'commit'

AI/A1_GenerateMarkdown.py

@@ -11,11 +11,6 @@ from Config import *
 class MarkdownGenerator:
     """Markdown教学大纲生成器"""
 
-    # 固定配置项
-    MODEL_R1 = "deepseek-r1"
-    MODEL_V3 = "deepseek-v3"
-    API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
-
     def __init__(
             self,
             course_name: str,
@@ -61,8 +56,8 @@ class MarkdownGenerator:
         )
 
         return Generation.call(
-            model=self.MODEL_R1,
-            api_key=self.API_KEY,
+            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"}

AI/B3_TextSummarize.py

@@ -4,16 +4,16 @@ from typing import Optional, Tuple, Iterator
 from openai import OpenAI, APIError, APITimeoutError
 import time
 import httpx
-
+from Config import *
 
 class ContentAnalyzer:
     """课程内容分析器（流式版本）"""
 
     def __init__(
             self,
-            api_key: str = "sk-01d13a39e09844038322108ecdbd1bbc",
-            base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1",
-            model: str = "deepseek-r1",
+            api_key: str = API_KEY,
+            base_url: str = MODEL_URL,
+            model: str = MODEL_R1,
             max_retries: int = 10,
             initial_timeout: int = 300
     ):

AI/C2_CheckZuoWen.py

@@ -2,13 +2,14 @@ import os
 import re
 from typing import Iterator
 from openai import OpenAI
+from Config import *
 
 
 class EnglishEssayAnalyzer:
     def __init__(self):
         self.client = OpenAI(
-            api_key=os.getenv("DEEPSEEK_API_KEY", "sk-01d13a39e09844038322108ecdbd1bbc"),
-            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
+            api_key=API_KEY,
+            base_url=MODEL_URL
         )
 
     def _build_prompt(self, essay: str) -> str:
@@ -27,7 +28,7 @@ class EnglishEssayAnalyzer:
         """流式分析作文（新增关键方法）"""
         try:
             stream = self.client.chat.completions.create(
-                model="deepseek-r1",
+                model=MODEL_R1,
                 messages=[{
                     "role": "user",
                     "content": self._build_prompt(essay)
@@ -140,4 +141,4 @@ if __name__ == "__main__":
 
     # 保存Markdown报告
     analyzer.save_markdown_report(result, "analysis_report.md")
-    print("\n\n✅ 分析结果已保存至 analysis_report.md")
+    print("\n\n✅ 分析结果已保存至 analysis_report.md")

AI/Config.py

@@ -1,5 +1,13 @@
 from pathlib import Path
 
+# 阿里云中用来调用deepseek r1的密钥
+API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
+
+# 固定配置项
+MODEL_R1 = "deepseek-r1"
+MODEL_V3 = "deepseek-v3"
+MODEL_URL='https://dashscope.aliyuncs.com/compatible-mode/v1'
+
 # 正确路径拼接方式
 mdWorkingPath = Path(__file__).parent / 'md-file' / 'readme'
 DEFAULT_TEMPLATE = mdWorkingPath / 'default.md'  # 使用 / 运算符

AI/Neo4j/KnowledgeGraph.py

@@ -0,0 +1,84 @@
+# -*- coding: utf-8 -*-
+import time
+from typing import Iterator, Optional
+
+from dashscope import Generation
+from dashscope.api_entities.dashscope_response import DashScopeAPIResponse
+
+from Config import *
+
+
+class KnowledgeGraph:
+    def __init__(
+            self,
+            shiti_content: str,
+    ):
+        """
+        初始化生成器
+        """
+        self.shiti_content = shiti_content
+
+    def _generate_stream(self) -> Iterator[DashScopeAPIResponse]:
+        """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、我准备把你返回的知识点和能力点，存入到neo4j中去，版本是neo4j-community-5.26.2,生成插入到数据库中的语句
+            '''
+        )
+
+        return Generation.call(
+            model=MODEL_R1,
+            api_key=API_KEY,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": shiti_content}
+            ],
+            result_format='message',
+            stream=True,
+            incremental_output=True
+        )
+
+    def run(self) -> bool:
+        """执行生成流程"""
+        start_time = time.time()
+        spinner = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
+        content_buffer = []
+
+        try:
+            print(f"🚀 开始生成知识点和能力点的总结和插入语句")
+            responses = self._generate_stream()
+
+            for idx, response in enumerate(responses):
+                # 显示进度
+                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)
+
+            # 保存结果
+            if content_buffer:
+                print(f"\n\n✅ 生成成功！耗时 {int(time.time() - start_time)}秒")
+                return True
+            return False
+
+        except Exception as e:
+            print(f"\n\n❌ 生成失败：{str(e)}")
+            return False
+
+
+if __name__ == '__main__':
+    shiti_content='''
+    下面是一道小学三年级的数学题目，巧求周长：
+    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？    
+    '''
+    KnowledgeGraph = KnowledgeGraph(shiti_content)
+    KnowledgeGraph.run()
+
+
+

AI/总结.txt

@@ -29,4 +29,7 @@ https://help.aliyun.com/zh/ocr/developer-reference/api-ocr-api-2021-07-07-recogn
 7. 尝试大模型+图数据库，实现知识点与能力点的智能总结，记录统计学生、教师与知识点能力点的关联关系，加速云校智能化进程。
 8. 尝试提供支持多步推理和复杂查询的AI智能体，服务教育理论研究。
 https://m.sohu.com/a/855727104_129720
-https://blog.csdn.net/qq_19841021/article/details/145503629
+https://blog.csdn.net/qq_19841021/article/details/145503629
+
+# 自动构建知识图谱
+https://github.com/neo4j-labs/llm-graph-builder