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# -*- coding: utf-8 -*-
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import re
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import time
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import hashlib
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from typing import Iterator, Tuple
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from openai import OpenAI
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from openai.types.chat import ChatCompletionChunk
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from Config import *
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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.client = OpenAI(api_key=MODEL_API_KEY, base_url=MODEL_API_URL)
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self.question_id = self._generate_question_id()
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def _generate_question_id(self) -> str:
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"""生成题目唯一标识符"""
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return hashlib.md5(self.content.encode()).hexdigest()[:8]
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def _generate_stream(self) -> Iterator[ChatCompletionChunk]:
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"""动态化提示词版本"""
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system_prompt = f'''请根据题目内容生成Neo4j Cypher语句，严格遵循以下规则：
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# 节点创建规范
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1. 知识点节点：
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- 标签: KnowledgePoint
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- 必须属性:
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* id: "KP_" + 知识点名称的MD5前6位（示例：name="分数运算" → id="KP_ae3b8c"）
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* name: 知识点名称（从题目内容中提取）
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* level: 学段（小学/初中/高中）
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2. 能力点节点：
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- 标签: AbilityPoint
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- 必须属性:
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* id: "AB_" + 能力名称的MD5前6位
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* name: 能力点名称
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* category: 能力类型（计算/推理/空间想象等）
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3. 题目节点：
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- 标签: Question
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- 必须属性:
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* id: "{self.question_id}"（已根据题目内容生成）
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* content: 题目文本摘要（50字内）
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* difficulty: 难度系数（1-5整数）
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# 关系规则
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1. 题目与知识点关系：
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(q:Question)-[:TESTS_KNOWLEDGE]->(kp:KnowledgePoint)
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需设置权重属性 weight（0.1-1.0）
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2. 题目与能力点关系：
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(q:Question)-[:REQUIRES_ABILITY]->(ab:AbilityPoint)
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需设置权重属性 weight
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# 生成步骤
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1. 先创建约束（必须）：
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CREATE CONSTRAINT IF NOT EXISTS FOR (kp:KnowledgePoint) REQUIRE kp.id IS UNIQUE;
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CREATE CONSTRAINT IF NOT EXISTS FOR (ab:AbilityPoint) REQUIRE ab.id IS UNIQUE;
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2. 使用MERGE创建节点（禁止使用CREATE）
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3. 最后创建关系（需先MATCH已存在节点）
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# 当前题目信息
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- 生成的问题ID: {self.question_id}
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- 题目内容: "{self.content[:50]}..."（已截断）'''
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return self.client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": self.content}
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],
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stream=True,
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timeout=300
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)
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def _extract_cypher(self, content: str) -> str:
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"""增强的Cypher提取（处理多代码块）"""
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cypher_blocks = []
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# 匹配所有cypher代码块（包含语言声明）
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pattern = r"```(?:cypher)?\n(.*?)```"
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for block in re.findall(pattern, content, re.DOTALL):
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# 清理注释和空行
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cleaned = [
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line.split('//')[0].strip()
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for line in block.split('\n')
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if line.strip() and not line.strip().startswith('//')
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]
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if cleaned:
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cypher_blocks.append('\n'.join(cleaned))
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return ';\n\n'.join(cypher_blocks)
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def run(self) -> Tuple[bool, str, str]:
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"""执行生成流程（确保所有路径都有返回值）"""
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start_time = time.time()
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spinner = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
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content_buffer = []
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try:
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print(f"🚀 开始生成知识点和能力点的总结和插入语句")
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stream = self._generate_stream()
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# 添加流数据检查
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if not stream:
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print("\n❌ 生成失败：无法获取生成流")
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return False, "生成流获取失败", ""
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for idx, chunk in enumerate(stream):
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print(f"\r{spinner[idx % 10]} 生成中（{int(time.time() - start_time)}秒）", end="")
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if chunk.choices and chunk.choices[0].delta.content:
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content_chunk = chunk.choices[0].delta.content
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content_buffer.append(content_chunk)
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if len(content_buffer) == 1:
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print("\n\n📝 内容生成开始：")
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print(content_chunk, end="", flush=True)
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# 确保最终返回
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if content_buffer:
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full_content = ''.join(content_buffer)
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cypher_script = self._extract_cypher(full_content)
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print(f"\n\n✅ 生成成功！耗时 {int(time.time() - start_time)}秒")
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print("\n================ 完整结果 ================")
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print(full_content)
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print("\n================ Cypher语句 ===============")
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print(cypher_script if cypher_script else "未检测到Cypher语句")
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print("==========================================")
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return True, full_content, cypher_script
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# 添加空内容处理
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print("\n⚠️ 生成完成但未获取到有效内容")
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return False, "空内容", ""
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except Exception as e:
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print(f"\n\n❌ 生成失败：{str(e)}")
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return False, str(e), ""
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if __name__ == '__main__':
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shiti_content = '''
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下面是一道小学三年级的数学题目，巧求周长：
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把7个完全相同的小长方形拼成如图的样子，已知每个小长方形的长是10厘米，则拼成的大长方形的周长是多少厘米？
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'''
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kg = KnowledgeGraph(shiti_content)
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success, result, cypher = kg.run()
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if success and cypher:
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with open("knowledge_graph.cypher", "w", encoding="utf-8") as f:
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f.write(cypher)
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print(f"\nCypher语句已保存至 knowledge_graph.cypher (题目ID: {kg.question_id})") |
