'commit'

AI/Text2Sql/Model/biModel.py

@@ -0,0 +1,70 @@
+from pydantic import BaseModel
+from sqlalchemy import create_engine, Column, Integer, String, SmallInteger, Date
+from sqlalchemy.orm import declarative_base  # 更新导入路径
+from sqlalchemy.orm import sessionmaker
+
+# 数据库连接配置
+DATABASE_URL = "postgresql+psycopg2://postgres:DsideaL147258369@10.10.14.71:5432/szjz_db"
+
+# 创建数据库引擎
+engine = create_engine(DATABASE_URL)
+
+# 创建 SessionLocal 类
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+# 创建 Base 类
+Base = declarative_base()
+
+# 定义 t_bi_question 表模型
+class TBIQuestion(Base):
+    __tablename__ = "t_bi_question"
+
+    id = Column(Integer, primary_key=True, index=True)
+    question = Column(String(255), nullable=False)
+    state_id = Column(SmallInteger, nullable=False, default=0)
+    sql = Column(String(2048))
+    bar_chart_x_columns = Column(String(255))
+    bar_chart_y_columns = Column(String(255))
+    pie_chart_category_columns = Column(String(255))
+    pie_chart_value_column = Column(String(255))
+    session_id = Column(String(255), nullable=False)
+    excel_file_name = Column(String(255))
+    bar_chart_file_name = Column(String(255))
+    pie_chart_file_name = Column(String(255))
+    report_file_name = Column(String(255))
+    create_time = Column(Date, nullable=False, default="CURRENT_TIMESTAMP")
+    is_system = Column(SmallInteger, nullable=False, default=0)
+    is_collect = Column(SmallInteger, nullable=False, default=0)
+
+# 创建 Pydantic 模型
+class TBIQuestionCreate(BaseModel):
+    question: str
+    state_id: int = 0
+    sql: str = None
+    bar_chart_x_columns: str = None
+    bar_chart_y_columns: str = None
+    pie_chart_category_columns: str = None
+    pie_chart_value_column: str = None
+    session_id: str
+    excel_file_name: str = None
+    bar_chart_file_name: str = None
+    pie_chart_file_name: str = None
+    report_file_name: str = None
+    is_system: int = 0
+    is_collect: int = 0
+
+class TBIQuestionUpdate(BaseModel):
+    question: str = None
+    state_id: int = None
+    sql: str = None
+    bar_chart_x_columns: str = None
+    bar_chart_y_columns: str = None
+    pie_chart_category_columns: str = None
+    pie_chart_value_column: str = None
+    session_id: str = None
+    excel_file_name: str = None
+    bar_chart_file_name: str = None
+    pie_chart_file_name: str = None
+    report_file_name: str = None
+    is_system: int = None
+    is_collect: int = None

AI/Text2Sql/Util/SaveToExcel.py

@@ -1,6 +1,9 @@
+import io
+import pandas as pd
+from openpyxl import Workbook
 from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
 from openpyxl.utils import get_column_letter
-import pandas as pd
+
 
