【大模型+本地自建知识图谱/GraphRAG/neo4j/ollama+Qwen千问（或llama3）】 python实战（中）

一、建立基本的知识图谱并导入neo4j

这里我举例用的属性表、关系表，大概格式如下

id	名字	颜色
a1	苹果	红色

start	rel	end
a1	属于	b1

启动neo4j（关于neo4j的安装此处不再赘述）

import pandas as pd
from py2neo import Graph, Node, Relationship

graph = Graph("bolt://localhost:7687", auth=("neo4j", "xxxxxx"))  # Xxxxx是你自己的密码

# 导入属性表
def create_nodes(file_name, label, tx):
    df_1 = pd.read_csv("./graph_data/"+ file_name)
    df = df_1.dropna(subset=['id'])
    for index, row in df.iterrows():
        node = Node(label, id=row['id'], **row.drop('id').to_dict())
        tx.merge(node, label, "id")

attribute_tables = {
    "attributes_a.csv":"a",
    "attributes_b.csv":"b",
}


tx = graph.begin()


try:
    for file_name, label in attribute_tables.items():
        create_nodes(file_name, label, tx)

    # 导入关系表
    relationship_types = {
        "rel_a_b.csv": "属于",
    }


    for file_name, rel_type in relationship_types.items():
        df = pd.read_csv("./graph_data/"+ file_name)
        for index, row in df.iterrows():
            start_label, end_label = file_name.split("_")[1], file_name.split("_")[2].split(".")[0]    # 这里的start_label, end_label根据自己的实际需求来定
            start_node = graph.nodes.match(start_label, id=row['start']).first()
            end_node = graph.nodes.match(end_label, id=row['end']).first()
            if start_node and end_node:
                rel = Relationship(start_node, rel_type, end_node)
                tx.merge(rel)
            else:
                print(f"无法找到节点: {row['start']} 或 {row['end']}")

    tx.commit()
    print("成功导入！")

except Exception as e:
    tx.rollback()
    print(f"导入失败：{e}")

运行后在neo4j中的效果大概是这样：

<