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@ -5,283 +5,145 @@ from py2neo import Graph
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from openai import OpenAI
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from Config import *
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# 切割试题
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def split_questions(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 使用正则表达式匹配题目块（包含答案）
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pattern = r'(\d+\.\s+【.*?】.*?(?=\n\d+\.|\Z))'
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questions = re.findall(pattern, content, re.DOTALL)
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# 清洗每个题目块的空白字符
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cleaned_questions = [q.strip() for q in questions]
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return cleaned_questions[:10] # 确保只返回前10题
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class KnowledgeGraph:
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def __init__(self, content: str):
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self.content = content
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self.question_id = hashlib.md5(content.encode()).hexdigest()[:8]
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self.graph = Graph(NEO4J_URI, auth=NEO4J_AUTH)
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# 双数据源加载
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self.knowledge_points = self._get_knowledge_points()
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self.client = OpenAI(api_key=MODEL_API_KEY, base_url=MODEL_API_URL)
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self.literacy_points = self._get_literacy_points()
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print(f"已加载知识点：{len(self.knowledge_points)}个，素养点：{len(self.literacy_points)}个")
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# self.knowledge_points = self._get_knowledge_points()
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print("加载知识点数量：", len(self.knowledge_points)) # 添加调试信息
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self.client = OpenAI(api_key=MODEL_API_KEY, base_url=MODEL_API_URL)
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def _get_knowledge_points(self) -> dict:
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"""保持ID原始大小写"""
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try:
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# 移除lower()转换
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return {row['n.id']: row['n.name'] # 直接使用原始ID
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return {row['n.id']: row['n.name']
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for row in self.graph.run("MATCH (n:KnowledgePoint) RETURN n.id, n.name")}
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except Exception as e:
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print(f"获取知识点失败：", str(e))
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print(f"知识点加载失败：{str(e)}")
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return {}
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def _make_prompt(self) -> str:
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"""生成知识点识别专用提示词"""
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example_ids = list(self.knowledge_points.keys())[:5]
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example_names = [self.knowledge_points[k] for k in example_ids]
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return f"""你是一个数学专家，请分析题目考查的知识点，严格：
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1. 只使用以下存在的知识点（格式：ID:名称）：
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{", ".join([f"{k}:{v}" for k, v in zip(example_ids, example_names)])}...
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共{len(self.knowledge_points)}个可用知识点
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2. 题目可能包含多个知识点，让仔细检查。
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3. 按此格式生成Cypher：
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MERGE (q:Question {{id: "{self.question_id}"}})
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SET q.content = "题目内容"
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WITH q
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MATCH (kp:KnowledgePoint {{id: "知识点ID"}})
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MERGE (q)-[:TESTS_KNOWLEDGE]->(kp)"""
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def _clean_cypher(self, code: str) -> str:
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"""完整Cypher清洗逻辑（增强版）"""
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safe = []
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content_keywords = {
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'行程问题': ['相遇', '相向而行', '追及', '速度', '路程'],
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'几何问题': ['面积', '体积', '周长', '三角形', '长方体'],
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'分数运算': ['分数', '百分比', '%', '分之']
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}
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def _get_literacy_points(self) -> dict:
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try:
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# 提取代码块
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cypher_block = re.findall(r"```(?:cypher)?\n(.*?)```", code, re.DOTALL)
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if not cypher_block:
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print("未检测到Cypher代码块")
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return ""
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# 预处理配置
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valid_ids_upper = [k.upper() for k in self.knowledge_points.keys()]
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detected_types = []
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raw_lines = cypher_block[0].split('\n')
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has_question = False
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# === 第一步：基础清洗 ===
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for line in raw_lines:
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# 清理注释和空白
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clean_line = line.split('//')[0].strip()
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if not clean_line:
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continue
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# 阻止CREATE操作
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if 'CREATE' in clean_line.upper():
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print(f"阻止CREATE操作: {clean_line}")
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continue
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# 强制Question节点在最前面
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if 'MERGE (q:Question' in clean_line:
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has_question = True
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safe.insert(0, clean_line)
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continue
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safe.append(clean_line)
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# === 第二步：检测题目类型 ===
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for pattern, keys in content_keywords.items():
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if any(k in self.content for k in keys):
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detected_types.append(pattern)
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print(f"检测到题目类型: {pattern}")
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# === 第三步：处理知识点ID ===
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knowledge_lines = []
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for line in safe.copy():
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if 'MATCH (kp:KnowledgePoint' in line:
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# 安全提取ID
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match = re.search(r"id: ['\"](.*?)['\"]", line)
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if not match:
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print(f"无效的MATCH语句: {line}")
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safe.remove(line)
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continue
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return {row['n.value']: row['n.title']
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for row in self.graph.run("MATCH (n:LiteracyNode) RETURN n.value, n.title")}
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except Exception as e:
