修复Azure OpenAI接口的各种bug
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qingxu fu
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@ -105,10 +105,10 @@ SLACK_CLAUDE_USER_TOKEN = ''
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# 如果需要使用AZURE 详情请见额外文档 docs\use_azure.md
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AZURE_ENDPOINT = "https://你的api名称.openai.azure.com/"
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AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
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AZURE_API_KEY = "填入azure openai api的密钥"
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AZURE_API_VERSION = "填入api版本"
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AZURE_ENGINE = "填入ENGINE"
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AZURE_API_VERSION = "2023-05-15" # 一般不修改
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AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
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# 使用Newbing
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@ -130,6 +130,11 @@ def request_gpt_model_in_new_thread_with_ui_alive(
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yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
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return final_result
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def can_multi_process(llm):
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if llm.startswith('gpt-'): return True
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if llm.startswith('api2d-'): return True
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if llm.startswith('azure-'): return True
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return False
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def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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inputs_array, inputs_show_user_array, llm_kwargs,
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@ -175,7 +180,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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except: max_workers = 8
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if max_workers <= 0: max_workers = 3
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# 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
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if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
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if not can_multi_process(llm_kwargs['llm_model']):
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max_workers = 1
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executor = ThreadPoolExecutor(max_workers=max_workers)
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@ -90,11 +90,12 @@
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到现在为止，申请操作就完成了，需要记下来的有下面几个东西：
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● 密钥（1或2都可以）
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● 密钥（对应AZURE_API_KEY，1或2都可以）
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● 终结点
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● 终结点 （对应AZURE_ENDPOINT）
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● 部署名（对应AZURE_ENGINE，不是模型名）
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● 部署名（不是模型名）
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# 修改 config.py
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@ -102,50 +103,14 @@
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AZURE_ENDPOINT = "填入终结点"
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AZURE_API_KEY = "填入azure openai api的密钥"
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AZURE_API_VERSION = "2023-05-15" # 默认使用 2023-05-15 版本，无需修改
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AZURE_ENGINE = "填入部署名"
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```
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# API的使用
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接下来就是具体怎么使用API了，还是可以参考官方文档：[快速入门 - 开始通过 Azure OpenAI 服务使用 ChatGPT 和 GPT-4 - Azure OpenAI Service | Microsoft Learn](https://learn.microsoft.com/zh-cn/azure/cognitive-services/openai/chatgpt-quickstart?pivots=programming-language-python)
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和openai自己的api调用有点类似，都需要安装openai库，不同的是调用方式
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```
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import openai
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openai.api_type = "azure" #固定格式，无需修改
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openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") #这里填入“终结点”
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openai.api_version = "2023-05-15" #固定格式，无需修改
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openai.api_key = os.getenv("AZURE_OPENAI_KEY") #这里填入“密钥1”或“密钥2”
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response = openai.ChatCompletion.create(
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engine="gpt-35-turbo", #这里填入的不是模型名，是部署名
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
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{"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
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{"role": "user", "content": "Do other Azure Cognitive Services support this too?"}
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]
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)
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print(response)
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print(response['choices'][0]['message']['content'])
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AZURE_ENGINE = "填入部署名" # 见上图
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```
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需要注意的是：
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1. engine那里填入的是部署名，不是模型名
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2. 通过openai库获得的这个 response 和通过 request 库访问 url 获得的 response 不同，不需要 decode，已经是解析好的 json 了，直接根据键值读取即可。
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更细节的使用方法，详见官方API文档。
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# 关于费用
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Azure OpenAI API 还是需要一些费用的（免费订阅只有1个月有效期），费用如下：
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Azure OpenAI API 还是需要一些费用的（免费订阅只有1个月有效期）
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具体可以可以看这个网址 ：[Azure OpenAI 服务 - 定价| Microsoft Azure](https://azure.microsoft.com/zh-cn/pricing/details/cognitive-services/openai-service/?cdn=disable)
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@ -14,7 +14,8 @@ import traceback
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import importlib
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import openai
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import time
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import requests
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import json
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# 读取config.py文件中关于AZURE OPENAI API的信息
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from toolbox import get_conf, update_ui, clip_history, trimmed_format_exc
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@ -43,7 +44,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
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additional_fn代表点击的哪个按钮，按钮见functional.py
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"""
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print(llm_kwargs["llm_model"])
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if additional_fn is not None:
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import core_functional
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@ -56,7 +56,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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logging.info(f'[raw_input] {raw_input}')
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chatbot.append((inputs, ""))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
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payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream)
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@ -64,20 +63,22 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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retry = 0
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while True:
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try:
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try:
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openai.api_type = "azure"
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openai.api_version = AZURE_API_VERSION
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openai.api_base = AZURE_ENDPOINT
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openai.api_key = AZURE_API_KEY
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response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
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except openai.error.AuthenticationError:
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = [chatbot[-1][0], tb_str]
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yield from update_ui(chatbot=chatbot, history=history, msg="openai返回错误") # 刷新界面
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return
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except:
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retry += 1
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chatbot[-1] = ((chatbot[-1][0], "获取response失败，重试中。。。"))
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retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
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yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
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traceback.print_exc()
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if retry > MAX_RETRY: raise TimeoutError
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if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
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gpt_replying_buffer = ""
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is_head_of_the_stream = True
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@ -141,20 +142,17 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
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retry = 0
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while True:
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try:
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openai.api_type = "azure"
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openai.api_version = AZURE_API_VERSION
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openai.api_base = AZURE_ENDPOINT
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openai.api_key = AZURE_API_KEY
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response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
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except:
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except:
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retry += 1
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traceback.print_exc()
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if retry > MAX_RETRY: raise TimeoutError
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if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
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if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
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stream_response = response
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result = ''
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@ -164,19 +162,14 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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break
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except:
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chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
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if len(chunk)==0: continue
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if not chunk.startswith('data:'):
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error_msg = get_full_error(chunk, stream_response)
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if "reduce the length" in error_msg:
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raise ConnectionAbortedError("AZURE OPENAI API拒绝了请求:" + error_msg)
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else:
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raise RuntimeError("AZURE OPENAI API拒绝了请求：" + error_msg)
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if ('data: [DONE]' in chunk): break
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delta = chunk["delta"]
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if len(delta) == 0: break
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if "role" in delta: continue
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json_data = json.loads(str(chunk))['choices'][0]
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delta = json_data["delta"]
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if len(delta) == 0:
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break
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if "role" in delta:
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continue
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if "content" in delta:
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result += delta["content"]
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if not console_slience: print(delta["content"], end='')
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@ -184,11 +177,14 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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# 观测窗，把已经获取的数据显示出去
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if len(observe_window) >= 1: observe_window[0] += delta["content"]
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# 看门狗，如果超过期限没有喂狗，则终止
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if len(observe_window) >= 2:
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if len(observe_window) >= 2000:
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if (time.time()-observe_window[1]) > watch_dog_patience:
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raise RuntimeError("用户取消了程序。")
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else: raise RuntimeError("意外Json结构："+delta)
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if chunk['finish_reason'] == 'length':
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else:
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raise RuntimeError("意外Json结构："+delta)
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if json_data['finish_reason'] == 'content_filter':
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raise RuntimeError("由于提问含不合规内容被Azure过滤。")
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if json_data['finish_reason'] == 'length':
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raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
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return result
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