add openaiSDK

README.md

@@ -16,11 +16,19 @@ Email：35252986@qq.com
 - 数据库管理
 - 支持针对特定站点的自定义爬虫集成，并提供本地定时扫描任务……
 
+## change log
+
+【2024.5.8】增加对openai SDK的支持，现在可以通过调用llms.openai_wrapper使用所有兼容openai SDK的大模型服务，具体见 [client/backend/llms/README.md](client/backend/llms/README.md)
+
 **产品介绍视频：**
 
 [![wiseflow repo demo](https://res.cloudinary.com/marcomontalbano/image/upload/v1714005731/video_to_markdown/images/youtube--80KqYgE8utE-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.bilibili.com/video/BV17F4m1w7Ed/?share_source=copy_web&vd_source=5ad458dc9dae823257e82e48e0751e25 "wiseflow repo demo")
 
-打不开看这里：https://www.bilibili.com/video/BV17F4m1w7Ed/?share_source=copy_web&vd_source=5ad458dc9dae823257e82e48e0751e25
+**打不开看这里**
+
+Youtube：https://www.youtube.com/watch?v=80KqYgE8utE&t=8s
+
+b站：https://www.bilibili.com/video/BV17F4m1w7Ed/?share_source=copy_web&vd_source=5ad458dc9dae823257e82e48e0751e25
 
 ## getting started
 

client/backend/llms/README.md

@@ -1,6 +1,8 @@
-## 使用阿里灵积提供的API接口服务
+## 考虑到很多LLM提供商都兼容openai SDK，所以本项目目前提供dashscope和openai两种wrapper，不管使用哪个都请提前设定
 
-export DASHSCOPE_API_KEY=
+export LLM_API_KEY=
+
+二者的用法也是完全一样的
 
 ```python
 from llms.dashscope_wrapper import dashscope_llm
@@ -9,14 +11,15 @@ result = dashscope_llm([{'role': 'system', 'content': '''}, {'role': 'user', 'co
                        logger=logger)
 ```
 
-## 使用智谱提供的API接口服务（暂时只支持glm4）
+*若要使用openai wrapper调用非openai服务，请提前设定
 
-export ZHIPUAI_API_KEY=
+export LLM_API_BASE=
 
 ```python
-from llms.zhipu_wrapper import zhipuai_llm
+from llms.openai_wrapper import openai_llm
 
-result = zhipuai_llm([{'role': 'system', 'content': ''}, {'role': 'user', 'content': ''}], logger=logger)
+result = openai_llm([{'role': 'system', 'content': '''}, {'role': 'user', 'content': '''}], 'deepseek-chat',
+                       logger=logger)
 ```
 
 ## 对于本地部署模型的支持

client/backend/llms/dashscope_wrapper.py

@@ -5,37 +5,32 @@ import time
 from http import HTTPStatus
 import dashscope
 import random
+import os
 
 
-def dashscope_llm(messages: list,
+DASHSCOPE_KEY = os.getenv("LLM_API_KEY")
-                  model: str,
+if not DASHSCOPE_KEY:
-                  seed: int = 1234,
+    raise ValueError("请指定LLM_API_KEY的环境变量")
-                  max_tokens: int = 2000,
+dashscope.api_key = DASHSCOPE_KEY
-                  temperature: float = 1,
+
-                  stop: list = None,
+
-                  enable_search: bool = False,
+def dashscope_llm(messages: list, model: str, logger=None, **kwargs) -> str:
-                  logger=None) -> str:
 
     if logger:
         logger.debug(f'messages:\n {messages}')
-        logger.debug(f'params:\n model: {model}, max_tokens: {max_tokens}, temperature: {temperature}, stop: {stop},'
+        logger.debug(f'model: {model}')
-                     f'enable_search: {enable_search}, seed: {seed}')
+        logger.debug(f'kwargs:\n {kwargs}')
 
-    for i in range(3):
     response = dashscope.Generation.call(
-            model=model,
         messages=messages,
-            max_tokens=max_tokens,
+        model=model,
-            temperature=temperature,
-            stop=stop,
-            enable_search=enable_search,
-            seed=seed,
         result_format='message',  # set the result to be "message" format.
+        **kwargs
     )
 
+    for i in range(2):
         if response.status_code == HTTPStatus.OK:
             break
-
         if response.message == "Input data may contain inappropriate content.":
             break
 
@@ -45,7 +40,13 @@ def dashscope_llm(messages: list,
             print(f"request failed. code: {response.code}, message:{response.message}\nretrying...")
 
