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73 lines
2.6 KiB
73 lines
2.6 KiB
# conda activate rag
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# pip install py2neo pyvis
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from py2neo import Graph
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from pyvis.network import Network
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from Config import Config
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# 连接Neo4j
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graph = Graph(Config.NEO4J_URI, auth=Config.NEO4J_AUTH)
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# 查询所有知识点节点和关系
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# query = """
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# MATCH (n:KnowledgePoint)-[r]->(m)
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# RETURN n, r, m
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# """
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query = """
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MATCH (n:KnowledgePoint)
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OPTIONAL MATCH (n)-[r1:PREREQUISITE]->(p)
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OPTIONAL MATCH (n)-[r2:RELATED]->(r)
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OPTIONAL MATCH (n)-[r3:HAS_SUB_POINT]->(s)
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RETURN n,
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COLLECT(DISTINCT {type: 'PREREQUISITE', node: p, rel: r1}) AS prerequisites,
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COLLECT(DISTINCT {type: 'RELATED', node: r, rel: r2}) AS relateds,
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COLLECT(DISTINCT {type: 'HAS_SUB_POINT', node: s, rel: r3}) AS sub_points
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"""
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data = graph.run(query).data()
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# 创建网络图
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net = Network(height="750px", width="100%", notebook=True, cdn_resources='in_line')
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# 添加节点和边
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# 修改后的添加节点和边逻辑
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for item in data:
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node_label = item['n'].get('name', '未命名')
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net.add_node(item['n'].identity,
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label=node_label,
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name=node_label,
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font={'size': 14, 'face': 'SimHei'})
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# 处理先修知识关系
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for pre in item['prerequisites']:
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if pre['node']:
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net.add_node(pre['node'].identity,
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label=pre['node'].get('name', '未命名'),
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name=pre['node'].get('name', '未命名'),
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font={'size': 14, 'face': 'SimHei'})
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net.add_edge(item['n'].identity, pre['node'].identity, title='PREREQUISITE')
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# 处理相关知识关系
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for rel in item['relateds']:
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if rel['node']:
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net.add_node(rel['node'].identity,
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label=rel['node'].get('name', '未命名'),
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name=rel['node'].get('name', '未命名'),
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font={'size': 14, 'face': 'SimHei'})
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net.add_edge(item['n'].identity, rel['node'].identity, title='RELATED')
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# 处理子知识点关系
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for sub in item['sub_points']:
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if sub['node']:
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net.add_node(sub['node'].identity,
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label=sub['node'].get('name', '未命名'),
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name=sub['node'].get('name', '未命名'),
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font={'size': 14, 'face': 'SimHei'})
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net.add_edge(item['n'].identity, sub['node'].identity, title='HAS_SUB_POINT')
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# 生成HTML文件
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with open("knowledge_graph.html", "w", encoding='utf-8') as f:
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f.write(net.generate_html())
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# 打开HTML文件
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import webbrowser
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webbrowser.open("knowledge_graph.html")
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