增加了日志，记录调用zhipu api的时间

app/services/zhipu_service.py

@@ -1,6 +1,6 @@
-from zhipuai import ZhipuAI
 import logging
-
+import time
+from zhipuai import ZhipuAI
 
 logger = logging.getLogger(__name__)
 
@@ -8,44 +8,61 @@ class ZhipuService:
     def __init__(self):
         self.model_name = "glm-4"
         self.app_secret_key = "d54f764a1d67c17d857bd3983b772016.GRjowY0fyiMNurLc"
+        logger.info("ZhipuService initialized with model: %s", self.model_name)
 
     def talk_to_zhipu(self, message):
-        client = ZhipuAI(api_key=self.app_secret_key) # 请填写您自己的APIKey
+        logger.info("Starting talk_to_zhipu call")
+        start_time = time.time()
+        client = ZhipuAI(api_key=self.app_secret_key)
+        try:
             response = client.chat.completions.create(
-            model=self.model_name,  # 填写需要调用的模型名称
+                model=self.model_name,
                 messages=[
                     {"role": "user", "content": message},
                 ],
-            stream=False, # 流式输出
+                stream=False,
-            temperature= 0.01,  #随机度，越大越发散，0.01则是一个比较确定和稳定的输出
+                temperature=0.01,
-            top_p= 0.1, #选择前 10% 概率的 tokens 作为候选，也是影响随机程度
+                top_p=0.1,
             )
             accum_resp = response.choices[0].message.content
-
+            end_time = time.time()
+            logger.info("talk_to_zhipu call completed in %.2f seconds", end_time - start_time)
             return accum_resp
+        except Exception as e:
+            logger.error("Error in talk_to_zhipu: %s", str(e))
+            raise
 
     def talk_to_zhipu_sse(self, message):
-        client = ZhipuAI(api_key=self.app_secret_key) # 请填写您自己的APIKey
+        logger.info("Starting talk_to_zhipu_sse call")
+        start_time = time.time()
+        client = ZhipuAI(api_key=self.app_secret_key)
+        try:
             response = client.chat.completions.create(
-            model=self.model_name,  # 填写需要调用的模型名称
+                model=self.model_name,
                 messages=[
                     {"role": "user", "content": message},
                 ],
-            stream=True, # 流式输出
+                stream=True,
-            temperature= 0.01,  #随机度，越大越发散，0.01则是一个比较确定和稳定的输出
+                temperature=0.01,
-            top_p= 0.1, #选择前 10% 概率的 tokens 作为候选，也是影响随机程度
+                top_p=0.1,
             )
-
             for chunk in response:
-            print(chunk.choices[0].delta.content)
                 yield chunk.choices[0].delta.content
+            end_time = time.time()
+            logger.info("talk_to_zhipu_sse call completed in %.2f seconds", end_time - start_time)
+        except Exception as e:
+            logger.error("Error in talk_to_zhipu_sse: %s", str(e))
+            raise
 
     def retrive(self, message, knowledge_id, prompt_template):
+        logger.info("Starting retrive call with knowledge_id: %s", knowledge_id)
+        start_time = time.time()
         client = ZhipuAI(api_key=self.app_secret_key)
         default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就用自身知识回答并且告诉用户该信息不是来自文档。\n不要复述问题，直接开始回答。"
 
         if prompt_template is None or prompt_template == "":
             prompt_template = default_prompt
+        try:
             response = client.chat.completions.create(
                 model="glm-4",
                 messages=[
@@ -64,15 +81,24 @@ class ZhipuService:
                 temperature=0.01,
                 top_p=0.1,
             )
-        return response.choices[0].message.content
+            result = response.choices[0].message.content
+            end_time = time.time()
+            logger.info("retrive call completed in %.2f seconds", end_time - start_time)
+            return result
+        except Exception as e:
+            logger.error("Error in retrive: %s", str(e))
+            raise
 
     def retrive_sse(self, message, knowledge_id, prompt_template):
-        client = ZhipuAI(api_key=self.app_secret_key) # 请填写您自己的APIKey
+        logger.info("Starting retrive_sse call with knowledge_id: %s", knowledge_id)
+        start_time = time.time()
+        client = ZhipuAI(api_key=self.app_secret_key)
         default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就用自身知识回答并且告诉用户该信息不是来自文档。\n不要复述问题，直接开始回答。"
         if prompt_template is None or prompt_template == "":
             prompt_template = default_prompt
+        try:
             response = client.chat.completions.create(
-            model="glm-4",  # 填写需要调用的模型名称
+                model="glm-4",
                 messages=[
                     {"role": "user", "content": message},
                 ],
@@ -83,12 +109,16 @@ class ZhipuService:
                             "knowledge_id": knowledge_id,
                             "prompt_template": prompt_template
                         }
-                    } # 标准prompt，可以在上面增加prompt文本，但不要改动已有的标准prompt
+                    }
                 ],
-            stream=True, # 流式输出
+                stream=True,
-            temperature= 0.01,  #随机度，越大越发散，0.01则是一个比较确定和稳定的输出
+                temperature=0.01,
-            top_p= 0.1, #选择前 10% 概率的 tokens 作为候选，也是影响随机程度
+                top_p=0.1,
             )
             for chunk in response:
-            print(chunk.choices[0].delta.content)
                 yield chunk.choices[0].delta.content
+            end_time = time.time()
+            logger.info("retrive_sse call completed in %.2f seconds", end_time - start_time)
+        except Exception as e:
+            logger.error("Error in retrive_sse: %s", str(e))
+            raise