QingLong/AI/Neo4j/N3_InputShiTi.py

# -*- coding: utf-8 -*-
"""
数学题目分析系统 v6.1（严格匹配版）
功能特性：
1. 纯大模型分析
2. 流式响应处理
3. 严格匹配已有节点
4. 不创建新知识点/素养点
"""

import re
import json
import hashlib
from typing import Dict, List
import requests
from py2neo import Graph
from Config import *


# ================== 配置区 ==================
class Config:
    # Neo4j配置
    NEO4J_URI = NEO4J_URI
    NEO4J_AUTH = NEO4J_AUTH

    # 大模型配置（示例为阿里云配置）
    MODEL_API_URL = MODEL_API_URL
    MODEL_API_KEY = MODEL_API_KEY
    MODEL_NAME = MODEL_NAME

    # 超时配置
    STREAM_TIMEOUT = 30  # 流式响应总超时
    CHUNK_TIMEOUT = 5  # 单次数据块等待超时

    # 系统参数
    MAX_CONTENT_LENGTH = 500


# ================== 流式大模型客户端 ==================
class StreamLLMClient:
    """支持流式响应的大模型客户端"""

    def __init__(self):
        self.base_url = Config.MODEL_API_URL
        self.headers = {
            "Authorization": f"Bearer {Config.MODEL_API_KEY}",
            "Content-Type": "application/json"
        }

    def analyze_problem(self, content: str) -> dict:
        """流式响应分析"""
        try:
            response = requests.post(
                f"{self.base_url}/chat/completions",
                headers=self.headers,
                json={
                    "model": Config.MODEL_NAME,
                    "messages": [{
                        "role": "user",
                        "content": f"""请严格按JSON格式分析数学题目：
{{
    "problem_types": ["题型列表"],
    "knowledge_points": ["知识点名称（必须与数据库完全一致）"],
    "literacy_points": ["素养点名称（必须与数据库完全一致）"]
}}
题目：{content}"""
                    }],
                    "temperature": 0.2,
                    "stream": True
                },
                timeout=Config.STREAM_TIMEOUT,
                stream=True
            )
            response.raise_for_status()

            return self._process_stream(response)

        except requests.exceptions.RequestException as e:
            print(f"🌐 网络错误: {str(e)}")
            return {}
        except Exception as e:
            print(f"🔴 解析失败: {str(e)}")
            return {}

    def _process_stream(self, response) -> dict:
        """处理流式响应"""
        full_content = ""
        for chunk in response.iter_lines():
            if chunk:
                decoded_chunk = chunk.decode('utf-8')
                if decoded_chunk.startswith("data:"):
                    try:
                        chunk_data = json.loads(decoded_chunk[5:])
                        content = chunk_data['choices'][0]['delta'].get('content', '')
                        full_content += content
                    except:
                        continue

        try:
            json_str = re.search(r'\{.*\}', full_content, re.DOTALL).group()
            return json.loads(json_str)
        except:
            print("🔴 无法解析大模型输出")
            return {}


# ================== 知识图谱管理 ==================
class KnowledgeManager:
    """严格匹配型知识图谱管理器"""

    def __init__(self):
        self.graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH)
        self.knowledge_map = self._load_knowledge()
        self.literacy_map = self._load_literacy()

    def _load_knowledge(self) -> Dict[str, str]:
        """加载知识点映射（id -> name）"""
        return {rec['n.id']: rec['n.name']
                for rec in self.graph.run("MATCH (n:KnowledgePoint) RETURN n.id, n.name")}

    def _load_literacy(self) -> Dict[str, str]:
        """加载素养点映射（value -> title）"""
        return {rec['n.value']: rec['n.title']
                for rec in self.graph.run("MATCH (n:LiteracyNode) RETURN n.value, n.title")}

    def store_analysis(self, question_id: str, content: str,
                       knowledge: List[str], literacy: List[str]):
        """使用参数化查询解决转义问题"""
        tx = self.graph.begin()
        try:
            # 使用参数化查询避免转义问题
            tx.run(
                "MERGE (q:Question {id: $question_id}) "
                "SET q.content = $content",
                {
                    "question_id": question_id,
                    "content": content
                }
            )

