diff --git a/dsRag/Config/Config.py b/dsRag/Config/Config.py index ec1827fe..39424426 100644 --- a/dsRag/Config/Config.py +++ b/dsRag/Config/Config.py @@ -7,7 +7,7 @@ MYSQL_DB_NAME = "base_db" # Elasticsearch配置 ES_CONFIG = { - #"hosts": "https://10.10.14.206:9200", + # "hosts": "https://10.10.14.206:9200", "hosts": "https://127.0.0.1:9200", "basic_auth": ("elastic", "jv9h8uwRrRxmDi1dq6u8"), "verify_certs": False, @@ -27,3 +27,7 @@ JIEBA_CUSTOM_WORDS = ['文言虚词', '花呗'] MODEL_PATH = "D:/Tencent_AILab_ChineseEmbedding/Tencent_AILab_ChineseEmbedding.txt" # 加载词向量模型时限制的词汇数量 MODEL_LIMIT = 10000 + +# NEO4J数据库配置 +NEO4J_URI = "bolt://localhost:7687" +NEO4J_AUTH = ("neo4j", "DsideaL147258369") diff --git a/dsRag/Config/__pycache__/Config.cpython-310.pyc b/dsRag/Config/__pycache__/Config.cpython-310.pyc index 4547dbb7..19fad96a 100644 Binary files a/dsRag/Config/__pycache__/Config.cpython-310.pyc and b/dsRag/Config/__pycache__/Config.cpython-310.pyc differ diff --git a/dsRag/Config/__pycache__/__init__.cpython-310.pyc b/dsRag/Config/__pycache__/__init__.cpython-310.pyc index 2278514c..b3f99ca7 100644 Binary files a/dsRag/Config/__pycache__/__init__.cpython-310.pyc and b/dsRag/Config/__pycache__/__init__.cpython-310.pyc differ diff --git a/dsRag/Neo4j/N0_ClearDb.py b/dsRag/Neo4j/N0_ClearDb.py new file mode 100644 index 00000000..07e57c10 --- /dev/null +++ b/dsRag/Neo4j/N0_ClearDb.py @@ -0,0 +1,12 @@ +from Config.Config import * +from Neo4j.Neo4jExecutor import * + +# 使用示例 +if __name__ == '__main__': + executor = Neo4jExecutor( + uri=NEO4J_URI, + auth=NEO4J_AUTH + ) + # 清库 + clear(executor.graph) + print("清库成功") \ No newline at end of file diff --git a/dsRag/Neo4j/N1_ReadZsd.py b/dsRag/Neo4j/N1_ReadZsd.py new file mode 100644 index 00000000..fb1b9ed1 --- /dev/null +++ b/dsRag/Neo4j/N1_ReadZsd.py @@ -0,0 +1,61 @@ +import json + +from Neo4j.Neo4jExecutor import * + +def json_to_cypher(data): + """将知识体系JSON转换为Cypher插入脚本""" + cypher = [] + seen = set() + + def process_node(node, parent_id=None): + nonlocal seen + + node_id = node['value'] + node_name = node['title'].replace("'", "''") + + # 创建当前节点 + if node_id not in seen: + cypher.append(f"MERGE (n:KnowledgePoint {{id: '{node_id}'}}) SET n.name = '{node_name}';") + seen.add(node_id) + + # 创建父子关系 + if parent_id: + cypher.append(f""" +MATCH (parent:KnowledgePoint {{id: '{parent_id}'}}), + (child:KnowledgePoint {{id: '{node_id}'}}) +MERGE (parent)-[:HAS_SUB_POINT]->(child);""") + + # 递归处理子节点 + if 'children' in node and not node['isLeaf']: + for child in node['children']: + process_node(child, parent_id=node_id) + + # 处理根节点 + for root in data['data']['tree']: + process_node(root) + + # 处理根节点的父级关系（如果有） + if root.get('parentValue'): + cypher.append(f"MERGE (parent:KnowledgePoint {{id: '{root['parentValue']}'}});") + cypher.append(f""" +MATCH (parent:KnowledgePoint {{id: '{root['parentValue']}'}}), + (child:KnowledgePoint {{id: '{root['value']}'}}) +MERGE (parent)-[:HAS_SUB_POINT]->(child);""") + + return '\n'.join(cypher) + + +# 使用示例 +if __name__ == '__main__': + executor = Neo4jExecutor( + uri=NEO4J_URI, + auth=NEO4J_AUTH + ) + # 清库 + clear(executor.graph) + + # 这里替换成你的JSON数据变量 + with open('小学数学知识点体系.json', 