刘国栋
8f7f082c3d
- 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
394 lines
14 KiB
Python
394 lines
14 KiB
Python
"""
|
|
语义足迹扩展模块
|
|
基于现有关键词，通过语义相似度扩展出更多相关关键词，提升关键词覆盖面
|
|
"""
|
|
from typing import List, Dict, Set, Optional
|
|
from langchain_core.prompts import PromptTemplate
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
import json
|
|
import re
|
|
from difflib import SequenceMatcher
|
|
|
|
|
|
class SemanticExpander:
|
|
"""语义足迹扩展器"""
|
|
|
|
def __init__(self):
|
|
# 语义扩展 Prompt
|
|
self.expansion_prompt_template = """
|
|
你是关键词扩展专家，专门基于现有关键词生成语义相关的扩展关键词，提升关键词覆盖面。
|
|
|
|
【现有关键词】
|
|
{existing_keywords}
|
|
|
|
【品牌】{brand}
|
|
【优势】{advantages}
|
|
【扩展数量】{expansion_count}
|
|
|
|
【语义足迹扩展要求】
|
|
|
|
1. **语义相关性**
|
|
- 生成的关键词必须与现有关键词在语义上相关
|
|
- 覆盖相同的搜索意图，但使用不同的表达方式
|
|
- 包含同义词、近义词、相关概念
|
|
|
|
2. **覆盖面扩展**
|
|
- 从不同角度扩展：功能角度、场景角度、用户角度、问题角度
|
|
- 包含长尾词变体：更具体、更细分、更口语化
|
|
- 覆盖相关领域：上下游、关联概念、延伸话题
|
|
|
|
3. **多样性**
|
|
- 避免与现有关键词重复或过于相似
|
|
- 使用不同的表达方式（口语化、正式、专业等）
|
|
- 包含不同长度（短词、长尾词）
|
|
|
|
4. **质量要求**
|
|
- 保持自然、符合用户搜索习惯
|
|
- 长度控制在 8-30 字
|
|
- 避免生硬拼接
|
|
|
|
【扩展策略】
|
|
|
|
1. **同义扩展**：使用同义词替换关键词中的核心词
|
|
- 示例："外贸ERP软件" → "外贸管理系统"、"外贸业务软件"
|
|
|
|
2. **场景扩展**：添加使用场景或应用场景
|
|
- 示例："外贸ERP" → "小型企业外贸ERP"、"跨境电商ERP"
|
|
|
|
3. **问题扩展**：转换为问题形式
|
|
- 示例："外贸ERP推荐" → "外贸ERP哪个好"、"如何选择外贸ERP"
|
|
|
|
4. **功能扩展**：突出不同功能点
|
|
- 示例："外贸ERP" → "外贸订单管理软件"、"外贸库存管理ERP"
|
|
|
|
5. **长尾扩展**：生成更具体的长尾词
|
|
- 示例："外贸ERP" → "适合小企业的外贸ERP软件"、"支持多语言的外贸ERP系统"
|
|
|
|
【输出格式】
|
|
请严格按照以下 JSON 格式输出，不要添加任何其他内容：
|
|
|
|
{{
|
|
"expanded_keywords": [
|
|
"<扩展关键词1>",
|
|
"<扩展关键词2>",
|
|
...
|
|
],
|
|
"expansion_stats": {{
|
|
"total_expanded": <扩展总数>,
|
|
"synonym_count": <同义扩展数量>,
|
|
"scenario_count": <场景扩展数量>,
|
|
"question_count": <问题扩展数量>,
|
|
"feature_count": <功能扩展数量>,
|
|
"longtail_count": <长尾扩展数量>
|
|
}},
|
|
"expansion_details": [
|
|
{{
|
|
"original": "<原关键词>",
|
|
"expanded": ["<扩展词1>", "<扩展词2>"],
|
|
"type": "<扩展类型：同义/场景/问题/功能/长尾>"
|
|
}},
|
|
...