         time.sleep(1 + i*30)
-        seed = random.randint(1, 10000)
+        kwargs['seed'] = random.randint(1, 10000)
+        response = dashscope.Generation.call(
+            messages=messages,
+            model=model,
+            result_format='message',  # set the result to be "message" format.
+            **kwargs
+        )
 
     if response.status_code != HTTPStatus.OK:
         if logger:

client/backend/llms/openai_wrapper.py

@@ -1,48 +1,60 @@
+'''
+除了openai外，很多大模型提供商也都使用openai的SDK，对于这一类可以统一使用本wrapper
+这里演示使用deepseek提供的DeepSeek-V2
+'''
+
+import random
 import os
-
+from openai import OpenAI
-from zhipuai import ZhipuAI
+import time
-
-zhipu_token = os.environ.get('ZHIPUAI_API_KEY', "")
-if not zhipu_token:
-    raise ValueError('请设置环境变量ZHIPUAI_API_KEY')
-
-client = ZhipuAI(api_key=zhipu_token)  # 填写您自己的APIKey
 
 
-def zhipuai_llm(messages: list,
+token = os.environ.get('LLM_API_KEY', "")
-                  model: str,
+if not token:
-                  seed: int = 1234,
+    raise ValueError('请设置环境变量LLM_API_KEY')
-                  max_tokens: int = 2000,
+
-                  temperature: float = 0.8,
+base_url = os.environ.get('LLM_API_BASE', "")
-                  stop: list = None,
+
-                  enable_search: bool = False,
+client = OpenAI(api_key=token, base_url=base_url)
-                  logger=None) -> str:
+
+
+def openai_llm(messages: list, model: str, logger=None, **kwargs) -> str:
 
     if logger:
         logger.debug(f'messages:\n {messages}')
-        logger.debug(f'params:\n model: {model}, max_tokens: {max_tokens}, temperature: {temperature}, stop: {stop},'
+        logger.debug(f'model: {model}')
-                     f'enable_search: {enable_search}, seed: {seed}')
+        logger.debug(f'kwargs:\n {kwargs}')
 
-    for i in range(3):
+    response = client.chat.completions.create(messages=messages, model=model, **kwargs)
-        try:
+
-            response = client.chat.completions.create(
+    for i in range(2):
-                model="glm-4",  # 填写需要调用的模型名称
-                seed=seed,
-                messages=messages,
-                temperature=temperature,
-                max_tokens=max_tokens,
-            )
         if response and response.choices:
             break
-        except Exception as e:
-            if logger:
-                logger.error(f'error:\n {e}')
-            else:
-                print(e)
-            continue
 
         if logger:
-        logger.debug(f'result:\n {response}')
+            logger.warning(f"request failed. code: {response}\nretrying...")
+        else:
+            print(f"request failed. code: {response}\nretrying...")
+
+        time.sleep(1 + i * 30)
+        kwargs['seed'] = random.randint(1, 10000)
+        response = client.chat.completions.create(
+            messages=messages,
+            model=model,
+            **kwargs
+        )
+
+    if not response or not response.choices:
+        if logger:
+            logger.warning(
+                f"request failed. code: {response}\nabort after multiple retries...")
+        else:
+            print(
+                f"request failed. code: {response}\naborted after multiple retries...")
+        return ''
+
+    if logger:
+        logger.debug(f'result:\n {response.choices[0]}')
         logger.debug(f'usage:\n {response.usage}')
 
     return response.choices[0].message.content
@@ -76,5 +88,5 @@ Hackers that breached Las Vegas casinos rely on violent threats, research shows'
 
     data = [{'role': 'user', 'content': user_content}]
     start_time = time.time()
-    pprint(zhipuai_llm(data, 'glm-4'))
+    pprint(openai_llm(data, "deepseek-chat"))
     print(f'time cost: {time.time() - start_time}')

client/backend/requirements.txt

@@ -2,6 +2,7 @@ fastapi
 pydantic
 uvicorn
 dashscope #optional（使用阿里灵积时安装）
+openai #optional（使用兼容openai sdk的llm服务时安装）
 volcengine #optional(使用火山翻译时安装）
 python-docx
 BCEmbedding==0.1.3

client/env_sample

@@ -1,4 +1,5 @@
-export DASHSCOPE_API_KEY=""
+export LLM_API_KEY=""
+export LLM_API_BASE="" ##使用本地模型服务或者使用openai_wrapper调用非openai服务时用
 export VOLC_KEY="AK|SK"
 
 #**for embeddig model**