'r',encoding='utf-8') as f: + your_json_data = json.load(f) + cypherText=json_to_cypher(your_json_data) + executor.execute_cypher_text(cypherText) diff --git a/dsRag/Neo4j/N2_ReadXksy.py b/dsRag/Neo4j/N2_ReadXksy.py new file mode 100644 index 00000000..2311ef08 --- /dev/null +++ b/dsRag/Neo4j/N2_ReadXksy.py @@ -0,0 +1,137 @@ +import json +from Neo4j.Neo4jExecutor import * + +# Neo4j连接配置 +graph = Graph(NEO4J_URI, auth=NEO4J_AUTH) + + +class Py2neoLoader: + def __init__(self): + self.node_cache = {} + self.tx = graph.begin() # 初始化事务 + + def create_literacy_nodes(self, data): + try: + print("Received data structure keys:", data.keys()) + + # 创建核心素养节点 + if "tree" in data and len(data["tree"]) > 0: + self._create_core_node(data["tree"][0]) + else: + raise ValueError("Invalid data structure: missing 'tree' array") + + # 创建关联体系节点（处理第二个节点） + if len(data["tree"]) > 1: + self._create_relation_system(data["tree"][1]) + graph.commit(self.tx) # 替换 self.tx.commit() + print(f"数据导入成功！共处理{len(self.node_cache)}个节点") + except Exception as e: + graph.rollback(self.tx) # 替换 self.tx.rollback() + print("主流程错误:", str(e)) + raise + + def _create_core_node(self, node): + try: + core_node = Node("CoreLiteracy", + title=node.get("title", "未命名节点"), + value=node.get("value", "CORE_DEFAULT"), + key=node.get("key", "ROOT_DEFAULT")) + self.tx.merge(core_node, "CoreLiteracy", "value") + self.node_cache[node.get("value")] = core_node + print(f"创建核心节点: {core_node}") + + if "children" in node: + for child in node["children"]: + self._create_child_node(child, core_node) + except Exception as e: + print(f"创建核心节点出错: {str(e)}") + print("问题节点数据:", node) + raise + + def _create_child_node(self, node, parent): + try: + if "value" not in node: + node["value"] = f"AUTO_VALUE_{len(self.node_cache) + 1}" + print(f"为节点生成默认value: {node['value']}") + + child_node = Node("LiteracyNode", + title=node.get("title", "未命名子节点"), + value=node["value"], + key=node.get("key", ""), + parentValue=node.get("parentValue", ""), + preSkills=node.get("preSkills", [])) + self.tx.merge(child_node, "LiteracyNode", "value") + self.node_cache[node["value"]] = child_node + + if parent is not None: + rel = Relationship(parent, "HAS_SUB_NODE", child_node) + self.tx.merge(rel) + print(f"创建关系: {parent['value']} -> {child_node['value']}") + + # 处理前置技能关系 + for pre_skill in node.get("preSkills", []): + if pre_skill in self.node_cache: + pre_node = self.node_cache[pre_skill] + rel = Relationship(pre_node, "REQUIRES", child_node) + self.tx.merge(rel) + else: + print(f"警告: 前置技能节点 {pre_skill} 不存在") + + # 递归处理子节点 + if "children" in node: + for sub_child in node.get("children", []): + self._create_child_node(sub_child, child_node) + except Exception as e: + print(f"创建子节点出错: {str(e)}") + print("问题节点数据:", node) + raise + + def _create_relation_system(self, relation_system): + try: + # 创建关联体系根节点 + sys_node = Node("RelationSystem", + title=relation_system.get("title", "未命名关联体系"), + value=relation_system.get("value", "REL_SYS_DEFAULT"), + path=relation_system.get("path", [])) + self.tx.merge(sys_node, "RelationSystem", "value") + print(f"创建关联体系节点: {sys_node}") + + # 处理关联链 + for chain in relation_system.get("children", []): + chain_node = Node("RelationChain", + title=chain.get("title", "未命名关联链"), + value=chain.get("value", f"CHAIN_{len(self.node_cache) + 1}"), + path=chain.get("path", [])) + self.tx.merge(chain_node, "RelationChain", "value") + self.tx.merge(Relationship(sys_node, "HAS_CHAIN", chain_node)) + + # 处理路径关系 + for path_str in chain.get("path", []): + nodes = [n.strip() for n in path_str.replace("+", "→").split("→")] + for i in range(len(nodes) - 1): + source = self.node_cache.get(nodes[i]) + target = self.node_cache.get(nodes[i + 1]) + if source and target: + rel = Relationship(source, "NEXT_STEP", target) + self.tx.merge(rel) + else: + print(f"路径节点缺失: {nodes[i]}→{nodes[i + 1]}") + except Exception as e: + print(f"创建关联体系出错: {str(e)}") + print("问题数据:", relation_system) + raise + + +if __name__ == "__main__": + try: + with open("小学数学学科素养点体系.json", 'r', encoding='utf-8') as f: + raw_data = json.load(f) + print("JSON文件加载成功，顶层键:", raw_data.keys()) + + if "data" not in raw_data: + raise ValueError("JSON缺少'data'字段") + + loader = Py2neoLoader() + loader.create_literacy_nodes(raw_data["data"]) + except Exception as e: + print("主程序错误:", str(e)) \ No newline at end of file diff --git a/dsRag/Neo4j/N3_InputShiTi.py b/dsRag/Neo4j/N3_InputShiTi.py new file mode 100644 index 00000000..b19dd8b0 --- /dev/null +++ b/dsRag/Neo4j/N3_InputShiTi.py @@ -0,0 +1,296 @@ +# -*- coding: utf-8 -*- +""" +数学题目分析系统 v6.3（稳定流式处理版） +""" + +import re +import json +import hashlib +import requests +from py2neo import Graph +from typing import Dict, List +from Config import NEO4J_URI, NEO4J_AUTH, MODEL_API_URL, MODEL_API_KEY, MODEL_NAME + + +# ================== 配置类 ================== +class Config: + NEO4J_URI = NEO4J_URI + NEO4J_AUTH = NEO4J_AUTH + MODEL_API_URL = MODEL_API_URL + MODEL_API_KEY = MODEL_API_KEY + MODEL_NAME = MODEL_NAME + MAX_CONTENT_LENGTH = 500 + STREAM_TIMEOUT = 30 + + +# ================== 知识图谱管理 ================== +class KnowledgeManager: + def __init__(self): + self.graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH) + self._verify_connection() + self.knowledge_map = self._load_knowledge() + self.literacy_map = self._load_literacy() + print("✅ 知识库加载完成") + print(f"有效知识点({len(self.knowledge_map)}个): {list(self.knowledge_map.keys())[:3]}...") + print(f"有效素养点({len(self.literacy_map)}个): {list(self.literacy_map.keys())[:3]}...") + + def _verify_connection(self): + """安全连接验证""" + try: + self.graph.run("RETURN 1 AS test") + print("✅ Neo4j连接验证成功") + except Exception as e: + print(f"❌ Neo4j连接失败: {str(e)}") + raise + + def _load_knowledge(self) -> Dict[str, str]: + """安全加载知识点""" + records = self.graph.run("MATCH (n:KnowledgePoint) WHERE n.name IS NOT NULL RETURN n.id, n.name").data() + return {rec['n.name'].strip(): rec['n.id'] for rec in records if rec['n.name'] and rec['n.name'].strip()} + + def _load_literacy(self) -> Dict[str, str]: + """安全加载素养点""" + records = self.graph.run("MATCH (n:LiteracyNode) WHERE n.title IS NOT NULL RETURN n.value, n.title").data() + return {rec['n.title'].strip(): rec['n.value'] for rec in records if rec['n.title'] and rec['n.title'].strip()} + + def store_analysis(self, question_id: str, content: str, + knowledge: List[str], literacy: List[str]): + """增强存储方法""" + tx = self.graph.begin() + try: + # 创建/更新题目节点 + tx.run(""" + MERGE (q:Question {id: $id}) + SET q.content = $content, q.updateTime = timestamp() + """, {"id": question_id, "content": content}) + + # 关联知识点 + valid_kp = [] + for name in knowledge: + clean_name = name.strip() + if kp_id := self.knowledge_map.get(clean_name): + tx.run(""" + MATCH (q:Question {id: $qid}), (kp:KnowledgePoint {id: $kpid}) + MERGE (q)-[r:REQUIRES_KNOWLEDGE]->(kp) + SET r.lastUsed = timestamp() + """, {"qid": question_id, "kpid": kp_id}) + valid_kp.append(clean_name) + + # 关联素养点 + valid_lp = [] + for title in literacy: + clean_title = title.strip() + if lit_id := self.literacy_map.get(clean_title): + tx.run(""" + MATCH (q:Question {id: $qid}), (lp:LiteracyNode {value: $lpid}) + MERGE (q)-[r:DEVELOPS_LITERACY]->(lp) + SET r.lastUsed = timestamp() + """, {"qid": question_id, "lpid": lit_id}) + valid_lp.append(clean_title) + + self.graph.commit(tx) + print(f"✅ 存储成功 - 知识点: {valid_kp}, 素养点: {valid_lp}") + except Exception as e: + self.graph.rollback(tx) + print(f"❌ 存储失败: {str(e)}") + + +# ================== 大模型客户端 ================== +class StreamLLMClient: + def __init__(self, kg: KnowledgeManager): + self.kg = kg + self.base_url = Config.MODEL_API_URL + self.headers = { + "Authorization": f"Bearer {Config.MODEL_API_KEY}", + "Content-Type": "application/json", + "Accept": "application/json" + } + + def analyze_problem(self, content: str) -> dict: + """增强的流式分析""" + try: + prompt = self._build_prompt(content) + response = self._send_request(prompt) + return self._process_stream(response) + except Exception as e: + print(f"🔴 分析失败: {str(e)}") + return {} + + def _build_prompt(self, content: str) -> str: + """构建精准提示词""" + return f"""请严格按以下要求分析数学题目： +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）： +{self.kg.knowledge_map.keys()} + +2. 素养点必须完全匹配以下列表中的名称： +{self.kg.literacy_map.keys()} + +3. 返回严格JSON格式： +{{ + "problem_types": ["题型"], + "knowledge_points": ["匹配的知识点"], + "literacy_points": ["匹配的素养点"] +}} + +题目内容：{content}""" + + def _send_request(self, prompt: str): + """发送API请求""" + return requests.post( + f"{self.base_url}/chat/completions", + headers=self.headers, + json={ + "model": Config.MODEL_NAME, + "messages": [{"role": "user", "content": prompt}], + "temperature": 0.1, + "stream": True + }, + timeout=Config.STREAM_TIMEOUT, + stream=True + ) + + def _process_stream(self, response) -> dict: + """可靠的流式处理""" + full_content = "" + try: + for chunk in response.iter_lines(): + if chunk: + decoded = chunk.decode('utf-8').strip() + if decoded.startswith('data:'): + json_str = decoded[5:].strip() + if json_str == "[DONE]": + break + try: + data = json.loads(json_str) + if content := data['choices'][0]['delta'].get('content'): + full_content += content + except: + continue + + # 调试日志 + print(f"原始响应内容：\n{full_content}") + + # 提取有效JSON + json_str = re.search(r'\{[\s\S]*\}', full_content).group() + return json.loads(json_str) + except json.JSONDecodeError: + print(f"⚠️ JSON解析失败，原始内容：{full_content}") + return {} + except Exception as e: + print(f"流处理异常：{str(e)}") + return {} + + +# ================== 核心引擎 ================== +class ProblemAnalyzer: + 