Merge pull request #664 from Ikaros-521/owner

智谱AI新增 应用 功能的对接；动态文案 前端新增文案组添加删除功能，新增LLM类型，可以指定动态文案选用的LLM

config.json

@@ -206,6 +206,7 @@
   "zhipu": {
     "api_key": "",
     "model": "chatglm_lite",
+    "app_id": "1761340125461340161",
     "top_p": "0.7",
     "temperature": "0.9",
     "history_enable": true,
@@ -744,6 +745,7 @@
   },
   "trends_copywriting": {
     "enable": false,
+    "llm_type": "chatgpt",
     "copywriting": [
       {
         "folder_path": "data/动态文案1",

config.json.bak

@@ -206,6 +206,7 @@
   "zhipu": {
     "api_key": "",
     "model": "chatglm_lite",
+    "app_id": "1761340125461340161",
     "top_p": "0.7",
     "temperature": "0.9",
     "history_enable": true,
@@ -744,6 +745,7 @@
   },
   "trends_copywriting": {
     "enable": false,
+    "llm_type": "chatgpt",
     "copywriting": [
       {
         "folder_path": "data/动态文案1",

main.py

@@ -611,7 +611,7 @@ async def run_trends_copywriting():
                             }
 
                             # 调用函数进行LLM处理，以及生成回复内容，进行音频合成，需要好好考虑考虑实现
-                            data_json["content"] = my_handle.llm_handle(config.get("chat_type"), data_json)
+                            data_json["content"] = my_handle.llm_handle(config.get("trends_copywriting", "llm_type"), data_json)
                         else:
                             data_json = {
                                 "username": "trends_copywriting",

requirements.txt

@@ -70,4 +70,5 @@ pygtrans
 jieba
 gradio==4.16.0
 TikTokLive
-azure-cognitiveservices-speech
+azure-cognitiveservices-speech
+pyjwt

requirements_common.txt

@@ -148,4 +148,5 @@ pygtrans==1.5.3
 jieba==0.42.1
 gradio==4.16.0
 TikTokLive==5.0.8
-azure-cognitiveservices-speech==1.35.0
+azure-cognitiveservices-speech==1.35.0
+pyjwt==2.8.0

tests/test_zhipu/api.py

@@ -1,5 +1,13 @@
 import zhipuai
 import logging
+import time
+import jwt  # 确保这是 PyJWT 库
+import requests
+import traceback
+from urllib.parse import urljoin
+
+# from utils.common import Common
+# from utils.logger import Configure_logger
 
 
 class Zhipu:
@@ -9,6 +17,8 @@ def __init__(self, data):
         # file_path = "./log/log-" + self.common.get_bj_time(1) + ".txt"
         # Configure_logger(file_path)
 
+        self.config_data = data
+
         zhipuai.api_key = data["api_key"]
         self.model = data["model"]
         self.top_p = float(data["top_p"])
@@ -21,8 +31,44 @@ def __init__(self, data):
         self.bot_name = data["bot_name"]
         self.user_name = data["user_name"]
 
+        # 非SDK
+        self.base_url = "https://open.bigmodel.cn"
+        self.token = None
+        self.headers = None
+        if self.model == "应用":
+            try:
+                self.token = self.generate_token(apikey=self.config_data["api_key"], exp_seconds=30 * 24 * 3600)
+
+                self.headers = {
+                    "Authorization": f"Bearer {self.token}",
+                }
+
+                url = urljoin(self.base_url, "/api/llm-application/open/application")
+
+                data = {
+                    "page": 1,
+                    "size": 100
+                }
+
+                # get请求
+                response = requests.get(url=url, data=data, headers=self.headers)
+
+                logging.debug(response.json())
+
+                resp_json = response.json()
+
+                tmp_content = "智谱应用列表："
+
+                for data in resp_json["data"]["list"]:
+                    tmp_content += f"\n应用名：{data['name']}，应用ID：{data['id']}，知识库：{data['knowledge_ids']}"
+
+                logging.info(tmp_content)
+            except Exception as e:
+                logging.error(traceback.format_exc())
+
         self.history = []
 
