镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
72dbe856d2
* use oai adaptive bridge function to handle vol engine * add vol engine deepseek v3 --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com>
436 行
17 KiB
Python
436 行
17 KiB
Python
import json
|
||
import time
|
||
import traceback
|
||
import requests
|
||
|
||
from loguru import logger
|
||
from toolbox import get_conf, is_the_upload_folder, update_ui, update_ui_latest_msg
|
||
|
||
proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
|
||
"proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
|
||
)
|
||
|
||
timeout_bot_msg = (
|
||
"[Local Message] Request timeout. Network error. Please check proxy settings in config.py."
|
||
+ "网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。"
|
||
)
|
||
|
||
|
||
def get_full_error(chunk, stream_response):
|
||
"""
|
||
尝试获取完整的错误信息
|
||
"""
|
||
while True:
|
||
try:
|
||
chunk += next(stream_response)
|
||
except:
|
||
break
|
||
return chunk
|
||
|
||
|
||
def decode_chunk(chunk):
|
||
"""
|
||
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）
|
||
"""
|
||
chunk = chunk.decode()
|
||
response = ""
|
||
reasoning_content = ""
|
||
finish_reason = "False"
|
||
|
||
# 考虑返回类型是 text/json 和 text/event-stream 两种
|
||
if chunk.startswith("data: "):
|
||
chunk = chunk[6:]
|
||
else:
|
||
chunk = chunk
|
||
|
||
try:
|
||
chunk = json.loads(chunk)
|
||
except:
|
||
response = ""
|
||
finish_reason = chunk
|
||
|
||
# 错误处理部分
|
||
if "error" in chunk:
|
||
response = "API_ERROR"
|
||
try:
|
||
chunk = json.loads(chunk)
|
||
finish_reason = chunk["error"]["code"]
|
||
except:
|
||
finish_reason = "API_ERROR"
|
||
return response, reasoning_content, finish_reason, str(chunk)
|
||
|
||
try:
|
||
if chunk["choices"][0]["delta"]["content"] is not None:
|
||
response = chunk["choices"][0]["delta"]["content"]
|
||
except:
|
||
pass
|
||
try:
|
||
if chunk["choices"][0]["delta"]["reasoning_content"] is not None:
|
||
reasoning_content = chunk["choices"][0]["delta"]["reasoning_content"]
|
||
except:
|
||
pass
|
||
try:
|
||
finish_reason = chunk["choices"][0]["finish_reason"]
|
||
except:
|
||
pass
|
||
return response, reasoning_content, finish_reason, str(chunk)
|
||
|
||
|
||
def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
|
||
"""
|
||
整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
|
||
"""
|
||
api_key = f"Bearer {key}"
|
||
|
||
headers = {"Content-Type": "application/json", "Authorization": api_key}
|
||
|
||
conversation_cnt = len(history) // 2
|
||
|
||
messages = [{"role": "system", "content": system_prompt}]
|
||
if conversation_cnt:
|
||
for index in range(0, 2 * conversation_cnt, 2):
|
||
what_i_have_asked = {}
|
||
what_i_have_asked["role"] = "user"
|
||
what_i_have_asked["content"] = history[index]
|
||
what_gpt_answer = {}
|
||
what_gpt_answer["role"] = "assistant"
|
||
what_gpt_answer["content"] = history[index + 1]
|
||
if what_i_have_asked["content"] != "":
|
||
if what_gpt_answer["content"] == "":
|
||
continue
|
||
if what_gpt_answer["content"] == timeout_bot_msg:
|
||
continue
|
||
messages.append(what_i_have_asked)
|
||
messages.append(what_gpt_answer)
|
||
else:
|
||
messages[-1]["content"] = what_gpt_answer["content"]
|
||
what_i_ask_now = {}
|
||
what_i_ask_now["role"] = "user"
|
||
what_i_ask_now["content"] = input
|
||
messages.append(what_i_ask_now)
|
||
payload = {
|
||
"model": model,
|
||