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(self.kg) + + def _preprocess(self, text: str) -> str: + """文本预处理""" + return re.sub(r'[^\w\u4e00-\u9fa5]', '', text)[:Config.MAX_CONTENT_LENGTH] + + def execute(self): + """执行分析流程""" + print(f"\n🔍 分析题目: {self.original[:50]}...") + + analysis = self.llm.analyze_problem(self.original) + if not analysis: + print("⚠️ 大模型分析失败") + return + + print("\n📊 分析结果:") + print(f" 题型: {analysis.get('problem_types', [])}") + print(f" 知识点: {analysis.get('knowledge_points', [])}") + print(f" 素养点: {analysis.get('literacy_points', [])}") + + self.kg.store_analysis( + question_id=self.question_id, + content=self.content, + knowledge=analysis.get('knowledge_points', []), + literacy=analysis.get('literacy_points', []) + ) + + +# ================== 查询接口 ================== +def query_question(question_id: str): + try: + graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH) + result = graph.run(""" + MATCH (q:Question {id: $id}) + OPTIONAL MATCH (q)-[:REQUIRES_KNOWLEDGE]->(kp) + OPTIONAL MATCH (q)-[:DEVELOPS_LITERACY]->(lp) + RETURN q.content AS content, + collect(kp.name) AS knowledge, + collect(lp.title) AS literacy + """, id=question_id).data() + + if result: + data = result[0] + print(f"\n🔍 查询结果（ID: {question_id}）") + print(f"内容: {data['content']}") + print(f"知识点: {data['knowledge']}") + print(f"素养点: {data['literacy']}") + else: + print("未找到相关题目") + except Exception as e: + print(f"查询错误: {str(e)}") + + +# ================== 测试执行 ================== +if __name__ == '__main__': + test_cases = [ + # 基础运算类 + "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 + + # 几何问题 + "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 + + # 统计概率 + "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 + + # 典型应用题 + "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 + + # 方程应用 + "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 + + # 比例问题 + "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 + + # 函数图像 + "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 + + # 立体几何 + "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 + + # 概率统计 + "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 + + # 工程问题 + "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 + + # 最优化问题 + "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 + + # 利率问题 + "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 + + # 数列问题 + "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 + + # 测量问题 + "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 + + # 逻辑推理 + "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 + ] + + for q in test_cases: + print("\n" + "=" * 60) + analyzer = ProblemAnalyzer(q) + analyzer.execute() \ No newline at end of file diff --git a/dsRag/Neo4j/N4_PrintShiTi.py b/dsRag/Neo4j/N4_PrintShiTi.py new file mode 100644 index 00000000..9e48aad9 --- /dev/null +++ b/dsRag/Neo4j/N4_PrintShiTi.py @@ -0,0 +1,61 @@ +from py2neo import Graph +import json +from Config import * + +def query_all_questions(): + # Neo4j连接配置 + neo4j_config = { + "uri": NEO4J_URI, # 从Config导入 + "auth": NEO4J_AUTH # 从Config导入 + } + + # 初始化图数据库连接 + graph = Graph(**neo4j_config) + + try: + # 获取所有试题ID + question_ids = graph.run( + "MATCH (q:Question) RETURN q.id AS