+    # 进行智谱AI 通用大模型请求
     def invoke_example(self, prompt):
         response = zhipuai.model_api.invoke(
             model=self.model,
@@ -34,6 +80,7 @@ def invoke_example(self, prompt):
 
         return response
 
+    # 进行characterglm请求
     def invoke_characterglm(self, prompt):
         response = zhipuai.model_api.invoke(
             model=self.model,
@@ -51,6 +98,7 @@ def invoke_characterglm(self, prompt):
 
         return response
 
+
     def async_invoke_example(self, prompt):
         response = zhipuai.model_api.async_invoke(
             model=self.model,
@@ -96,6 +144,28 @@ def query_async_invoke_result_example(self):
 
         return response
 
+    # 非SDK鉴权
+    def generate_token(self, apikey: str, exp_seconds: int):
+        try:
+            id, secret = apikey.split(".")
+        except Exception as e:
+            raise Exception("invalid apikey", e)
+
+        payload = {
+            "api_key": id,
+            "exp": int(round(time.time())) + exp_seconds,  # PyJWT中exp字段期望的是秒级的时间戳
+            "timestamp": int(round(time.time() * 1000)),  # 如果需要毫秒级时间戳，可以保留这一行
+        }
+
+        # 使用PyJWT编码payload
+        token = jwt.encode(
+            payload,
+            secret,
+            headers={"alg": "HS256", "sign_type": "SIGN"}
+        )
+
+        return token
+
 
     def get_resp(self, prompt):
         """请求对应接口，获取返回值
@@ -117,13 +187,61 @@ def get_resp(self, prompt):
 
             if self.model == "characterglm":
                 ret = self.invoke_characterglm(data_json)
+            elif self.model == "应用":
+                url = urljoin(self.base_url, f"/api/llm-application/open/model-api/{self.config_data['app_id']}/invoke")
+
+                self.history.append({"role": "user", "content": prompt})
+                data = {
+                    "prompt": self.history,
+                    "returnType": "json_string",
+                    # "knowledge_ids": [],
+                    # "document_ids": []
+                }
+
+                response = requests.post(url=url, json=data, headers=self.headers)
+
+                try:
+                    resp_json = response.json()
+
+                    logging.debug(resp_json)
+
+                    resp_content = resp_json["data"]["content"]
+
+                    # 启用历史就给我记住！
+                    if self.history_enable:
+                        # 把机器人回答添加到历史记录中
+                        self.history.append({"role": "assistant", "content": resp_content})
+
+                        while True:
+                            # 获取嵌套列表中所有字符串的字符数
+                            total_chars = sum(len(string) for sublist in self.history for string in sublist)
+                            # 如果大于限定最大历史数，就剔除第1 2个元素
+                            if total_chars > self.history_max_len:
+                                self.history.pop(0)
+                                self.history.pop(0)
+                            else:
+                                break
+
+                    return resp_content
+                except Exception as e:
+                    def is_odd(number):
+                        # 检查数除以2的余数是否为1
+                        return number % 2 != 0
+
+                    # 保持history始终为偶数个
+                    if is_odd(len(self.history)):
+                        self.history.pop(0)
+
+                    logging.error(traceback.format_exc())
+                    return None
+
             else:
                 ret = self.invoke_example(data_json)
 
             logging.debug(f"ret={ret}")
 
             if False == ret['success']:
-                logging.error(f"请求zhipuai失败，错误代码：{ret['code']}，{ret['msg']}")
+                logging.error(f"请求智谱ai失败，错误代码：{ret['code']}，{ret['msg']}")
                 return None
 
             # 启用历史就给我记住！
@@ -140,7 +258,7 @@ def get_resp(self, prompt):
 
             return ret['data']['choices'][0]['content']
         except Exception as e:
-            logging.error(e)
+            logging.error(traceback.format_exc())
             return None
 
