TDSQL-C Serverless 数据库技术实战营——学习实践全流程记录

SQLCREATE TABLE ecommerce_sales_stats ( category_id int NOT NULL COMMENT '分类ID（主键）', category_name varchar(100) NOT NULL COMMENT '分类名称', total_sales decimal(15,2) NOT NULL COMMENT '总销售额', steam_sales decimal(15,2) NOT NULL COMMENT 'Steam平台销售额', offline_sales decimal(15,2) NOT NULL COMMENT '线下实体销售额', official_online_sales decimal(15,2) NOT NULL COMMENT '官方在线销售额',PRIMARY KEY (category_id)) ENGINE=INNODB DEFAULT CHARSET=utf8mb4 AUTO_INCREMENT=1 COMMENT='电商分类销售统计表';INSERT INTO ecommerce_sales_stats VALUES (1,'电子产品',150000.00,80000.00,30000.00,40000.00),(2,'服装',120000.00,20000.00,60000.00,40000.00),(3,'家居用品',90000.00,10000.00,50000.00,30000.00),(4,'玩具',60000.00,5000.00,30000.00,25000.00),(5,'书籍',45000.00,2000.00,20000.00,23000.00),(6,'运动器材',70000.00,15000.00,25000.00,30000.00),(7,'美容护肤',80000.00,10000.00,30000.00,40000.00),(8,'食品',50000.00,5000.00,25000.00,20000.00),(9,'珠宝首饰',30000.00,2000.00,10000.00,18000.00),(10,'汽车配件',40000.00,10000.00,15000.00,25000.00),(11,'手机配件',75000.00,30000.00,20000.00,25000.00),(12,'电脑配件',85000.00,50000.00,15000.00,20000.00),(13,'摄影器材',50000.00,20000.00,15000.00,15000.00),(14,'家电',120000.00,60000.00,30000.00,30000.00),(15,'宠物用品',30000.00,3000.00,12000.00,16800.00),(16,'母婴用品',70000.00,10000.00,30000.00,30000.00),(17,'旅行用品',40000.00,5000.00,15000.00,20000.00),(18,'艺术品',25000.00,1000.00,10000.00,14000.00),(19,'健康产品',60000.00,8000.00,25000.00,27000.00),(20,'办公用品',55000.00,2000.00,20000.00,33000.00);CREATE TABLE users ( user_id int NOT NULL AUTO_INCREMENT COMMENT '用户ID（主键，自增）', full_name varchar(100) NOT NULL COMMENT '用户全名', username varchar(50) NOT NULL COMMENT '用户名', email varchar(100) NOT NULL COMMENT '用户邮箱', password_hash varchar(255) NOT NULL COMMENT '用户密码的哈希值', created_at datetime DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', updated_at datetime DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间', is_active tinyint(1) DEFAULT '1' COMMENT '是否激活',PRIMARY KEY (user_id),UNIQUE KEY email (email)) ENGINE=INNODB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8mb4 COMMENT='用户表';INSERT INTO users VALUES (1,'张伟','zhangwei','zhangwei@example.com','hashed_password_1','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(2,'李娜','lina','lina@example.com','hashed_password_2','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(3,'王芳','wangfang','wangfang@example.com','hashed_password_3','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(4,'刘洋','liuyang','liuyang@example.com','hashed_password_4','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(5,'陈杰','chenjie','chenjie@example.com','hashed_password_5','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(6,'杨静','yangjing','yangjing@example.com','hashed_password_6','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(7,'赵强','zhaoqiang','zhaoqiang@example.com','hashed_password_7','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(8,'黄丽','huangli','huangli@example.com','hashed_password_8','