"messages": messages,
|
||
"temperature": temperature,
|
||
"stream": True,
|
||
"max_tokens": max_output_token,
|
||
}
|
||
|
||
return headers, payload
|
||
|
||
|
||
def get_predict_function(
|
||
api_key_conf_name,
|
||
max_output_token,
|
||
disable_proxy = False,
|
||
model_remove_prefix = [],
|
||
):
|
||
"""
|
||
为openai格式的API生成响应函数，其中传入参数：
|
||
api_key_conf_name：
|
||
`config.py`中此模型的APIKEY的名字，例如"YIMODEL_API_KEY"
|
||
max_output_token：
|
||
每次请求的最大token数量，例如对于01万物的yi-34b-chat-200k，其最大请求数为4096
|
||
⚠️请不要与模型的最大token数量相混淆。
|
||
disable_proxy：
|
||
是否使用代理，True为不使用，False为使用。
|
||
"""
|
||
|
||
APIKEY = get_conf(api_key_conf_name)
|
||
|
||
def remove_prefix(model_name):
|
||
# 去除模型名字的前缀，输入 volcengine-deepseek-r1-250120 会返回 deepseek-r1-250120
|
||
if not model_remove_prefix:
|
||
return model_name
|
||
model_without_prefix = model_name
|
||
for prefix in model_remove_prefix:
|
||
if model_without_prefix.startswith(prefix):
|
||
model_without_prefix = model_without_prefix[len(prefix):]
|
||
return model_without_prefix
|
||
|
||
def predict_no_ui_long_connection(
|
||
inputs,
|
||
llm_kwargs,
|
||
history=[],
|
||
sys_prompt="",
|
||
observe_window=None,
|
||
console_silence=False,
|
||
):
|
||
"""
|
||
发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
|
||
inputs：
|
||
是本次问询的输入
|
||
sys_prompt:
|
||
系统静默prompt
|
||
llm_kwargs：
|
||
chatGPT的内部调优参数
|
||
history：
|
||
是之前的对话列表
|
||
observe_window = None：
|
||
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
|
||
"""
|
||
from .bridge_all import model_info
|
||
watch_dog_patience = 5 # 看门狗的耐心，设置5秒不准咬人 (咬的也不是人)
|
||
if len(APIKEY) == 0:
|
||
raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
|
||
if inputs == "":
|
||
inputs = "你好👋"
|
||
|
||
|
||
headers, payload = generate_message(
|
||
input=inputs,
|
||
model=remove_prefix(llm_kwargs["llm_model"]),
|
||
key=APIKEY,
|
||
history=history,
|
||
max_output_token=max_output_token,
|
||
system_prompt=sys_prompt,
|
||
temperature=llm_kwargs["temperature"],
|
||
)
|
||
|
||
reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
|
||
|
||
retry = 0
|
||
while True:
|
||
try:
|
||
endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
|
||
response = requests.post(
|
||
endpoint,
|
||
headers=headers,
|
||
proxies=None if disable_proxy else proxies,
|
||
json=payload,
|
||
stream=True,
|
||
timeout=TIMEOUT_SECONDS,
|
||
)
|
||
break
|
||
except:
|
||
retry += 1
|
||
traceback.print_exc()
|
||
if retry > MAX_RETRY:
|
||
raise TimeoutError
|
||
if MAX_RETRY != 0:
|
||
logger.error(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
|
||
|
||
result = ""
|
||
finish_reason = ""
|
||
if reasoning:
|
||
reasoning_buffer = ""
|
||
|
||
stream_response = response.iter_lines()
|
||
while True:
|
||
try:
|
||
chunk = next(stream_response)
|
||
except StopIteration:
|
||
if result == "":
|
||
raise RuntimeError(f"获得空的回复，可能原因:{finish_reason}")
|
||
break
|
||
except requests.exceptions.ConnectionError:
|
||
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
|
||
response_text, reasoning_content, finish_reason, decoded_chunk = decode_chunk(chunk)
|
||
# 返回的数据流第一次为空，继续等待
|
||
if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
|
||
continue
|
||
if response_text == "API_ERROR" and (
|
||
finish_reason != "False" or finish_reason != "stop"
|
||
):
|
||
chunk = get_full_error(chunk, stream_response)
|
||
chunk_decoded = chunk.decode()
|
||
logger.error(chunk_decoded)