 
@@ -154,6 +272,7 @@ def get_resp(self, prompt):
 
     data = {
         "api_key": "",
+        "app_id": "1761340125461340161",
         # chatglm_pro/chatglm_std/chatglm_lite/characterglm
         "model": "characterglm",
         "top_p": 0.7,

tests/test_zhipu/app_api.py

@@ -0,0 +1,112 @@
+# import time
+# import jwt
+
+# def generate_token(apikey: str, exp_seconds: int):
+#     try:
+#         id, secret = apikey.split(".")
+#     except Exception as e:
+#         raise Exception("invalid apikey", e)
+
+#     payload = {
+#         "api_key": id,
+#         "exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
+#         "timestamp": int(round(time.time() * 1000)),
+#     }
+
+#     tmp = jwt.JWT()
+
+#     return tmp.encode(
+#         payload,
+#         secret,
+#         alg="HS256",
+#         optional_headers={"alg": "HS256", "sign_type": "SIGN"},
+#     )
+
+# print(generate_token("25571a2d0af8583121c3248be7e89d29.AuIX3O5ktKuQVMtC", 30 * 24 * 3600))
+
+import time
+import jwt  # 确保这是 PyJWT 库
+import requests
+from urllib.parse import urljoin
+
+def generate_token(apikey: str, exp_seconds: int):
+    try:
+        id, secret = apikey.split(".")
+    except Exception as e:
+        raise Exception("invalid apikey", e)
+
+    payload = {
+        "api_key": id,
+        "exp": int(round(time.time())) + exp_seconds,  # PyJWT中exp字段期望的是秒级的时间戳
+        "timestamp": int(round(time.time() * 1000)),  # 如果需要毫秒级时间戳，可以保留这一行
+    }
+
+    # 使用PyJWT编码payload
+    token = jwt.encode(
+        payload,
+        secret,
+        headers={"alg": "HS256", "sign_type": "SIGN"}
+    )
+
+    return token
+
+
+token = generate_token("", 30 * 24 * 3600)
+
+print(token)
+
+base_url = "https://open.bigmodel.cn"
+
+headers = {
+    "Authorization": f"Bearer {token}",
+}
+
+url = urljoin(base_url, "/api/llm-application/open/application")
+
+data = {
+    "page": 1,
+    "size": 20
+}
+
+# get请求
+response = requests.get(url=url, data=data, headers=headers)
+
+print(response.json())
+
+resp_json = response.json()
+
+tmp_content = "智谱应用列表："
+
+app_id = None
+
+try:
+    for data in resp_json["data"]["list"]:
+        tmp_content += f"\n应用名：{data['name']}，应用ID：{data['id']}，知识库：{data['knowledge_ids']}"
+        app_id = data['id']
+
+    print(tmp_content)
+except Exception as e:
+    print(e)
+
+def get_resp(prompt):
+    url = urljoin(base_url, f"/api/llm-application/open/model-api/{app_id}/invoke")
+    data = {
+        "prompt": [{"role": "user", "content": prompt}],
+        "returnType": "json_string",
+        # "knowledge_ids": [],
+        # "document_ids": []
+    }
+
+    response = requests.post(url=url, json=data, headers=headers)
+
+    try:
+        print(response.json())
+
+        resp_json = response.json()
+        resp_content = resp_json["data"]["content"]
+
+        print(resp_content)
+    except Exception as e:
+        print(e)
+
+get_resp("伊卡洛斯和妮姆芙的关系")

utils/common.py

@@ -733,11 +733,11 @@ def write_content_to_file(self, file_path, content, write_log=True):
         try:
             with open(file_path, 'w', encoding='utf-8') as file:
                 file.write(content)
-            logging.info(f"内容已成功写入文件:{file_path}")
+            logging.info(f"写入文件:{file_path}，内容：【{content}】")
 
             return True
         except IOError as e:
-            logging.error(f"无法写入文件:{file_path}\n{e}")
+            logging.error(f"无法写入 【{content}】 到文件:{file_path}\n{e}")
             return False
 
     # 移动文件到指定路径 src dest