'commit'

AI/Config.py

@@ -34,3 +34,10 @@ DIFY_URL='http://10.10.14.207/v1'
 
 # Vanna Postgresql
 VANNA_POSTGRESQL_URI = "postgresql://postgres:DsideaL147258369@10.10.14.71:5432/szjz_db"
+
+# Postgresql配置信息
+PG_HOST="10.10.14.71"
+PG_PORT=5432
+PG_DATABASE="szjz_db"
+PG_USER="postgres"
+PG_PASSWORD="DsideaL147258369"

AI/Text2Sql/Model/biModel.py

@@ -1,78 +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
+from Text2Sql.Util.PostgreSQLUtil import PostgreSQLUtil
 
-# 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)
+# 删除数据
+def delete_question(db, question_id: str):
+    # 删除 t_bi_question 表中的数据
+    delete_sql = """
+                DELETE FROM t_bi_question WHERE id = %s
+                """
+    db.execute_query(delete_sql, (question_id,))
 
-# 创建 SessionLocal 类
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 
-# 创建 Base 类
-Base = declarative_base()
+# 插入数据
+def insert_question(db, question_id: str, question: str):
+    # 向 t_bi_question 表插入数据
+    insert_sql = """
+               INSERT INTO t_bi_question (id,question, state_id, is_system, is_collect)
+               VALUES (%s,%s, %s, %s, %s)
+           """
+    db.execute_query(insert_sql, (question_id, question, 0, 0, 0))
 
-# 获取数据库会话
-def get_db():
-    db = SessionLocal()
-    try:
-        yield db
-    finally:
-        db.close()
 
-# 定义 t_bi_question 表模型
-class TBIQuestion(Base):
-    __tablename__ = "t_bi_question"
+# 修改数据
+'''
+示例:
+# 更新 question 和 state_id 字段
+update_question_by_id(db, question_id=1, question="新的问题描述", state_id=1)
+
+# 只更新 excel_file_name 字段
+update_question_by_id(db, question_id=1, excel_file_name="new_excel.xlsx")
+
+# 只更新 is_collect 字段
+update_question_by_id(db, question_id=1, is_collect=1)
 
-    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)
+# 不更新任何字段（因为所有参数都是 None）
+update_question_by_id(db, question_id=1, question=None, state_id=None)
+'''
 
-# 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
+def update_question_by_id(db: PostgreSQLUtil, question_id: str, **kwargs):
+    """
+    根据主键更新 t_bi_question 表，只更新非 None 的字段
+    :param db: PostgreSQLUtil 实例
+    :param question_id: 主键 id
+    :param kwargs: 需要更新的字段和值
+    :return: 更新是否成功
+    """
+    # 过滤掉值为 None 的字段
+    update_fields = {k: v for k, v in kwargs.items() if v is not None}
+    if not update_fields:
+        return False  # 没有需要更新的字段
+
+    # 动态构建 SET 子句
+    set_clause = ", ".join([f"{field} = %s" for field in update_fields.keys()])
+    # 构建完整 SQL
+    sql = f"""
+        UPDATE t_bi_question
+        SET {set_clause}
+        WHERE id = %s
+    """
+    # 参数列表
+    params = list(update_fields.values()) + [question_id]
+
+    # 执行更新
+    try:
+        db.execute_query(sql, params)
+        return True
+    except Exception as e:
+        print(f"更新失败: {e}")
+        return False

AI/Text2Sql/Util/PostgreSQLUtil.py

@@ -1,41 +1,26 @@
-import psycopg2
-from psycopg2 import OperationalError
-from psycopg2.extras import RealDictCursor
 import json
 from datetime import date, datetime
 
+import psycopg2
+from psycopg2 import pool
+from psycopg2.extras import RealDictCursor
+from Config import *
 
-class PostgreSQLUtil:
-    def __init__(self, host="10.10.14.71", port=5432,
-                 dbname="szjz_db", user="postgres", password="DsideaL147258369"):
-        self.conn_params = {
-            "host": host,
-            "port": port,
-            "dbname": dbname,
-            "user": user,
-            "password": password
-        }
-        self.connection = None
-
-    def __enter__(self):
-        self.connect()
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
+# 创建连接池
+postgresql_pool = psycopg2.pool.SimpleConnectionPool(
+    minconn=1,
+    maxconn=10,
+    host=PG_HOST,
+    port=PG_PORT,
+    dbname=PG_DATABASE,
+    user=PG_USER,
+    password=PG_PASSWORD
+)
 