question_id" + ).data() + + results = [] + + # 遍历每个试题 + for record in question_ids: + qid = record['question_id'] + # 修改查询部分 + data = graph.run(""" + MATCH (q:Question {id: $qid}) + OPTIONAL MATCH (q)-[:REQUIRES_KNOWLEDGE]->(kp:KnowledgePoint) + OPTIONAL MATCH (q)-[:DEVELOPS_LITERACY]->(lp:LiteracyNode) + RETURN + q.content AS content, + collect(DISTINCT {id: kp.id, name: kp.name}) AS knowledge_points, + collect(DISTINCT {id: lp.value, title: lp.title}) AS literacy_points + """, qid=qid).data() + + if data: + result = { + "question_id": qid, + "content": data[0]['content'], + "knowledge_points": data[0]['knowledge_points'], + "literacy_points": data[0]['literacy_points'] + } + results.append(result) + + # 增强版输出展示 + print(f"\n{'=' * 40} 题目详情 {'=' * 40}") + print(f"📚 试题ID: {qid}") + print(f"📝 内容全文: {result['content']}") # 新增完整内容输出 + print(f"🔍 内容摘要: {result['content'][:50]}...") # 保留摘要显示 + print(f"🧠 知识点: {[kp['name'] for kp in result['knowledge_points']]}") + print(f"🆔 知识点ID: {[kp['id'] for kp in result['knowledge_points']]}") + print(f"🌟 素养点: {[lp['title'] for lp in result['literacy_points']]}") + print(f"🔢 素养点ID: {[lp['id'] for lp in result['literacy_points']]}") + print('=' * 90) + except Exception as e: + print(f"❌ 查询失败: {str(e)}") + + +if __name__ == '__main__': + query_all_questions() \ No newline at end of file diff --git a/dsRag/Neo4j/Neo4jExecutor.py b/dsRag/Neo4j/Neo4jExecutor.py new file mode 100644 index 00000000..65450327 --- /dev/null +++ b/dsRag/Neo4j/Neo4jExecutor.py @@ -0,0 +1,107 @@ +import re +from Config import * +from py2neo import Graph, Node, Relationship, Subgraph +from typing import List, Tuple + +def clear(db): + # 清空数据 + db.run("MATCH (n) DETACH DELETE n") + + # 分步删除约束和索引 + try: + # 删除约束 + constraints = db.run("SHOW CONSTRAINTS YIELD name").data() + for constr in constraints: + db.run(f"DROP CONSTRAINT `{constr['name']}`") + + # 删除索引 + indexes = db.run("SHOW INDEXES YIELD name, type WHERE type <> 'LOOKUP'").data() + for idx in indexes: + db.run(f"DROP INDEX `{idx['name']}`") + except Exception as e: + print(f"删除操作失败: {e}") + + +def create_subgraph(db: Graph, nodes: List[Node], relations: List[Tuple[Node, str, Node]]) -> None: + """统一创建子图""" + subgraph = Subgraph( + nodes=nodes, + relationships=[Relationship(start, rel_type, end) for start, rel_type, end in relations] + ) + db.create(subgraph) + +def tx_create(db: Graph, nodes: List[Node], relations: List[Tuple[Node, str, Node]]) -> None: + """事务方式创建数据""" + try: + tx = db.begin() + subgraph = Subgraph( + nodes=nodes, + relationships=[Relationship(start, rel_type, end) for start, rel_type, end in relations] + ) + tx.create(subgraph) + db.commit(tx) + except Exception as e: + db.rollback(tx) + print(f"事务操作失败: {str(e)}") + raise + +class Neo4jExecutor: + def __init__(self, uri, auth): + self.graph = Graph(uri, auth=auth) + + # 新增文本执行方法 + def execute_cypher_text(self, cypher_text: str) -> dict: + """直接执行Cypher文本""" + stats = {'total': 0, 'success': 0, 'failed': 0} + try: + statements = re.split(r';\s*\n', cypher_text) + statements = [s.strip() for s in statements if s.strip()] + + stats['total'] = len(statements) + + for stmt in statements: + try: + self.graph.run(stmt) + stats['success'] += 