 def save_to_excel(data, filename):
     """
@@ -61,4 +64,77 @@ def save_to_excel(data, filename):
             column_width = min(max(max_length + 2, 10)*2, 120)  # 加 2 是为了留出一些空白
             # 设置列宽
             column_letter = get_column_letter(idx + 1)
-            worksheet.column_dimensions[column_letter].width = column_width
+            worksheet.column_dimensions[column_letter].width = column_width
+
+
+def save_to_excel_stream(data):
+    """
+    将数据集保存为格式化的Excel文件流
+
+    参数：
+    data - 数据集 (列表字典格式，例如：[{"列1": "值1", "列2": "值2"}, ...])
+
+    返回：
+    BytesIO - 包含Excel文件内容的流
+    """
+    # 转换数据为DataFrame
+    df = pd.DataFrame(data)
+
+    # 创建一个 BytesIO 对象作为缓冲区
+    output = io.BytesIO()
+
+    # 创建Excel工作簿
+    wb = Workbook()
+    ws = wb.active
+    ws.title = '统计报表'
+
+    # 写入数据
+    for row in pd.DataFrame(data).itertuples(index=False):
+        ws.append(row)
+
+    # 定义边框样式
+    thin_border = Border(left=Side(style='thin'),
+                         right=Side(style='thin'),
+                         top=Side(style='thin'),
+                         bottom=Side(style='thin'))
+
+    # 设置全局行高
+    for row in ws.iter_rows():
+        ws.row_dimensions[row[0].row].height = 20
+        # 为所有单元格添加边框
+        for cell in row:
+            cell.border = thin_border
+
+    # 设置标题样式
+    header_font = Font(bold=True, size=14)
+    header_fill = PatternFill(start_color='ADD8E6', end_color='ADD8E6', fill_type='solid')
+
+    for cell in ws[1]:
+        cell.font = header_font
+        cell.fill = header_fill
+        cell.alignment = Alignment(horizontal='center', vertical='center')
+
+    # 设置数据行样式
+    data_font = Font(size=14)
+    for row in ws.iter_rows(min_row=2):
+        for cell in row:
+            cell.font = data_font
+            cell.alignment = Alignment(vertical='center', wrap_text=True)
+
+    # 动态设置列宽
+    for idx, column in enumerate(df.columns):
+        # 获取列的最大长度
+        max_length = max(
+            df[column].astype(str).map(len).max(),  # 数据列的最大长度
+            len(str(column))  # 列名的长度
+        )
+        # 计算列宽，确保在 10 到 120 之间
+        column_width = min(max(max_length + 2, 10) * 2, 120)  # 加 2 是为了留出一些空白
+        # 设置列宽
+        column_letter = get_column_letter(idx + 1)
+        ws.column_dimensions[column_letter].width = column_width
+
+    # 将工作簿保存到 BytesIO 对象
+    wb.save(output)
+    output.seek(0)  # 将指针移动到流的开头
+    return output

AI/Text2Sql/Util/VannaUtil.py

@@ -1,61 +1,128 @@
 import re
 from typing import List, Dict, Any
 import requests
+import sqlite3
 from vanna.base import VannaBase
 from Config import *
 
 
 class VannaUtil(VannaBase):
-    def __init__(self):
+    def __init__(self, db_type='sqlite', db_uri=None):
         super().__init__()
         self.api_key = MODEL_API_KEY
         self.base_url = MODEL_GENERATION_TEXT_URL  # 阿里云专用API地址
         self.model = QWEN_MODEL_NAME  # 根据实际模型名称调整
         self.training_data = []
         self.chat_history = []
+        self.db_type = db_type
+        self.db_uri = db_uri or 'vanna.db'  # 默认使用 SQLite
+        self._init_db()
+
+    def _init_db(self):
+        """初始化数据库连接"""
+        if self.db_type == 'sqlite':
+            self.conn = sqlite3.connect(self.db_uri)
+            self._create_tables()
+        elif self.db_type == 'postgres':
+            import psycopg2
+            self.conn = psycopg2.connect(self.db_uri)
+            self._create_tables()
+        else:
+            raise ValueError(f"Unsupported database type: {self.db_type}")
+
+    def _create_tables(self):
+        """创建训练数据表"""
+        cursor = self.conn.cursor()
+        if self.db_type == 'sqlite':
+            cursor.execute('''
+                CREATE TABLE IF NOT EXISTS training_data (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    type TEXT,
+                    question TEXT,
+                    sql TEXT,
+                    content TEXT
+                )
+            ''')
+        elif self.db_type == 'postgres':
+            cursor.execute('''
+                CREATE TABLE IF NOT EXISTS training_data (
+                    id SERIAL PRIMARY KEY,
+                    type TEXT,
+                    question TEXT,
+                    sql TEXT,
+                    content TEXT
+                )
+            ''')
+        self.conn.commit()
 
