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# -*- coding: utf-8 -*-
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"""
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数学题目分析系统 v6.3（稳定流式处理版）
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"""
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import re
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import json
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import hashlib
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import requests
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from py2neo import Graph
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from typing import Dict, List
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from Config import NEO4J_URI, NEO4J_AUTH, MODEL_API_URL, MODEL_API_KEY, MODEL_NAME
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# ================== 配置类 ==================
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class Config:
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NEO4J_URI = NEO4J_URI
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NEO4J_AUTH = NEO4J_AUTH
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MODEL_API_URL = MODEL_API_URL
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MODEL_API_KEY = MODEL_API_KEY
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MODEL_NAME = MODEL_NAME
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MAX_CONTENT_LENGTH = 500
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STREAM_TIMEOUT = 30
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# ================== 知识图谱管理 ==================
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class KnowledgeManager:
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def __init__(self):
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self.graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH)
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self._verify_connection()
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self.knowledge_map = self._load_knowledge()
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self.literacy_map = self._load_literacy()
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print("✅ 知识库加载完成")
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print(f"有效知识点({len(self.knowledge_map)}个): {list(self.knowledge_map.keys())[:3]}...")
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print(f"有效素养点({len(self.literacy_map)}个): {list(self.literacy_map.keys())[:3]}...")
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def _verify_connection(self):
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"""安全连接验证"""
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try:
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self.graph.run("RETURN 1 AS test")
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print("✅ Neo4j连接验证成功")
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except Exception as e:
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print(f"❌ Neo4j连接失败: {str(e)}")
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raise
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def _load_knowledge(self) -> Dict[str, str]:
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"""安全加载知识点"""
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records = self.graph.run("MATCH (n:KnowledgePoint) WHERE n.name IS NOT NULL RETURN n.id, n.name").data()
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return {rec['n.name'].strip(): rec['n.id'] for rec in records if rec['n.name'] and rec['n.name'].strip()}
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def _load_literacy(self) -> Dict[str, str]:
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"""安全加载素养点"""
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records = self.graph.run("MATCH (n:LiteracyNode) WHERE n.title IS NOT NULL RETURN n.value, n.title").data()
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return {rec['n.title'].strip(): rec['n.value'] for rec in records if rec['n.title'] and rec['n.title'].strip()}
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def store_analysis(self, question_id: str, content: str,
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knowledge: List[str], literacy: List[str]):
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"""增强存储方法"""
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tx = self.graph.begin()
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try:
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# 创建/更新题目节点
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tx.run("""
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MERGE (q:Question {id: $id})
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SET q.content = $content, q.updateTime = timestamp()
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""", {"id": question_id, "content": content})
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# 关联知识点
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valid_kp = []
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for name in knowledge:
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clean_name = name.strip()
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if kp_id := self.knowledge_map.get(clean_name):
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tx.run("""
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MATCH (q:Question {id: $qid}), (kp:KnowledgePoint {id: $kpid})
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MERGE (q)-[r:REQUIRES_KNOWLEDGE]->(kp)
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SET r.lastUsed = timestamp()
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""", {"qid": question_id, "kpid": kp_id})
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valid_kp.append(clean_name)
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# 关联素养点
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valid_lp = []
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for title in literacy:
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clean_title = title.strip()
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if lit_id := self.literacy_map.get(clean_title):
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tx.run("""
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MATCH (q:Question {id: $qid}), (lp:LiteracyNode {value: $lpid})
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MERGE (q)-[r:DEVELOPS_LITERACY]->(lp)
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SET r.lastUsed = timestamp()
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""", {"qid": question_id, "lpid": lit_id})
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valid_lp.append(clean_title)
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self.graph.commit(tx)
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print(f"✅ 存储成功 - 知识点: {valid_kp}, 素养点: {valid_lp}")
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except Exception as e:
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self.graph.rollback(tx)
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print(f"❌ 存储失败: {str(e)}")
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# ================== 大模型客户端 ==================
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class StreamLLMClient:
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def __init__(self, kg: KnowledgeManager):
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self.kg = kg
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self.base_url = Config.MODEL_API_URL
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self.headers = {
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"Authorization": f"Bearer {Config.MODEL_API_KEY}",
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"Content-Type": "application/json",
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"Accept": "application/json"
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}
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def analyze_problem(self, content: str) -> dict:
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"""增强的流式分析"""
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try:
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prompt = self._build_prompt(content)
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response = self._send_request(prompt)
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return self._process_stream(response)
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except Exception as e:
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print(f"🔴 分析失败: {str(e)}")
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return {}
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def _build_prompt(self, content: str) -> str:
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"""构建精准提示词"""
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return f"""请严格按以下要求分析数学题目：
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1. 知识点必须完全匹配以下列表中的名称（不要新增或修改）：
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{self.kg.knowledge_map.keys()}
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2. 素养点必须完全匹配以下列表中的名称：
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{self.kg.literacy_map.keys()}
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3. 返回严格JSON格式：
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{{
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"problem_types": ["题型"],
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"knowledge_points": ["匹配的知识点"],
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"literacy_points": ["匹配的素养点"]
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}}
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题目内容：{content}"""
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def _send_request(self, prompt: str):
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"""发送API请求"""
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return requests.post(
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f"{self.base_url}/chat/completions",
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headers=self.headers,
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json={
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"model": Config.MODEL_NAME,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.1,
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"stream": True
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},
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timeout=Config.STREAM_TIMEOUT,
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stream=True
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)
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def _process_stream(self, response) -> dict:
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"""可靠的流式处理"""
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full_content = ""
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try:
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for chunk in response.iter_lines():
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if chunk:
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decoded = chunk.decode('utf-8').strip()
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if decoded.startswith('data:'):
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json_str = decoded[5:].strip()
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if json_str == "[DONE]":
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break
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try:
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data = json.loads(json_str)
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if content := data['choices'][0]['delta'].get('content'):
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full_content += content
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except:
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continue
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# 调试日志
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print(f"原始响应内容：\n{full_content}")
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# 提取有效JSON
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json_str = re.search(r'\{[\s\S]*\}', full_content).group()
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return json.loads(json_str)
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except json.JSONDecodeError:
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print(f"⚠️ JSON解析失败，原始内容：{full_content}")
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return {}
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except Exception as e:
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print(f"流处理异常：{str(e)}")
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return {}
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# ================== 核心引擎 ==================
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class ProblemAnalyzer:
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def __init__(self, content: str):
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self.original = content
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self.content = self._preprocess(content)
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self.question_id = hashlib.sha256(content.encode()).hexdigest()[:12]
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self.kg = KnowledgeManager()
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self.llm = StreamLLMClient(self.kg)
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def _preprocess(self, text: str) -> str:
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"""文本预处理"""
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return re.sub(r'[^\w\u4e00-\u9fa5]', '', text)[:Config.MAX_CONTENT_LENGTH]
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def execute(self):
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"""执行分析流程"""
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print(f"\n🔍 分析题目: {self.original[:50]}...")
