'commit'

AI/A1_GenerateMarkdown.py

@@ -1,15 +1,13 @@
 # -*- coding: utf-8 -*-
 import time
+from pathlib import Path
 from typing import Iterator, Optional
-
-from dashscope import Generation
-from dashscope.api_entities.dashscope_response import DashScopeAPIResponse
-
+from openai import OpenAI, APIError, APITimeoutError
 from Config import *
 
 
 class MarkdownGenerator:
-    """Markdown教学大纲生成器"""
+    """Markdown教学大纲生成器（OpenAI版本）"""
 
     def __init__(
             self,
@@ -27,11 +25,12 @@ class MarkdownGenerator:
         self.course_name = course_name
         self.template_path = template_path or DEFAULT_TEMPLATE
         self.output_path = output_path or DEFAULT_OUTPUT_DIR / f"{course_name}.md"
+        self.client = OpenAI(api_key=API_KEY, base_url=MODEL_URL)  # 初始化OpenAI客户端
 
         self._validate_paths()
 
     def _validate_paths(self):
-        """路径验证"""
+        """路径验证（保持不变）"""
         if not self.template_path.exists():
             raise FileNotFoundError(f"模板文件不存在: {self.template_path}")
 
@@ -40,11 +39,11 @@ class MarkdownGenerator:
             raise NotADirectoryError(f"无效的输出目录: {self.output_path.parent}")
 
     def _load_template(self) -> str:
-        """加载模板内容"""
+        """加载模板内容（保持不变）"""
         return self.template_path.read_text(encoding='utf-8')
 
-    def _generate_stream(self) -> Iterator[DashScopeAPIResponse]:
-        """流式生成内容"""
+    def _generate_stream(self) -> Iterator:
+        """流式生成内容（修改为OpenAI格式）"""
         system_prompt = (
             "请严格按照以下Markdown格式生成教学大纲：\n"
             f"{self._load_template()}\n"
@@ -55,49 +54,55 @@ class MarkdownGenerator:
             "4. 内容中不要包含图片\n"
         )
 
-        return Generation.call(
+        return self.client.chat.completions.create(
             model=MODEL_R1,
-            api_key=API_KEY,
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": f"请生成《{self.course_name}》教学大纲"}
             ],
-            result_format='message',
             stream=True,
-            incremental_output=True
+            timeout=30  # 设置超时时间
         )
 
     def run(self) -> bool:
-        """执行生成流程"""
+        """执行生成流程（调整流式处理逻辑）"""
         start_time = time.time()
         spinner = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
         content_buffer = []
 
         try:
             print(f"🚀 开始生成【{self.course_name}】教学大纲")
-            responses = self._generate_stream()
+            stream = self._generate_stream()
 
-            for idx, response in enumerate(responses):
-                # 显示进度
+            for idx, chunk in enumerate(stream):
                 print(f"\r{spinner[idx % 10]} 生成中（{int(time.time() - start_time)}秒）", end="")
 
-                if response.status_code == 200 and response.output:
-                    if chunk := response.output.choices[0].message.content:
-                        content_buffer.append(chunk)
-                        if len(content_buffer) == 1:
-                            print("\n\n📝 内容生成开始：")
-                        print(chunk, end="", flush=True)
+                # 处理OpenAI流式响应
+                if chunk.choices and chunk.choices[0].delta.content:
+                    content_chunk = chunk.choices[0].delta.content
+                    content_buffer.append(content_chunk)
+
+                    if len(content_buffer) == 1:
+                        print("\n\n📝 内容生成开始：")
+                    print(content_chunk, end="", flush=True)
 
             # 保存结果
             if content_buffer:
-                self.output_path.write_text(''.join(content_buffer), encoding='utf-8')
+                full_content = ''.join(content_buffer)
+                self.output_path.write_text(full_content, encoding='utf-8')
                 print(f"\n\n✅ 生成成功！耗时 {int(time.time() - start_time)}秒")
                 print(f"📂 文件已保存至：{self.output_path}")
                 return True
             return False
 
+        except APITimeoutError as e:
+            print(f"\n\n❌ 请求超时：{str(e)}")
+            return False
+        except APIError as e:
+            print(f"\n\n❌ API错误：{str(e)}")
+            return False
         except Exception as e:
-            print(f"\n\n❌ 生成失败：{str(e)}")
+            print(f"\n\n❌ 未处理异常：{str(e)}")
             return False
 
 
@@ -107,11 +112,7 @@ def generate_document(
         template_path: Optional[str] = None
 ):
     """
-    生成文档入口函数
-
-    :param course_name: 课程名称
-    :param output_path: 输出路径（可选）
-    :param template_path: 模板路径（可选）
+    生成文档入口函数（保持不变）
     """
     try:
         generator = MarkdownGenerator(
@@ -122,9 +123,4 @@ def generate_document(
         return generator.run()
     except Exception as e:
         print(f"❌ 初始化失败：{str(e)}")
-        return False
-
-
-
-
-
+        return False

AI/Neo4j/KnowledgeGraph.py

@@ -1,30 +1,20 @@
 # -*- coding: utf-8 -*-
 import time
-from typing import Iterator
-
+from typing import Iterator, Tuple
 from openai import OpenAI
 from openai.types.chat import ChatCompletionChunk
-
-from Config import API_KEY, MODEL_R1, MODEL_URL  # 确保导入配置
+from Config import API_KEY, MODEL_R1, MODEL_URL
 
 
 class KnowledgeGraph:
     def __init__(self, shiti_content: str):
-        """
-        初始化生成器
-        """
         self.shiti_content = shiti_content
-        self.client = OpenAI(
-            api_key=API_KEY,
-            base_url=MODEL_URL
-        )
+        self.client = OpenAI(api_key=API_KEY, base_url=MODEL_URL)
 
     def _generate_stream(self) -> Iterator[ChatCompletionChunk]:
-        """流式生成内容"""
         system_prompt = '''回答以下内容:
 1. 这道题目有哪些知识点，哪些能力点
 2. 生成Neo4j 5.26.2的插入语句'''
-
         return self.client.chat.completions.create(
             model=MODEL_R1,
             messages=[
@@ -35,8 +25,8 @@ class KnowledgeGraph:
             timeout=300
         )
 
-    def run(self) -> bool:
-        """执行生成流程"""
+    def run(self) -> Tuple[bool, str]:
+        """执行生成流程（返回状态和完整内容）"""
         start_time = time.time()
         spinner = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
         content_buffer = []
@@ -57,13 +47,17 @@ class KnowledgeGraph:
                     print(content_chunk, end="", flush=True)
 
             if content_buffer:
+                full_content = ''.join(content_buffer)
                 print(f"\n\n✅ 生成成功！耗时 {int(time.time() - start_time)}秒")
-                return True
-            return False
+                print("\n================ 完整结果 ================")
+                print(full_content)
+                print("========================================")
+                return True, full_content
+            return False, ""
 
         except Exception as e:
             print(f"\n\n❌ 生成失败：{str(e)}")
-            return False
+            return False, str(e)
 
 
 if __name__ == '__main__':
@@ -72,4 +66,9 @@ if __name__ == '__main__':
     把7个完全相同的小长方形拼成如图的样子，已知每个小长方形的长是10厘米，则拼成的大长方形的周长是多少厘米？    
     '''
     kg = KnowledgeGraph(shiti_content)
-    kg.run()
+    success, result = kg.run()  # 获取返回结果
+
+    # 如果需要进一步处理结果
+    if success:
+        with open("result.txt", "w", encoding="utf-8") as f:
+            f.write(result)