-    # ---------- 必须实现的抽象方法 ----------
     def add_ddl(self, ddl: str, **kwargs) -> None:
-        self.training_data.append({"type": "ddl", "content": ddl})
+        """添加 DDL"""
+        cursor = self.conn.cursor()
+        cursor.execute('INSERT INTO training_data (type, content) VALUES (?, ?)', ('ddl', ddl))
+        self.conn.commit()
 
     def add_documentation(self, doc: str, **kwargs) -> None:
-        self.training_data.append({"type": "documentation", "content": doc})
+        """添加文档"""
+        cursor = self.conn.cursor()
+        cursor.execute('INSERT INTO training_data (type, content) VALUES (?, ?)', ('documentation', doc))
+        self.conn.commit()
 
     def add_question_sql(self, question: str, sql: str, **kwargs) -> None:
-        self.training_data.append({"type": "qa", "question": question, "sql": sql})
+        """添加问答对"""
+        cursor = self.conn.cursor()
+        cursor.execute('INSERT INTO training_data (type, question, sql) VALUES (?, ?, ?)', ('qa', question, sql))
+        self.conn.commit()
 
     def get_related_ddl(self, question: str, **kwargs) -> str:
-        return "\n".join([item["content"] for item in self.training_data if item["type"] == "ddl"])
+        """获取相关 DDL"""
+        cursor = self.conn.cursor()
+        cursor.execute('SELECT content FROM training_data WHERE type = ?', ('ddl',))
+        return "\n".join(row[0] for row in cursor.fetchall())
 
     def get_related_documentation(self, question: str, **kwargs) -> str:
-        return "\n".join([item["content"] for item in self.training_data if item["type"] == "documentation"])
+        """获取相关文档"""
+        cursor = self.conn.cursor()
+        cursor.execute('SELECT content FROM training_data WHERE type = ?', ('documentation',))
+        return "\n".join(row[0] for row in cursor.fetchall())
 
     def get_training_data(self, **kwargs) -> List[Dict[str, Any]]:
-        return self.training_data
+        """获取所有训练数据"""
+        cursor = self.conn.cursor()
+        cursor.execute('SELECT * FROM training_data')
+        columns = [column[0] for column in cursor.description]
+        return [dict(zip(columns, row)) for row in cursor.fetchall()]
+
+    def remove_training_data(self, id: str, **kwargs) -> bool:
+        """删除训练数据"""
+        cursor = self.conn.cursor()
+        cursor.execute('DELETE FROM training_data WHERE id = ?', (id,))
+        self.conn.commit()
+        return cursor.rowcount > 0
+
+    def generate_embedding(self, text: str, **kwargs) -> List[float]:
+        """生成嵌入向量"""
+        return []
+
+    def get_similar_question_sql(self, question: str, **kwargs) -> List[Dict[str, Any]]:
+        """获取相似问答对"""
+        return []
 
-    # ---------- 对话方法 ----------
     def system_message(self, message: str) -> None:
+        """添加系统消息"""
         self.chat_history = [{"role": "system", "content": message}]
 
     def user_message(self, message: str) -> None:
+        """添加用户消息"""
         self.chat_history.append({"role": "user", "content": message})
 
     def assistant_message(self, message: str) -> None:
+        """添加助手消息"""
         self.chat_history.append({"role": "assistant", "content": message})
 
     def submit_prompt(self, prompt: str, **kwargs) -> str:
+        """提交提示词"""
         return self.generate_sql(question=prompt)
 
-    # ---------- 其他方法 ----------
-    def remove_training_data(self, id: str, **kwargs) -> bool:
-        return True
-
-    def generate_embedding(self, text: str, **kwargs) -> List[float]:
-        return []
-
-    def get_similar_question_sql(self, question: str, **kwargs) -> List[Dict[str, Any]]:
-        return []
-
     def _clean_sql_output(self, raw_sql: str) -> str:
         """增强版清洗逻辑"""
         # 移除所有非SQL内容
@@ -119,4 +186,4 @@ class VannaUtil(VannaBase):
 
         except Exception as e:
             print(f"\nAPI请求错误: {str(e)}")
-            return ""
+            return ""

