refactor, bug fix(cache donot work when file changed)

mkdocs_ai_summary/chatgpt_api.py

@@ -21,6 +21,31 @@ def ask(prompt, model="gpt-3.5-turbo"):
     return completion.choices[0].message.content
 
 
+def get_cache_dict(cache_dir, file_suffix="_ai_summary_cache2"):
+    cache_file = cache_dir + file_suffix
+    if os.path.exists(cache_file):
+        with open(cache_file, "r+") as f:
+            cache_dict = json.load(f)
+    else:
+        cache_dict = {}
+    return cache_dict
+
+
+def ask_with_cache(question, page, content_md5, model, cache_dict, logger):
+    # asked before
+    if page in cache_dict:
+        if content_md5 == cache_dict[page]["content_md5"]:
+            ai_summary = cache_dict[page]["ai_summary"]
+            logger.info("Using cache.")
+        # asked before, but content changed
+        else:
+            ai_summary = ask(question, model=model)
+    # do not aksed before
+    else:
+        ai_summary = ask(question, model=model)
+    return ai_summary
+
+
 def get_summary_chatgpt(
     page,
     prompt,
@@ -33,29 +58,17 @@ def get_summary_chatgpt(
     question = prompt + markdown
     if cache:
         content_md5 = md5(markdown.encode("utf-8")).hexdigest()
-        cache_file = f"{cache_dir}_ai_summary_cache2.json"
-        if os.path.exists(cache_file):
-            with open(cache_file, "r+") as f:
-                cache_dict = json.load(f)
-        else:
-            cache_dict = {}
-
-        # asked before
-        if page in cache_dict:
-            if content_md5 == cache_dict[page]["content_md5"]:
-                ai_summary = cache_dict[page]["ai_summary"]
-                logger.info("Using cache.")
-            # asked before, but content changed
-            else:
-                ai_summary = ask(question, model=model)
-        # do not aksed before
-        else:
-            ai_summary = ask(question, model=model)
-            cache_dict[page] = {"content_md5": content_md5, "ai_summary": ai_summary}
-            with open(f"{cache_dir}/_ai_summary_cache2.json", "w+") as f:
-                cache_dict = json.dump(cache_dict, f)
+        cache_dict = get_cache_dict(cache_dir, file_suffix="_ai_summary_cache2.json")
+        ai_summary = ask_with_cache(
+            question, page, content_md5, model, cache_dict, logger
+        )
+        # always refresh the cache
+        cache_dict[page] = {"content_md5": content_md5, "ai_summary": ai_summary}
+        with open(f"{cache_dir}/_ai_summary_cache2.json", "w+") as f:
+            cache_dict = json.dump(cache_dict, f, indent=4)
     else:
         ai_summary = ask(question, model=model)
+    removed_line_break = ai_summary.replace(r"\n", "")
     return f"""!!! chatgpt-summary "AI Summary powered by [ChatGPT](https://chat.openai.com/)"
-    {ai_summary}
+    {removed_line_break}
 """

mkdocs_ai_summary/tongyi_api.py

@@ -6,6 +6,8 @@
 from dashscope import Generation
 import logging
 
+from .chatgpt_api import get_cache_dict, ask_with_cache
+
 MAX_LENGTH = 6000
 
 
@@ -52,29 +54,17 @@ def get_summary_tongyi(
     question = (prompt + markdown)[: MAX_LENGTH - 10]
     if cache:
         content_md5 = md5(markdown.encode("utf-8")).hexdigest()
-        cache_file = f"{cache_dir}_ai_summary_cache.json"
-        if os.path.exists(cache_file):
-            with open(cache_file, "r+") as f:
-                cache_dict = json.load(f)
-        else:
-            cache_dict = {}
-
-        # asked before
-        if page in cache_dict:
-            if content_md5 == cache_dict[page]["content_md5"]:
-                ai_summary = cache_dict[page]["ai_summary"]
-                logger.info("Using cache.")
-            # asked before, but content changed
-            else:
-                ai_summary = ask(question, model=model)
-        # do not aksed before
-        else:
-            ai_summary = ask(question, model=model)
-            cache_dict[page] = {"content_md5": content_md5, "ai_summary": ai_summary}
-            with open(f"{cache_dir}/_ai_summary_cache.json", "w+") as f:
-                cache_dict = json.dump(cache_dict, f)
+        cache_dict = get_cache_dict(cache_dir, file_suffix="_ai_summary_cache1.json")
+        ai_summary = ask_with_cache(
+            question, page, content_md5, model, cache_dict, logger
+        )
+        # always refresh the cache
+        cache_dict[page] = {"content_md5": content_md5, "ai_summary": ai_summary}
+        with open(f"{cache_dir}/_ai_summary_cache2.json", "w+") as f:
+            cache_dict = json.dump(cache_dict, f, indent=4)
     else:
         ai_summary = ask(question, model=model)
+    removed_line_break = ai_summary.replace(r"\n", "")
     return f"""!!! tongyiai-summary "AI Summary powered by [通义千问](https://tongyi.aliyun.com/)"
-    {ai_summary}
+    {removed_line_break}
 """

pyproject.toml

@@ -7,7 +7,7 @@ where = ["."]
 
 [project]
 name = "mkdocs-ai-summary"
-version = "0.2.3"
+version = "0.2.4"
 requires-python = ">=3.10"
 dependencies = [
     "mkdocs>=1.5.3",

site/withouth1/index.html

@@ -706,10 +706,6 @@
 
   <h1>内置关键字(without h1, tongyi ai summary)</h1>
 
-<div class="admonition tongyiai-summary">
-<p class="admonition-title">AI Summary powered by <a href="https://tongyi.aliyun.com/">通义千问</a></p>
-<p>Python的语法学习包含单行逻辑处理和多行逻辑块，如缩进表示代码结构，4个空格为推荐缩进。自定义函数通过<code>def</code>声明，接受参数，可返回值，支持按位置、名或混合方式传递。<code>lambda</code>用于定义匿名函数。类定义涉及<code>__new__</code>和<code>__init__</code>方法，用于对象创建和初始化，支持类的继承。条件语句如<code>if</code>、<code>elif</code>用于基于布尔表达式的逻辑判断，<code>input</code>函数获取用户输入。<code>for</code>循环遍历可迭代对象，<code>range</code>和<code>iterable</code>是关键。<code>while</code>循环在条件为真时持续执行。循环控制关键字包括<code>continue</code>跳过当前循环，<code>break</code>退出循环，<code>pass</code>无操作。循环的<code>else</code>子句在正常结束时执行。Python 3.10引入了<code>match</code>语句。<code>for</code>和<code>while</code>循环有不同的适用场景和控制结构。</p>
-</div>
 <blockquote>
 <p>copy from <a href="https://yangzhang.site/Python/BasicSyntax/builtin_keyword/">https://yangzhang.site/Python/BasicSyntax/builtin_keyword/</a></p>
 </blockquote>