            # 关联知识点（参数化版本）
            for kp_name in knowledge:
                if kp_id := next((k for k, v in self.knowledge_map.items() if v == kp_name), None):
                    tx.run(
                        "MATCH (kp:KnowledgePoint {id: $kp_id}) "
                        "MATCH (q:Question {id: $qid}) "
                        "MERGE (q)-[:REQUIRES_KNOWLEDGE]->(kp)",
                        {"kp_id": kp_id, "qid": question_id}
                    )

            # 关联素养点（参数化版本）
            for lit_name in literacy:
                if lit_id := next((k for k, v in self.literacy_map.items() if v == lit_name), None):
                    tx.run(
                        "MATCH (lp:LiteracyNode {value: $lit_id}) "
                        "MATCH (q:Question {id: $qid}) "
                        "MERGE (q)-[:DEVELOPS_LITERACY]->(lp)",
                        {"lit_id": lit_id, "qid": question_id}
                    )

            self.graph.commit(tx)
            print("✅ 数据存储成功")
        except Exception as e:
            self.graph.rollback(tx)
            print(f"❌ 存储失败: {str(e)}")


# ================== 核心分析引擎 ==================
class ProblemAnalyzer:
    """纯大模型分析引擎"""

    def __init__(self, content: str):
        self.original = content
        self.content = self._preprocess(content)
        self.question_id = hashlib.sha256(content.encode()).hexdigest()[:12]
        self.kg = KnowledgeManager()
        self.llm = StreamLLMClient()

    def _preprocess(self, text: str) -> str:
        """文本预处理"""
        return re.sub(r'[^\w\u4e00-\u9fa5]', '', text)[:Config.MAX_CONTENT_LENGTH]

    def analyze(self) -> dict:
        """纯大模型分析"""
        result = self.llm.analyze_problem(self.original)
        return {
            "problem_id": self.question_id,
            "problem_types": result.get('problem_types', [])[:3],
            "knowledge_points": result.get('knowledge_points', [])[:2],
            "literacy_points": result.get('literacy_points', [])[:2]
        }

    def execute(self):
        """执行完整流程"""
        print(f"\n🔍 开始分析题目：{self.original[:50]}...")

        analysis = self.analyze()

        print("\n📊 大模型分析报告：")
        print(f"  题型识别：{analysis.get('problem_types', [])}")
        print(f"  推荐知识点：{analysis.get('knowledge_points', [])}")
        print(f"  关联素养点：{analysis.get('literacy_points', [])}")

        # 存储到知识图谱
        self.kg.store_analysis(
            question_id=analysis['problem_id'],
            content=self.content,
            knowledge=analysis.get('knowledge_points', []),
            literacy=analysis.get('literacy_points', [])
        )


# ================== 测试用例 ==================
if __name__ == '__main__':
    test_cases = [
        "小明用50元买了3本笔记本，每本8元，还剩多少钱？",
        "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？"
    ]

    for question in test_cases:
        print("\n" + "=" * 80)
        print(f"📚 处理题目：{question}")
        analyzer = ProblemAnalyzer(question)
        analyzer.execute()

'''
// 查询题目关联信息
MATCH (q:Question {id: '6fff79108736'})
OPTIONAL MATCH (q)-[:REQUIRES_KNOWLEDGE]->(kp:KnowledgePoint)
OPTIONAL MATCH (q)-[:DEVELOPS_LITERACY]->(lp:LiteracyNode)
RETURN 
  q.content AS 题目内容,
  COLLECT(DISTINCT kp.name) AS 关联知识点,
  COLLECT(DISTINCT lp.title) AS 关联素养点
'''