AI/Text2Sql/app.py

@@ -1,84 +1,13 @@
 import re
-from typing import io
 
-import pandas as pd
-from fastapi import FastAPI, HTTPException, Depends
-from pydantic import BaseModel
-from sqlalchemy import create_engine, Column, Integer, String, SmallInteger, Date
-from sqlalchemy.orm import declarative_base  # 更新导入路径
-from sqlalchemy.orm import sessionmaker, Session
 import uvicorn  # 导入 uvicorn
+from fastapi import FastAPI, HTTPException, Depends
+from sqlalchemy.orm import Session
 from starlette.responses import StreamingResponse
-
 from Text2Sql.Util.PostgreSQLUtil import PostgreSQLUtil
-from Text2Sql.Util.SaveToExcel import save_to_excel
+from Text2Sql.Util.SaveToExcel import save_to_excel_stream
 from Text2Sql.Util.VannaUtil import VannaUtil
-
+from Model.biModel import *
-# 数据库连接配置
-DATABASE_URL = "postgresql+psycopg2://postgres:DsideaL147258369@10.10.14.71:5432/szjz_db"
-
-# 创建数据库引擎
-engine = create_engine(DATABASE_URL)
-
-# 创建 SessionLocal 类
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
-
-# 创建 Base 类
-Base = declarative_base()
-
-# 定义 t_bi_question 表模型
-class TBIQuestion(Base):
-    __tablename__ = "t_bi_question"
-
-    id = Column(Integer, primary_key=True, index=True)
-    question = Column(String(255), nullable=False)
-    state_id = Column(SmallInteger, nullable=False, default=0)
-    sql = Column(String(2048))
-    bar_chart_x_columns = Column(String(255))
-    bar_chart_y_columns = Column(String(255))
-    pie_chart_category_columns = Column(String(255))
-    pie_chart_value_column = Column(String(255))
-    session_id = Column(String(255), nullable=False)
-    excel_file_name = Column(String(255))
-    bar_chart_file_name = Column(String(255))
-    pie_chart_file_name = Column(String(255))
-    report_file_name = Column(String(255))
-    create_time = Column(Date, nullable=False, default="CURRENT_TIMESTAMP")
-    is_system = Column(SmallInteger, nullable=False, default=0)
-    is_collect = Column(SmallInteger, nullable=False, default=0)
-
-# 创建 Pydantic 模型
-class TBIQuestionCreate(BaseModel):
-    question: str
-    state_id: int = 0
-    sql: str = None
-    bar_chart_x_columns: str = None
-    bar_chart_y_columns: str = None
-    pie_chart_category_columns: str = None
-    pie_chart_value_column: str = None
-    session_id: str
-    excel_file_name: str = None
-    bar_chart_file_name: str = None
-    pie_chart_file_name: str = None
-    report_file_name: str = None
-    is_system: int = 0
-    is_collect: int = 0
-
-class TBIQuestionUpdate(BaseModel):
-    question: str = None
-    state_id: int = None
-    sql: str = None
-    bar_chart_x_columns: str = None
-    bar_chart_y_columns: str = None
-    pie_chart_category_columns: str = None
-    pie_chart_value_column: str = None
-    session_id: str = None
-    excel_file_name: str = None
-    bar_chart_file_name: str = None
-    pie_chart_file_name: str = None
-    report_file_name: str = None
-    is_system: int = None
-    is_collect: int = None
 
 # 初始化 FastAPI
 app = FastAPI()
@@ -87,6 +16,7 @@ app = FastAPI()
 def read_root():
     return {"message": "Hello, World!"}
 
+
 # 获取数据库会话
 def get_db():
     db = SessionLocal()
@@ -95,6 +25,7 @@ def get_db():
     finally:
         db.close()
 
