基于neo4j存储知识树-mac

1、安装jdk21 for mac(jdk-21_macos-aarch64_bin.dmg)

2、安装neo4j for mac(neo4j-community-5.26.0-unix.tar.gz)

3、使用默认neo4j/neo4j登录http://localhost:7474

修改登录密码，可以使用生成按钮生成密码，连接数据库，默认设置为neo4j://localhost:7687。

3、具体代码如下：

import networkx as nx
import json
import requests
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from py2neo import Graph, Node, Relationship# 设置字体路径
font_path = "/System/Library/Fonts/STHeiti Light.ttc"  # macOS系统自带的黑体字体路径
font_prop = FontProperties(fname=font_path)# 连接到Neo4j数据库
def connect_to_neo4j(uri, user, password):graph = Graph(uri, auth=(user, password))return graph# 将知识树保存到Neo4j数据库
def save_knowledge_tree_to_neo4j(graph, tree):# 清空数据库graph.delete_all()# 添加节点和关系for node in tree.nodes():neo4j_node = Node("Concept", name=node)graph.create(neo4j_node)for u, v in tree.edges():u_node = graph.nodes.match("Concept", name=u).first()v_node = graph.nodes.match("Concept", name=v).first()if u_node and v_node:rel = Relationship(u_node, "ANSWERED_BY", v_node)graph.create(rel)# 可视化知识树
def visualize_tree(tree):pos = nx.spring_layout(tree, k=0.5, iterations=20)  # 节点布局nx.draw(tree, pos, with_labels=True, node_size=500, node_color="skyblue", font_size=8, font_weight='bold', font_family=font_prop.get_name())plt.title("知识树", fontproperties=font_prop)plt.axis('off')  # 关闭坐标轴plt.show()# 示例对话内容
dialogue = """
客户：你好，我想了解一下产品的保修政策。
坐席：您好！我们的产品保修期为一年，从购买之日起计算。
客户：如果产品在保修期内出现故障，我该怎么办？
坐席：您可以在保修期内将产品送到我们指定的维修点进行免费维修。
客户：维修点在哪里？
坐席：您可以在我们的官方网站上查询最近的维修点地址。
客户：好的，谢谢！
坐席：不客气，祝您生活愉快！
"""# 手动解析对话内容
def parse_dialogue(dialogue):lines = dialogue.strip().split("\n")questions = []answers = []for i in range(0, len(lines), 2):question = lines[i].replace("客户：", "").strip()answer = lines[i + 1].replace("坐席：", "").strip()questions.append(question)answers.append(answer)return questions, answersquestions, answers = parse_dialogue(dialogue)
print("问题：", questions)
print("答案：", answers)def build_knowledge_tree(questions, answers):tree = nx.DiGraph()root = "根据对话初始化的知识树"tree.add_node(root)for q, a in zip(questions, answers):tree.add_node(q)tree.add_edge(root, q)tree.add_node(a)tree.add_edge(q, a)return treeknowledge_tree = build_knowledge_tree(questions, answers)# 可视化知识树
visualize_tree(knowledge_tree)# 生成需要补充的问题
def generate_supplement_questions(questions, answers):prompt = "基于以上对话内容，您认为还需要补充哪些问题？请以问题的形式列出。\n"for q, a in zip(questions, answers):prompt += f"问题：{q}\n答案：{a}\n"prompt += "需要补充的问题："print('user:', prompt)# 调用 Ollama API 生成补充问题data = {"model": "qwen2.5:14b","prompt": prompt,"stream": False,"temperature": 0.7,"max_tokens": 200}try:response = requests.post("http://127.0.0.1:11434/api/generate", json=data)if response.status_code == 200:return response.json().get("response", "")else:return f"API 请求失败，状态码：{response.status_code}"except Exception as e:return f"API 请求失败，错误信息：{e}"# 生成需要补充的问题
supplement_questions = generate_supplement_questions(questions, answers)
print("assistant:需要补充的问题：", supplement_questions)# 专家回答
def expert_answers(supplement_questions):print("专家，请回答以下问题：")print(supplement_questions)answers = []for q in supplement_questions.split("\n"):if q.strip():print(f"问题：{q}")answer = input("专家回答输入：")answers.append(answer)return answers# 专家回答
expert_answers_list = expert_answers(supplement_questions)
print("专家的回答：", expert_answers_list)# 构建知识树
def build_knowledge_tree(questions, answers, supplement_questions, expert_answers):tree = nx.DiGraph()root = "专家补充回答后的知识树"tree.add_node(root)for q, a in zip(questions, answers):tree.add_node(q)tree.add_edge(root, q)tree.add_node(a)tree.add_edge(q, a)for q, a in zip(supplement_questions.split("\n"), expert_answers):if q.strip():tree.add_node(q)tree.add_edge(root, q)tree.add_node(a)tree.add_edge(q, a)return tree# 构建知识树
knowledge_tree = build_knowledge_tree(questions, answers, supplement_questions, expert_answers_list)# 连接到Neo4j数据库
neo4j_uri = "neo4j://localhost:7687"
neo4j_user = "neo4j"
neo4j_password = "密码"  # 替换为你的Neo4j密码（生成或设置的密码）
graph = connect_to_neo4j(neo4j_uri, neo4j_user, neo4j_password)# 将知识树保存到Neo4j数据库
save_knowledge_tree_to_neo4j(graph, knowledge_tree)# 查询功能
def query_knowledge_tree(graph, question):query = """MATCH (q:Concept {name: $question})-[:ANSWERED_BY]->(a:Concept)RETURN a.name AS answer"""result = graph.run(query, question=question)return [record["answer"] for record in result]# 测试查询功能
for question in questions:result = query_knowledge_tree(graph, question)print("原始问题：", question)if result:print(f"查询结果：")for node in result:print(node)else:print("未找到相关问题的答案。")for question in supplement_questions.split("\n"):result = query_knowledge_tree(graph, question)print("补充问题：", question)if result:print(f"查询结果：")for node in result:print(node)else:print("未找到相关问题的答案。")

4、显示效果：