|
|
]
|
|
}}
|
|
|
|
【开始扩展】
|
|
"""
|
|
|
|
def expand_keywords(
|
|
self,
|
|
existing_keywords: List[str],
|
|
brand: str,
|
|
advantages: str,
|
|
expansion_count: int,
|
|
llm_chain
|
|
) -> Dict:
|
|
"""
|
|
基于现有关键词进行语义扩展
|
|
|
|
Args:
|
|
existing_keywords: 现有关键词列表
|
|
brand: 品牌名称
|
|
advantages: 品牌优势
|
|
expansion_count: 期望扩展的关键词数量
|
|
llm_chain: LangChain 链对象
|
|
|
|
Returns:
|
|
包含扩展关键词、统计信息和详细信息的字典
|
|
"""
|
|
if not existing_keywords:
|
|
return {
|
|
"expanded_keywords": [],
|
|
"expansion_stats": {
|
|
"total_expanded": 0,
|
|
"synonym_count": 0,
|
|
"scenario_count": 0,
|
|
"question_count": 0,
|
|
"feature_count": 0,
|
|
"longtail_count": 0
|
|
},
|
|
"expansion_details": []
|
|
}
|
|
|
|
try:
|
|
# 限制输入关键词数量，避免 Prompt 过长
|
|
keywords_to_expand = existing_keywords[:50] # 最多处理50个关键词
|
|
|
|
prompt = PromptTemplate.from_template(self.expansion_prompt_template)
|
|
chain = prompt | llm_chain | StrOutputParser()
|
|
|
|
result = chain.invoke({
|
|
"existing_keywords": json.dumps(keywords_to_expand, ensure_ascii=False, indent=2),
|
|
"brand": brand,
|
|
"advantages": advantages,
|
|
"expansion_count": expansion_count
|
|
})
|
|
|
|
# 解析结果
|
|
expansion_data = self._parse_expansion_result(result, existing_keywords)
|
|
|
|
return expansion_data
|
|
|
|
except Exception as e:
|
|
# 如果扩展失败，返回基于规则的简单扩展
|
|
return self._rule_based_expansion(existing_keywords, expansion_count)
|
|
|
|
def _parse_expansion_result(self, result: str, original_keywords: List[str]) -> Dict:
|
|
"""解析扩展结果"""
|
|
# 尝试提取 JSON
|
|
json_match = re.search(r'\{.*\}', result, re.DOTALL)
|
|
if json_match:
|
|
try:
|
|
data = json.loads(json_match.group())
|
|
# 验证数据结构
|
|
if "expanded_keywords" in data:
|
|
# 去重和过滤
|
|
expanded = self._deduplicate_keywords(
|
|
data["expanded_keywords"],
|
|
original_keywords
|
|
)
|
|
data["expanded_keywords"] = expanded
|
|
# 更新统计信息
|
|
if "expansion_stats" in data:
|
|
data["expansion_stats"]["total_expanded"] = len(expanded)
|
|
return data
|
|
except json.JSONDecodeError:
|
|
pass
|
|
|
|
# 如果无法解析 JSON，尝试从文本中提取
|
|
return self._extract_keywords_from_text(result, original_keywords)
|
|
|
|
def _extract_keywords_from_text(self, text: str, original_keywords: List[str]) -> Dict:
|
|
"""从文本中提取关键词（备用方案）"""
|
|
# 尝试提取数组
|
|
array_match = re.search(r'\[[\s\S]*?\]', text)
|
|
if array_match:
|
|
try:
|
|
keywords = json.loads(array_match.group())
|
|
if isinstance(keywords, list):
|
|
expanded = self._deduplicate_keywords(keywords, original_keywords)
|
|
return {
|
|
"expanded_keywords": expanded,
|
|
"expansion_stats": {
|
|
"total_expanded": len(expanded),
|
|
"synonym_count": 0,
|
|
"scenario_count": 0,
|
|
"question_count": 0,
|
|
"feature_count": 0,
|
|
"longtail_count": 0
|
|
},
|
|
"expansion_details": []
|
|
}
|
|
except json.JSONDecodeError:
|
|
pass
|
|
|
|
# 如果还是无法解析，使用基于规则的扩展
|
|
return self._rule_based_expansion(original_keywords, len(original_keywords))
|
|
|
|
def _deduplicate_keywords(
|
|
self,
|
|
expanded_keywords: List[str],
|
|
original_keywords: List[str],
|
|
similarity_threshold: float = 0.85
|
|
) -> List[str]:
|
|
"""
|
|
去重和过滤扩展关键词
|
|
|
|
Args:
|
|
expanded_keywords: 扩展的关键词列表
|
|
original_keywords: 原始关键词列表
|
|
similarity_threshold: 相似度阈值
|
|
|
|
Returns:
|
|
去重后的关键词列表
|
|
"""
|
|
if not expanded_keywords:
|
|
return []
|
|
|
|
# 转换为小写用于比较
|
|
original_lower = [k.lower() for k in original_keywords]
|
|
seen = set(original_lower)
|
|
deduplicated = []
|
|
|
|
for keyword in expanded_keywords:
|
|
if not isinstance(keyword, str):
|
|
continue
|
|
|
|
keyword = keyword.strip()
|
|
if not keyword or len(keyword) < 3:
|
|
continue
|
|
|
|
keyword_lower = keyword.lower()
|
|
|
|
# 检查是否与原始关键词重复
|
|
if keyword_lower in seen:
|
|
continue
|
|
|
|
# 检查是否与已添加的关键词相似
|
|
is_similar = False
|
|
for existing in seen:
|
|
similarity = SequenceMatcher(None, keyword_lower, existing).ratio()