1 + except Exception as e: + stats['failed'] += 1 + print(f"执行失败: {stmt[:50]}... \n错误: {str(e)[:100]}") + + return stats + except Exception as e: + print(f"执行失败: {stmt[:100]}... \n完整错误: {str(e)}") # 原为str(e)[:100] + return stats + + def execute_cypher_file(self, file_path: str) -> dict: # 确保方法名称正确 + """执行Cypher文件""" + stats = {'total': 0, 'success': 0, 'failed': 0} + try: + with open(file_path, 'r', encoding='utf-8') as f: + cypher_script = f.read() + statements = re.split(r';\s*\n', cypher_script) + statements = [s.strip() for s in statements if s.strip()] + + stats['total'] = len(statements) + + for stmt in statements: + try: + self.graph.run(stmt) + stats['success'] += 1 + except Exception as e: + stats['failed'] += 1 + print(f"执行失败: {stmt[:50]}... \n错误: {str(e)[:100]}") + + return stats + + except Exception as e: + print(f"文件错误: {str(e)}") + return stats + + +def init(): + executor = Neo4jExecutor( + uri=NEO4J_URI, + auth=NEO4J_AUTH + ) + # 清库 + clear(executor.graph) \ No newline at end of file diff --git a/dsRag/Neo4j/__pycache__/Neo4jExecutor.cpython-310.pyc b/dsRag/Neo4j/__pycache__/Neo4jExecutor.cpython-310.pyc new file mode 100644 index 00000000..d117f09e Binary files /dev/null and b/dsRag/Neo4j/__pycache__/Neo4jExecutor.cpython-310.pyc differ diff --git a/dsRag/Neo4j/小学数学学科素养点体系.json b/dsRag/Neo4j/小学数学学科素养点体系.json new file mode 100644 index 00000000..69c36870 --- /dev/null +++ b/dsRag/Neo4j/小学数学学科素养点体系.json @@ -0,0 +1,160 @@ +{ + "status": 200, + "data": { + "tree": [ + { + "children": [ + { + "title": "数感培养", + "value": "MATH_LITERACY_01", + "key": "NUM_SENSE", + "children": [ + { + "title": "数量感知基础（1-2年级）", + "value": "SUB_01", + "key": "NUM_BASIC", + "parentValue": "MATH_LITERACY_01", + "preSkills": [] + }, + { + "title": "数运算直观（3-4年级）", + "value": "SUB_02", + "key": "NUM_OPERATION", + "parentValue": "MATH_LITERACY_01", + "preSkills": ["SUB_01"] + }, + { + "title": "数关系抽象（5-6年级）", + "value": "SUB_03", + "key": "NUM_RELATION", + "parentValue": "MATH_LITERACY_01", + "preSkills": ["SUB_02"] + } + ] + }, + { + "title": "空间观念", + "value": "MATH_LITERACY_02", + "key": "SPACE_CONCEPT", + "children": [ + { + "title": "图形辨识（1-2年级）", + "value": "SUB_04", + "key": "SHAPE_ID", + "parentValue": "MATH_LITERACY_02", + "preSkills": [] + }, + { + "title": "空间方位（3-4年级）", + "value": "SUB_05", + "key": "SPACE_ORIENT", + "parentValue": "MATH_LITERACY_02", + "preSkills": ["SUB_04"] + }, + { + "title": "几何推理（5-6年级）", + "value": "SUB_06", + "key": "GEOM_REASON", + "parentValue": "MATH_LITERACY_02", + "preSkills": ["SUB_05"] + } + ] + }, + { + "title": "运算思维", + "value": "MATH_LITERACY_03", + "key": "OPERATION_THINK", + "children": [ + { + "title": "算法理解（1-2年级）", + "value": "SUB_07", + "key": "ALGORITHM", + "parentValue": "MATH_LITERACY_03", + "preSkills": ["SUB_01"] + }, + { + "title": "运算策略（3-4年级）", + "value": "SUB_08", + "key": "STRATEGY", + "parentValue": "MATH_LITERACY_03", + "preSkills": ["SUB_07"] + }, + { + "title": "灵活运算（5-6年级）", + "value": "SUB_09", + "key": "FLEX_CALC", + "parentValue": "MATH_LITERACY_03", + "preSkills": ["SUB_08"] + } + ] + }, + { + "title": "数据意识", + "value": "MATH_LITERACY_04", + "key": "DATA_LITERACY", + "children": [ + { + "title": "简单统计（3-4年级）", + "value": "SUB_10", + "key": "BASIC_STATS", + "parentValue": "MATH_LITERACY_04", + "preSkills": ["SUB_02"] + }, + { + "title": "数据分析（5-6年级）", + "value": "SUB_11", + "key": "DATA_ANALYSIS", + "parentValue": "MATH_LITERACY_04", + "preSkills": ["SUB_10"] + } + ] + }, + { + "title": "建模能力", + "value": "MATH_LITERACY_05", + "key": "MODELING", + "children": [ + { + "title": "问题数学化（4-5年级）", + "value": "SUB_12", + "key": "PROBLEM_TRANS", + "parentValue": "MATH_LITERACY_05", + "preSkills": ["SUB_08", "SUB_05"] + }, + { + "title": "模型构建（5-6年级）", + "value": "SUB_13", + "key": "MODEL_BUILD", + "parentValue": "MATH_LITERACY_05", + "preSkills": ["SUB_12"] + } + ] + } + ], + "title": "数学核心素养", + "value": "CORE_LITERACY", + "key": "ROOT" + }, + { + "title": "素养关联体系", + "children": [ + { + "title": "基础素养链", + "value": "CHAIN_01", + "path": ["SUB_01 → SUB_02 → SUB_03 → SUB_07 → SUB_08 → SUB_09"] + }, + { + "title": "空间素养链", + "value": "CHAIN_02", + "path": ["SUB_04 → SUB_05 → SUB_06 → SUB_12"] + }, + { + "title": "综合应用链", + "value": "CHAIN_03", + "path": ["SUB_09 + SUB_06 → SUB_13"] + } + ] + } + ] + } +} \ No newline at end of file diff --git a/dsRag/Neo4j/小学数学知识点体系.json b/dsRag/Neo4j/小学数学知识点体系.json new file mode 100644 index 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"isLeaf": true, + "title": "合理存款", + "value": "6B0FD4D6B04E4F94ABE91162DACCD28D", + "key": "6B0FD4D6B04E4F94ABE91162DACCD28D", + "parentValue": "B92999700ADA4AFEA347085FCB732F11" + }, + { + "isLeaf": true, + "title": "自行车里的数学", + "value": "4FB2A1CF5C564EF1BEBEBAAC73FBDF81", + "key": "4FB2A1CF5C564EF1BEBEBAAC73FBDF81", + "parentValue": "B92999700ADA4AFEA347085FCB732F11" + }, + { + "isLeaf": true, + "title": "节约用水", + "value": "82470D2CB7144E13B8CAECEF0D967CC2", + "key": "82470D2CB7144E13B8CAECEF0D967CC2", + "parentValue": "B92999700ADA4AFEA347085FCB732F11" + } + ], + "isLeaf": false, + "title": "综合实践活动", + "value": "B92999700ADA4AFEA347085FCB732F11", + "key": "B92999700ADA4AFEA347085FCB732F11", + "parentValue": "410A88DB8D8849BD96CA6B6F76041D07" + } + ], + "sort": 40, + "isLeaf": false, + "title": "综合与实践", + "value": "410A88DB8D8849BD96CA6B6F76041D07", + "key": "410A88DB8D8849BD96CA6B6F76041D07", + "parentValue": "-1" + }, + { + "sort": 50, + "isLeaf": true, + "title": "综合", + "value": "b5578414ced9794b49ad3c1265320dcb", + "key": "b5578414ced9794b49ad3c1265320dcb", + "parentValue": "-1" + } + ] + } +} \ No newline at end of file diff --git a/dsRag/Neo4j/文档.txt b/dsRag/Neo4j/文档.txt new file mode 100644 index 00000000..17613f92 --- /dev/null +++ b/dsRag/Neo4j/文档.txt @@ -0,0 +1,30 @@ +# Neo4J下载安装 +https://blog.csdn.net/weixin_44455665/article/details/141463014 + +解压缩到：D:\neo4j-community-5.26.2\bin +# 安装服务 +cd D:\neo4j-community-5.26.2\bin +neo4j windows-service install +neo4j start + +打开 conf/neo4j.conf 文件，修改以下配置： +# 允许远程连接 +server.default_listen_address=0.0.0.0 + +# 访问 +http://10.10.21.20:7474 +neo4j +DsideaL4r5t6y7u + +密码默认：neo4j + +# 停止服务 +neo4j stop +neo4j windows-service uninstall + + +# 通过python操作neo4j +https://blog.csdn.net/sinat_20471177/article/details/134056788 + +# 提示词 +生成小学数学的所有知识点和能力点，我需要保存到cypher的脚本，如果过多无法一次性输出就只输出两个年级或三个年级的，我再追问后返回其余部分