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print(f"素养点加载失败：{str(e)}")
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return {}
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original_id = match.group(1)
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upper_id = original_id.upper()
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def _make_prompt(self) -> str:
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kp_samples = "\n".join([f"• {k}: {v}" for k, v in list(self.knowledge_points.items())[:3]])
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lp_samples = "\n".join([f"• {k}: {v}" for k, v in list(self.literacy_points.items())[:3]])
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# 验证ID存在性
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if upper_id not in valid_ids_upper:
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print(f"忽略无效知识点ID: {original_id}")
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safe.remove(line)
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continue
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return f"""请分析题目考查的知识点和核心素养：
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# 替换为正确的大写ID
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new_line = line.replace(original_id, upper_id)
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safe[safe.index(line)] = new_line
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knowledge_lines.append(new_line)
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可用知识点（ID:名称）：
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{kp_samples}
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...共{len(self.knowledge_points)}个知识点
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# === 第四步：自动补充知识点 ===
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for dtype in detected_types:
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# 安全获取已关联知识点ID
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extracted_ids = []
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for line in knowledge_lines:
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try:
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match = re.search(r"id: ['\"](.*?)['\"]", line)
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if match:
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kp_id = match.group(1).upper()
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extracted_ids.append(kp_id)
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except AttributeError:
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continue
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可用素养点（ID:名称）：
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{lp_samples}
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...共{len(self.literacy_points)}个素养点
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# 获取对应的知识点名称（确保为字符串）
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type_exists = any(
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dtype in str(self.knowledge_points.get(kp_id, ''))
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for kp_id in extracted_ids
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)
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生成要求：
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1. 必须使用上述ID
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2. 按以下格式生成Cypher代码：
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if not type_exists:
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# 查找匹配的知识点（添加空值过滤）
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candidates = [
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(k, v) for k, v in self.knowledge_points.items()
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if v and dtype in str(v) # 确保v是字符串
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and k.upper() in valid_ids_upper
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]
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MERGE (q:Question {{id: "{self.question_id}"}})
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SET q.content = "题目内容"
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WITH q
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MATCH (kp:KnowledgePoint {{id: "知识点ID"}})
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MERGE (q)-[:TESTS_KNOWLEDGE]->(kp)
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WITH q
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MATCH (lp:LiteracyNode {{value: "素养点ID"}})
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MERGE (q)-[:RELATES_TO_LITERACY]->(lp)"""
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# 按名称匹配度排序
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candidates.sort(key=lambda x: (
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dtype in x[1], # 优先完全匹配
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-len(x[1]) # 次优先名称长度短的
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), reverse=True)
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def _clean_cypher(self, code: str) -> str:
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valid_kp_ids = [k.upper() for k in self.knowledge_points.keys()]
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valid_lp_ids = [k.upper() for k in self.literacy_points.keys()]
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if candidates:
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target_id, target_name = candidates[0]
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print(f"补充知识点: {target_id} - {target_name}")
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safe.extend([
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"WITH q",
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f"MATCH (kp:KnowledgePoint {{id: \"{target_id.upper()}\"}})",
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"MERGE (q)-[:TESTS_KNOWLEDGE]->(kp)"
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])
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else:
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print(f"未找到匹配的{dtype}知识点")
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cleaned = []
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lines = [line.strip() for line in code.split('\n') if line.strip()]
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# === 第五步：语法修正 ===
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# 确保Question节点后紧跟WITH
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if has_question:
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for i, line in enumerate(safe):
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if 'MERGE (q:Question' in line:
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# 检查下一条是否是WITH
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if i + 1 >= len(safe) or not safe[i + 1].startswith('WITH'):
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safe.insert(i + 1, "WITH q")
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break
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for line in lines:
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# 处理知识点匹配
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if 'MATCH (kp:KnowledgePoint' in line:
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if match := re.search(r'id: ["\'](.*?)["\']', line):
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kp_id = match.group(1).upper()
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if kp_id in valid_kp_ids:
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cleaned.append(line.replace(match.group(1), kp_id))
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# 移除重复的WITH语句
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final_safe = []
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prev_was_with = False
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for line in safe:
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if line.startswith('WITH'):
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if not prev_was_with:
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final_safe.append(line)
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prev_was_with = True
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else:
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final_safe.append(line)
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prev_was_with = False
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# 处理素养点匹配
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elif 'MATCH (lp:LiteracyNode' in line:
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if match := re.search(r'value: ["\'](.*?)["\']', line):
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lp_id = match.group(1).upper()