-    def connect(self):
-        try:
-            self.connection = psycopg2.connect(**self.conn_params)
-            print("成功连接到PostgreSQL数据库")
-        except OperationalError as e:
-            print(f"连接错误: {e}")
-            raise
 
-    def close(self):
-        if self.connection:
-            self.connection.close()
-            print("数据库连接已关闭")
+class PostgreSQLUtil:
+    def __init__(self, connection):
+        self.connection = connection
 
     def execute_query(self, sql, params=None, return_dict=True):
         """执行查询并返回结果"""
@@ -77,13 +62,32 @@ class PostgreSQLUtil:
         raise TypeError(f"Type {type(obj)} not serializable")
 
 
+def get_db():
+    connection = postgresql_pool.getconn()
+    try:
+        yield PostgreSQLUtil(connection)
+    finally:
+        postgresql_pool.putconn(connection)
+
+
 # 使用示例
 if __name__ == "__main__":
-    with PostgreSQLUtil() as db:
+    '''
+    db_gen = get_db()：调用生成器函数，返回生成器对象。
+    db = next(db_gen)：从生成器中获取 PostgreSQLUtil 实例。
+    生成器函数确保连接在使用后正确归还到连接池。
+    '''
+    # 从生成器中获取数据库实例
+    db_gen = get_db()
+    db = next(db_gen)
+    try:
         # 示例查询
         result = db.execute_query("SELECT version()")
         print("数据库版本:", result)
 
         # 返回JSON
         json_data = db.query_to_json("SELECT * FROM t_base_class LIMIT 2")
-        print("JSON结果:", json_data)
+        print("JSON结果:", json_data)
+    finally:
+        # 手动关闭生成器
+        db_gen.close()

AI/Text2Sql/app.py

@@ -1,13 +1,15 @@
+import json
 import uuid
 
 import uvicorn  # 导入 uvicorn
-from fastapi import FastAPI, HTTPException, Depends, Form
-from sqlalchemy.orm import Session
-from starlette.responses import JSONResponse
+from fastapi import FastAPI, Depends, Form
+from openai import OpenAI
 from starlette.staticfiles import StaticFiles
 
+from Config import MODEL_API_KEY, MODEL_API_URL, MODEL_NAME
 from Model.biModel import *
-from Text2Sql.Util.PostgreSQLUtil import PostgreSQLUtil
+from Text2Sql.Util.MarkdownToDocxUtil import markdown_to_docx
+from Text2Sql.Util.PostgreSQLUtil import get_db
 from Text2Sql.Util.SaveToExcel import save_to_excel
 from Text2Sql.Util.VannaUtil import VannaUtil
 
@@ -15,81 +17,113 @@ from Text2Sql.Util.VannaUtil import VannaUtil
 app = FastAPI()
 # 配置静态文件目录
 app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# 初始化一次vanna的类
 vn = VannaUtil()
+
+
 @app.get("/")
 def read_root():
-    return {"message": "Welcome to Vanna AI SQL !"}
-
-
-# 创建记录
-@app.post("/questions/", response_model=TBIQuestionCreate)
-def create_question(question: TBIQuestionCreate, db: Session = Depends(get_db)):
-    db_question = TBIQuestion(**question.dict())
-    db.add(db_question)
-    db.commit()
-    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)):
-    db_question = db.query(TBIQuestion).filter(TBIQuestion.id == question_id).first()
-    if db_question is None:
-        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)):
-    db_question = db.query(TBIQuestion).filter(TBIQuestion.id == question_id).first()
-    if db_question is None:
-        raise HTTPException(status_code=404, detail="Question not found")
-    for key, value in question.dict().items():
-        if value is not None:
-            setattr(db_question, key, value)
-    db.commit()
-    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)):
-    db_question = db.query(TBIQuestion).filter(TBIQuestion.id == question_id).first()
-    if db_question is None:
-        raise HTTPException(status_code=404, detail="Question not found")
-    db.delete(db_question)
-    db.commit()
-    return {"message": "Question deleted successfully"}
+    return {"message": "Welcome to AI SQL World!"}
 
