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add openaiSDK
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README.md
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README.md
@ -16,11 +16,19 @@ Email：35252986@qq.com
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- 数据库管理
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- 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……
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## change log
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【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)
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**产品介绍视频：**
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[](https://www.bilibili.com/video/BV17F4m1w7Ed/?share_source=copy_web&vd_source=5ad458dc9dae823257e82e48e0751e25 "wiseflow repo demo")
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打不开看这里：https://www.bilibili.com/video/BV17F4m1w7Ed/?share_source=copy_web&vd_source=5ad458dc9dae823257e82e48e0751e25
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**打不开看这里**
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Youtube：https://www.youtube.com/watch?v=80KqYgE8utE&t=8s
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b站：https://www.bilibili.com/video/BV17F4m1w7Ed/?share_source=copy_web&vd_source=5ad458dc9dae823257e82e48e0751e25
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## getting started
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@ -1,6 +1,8 @@
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## 使用阿里灵积提供的API接口服务
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## 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
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export DASHSCOPE_API_KEY=
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export LLM_API_KEY=
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二者的用法也是完全一样的
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```python
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from llms.dashscope_wrapper import dashscope_llm
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@ -9,14 +11,15 @@ result = dashscope_llm([{'role': 'system', 'content': '''}, {'role': 'user', 'co
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logger=logger)
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```
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## 使用智谱提供的API接口服务（暂时只支持glm4）
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*若要使用openai wrapper调用非openai服务，请提前设定
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export ZHIPUAI_API_KEY=
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export LLM_API_BASE=
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```python
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from llms.zhipu_wrapper import zhipuai_llm
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from llms.openai_wrapper import openai_llm
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result = zhipuai_llm([{'role': 'system', 'content': ''}, {'role': 'user', 'content': ''}], logger=logger)
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result = openai_llm([{'role': 'system', 'content': '''}, {'role': 'user', 'content': '''}], 'deepseek-chat',
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logger=logger)
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```
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## 对于本地部署模型的支持
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@ -5,37 +5,32 @@ import time
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from http import HTTPStatus
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import dashscope
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import random
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import os
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def dashscope_llm(messages: list,
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model: str,
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seed: int = 1234,
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max_tokens: int = 2000,
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temperature: float = 1,
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stop: list = None,
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enable_search: bool = False,
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logger=None) -> str:
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DASHSCOPE_KEY = os.getenv("LLM_API_KEY")
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if not DASHSCOPE_KEY:
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raise ValueError("请指定LLM_API_KEY的环境变量")
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dashscope.api_key = DASHSCOPE_KEY
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def dashscope_llm(messages: list, model: str, logger=None, **kwargs) -> str:
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if logger:
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logger.debug(f'messages:\n {messages}')
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logger.debug(f'params:\n model: {model}, max_tokens: {max_tokens}, temperature: {temperature}, stop: {stop},'
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f'enable_search: {enable_search}, seed: {seed}')
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logger.debug(f'model: {model}')
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logger.debug(f'kwargs:\n {kwargs}')
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for i in range(3):
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response = dashscope.Generation.call(
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model=model,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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stop=stop,
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enable_search=enable_search,
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seed=seed,
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result_format='message', # set the result to be "message" format.
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)
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response = dashscope.Generation.call(
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messages=messages,
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model=model,
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result_format='message', # set the result to be "message" format.
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**kwargs
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)
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for i in range(2):
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if response.status_code == HTTPStatus.OK:
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break
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if response.message == "Input data may contain inappropriate content.":
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break
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@ -45,7 +40,13 @@ def dashscope_llm(messages: list,
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print(f"request failed. code: {response.code}, message:{response.message}\nretrying...")
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time.sleep(1 + i*30)
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seed = random.randint(1, 10000)
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kwargs['seed'] = random.randint(1, 10000)
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response = dashscope.Generation.call(
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messages=messages,
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model=model,
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result_format='message', # set the result to be "message" format.
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**kwargs
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)
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if response.status_code != HTTPStatus.OK:
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if logger:
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@ -1,48 +1,60 @@
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'''
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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
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这里演示使用deepseek提供的DeepSeek-V2
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'''
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import random
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import os
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from zhipuai import ZhipuAI
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zhipu_token = os.environ.get('ZHIPUAI_API_KEY', "")
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if not zhipu_token:
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raise ValueError('请设置环境变量ZHIPUAI_API_KEY')
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client = ZhipuAI(api_key=zhipu_token) # 填写您自己的APIKey
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from openai import OpenAI
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import time
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def zhipuai_llm(messages: list,
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model: str,
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seed: int = 1234,
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max_tokens: int = 2000,
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temperature: float = 0.8,
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stop: list = None,
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enable_search: bool = False,
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logger=None) -> str:
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token = os.environ.get('LLM_API_KEY', "")
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if not token:
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raise ValueError('请设置环境变量LLM_API_KEY')
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base_url = os.environ.get('LLM_API_BASE', "")
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client = OpenAI(api_key=token, base_url=base_url)
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def openai_llm(messages: list, model: str, logger=None, **kwargs) -> str:
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if logger:
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logger.debug(f'messages:\n {messages}')
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logger.debug(f'params:\n model: {model}, max_tokens: {max_tokens}, temperature: {temperature}, stop: {stop},'
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f'enable_search: {enable_search}, seed: {seed}')
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logger.debug(f'model: {model}')
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logger.debug(f'kwargs:\n {kwargs}')
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for i in range(3):
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try:
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response = client.chat.completions.create(
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model="glm-4", # 填写需要调用的模型名称
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seed=seed,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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if response and response.choices:
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break
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except Exception as e:
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if logger:
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logger.error(f'error:\n {e}')
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else:
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print(e)
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continue
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response = client.chat.completions.create(messages=messages, model=model, **kwargs)
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for i in range(2):
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if response and response.choices:
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break
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if logger:
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logger.warning(f"request failed. code: {response}\nretrying...")
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else:
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print(f"request failed. code: {response}\nretrying...")
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time.sleep(1 + i * 30)
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kwargs['seed'] = random.randint(1, 10000)
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response = client.chat.completions.create(
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messages=messages,
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model=model,
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**kwargs
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)
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if not response or not response.choices:
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if logger:
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logger.warning(
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f"request failed. code: {response}\nabort after multiple retries...")
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else:
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print(
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f"request failed. code: {response}\naborted after multiple retries...")
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return ''
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if logger:
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logger.debug(f'result:\n {response}')
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logger.debug(f'result:\n {response.choices[0]}')
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logger.debug(f'usage:\n {response.usage}')
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return response.choices[0].message.content
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@ -76,5 +88,5 @@ Hackers that breached Las Vegas casinos rely on violent threats, research shows'
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data = [{'role': 'user', 'content': user_content}]
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start_time = time.time()
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pprint(zhipuai_llm(data, 'glm-4'))
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pprint(openai_llm(data, "deepseek-chat"))
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print(f'time cost: {time.time() - start_time}')
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@ -2,6 +2,7 @@ fastapi
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pydantic
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uvicorn
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dashscope #optional（使用阿里灵积时安装）
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openai #optional（使用兼容openai sdk的llm服务时安装）
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volcengine #optional(使用火山翻译时安装）
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python-docx
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BCEmbedding==0.1.3
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@ -1,4 +1,5 @@
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export DASHSCOPE_API_KEY=""
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export LLM_API_KEY=""
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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
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export VOLC_KEY="AK|SK"
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#**for embeddig model**
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