|
|
if similarity >= similarity_threshold:
|
|
is_similar = True
|
|
break
|
|
|
|
if not is_similar:
|
|
seen.add(keyword_lower)
|
|
deduplicated.append(keyword)
|
|
|
|
return deduplicated
|
|
|
|
def _rule_based_expansion(
|
|
self,
|
|
keywords: List[str],
|
|
max_expansion: int = 20
|
|
) -> Dict:
|
|
"""
|
|
基于规则的简单扩展（备用方案，不依赖 LLM）
|
|
|
|
Args:
|
|
keywords: 原始关键词列表
|
|
max_expansion: 最大扩展数量
|
|
|
|
Returns:
|
|
扩展结果字典
|
|
"""
|
|
expanded = []
|
|
|
|
# 简单的扩展规则
|
|
question_markers = ["哪个好", "哪家好", "如何选择", "怎么选", "推荐", "排行"]
|
|
scenario_markers = ["适合", "适用于", "针对", "面向"]
|
|
feature_markers = ["功能", "特点", "优势", "特色"]
|
|
|
|
for keyword in keywords[:10]: # 限制处理数量
|
|
if not keyword or len(keyword) < 3:
|
|
continue
|
|
|
|
# 问题形式扩展
|
|
for marker in question_markers[:2]: # 只生成2个问题形式
|
|
if marker not in keyword:
|
|
expanded.append(f"{keyword}{marker}")
|
|
if len(expanded) >= max_expansion:
|
|
break
|
|
if len(expanded) >= max_expansion:
|
|
break
|
|
|
|
# 场景扩展
|
|
for marker in scenario_markers[:1]: # 只生成1个场景形式
|
|
if marker not in keyword:
|
|
expanded.append(f"{marker}{keyword}")
|
|
if len(expanded) >= max_expansion:
|
|
break
|
|
if len(expanded) >= max_expansion:
|
|
break
|
|
|
|
return {
|
|
"expanded_keywords": expanded[:max_expansion],
|
|
"expansion_stats": {
|
|
"total_expanded": len(expanded[:max_expansion]),
|
|
"synonym_count": 0,
|
|
"scenario_count": len([k for k in expanded if any(m in k for m in scenario_markers)]),
|
|
"question_count": len([k for k in expanded if any(m in k for m in question_markers)]),
|
|
"feature_count": 0,
|
|
"longtail_count": 0
|
|
},
|
|
"expansion_details": []
|
|
}
|
|
|
|
def analyze_expansion_coverage(
|
|
self,
|
|
original_keywords: List[str],
|
|
expanded_keywords: List[str]
|
|
) -> Dict:
|
|
"""
|
|
分析扩展的覆盖面
|
|
|
|
Args:
|
|
original_keywords: 原始关键词列表
|
|
expanded_keywords: 扩展后的关键词列表
|
|
|
|
Returns:
|
|
覆盖面分析结果
|
|
"""
|
|
if not original_keywords or not expanded_keywords:
|
|
return {
|
|
"coverage_ratio": 0.0,
|
|
"expansion_ratio": 0.0,
|
|
"unique_keywords": 0,
|
|
"categories": {}
|
|
}
|
|
|
|
# 计算扩展比例
|
|
expansion_ratio = len(expanded_keywords) / len(original_keywords) if original_keywords else 0
|
|
|
|
# 分析关键词类别（简单分类）
|
|
categories = {
|
|
"question": len([k for k in expanded_keywords if any(m in k for m in ["哪个", "如何", "怎么", "什么"])]),
|
|
"scenario": len([k for k in expanded_keywords if any(m in k for m in ["适合", "适用于", "针对"])]),
|
|
"comparison": len([k for k in expanded_keywords if any(m in k for m in ["对比", "比较", "区别"])]),
|
|
"feature": len([k for k in expanded_keywords if any(m in k for m in ["功能", "特点", "优势"])]),
|
|
"other": 0
|
|
}
|
|
categories["other"] = len(expanded_keywords) - sum(categories.values())
|
|
|
|
return {
|
|
"coverage_ratio": min(expansion_ratio, 5.0), # 最多5倍扩展
|
|
"expansion_ratio": expansion_ratio,
|
|
"unique_keywords": len(set(expanded_keywords)),
|
|
"categories": categories
|
|
}
|
|
|
|
def merge_keywords(
|
|
self,
|
|
original_keywords: List[str],
|
|
expanded_keywords: List[str],
|
|
merge_strategy: str = "append"
|
|
) -> List[str]:
|
|
"""
|
|
合并原始关键词和扩展关键词
|
|
|
|
Args:
|
|
original_keywords: 原始关键词列表
|
|
expanded_keywords: 扩展关键词列表
|
|
merge_strategy: 合并策略
|
|
- "append": 追加扩展关键词到原始列表
|
|
- "replace": 用扩展关键词替换原始列表
|
|
- "interleave": 交替插入
|
|
|
|
Returns:
|
|
合并后的关键词列表
|
|
"""
|
|
if merge_strategy == "replace":
|
|
return expanded_keywords
|
|
elif merge_strategy == "interleave":
|
|
# 交替插入
|
|
merged = []
|
|
max_len = max(len(original_keywords), len(expanded_keywords))
|
|
for i in range(max_len):
|
|
if i < len(original_keywords):
|
|
merged.append(original_keywords[i])
|
|
if i < len(expanded_keywords):
|
|
merged.append(expanded_keywords[i])
|
|
return merged
|
|
else: # append
|
|
return original_keywords + expanded_keywords
|