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if lp_id in valid_lp_ids:
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cleaned.append(line.replace(match.group(1), lp_id))
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return '\n'.join(final_safe)
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# 保留其他合法语句
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elif line.startswith(('MERGE', 'WITH', 'SET')):
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cleaned.append(line)
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except Exception as e:
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print(f"清洗Cypher时发生错误: {str(e)}")
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return ""
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return '\n'.join(cleaned)
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def run(self) -> str:
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"""执行知识点关联流程"""
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try:
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response = self.client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{
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"role": "system",
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"content": self._make_prompt()
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},
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{
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"role": "user",
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"content": f"题目内容：{self.content}\n请分析考查的知识点，只返回Cypher代码"
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}
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{"role": "system", "content": self._make_prompt()},
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{"role": "user", "content": f"题目内容：{self.content}"}
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]
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)
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raw_cypher = response.choices[0].message.content
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cleaned_cypher = self._clean_cypher(raw_cypher)
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if cleaned_cypher:
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print("验证通过的Cypher：\n", cleaned_cypher)
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return cleaned_cypher
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return ""
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return self._clean_cypher(response.choices[0].message.content)
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except Exception as e:
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print("知识点分析失败：", str(e))
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print(f"分析失败：{str(e)}")
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return ""
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def query_related_knowledge(self):
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"""查询题目关联的知识点"""
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def query_relations(self):
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cypher = f"""
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MATCH (q:Question {{id: "{self.question_id}"}})-[:TESTS_KNOWLEDGE]->(kp)
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RETURN kp.id AS knowledge_id, kp.name AS knowledge_name
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"""
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try:
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result = self.graph.run(cypher).data()
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if result:
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print(f"题目关联的知识点（{self.question_id}）：")
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for row in result:
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print(f"- {row['knowledge_name']} (ID: {row['knowledge_id']})")
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else:
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print("该题目尚未关联知识点")
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return result
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except Exception as e:
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print("查询失败：", str(e))
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return []
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# 切割试题
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def split_questions(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 使用正则表达式匹配题目块（包含答案）
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pattern = r'(\d+\.\s+【.*?】.*?(?=\n\d+\.|\Z))'
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questions = re.findall(pattern, content, re.DOTALL)
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# 清洗每个题目块的空白字符
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cleaned_questions = [q.strip() for q in questions]
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return cleaned_questions[:10] # 确保只返回前10题
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# 测试用例
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MATCH (q:Question {{id: "{self.question_id}"}})
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OPTIONAL MATCH (q)-[:TESTS_KNOWLEDGE]->(kp)
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OPTIONAL MATCH (q)-[:RELATES_TO_LITERACY]->(lp)
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RETURN
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kp.id AS knowledge_id,
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kp.name AS knowledge_name,
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lp.value AS literacy_id,
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lp.title AS literacy_title"""
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return self.graph.run(cypher).data()
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# 使用示例
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if __name__ == '__main__':
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# 分段读入题目
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question_blocks = split_questions('Backup/ShiTi.md')
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question_blocks = split_questions('ShiTi.md')
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# 验证分割结果
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for i, block in enumerate(question_blocks, 1):
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print(f"第{i}题块：")
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print("-" * 50)
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kg = KnowledgeGraph(block)
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cypher = kg.run()
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if cypher:
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# 插入数据
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kg.graph.run(cypher)
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print("执行成功！关联知识点：")
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kg.query_related_knowledge() # 新增查询
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else:
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print("未生成有效Cypher")
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'''
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# 基本可视化查询
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MATCH path=(q:Question {id: "07ece550"})-[:TESTS_KNOWLEDGE]->(kp)
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RETURN path
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# 带样式的可视化
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MATCH (q:Question {id: "07ece550"})-[:TESTS_KNOWLEDGE]->(kp)
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RETURN q, kp
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// 在浏览器中点击左侧样式图标，设置：
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// - Question节点颜色：橙色
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// - KnowledgePoint节点颜色：蓝色
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// - 关系线宽：3px
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'''
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if cypher := kg.run():
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print("生成的Cypher:\n", cypher)
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kg.graph.run(cypher)
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print("关联结果：")
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for record in kg.query_relations():
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print(f"知识点：{record['knowledge_name']} ({record['knowledge_id']})")
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print(f"素养点：{record['literacy_title']} ({record['literacy_id']})")
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