+
 # 创建记录
 @app.post("/questions/", response_model=TBIQuestionCreate)
 def create_question(question: TBIQuestionCreate, db: Session = Depends(get_db)):
@@ -104,6 +35,7 @@ def create_question(question: TBIQuestionCreate, db: Session = Depends(get_db)):
     db.refresh(db_question)
     return db_question
 
+
 # 读取记录
 @app.get("/questions/{question_id}", response_model=TBIQuestionCreate)
 def read_question(question_id: int, db: Session = Depends(get_db)):
@@ -112,6 +44,7 @@ def read_question(question_id: int, db: Session = Depends(get_db)):
         raise HTTPException(status_code=404, detail="Question not found")
     return db_question
 
+
 # 更新记录
 @app.put("/questions/{question_id}", response_model=TBIQuestionCreate)
 def update_question(question_id: int, question: TBIQuestionUpdate, db: Session = Depends(get_db)):
@@ -125,6 +58,7 @@ def update_question(question_id: int, question: TBIQuestionUpdate, db: Session =
     db.refresh(db_question)
     return db_question
 
+
 # 删除记录
 @app.delete("/questions/{question_id}")
 def delete_question(question_id: int, db: Session = Depends(get_db)):
@@ -135,16 +69,17 @@ def delete_question(question_id: int, db: Session = Depends(get_db)):
     db.commit()
     return {"message": "Question deleted successfully"}
 
+
 # 通过语义生成Excel
-@app.post("/questions/getExcel")
+@app.post("/questions/get_excel")
-def getExcel(question: str):
+def get_excel(question: str):
     # 指定学段
-    question = '''
+    # question = '''
-                    查询:
+    #                 查询:
-                    1、发布时间是2024年度
+    #                 1、发布时间是2024年度
-                    2、每个学段，每个科目，上传课程数量，按由多到少排序
+    #                 2、每个学段，每个科目，上传课程数量，按由多到少排序
-                    3、字段名: 学段,科目,排名,课程数量
+    #                 3、字段名: 学段,科目,排名,课程数量
-                    '''
+    #                 '''
     common_prompt = '''
         返回的信息要求：
         1、行政区划为NULL 或者是空字符的不参加统计
@@ -165,45 +100,10 @@ def getExcel(question: str):
             media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
             headers={"Content-Disposition": "attachment; filename=导出信息.xlsx"}
         )
-def save_to_excel_stream(data):
-    # 将数据保存为Excel文件流
-    df = pd.DataFrame(data)
-    output = io.BytesIO()
-    with pd.ExcelWriter(output, engine='openpyxl') as writer:
-        df.to_excel(writer, index=False)
-    output.seek(0)  # 将指针移动到流的开头
-    return output
-# 添加 main 函数
-def main():
-    # 开始训练
-    print("开始训练...")
-    # 打开AreaSchoolLesson.sql文件内容
-    with open("Sql/AreaSchoolLessonDDL.sql", "r", encoding="utf-8") as file:
-        ddl = file.read()
-    # 训练数据
-    vn.train(
-        ddl=ddl
-    )
-    # 添加有关业务术语或定义的文档
-    # vn.train(documentation="Sql/AreaSchoolLesson.md")
-
-    # 使用 SQL 进行训练
-    with open('Sql/AreaSchoolLessonGenerate.sql', 'r', encoding='utf-8') as file:
-        sql_content = file.read()
-    # 使用正则表达式提取注释和 SQL 语句
-    sql_pattern = r'/\*(.*?)\*/(.*?);'
-    sql_snippets = re.findall(sql_pattern, sql_content, re.DOTALL)
-
-    # 打印提取的注释和 SQL 语句
-    for i, (comment, sql) in enumerate(sql_snippets, 1):
-        vn.train(sql=comment.strip() + '\n' + sql.strip() + '\n')
-
-
-    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
 
 
 
 # 确保直接运行脚本时启动 FastAPI 应用
 if __name__ == "__main__":
     vn = VannaUtil()
-    main()
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)