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analysis = self.llm.analyze_problem(self.original)
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if not analysis:
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print("⚠️ 大模型分析失败")
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return
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print("\n📊 分析结果:")
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print(f" 题型: {analysis.get('problem_types', [])}")
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print(f" 知识点: {analysis.get('knowledge_points', [])}")
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print(f" 素养点: {analysis.get('literacy_points', [])}")
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self.kg.store_analysis(
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question_id=self.question_id,
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content=self.content,
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knowledge=analysis.get('knowledge_points', []),
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literacy=analysis.get('literacy_points', [])
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)
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# ================== 查询接口 ==================
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def query_question(question_id: str):
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try:
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graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH)
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result = graph.run("""
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MATCH (q:Question {id: $id})
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OPTIONAL MATCH (q)-[:REQUIRES_KNOWLEDGE]->(kp)
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OPTIONAL MATCH (q)-[:DEVELOPS_LITERACY]->(lp)
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RETURN q.content AS content,
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collect(kp.name) AS knowledge,
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collect(lp.title) AS literacy
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""", id=question_id).data()
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if result:
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data = result[0]
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print(f"\n🔍 查询结果（ID: {question_id}）")
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print(f"内容: {data['content']}")
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print(f"知识点: {data['knowledge']}")
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print(f"素养点: {data['literacy']}")
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else:
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print("未找到相关题目")
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except Exception as e:
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print(f"查询错误: {str(e)}")
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# ================== 测试执行 ==================
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if __name__ == '__main__':
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test_cases = [
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# 基础运算类
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"小明用50元买了3本笔记本，每本8元，还剩多少钱？", # 知识点：四则运算应用 素养点：数学运算能力
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# 几何问题
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"一个长方形花坛长5米，宽3米，四周铺1米宽的小路，求小路面积？", # 知识点：面积计算 素养点：空间观念
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# 统计概率
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"某班30人，数学成绩90分以上占1/5，80-89分占1/3，求80分以下人数？", # 知识点：分数运算 素养点：数据分析
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# 典型应用题
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"甲乙两车相距300公里，甲车速度60km/h，乙车40km/h，几小时后相遇？", # 知识点：相遇问题 素养点：数学建模
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# 方程应用
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"鸡兔同笼，头共10个，脚共28只，问鸡兔各多少？", # 知识点：二元一次方程 素养点：代数思维
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# 比例问题
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"配制农药药液，药粉和水的比例是3:100，现有水500kg，需要多少药粉？", # 知识点：比例应用 素养点：量化分析
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# 函数图像
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"某物体做匀加速运动，初速度2m/s，加速度0.5m/s²，写出速度v与时间t的关系式", # 知识点：一次函数 素养点：数形结合
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# 立体几何
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"圆柱形水桶底面半径0.4米，高1.2米，求它的容积（π取3.14）", # 知识点：圆柱体积 素养点：空间想象
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# 概率统计
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"掷两个骰子，点数和为7的概率是多少？", # 知识点：概率计算 素养点：数据预测
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# 工程问题
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"甲队单独完成工程需20天，乙队需30天，两队合作需要多少天？", # 知识点：工程问题 素养点：模型构建
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# 最优化问题
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"用20米篱笆围矩形菜地，怎样围面积最大？最大面积是多少？", # 知识点：二次函数 素养点：优化思想
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# 利率问题
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"本金10000元，年利率3.5%，存3年到期本息合计多少？", # 知识点：单利计算 素养点：金融素养
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# 数列问题
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"等差数列首项5，公差3，求第10项的值", # 知识点：等差数列 素养点：模式识别
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# 测量问题
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"在比例尺1:5000的地图上，2cm线段代表实际距离多少米？", # 知识点：比例尺应用 素养点：单位换算
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# 逻辑推理
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"A说B在说谎，B说C在说谎，C说AB都在说谎，问谁在说真话？" # 知识点：逻辑推理 素养点：批判性思维
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]
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for q in test_cases:
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print("\n" + "=" * 60)
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analyzer = ProblemAnalyzer(q)
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analyzer.execute() |