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(9,'周杰','zhoujie','zhoujie@example.com','hashed_password_9','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(10,'吴敏','wumin','wumin@example.com','hashed_password_10','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(11,'郑伟','zhengwei','zhengwei@example.com','hashed_password_11','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(12,'冯婷','fengting','fengting@example.com','hashed_password_12','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(13,'蔡明','caiming','caiming@example.com','hashed_password_13','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(14,'潘雪','panxue','panxue@example.com','hashed_password_14','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(15,'蒋磊','jianglei','jianglei@example.com','hashed_password_15','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(16,'陆佳','lujia','lujia@example.com','hashed_password_16','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(17,'邓超','dengchao','dengchao@example.com','hashed_password_17','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(18,'任丽','renli','renli@example.com','hashed_password_18','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(19,'彭涛','pengtao','pengtao@example.com','hashed_password_19','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(20,'方圆','fangyuan','fangyuan@example.com','hashed_password_20','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(21,'段飞','duanfei','duanfei@example.com','hashed_password_21','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(22,'雷鸣','leiming','leiming@example.com','hashed_password_22','2024-08-18 04:07:18','2024-08-18 04:07:18',1),(23,'贾玲','jialing','jialing@example.com','hashed_password_23','2024-08-18 04:07:18','2024-08-18 04:07:18',1);CREATE TABLE orders ( order_id int NOT NULL AUTO_INCREMENT, user_id int DEFAULT NULL, order_amount decimal(10,2) DEFAULT NULL, order_status varchar(20) DEFAULT NULL, order_time datetime DEFAULT NULL,PRIMARY KEY (order_id)) ENGINE=InnoDB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8mb4 ;INSERT INTO orders VALUES (1,3,150.50,'已支付','2024-08-23 10:01:00'),(2,7,89.20,'待支付','2024-08-23 10:03:15'),(3,12,230.00,'已支付','2024-08-23 10:05:30'),(4,2,99.90,'已发货','2024-08-23 10:07:45'),(5,15,120.00,'待发货','2024-08-23 10:10:00'),(6,21,180.50,'已支付','2024-08-23 10:12:15'),(7,4,105.80,'待支付','2024-08-23 10:14:30'),(8,18,210.00,'已支付','2024-08-23 10:16:45'),(9,6,135.20,'已发货','2024-08-23 10:19:00'),(10,10,160.00,'待发货','2024-08-23 10:21:15'),(11,1,110.50,'已支付','2024-08-23 10:23:30'),(12,22,170.80,'待支付','2024-08-23 10:25:45'),(13,8,145.20,'已发货','2024-08-23 10:28:00'),(14,16,190.00,'待发货','2024-08-23 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Plain Textpip install openai pip install langchain pip install langchain-core pip install langchain-community pip install mysql-connector-python pip install streamlit pip install plotly pip install numpypip install pandaspip install watchdogpip install matplotlibpip install kaleido

