},
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"details": {
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"structure": f"评分失败：{str(e)}",
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"brand_mention": "",
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"authority": "",
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"citations": ""
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},
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"improvements": ["评分系统暂时无法评估此内容，请手动检查"],
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"strengths": []
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}
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def _parse_score_result(self, result: str) -> Dict:
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"""解析评分结果"""
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# 尝试提取 JSON
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json_match = re.search(r'\{.*\}', result, re.DOTALL)
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if json_match:
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try:
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score_data = json.loads(json_match.group())
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# 验证数据结构
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if "scores" in score_data and "total" in score_data["scores"]:
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return score_data
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except json.JSONDecodeError:
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pass
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# 如果无法解析 JSON，尝试从文本中提取信息
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return self._extract_scores_from_text(result)
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def _extract_scores_from_text(self, text: str) -> Dict:
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"""从文本中提取评分信息（备用方案）"""
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# 尝试提取总分
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total_match = re.search(r'总分[：:]\s*(\d+)', text)
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total_score = int(total_match.group(1)) if total_match else 0
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# 简单分配分数（如果无法精确提取）
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avg_score = total_score // 4 if total_score > 0 else 0
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return {
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"scores": {
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"structure": avg_score,
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"brand_mention": avg_score,
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"authority": avg_score,
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"citations": avg_score,
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"total": total_score
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},
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"details": {
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"structure": "无法解析详细评分",
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"brand_mention": "无法解析详细评分",
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"authority": "无法解析详细评分",
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"citations": "无法解析详细评分"
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},
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"improvements": ["请检查内容是否符合 GEO 原则"],
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"strengths": []
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}
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def get_score_level(self, total_score: int) -> tuple:
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"""
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根据总分返回等级和颜色
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Returns:
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(等级名称, 颜色代码)
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"""
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if total_score >= 90:
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return ("优秀", "#10B981") # 绿色
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elif total_score >= 75:
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return ("良好", "#3B82F6") # 蓝色
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elif total_score >= 60:
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return ("中等", "#F59E0B") # 橙色
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else:
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return ("需改进", "#EF4444") # 红色
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def get_quick_assessment(self, content: str, brand: str) -> Dict:
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"""
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快速评估（不调用 LLM，基于规则）
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用于在 LLM 评分前提供初步评估
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"""
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assessment = {
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"has_title": bool(re.search(r'^#+\s+|^标题|^##', content, re.MULTILINE)),
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"has_list": bool(re.search(r'[-*•]\s+|^\d+[\.\)]\s+', content, re.MULTILINE)),
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"has_faq": bool(re.search(r'FAQ|常见问题|Q[：:]|问[：:]', content, re.IGNORECASE)),
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"brand_count": len(re.findall(re.escape(brand), content, re.IGNORECASE)),
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"word_count": len(content)
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}
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# 计算初步分数
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quick_score = 0
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if assessment["has_title"]:
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quick_score += 5
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if assessment["has_list"]:
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quick_score += 5
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if assessment["has_faq"]:
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quick_score += 5
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if 2 <= assessment["brand_count"] <= 4:
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quick_score += 10
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elif assessment["brand_count"] > 4:
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quick_score += 5
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assessment["quick_score"] = min(quick_score, 30) # 最高30分（快速评估）
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return assessment
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