镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
add support for Deepseek R1 model and display CoT (#2118)
* feat: add support for R1 model and display CoT * fix unpacking * feat: customized font & font size * auto hide tooltip when scoll down * tooltip glass transparent css * fix: Enhance API key validation in is_any_api_key function (#2113) * support qwen2.5-max! * update minior adjustment --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> Co-authored-by: Steven Moder <java20131114@gmail.com>
这个提交包含在：
Memento mori.
@@ -36,10 +36,11 @@ def get_full_error(chunk, stream_response):
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def decode_chunk(chunk):
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"""
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用于解读"content"和"finish_reason"的内容
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用于解读"content"和"finish_reason"的内容（如果支持思维链也会返回"reasoning_content"内容）
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"""
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chunk = chunk.decode()
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respose = ""
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reasoning_content = ""
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finish_reason = "False"
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try:
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chunk = json.loads(chunk[6:])
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@@ -57,14 +58,20 @@ def decode_chunk(chunk):
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return respose, finish_reason
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try:
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respose = chunk["choices"][0]["delta"]["content"]
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if chunk["choices"][0]["delta"]["content"] is not None:
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respose = chunk["choices"][0]["delta"]["content"]
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except:
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pass
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try:
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if chunk["choices"][0]["delta"]["reasoning_content"] is not None:
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reasoning_content = chunk["choices"][0]["delta"]["reasoning_content"]
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except:
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pass
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try:
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finish_reason = chunk["choices"][0]["finish_reason"]
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except:
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pass
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return respose, finish_reason
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return respose, reasoning_content, finish_reason
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def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
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@@ -149,6 +156,7 @@ def get_predict_function(
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observe_window = None：
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用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
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"""
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from .bridge_all import model_info
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watch_dog_patience = 5 # 看门狗的耐心，设置5秒不准咬人(咬的也不是人
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if len(APIKEY) == 0:
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raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
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@@ -163,29 +171,21 @@ def get_predict_function(
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system_prompt=sys_prompt,
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temperature=llm_kwargs["temperature"],
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)
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reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
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retry = 0
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while True:
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try:
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from .bridge_all import model_info
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endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
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if not disable_proxy:
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response = requests.post(
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endpoint,
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headers=headers,
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proxies=proxies,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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else:
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response = requests.post(
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endpoint,
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headers=headers,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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response = requests.post(
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endpoint,
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headers=headers,
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proxies=None if disable_proxy else proxies,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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break
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except:
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retry += 1
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@@ -194,10 +194,13 @@ def get_predict_function(
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raise TimeoutError
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if MAX_RETRY != 0:
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logger.error(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
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stream_response = response.iter_lines()
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result = ""
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finish_reason = ""
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if reasoning:
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resoning_buffer = ""
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stream_response = response.iter_lines()
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while True:
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try:
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chunk = next(stream_response)
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@@ -207,9 +210,9 @@ def get_predict_function(
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break
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except requests.exceptions.ConnectionError:
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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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response_text, finish_reason = decode_chunk(chunk)
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response_text, reasoning_content, finish_reason = decode_chunk(chunk)
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# 返回的数据流第一次为空，继续等待
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if response_text == "" and finish_reason != "False":
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if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
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continue
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if response_text == "API_ERROR" and (
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finish_reason != "False" or finish_reason != "stop"
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@@ -227,6 +230,8 @@ def get_predict_function(
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print(f"[response] {result}")
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break
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result += response_text
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if reasoning:
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resoning_buffer += reasoning_content
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if observe_window is not None:
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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:
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@@ -241,6 +246,10 @@ def get_predict_function(
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error_msg = chunk_decoded
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logger.error(error_msg)
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raise RuntimeError("Json解析不合常规")
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if reasoning:
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# reasoning 的部分加上框 (>)
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return '\n'.join(map(lambda x: '> ' + x, resoning_buffer.split('\n'))) + \
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'\n\n' + result
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return result
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def predict(
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@@ -262,6 +271,7 @@ def get_predict_function(
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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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from .bridge_all import model_info
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if len(APIKEY) == 0:
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raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
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if inputs == "":
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@@ -298,32 +308,23 @@ def get_predict_function(
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system_prompt=system_prompt,
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temperature=llm_kwargs["temperature"],
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)
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reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
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history.append(inputs)
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history.append("")
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retry = 0
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while True:
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try:
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from .bridge_all import model_info
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endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
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if not disable_proxy:
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response = requests.post(
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endpoint,
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headers=headers,
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proxies=proxies,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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else:
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response = requests.post(
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endpoint,
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headers=headers,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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response = requests.post(
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endpoint,
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headers=headers,
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proxies=None if disable_proxy else proxies,
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json=playload,
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stream=True,
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timeout=TIMEOUT_SECONDS,
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)
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break
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except:
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retry += 1
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@@ -338,6 +339,8 @@ def get_predict_function(
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raise TimeoutError
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gpt_replying_buffer = ""
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if reasoning:
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gpt_reasoning_buffer = ""
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stream_response = response.iter_lines()
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while True:
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@@ -347,9 +350,9 @@ def get_predict_function(
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break
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except requests.exceptions.ConnectionError:
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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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response_text, finish_reason = decode_chunk(chunk)
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response_text, reasoning_content, finish_reason = decode_chunk(chunk)
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# 返回的数据流第一次为空，继续等待
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if response_text == "" and finish_reason != "False":
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if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
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status_text = f"finish_reason: {finish_reason}"
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yield from update_ui(
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chatbot=chatbot, history=history, msg=status_text
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@@ -379,9 +382,14 @@ def get_predict_function(
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logger.info(f"[response] {gpt_replying_buffer}")
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break
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status_text = f"finish_reason: {finish_reason}"
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gpt_replying_buffer += response_text
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# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
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history[-1] = gpt_replying_buffer
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if reasoning:
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gpt_replying_buffer += response_text
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gpt_reasoning_buffer += reasoning_content
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history[-1] = '\n'.join(map(lambda x: '> ' + x, gpt_reasoning_buffer.split('\n'))) + '\n\n' + gpt_replying_buffer
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else:
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gpt_replying_buffer += response_text
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# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
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history[-1] = gpt_replying_buffer
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chatbot[-1] = (history[-2], history[-1])
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yield from update_ui(
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chatbot=chatbot, history=history, msg=status_text
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在新工单中引用
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