Pythondatabase: db_user: root db_password: your password db_host: bj-cynosdbmysql-grp-kjfaeho8.sql.tencentcdb.com db_port: 22696 db_name: shophai: model: llama3.1:8b base_url: http://62.234.25.23:6399/

Pythonfrom langchain_community.utilities import SQLDatabasefrom langchain_core.prompts import ChatPromptTemplatefrom langchain_community.chat_models import ChatOllamafrom langchain_core.output_parsers import StrOutputParserfrom langchain_core.runnables import RunnablePassthroughimport yamlimport mysql.connectorfrom decimal import Decimalimport plotly.graph_objects as goimport plotlyimport pkg_resourcesimport matplotlibyaml_file_path = 'config.yaml'with open(yaml_file_path, 'r') as file: config_data = yaml.safe_load(file)#获取所有的已安装的pip包def get_piplist(p):return [d.project_name for d in pkg_resources.working_set]#获取llm用于提供AI交互ollama = ChatOllama(model=config_data['hai']['model'],base_url=config_data['hai']['base_url'])db_user = config_data['database']['db_user']db_password = config_data['database']['db_password']db_host = config_data['database']['db_host']db_port= config_data['database']['db_port']db_name = config_data['database']['db_name']获得schemadef get_schema(db): schema = mysql_db.get_table_info()return schemadef getResult(content):global mysql_db数据库连接 mysql_db = SQLDatabase.from_uri(f"mysql+mysqlconnector://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}")获得 数据库中表的信息#mysql_db_schema = mysql_db.get_table_info()#print(mysql_db_schema) template = """基于下面提供的数据库schema, 根据用户提供的要求编写sql查询语句,要求尽量使用最优sql,每次查询都是独立的问题，不要收到其他查询的干扰: {schema} Question: {question} 只返回sql语句，不要任何其他多余的字符，例如markdown的格式字符等: 如果有异常抛出不要显示出来 """ prompt = ChatPromptTemplate.from_template(template) text_2_sql_chain = ( RunnablePassthrough.assign(schema=get_schema) | prompt | ollama | StrOutputParser() ) 执行langchain 获取操作的sql语句 sql = text_2_sql_chain.invoke({"question": content})print(sql)#连接数据库进行数据的获取配置连接信息 conn = mysql.connector.connect( host=db_host, port=db_port, user=db_user, password=db_password, database=db_name )创建游标对象 cursor = conn.cursor()查询数据 cursor.execute(sql.strip("```").strip("```sql")) info = cursor.fetchall()打印结果#for row in info:#print(row)关闭游标和数据库连接 cursor.close() conn.close()#根据数据生成对应的图表print(info) template2 = """ 以下提供当前python环境已经安装的pip包集合: {installed_packages}; 请根据data提供的信息，生成是一个适合展示数据的plotly的图表的可执行代码,要求如下： 1.不要导入没有安装的pip包代码 2.如果存在多个数据类别，尽量使用柱状图，循环生成时图表中对不同数据请使用不同颜色区分， 3.图表要生成图片格式,保存在当前文件夹下即可，名称固定为：图表.png, 4.我需要您生成的代码是没有 Markdown 标记的，纯粹的编程语言代码。 5.生成的代码请注意将所有依赖包提前导入， 6.不要使用iplot等需要特定环境的代码 7.请注意数据之间是否可以转换，使用正确的代码 8.不需要生成注释 data:{data} 这是查询的sql语句与文本: sql:{sql} question:{question} 返回数据要求: 仅仅返回python代码，不要有额外的字符 """ prompt2 = ChatPromptTemplate.from_template(template2) data_2_code_chain = ( RunnablePassthrough.assign(installed_packages=get_piplist) | prompt2 | ollama | StrOutputParser() ) 执行langchain 获取操作的sql语句 code = data_2_code_chain.invoke({"data": info,"sql":sql,'question':content}) #删除数据两端可能存在的markdown格式print(code.strip("```").strip("```python"))exec(code.strip("```").strip("```python"))return {"code":code,"SQL":sql,"Query":info}构建展示页面import streamlit设置页面标题streamlit.title('AI驱动的数据库TDSQL-C 电商可视化分析小助手')设置对话框content = streamlit.text_area('请输入想查询的信息', value='', max_chars=None)提问按钮 # 设置点击操作if streamlit.button('提问'):#开始ai及langchain操作if content:#进行结果获取 result = getResult(content)#显示操作结果 streamlit.write('AI生成的SQL语句:') streamlit.write(result['SQL']) streamlit.write('SQL语句的查询结果:') streamlit.write(result['Query']) streamlit.write('plotly图表代码:') streamlit.write(result['code'])显示图表内容（生成在getResult中） streamlit.image('./图表.png', width=800)

streamlit run text2sql2plotly.py

Pythonimport plotly.express as pxfrom decimal import Decimalimport pandas as pdimport matplotlib.pyplot as pltimport numpy as np生成数据data = [ ('电子产品', Decimal('150000.00')), ('服装', Decimal('120000.00')), ('家居用品', Decimal('90000.00')), ('玩具', Decimal('60000.00')), ('书籍', Decimal('45000.00')), ('运动器材', Decimal('70000.00')), ('美容护肤', Decimal('80000.00')), ('食品', Decimal('50000.00')), ('珠宝首饰', Decimal('30000.00')), ('汽车配件', Decimal('40000.00')), ('手机配件', Decimal('75000.00')), ('电脑配件', Decimal('85000.00')), ('摄影器材', Decimal('50000.00')), ('家电', Decimal('120000.00')), ('宠物用品', Decimal('30000.00')), ('母婴用品', Decimal('70000.00')), ('旅行用品', Decimal('40000.00')), ('艺术品', Decimal('25000.00')), ('健康产品', Decimal('60000.00')), ('办公用品', Decimal('55000.00'))]生成DataFramedf = pd.DataFrame(data, columns=['category_name', 'total_sales'])将数据转换为数字类型df['total_sales'] = df['total_sales'].astype(float)使用柱状图进行可视化fig = px.bar(df, x='category_name', y='total_sales', color_discrete_sequence=px.colors.sequential.Plotly3)fig.update_layout(title='各类商品销售总额', xaxis_title='类别', yaxis_title='销售金额')fig.show()保存图片plt.savefig('图表.png')