 
 # 通过语义生成Excel
 # http://10.10.21.20:8000/questions/get_excel
-# 参数：question
 @app.post("/questions/get_excel")
-def get_excel(question: str = Form(...)):
+def get_excel(question_id: str = Form(...), question_str: str = Form(...), db: PostgreSQLUtil = Depends(get_db)):
+    # 只接受guid号
+    if len(question_id) != 36:
+        return {"success": False, "message": "question_id格式错误"}
 
     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
         2、目标数据库是Postgresql 16
         '''
-    question = question + common_prompt
+    question = question_str + common_prompt
+
+    # 先删除后插入,防止重复插入
+    delete_question(db, question_id)
+    insert_question(db, question_id, question)
+
     # 获取完整 SQL
     sql = vn.generate_sql(question)
     print("生成的查询 SQL:\n", sql)
-    # 执行SQL查询
-    with PostgreSQLUtil() as db:
-        _data = db.execute_query(sql)
-        # 在static目录下，生成一个guid号的临时文件
-        uuidStr = str(uuid.uuid4())
-        filename = f"static/{uuidStr}.xlsx"
-        save_to_excel(_data, filename)
-        # 返回静态文件URL
-        return {"success": True, "message": "Excel文件生成成功", "download_url": f"/static/{uuidStr}.xlsx"}
 
+    # 更新question_id
+    update_question_by_id(db, question_id=question_id, sql=sql, state_id=1)
+
+    # 执行SQL查询
+    _data = db.execute_query(sql)
+    # 在static目录下，生成一个guid号的临时文件
+    uuid_str = str(uuid.uuid4())
+    filename = f"static/{uuid_str}.xlsx"
+    save_to_excel(_data, filename)
+    # 更新EXCEL文件名称
+    update_question_by_id(db, question_id, excel_file_name=filename)
+    # 返回静态文件URL
+    return {"success": True, "message": "Excel文件生成成功", "download_url": f"/static/{uuid_str}.xlsx"}
+
+
+# 获取docx
+# http://10.10.21.20:8000/questions/get_docx
+@app.post("/questions/get_docx")
+def get_docx(question_id: str = Form(...), db: PostgreSQLUtil = Depends(get_db)):
+    select_sql = """
+                    select * from t_bi_question where id=%s
+                    """
+    _data = db.execute_query(select_sql, (question_id,))
+    sql = _data[0]['sql']
+
+    # 4、生成word报告
+    prompt = '''
+    请根据以下 JSON 数据，整理出2000字左右的话描述当前数据情况。要求：
+    1、以Markdown格式返回，我将直接通过markdown格式生成Word。
+    2、标题统一为：长春云校数据分析报告
+    3、内容中不要提到JSON数据，统一称：数据
+    4、尽量以条目列出，这样更清晰
+    5、数据：
+    '''
+    _data = db.execute_query(sql)
+    prompt = prompt + json.dumps(_data, ensure_ascii=False)
+
+    # 初始化 OpenAI 客户端
+    client = OpenAI(
+        api_key=MODEL_API_KEY,
+        base_url=MODEL_API_URL,
+    )
+
+    # 调用 OpenAI API 生成总结（流式输出）
+    response = client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=[
+            {"role": "system", "content": "你是一个数据分析助手，擅长从 JSON 数据中提取关键信息并生成详细的总结。"},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=3000,  # 控制生成内容的长度
+        temperature=0.7,  # 控制生成内容的创造性
+        stream=True  # 启用流式输出
+    )
+
+    # 初始化变量用于存储流式输出的内容
+    summary = ""
+
+    # 处理流式输出
+    for chunk in response:
+        if chunk.choices[0].delta.content:  # 检查是否有内容
+            chunk_content = chunk.choices[0].delta.content
+            print(chunk_content, end="", flush=True)  # 实时打印到控制台
+            summary += chunk_content  # 将内容拼接到 summary 中
+
+    # 最终 summary 为完整的 Markdown 内容
+    print("\n\n流式输出完成，summary 已拼接为完整字符串。")
+    # 生成 Word 文档
+    uuid_str = str(uuid.uuid4())
+    filename = f"static/{uuid_str}.docx"
+    markdown_to_docx(summary, output_file=filename)
+
+    # 返回静态文件URL
+    return {"success": True, "message": "Word文件生成成功", "download_url": f"/static/{uuid_str}.docx"}
 
 
 # 确保直接运行脚本时启动 FastAPI 应用