|
||
raise RuntimeError(
|
||
f"API异常,请检测终端输出。可能的原因是:{finish_reason}"
|
||
)
|
||
if chunk:
|
||
try:
|
||
if finish_reason == "stop":
|
||
if not console_silence:
|
||
print(f"[response] {result}")
|
||
break
|
||
result += response_text
|
||
if reasoning:
|
||
reasoning_buffer += reasoning_content
|
||
if observe_window is not None:
|
||
# 观测窗，把已经获取的数据显示出去
|
||
if len(observe_window) >= 1:
|
||
observe_window[0] += response_text
|
||
# 看门狗，如果超过期限没有喂狗，则终止
|
||
if len(observe_window) >= 2:
|
||
if (time.time() - observe_window[1]) > watch_dog_patience:
|
||
raise RuntimeError("用户取消了程序。")
|
||
except Exception as e:
|
||
chunk = get_full_error(chunk, stream_response)
|
||
chunk_decoded = chunk.decode()
|
||
error_msg = chunk_decoded
|
||
logger.error(error_msg)
|
||
raise RuntimeError("Json解析不合常规")
|
||
if reasoning:
|
||
paragraphs = ''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in reasoning_buffer.split('\n')])
|
||
return f'''<div class="reasoning_process" >{paragraphs}</div>\n\n''' + result
|
||
return result
|
||
|
||
def predict(
|
||
inputs,
|
||
llm_kwargs,
|
||
plugin_kwargs,
|
||
chatbot,
|
||
history=[],
|
||
system_prompt="",
|
||
stream=True,
|
||
additional_fn=None,
|
||
):
|
||
"""
|
||
发送至chatGPT，流式获取输出。
|
||
用于基础的对话功能。
|
||
inputs 是本次问询的输入
|
||
top_p, temperature是chatGPT的内部调优参数
|
||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||
chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
|
||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||
"""
|
||
from .bridge_all import model_info
|
||
if len(APIKEY) == 0:
|
||
raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
|
||
if inputs == "":
|
||
inputs = "你好👋"
|
||
if additional_fn is not None:
|
||
from core_functional import handle_core_functionality
|
||
|
||
inputs, history = handle_core_functionality(
|
||
additional_fn, inputs, history, chatbot
|
||
)
|
||
logger.info(f"[raw_input] {inputs}")
|
||
chatbot.append((inputs, ""))
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg="等待响应"
|
||
) # 刷新界面
|
||
|
||
# check mis-behavior
|
||
if is_the_upload_folder(inputs):
|
||
chatbot[-1] = (
|
||
inputs,
|
||
f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。",
|
||
)
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg="正常"
|
||
) # 刷新界面
|
||
time.sleep(2)
|
||
|
||
headers, payload = generate_message(
|
||
input=inputs,
|
||
model=remove_prefix(llm_kwargs["llm_model"]),
|
||
key=APIKEY,
|
||
history=history,
|
||
max_output_token=max_output_token,
|
||
system_prompt=system_prompt,
|
||
temperature=llm_kwargs["temperature"],
|
||
)
|
||
|
||
reasoning = model_info[llm_kwargs['llm_model']].get('enable_reasoning', False)
|
||
|
||
history.append(inputs)
|
||
history.append("")
|
||
retry = 0
|
||
while True:
|
||
try:
|
||
endpoint = model_info[llm_kwargs["llm_model"]]["endpoint"]
|
||
response = requests.post(
|
||
endpoint,
|
||
headers=headers,
|
||
proxies=None if disable_proxy else proxies,
|
||
json=payload,
|
||
stream=True,
|
||
timeout=TIMEOUT_SECONDS,
|
||
)
|
||
break
|
||
except:
|
||
retry += 1
|
||
chatbot[-1] = (chatbot[-1][0], timeout_bot_msg)
|
||
retry_msg = (
|
||
f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
|
||
)
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg="请求超时" + retry_msg
|
||
) # 刷新界面
|
||
if retry > MAX_RETRY:
|
||
raise TimeoutError
|
||
|
||
gpt_replying_buffer = ""
|
||
if reasoning:
|
||
gpt_reasoning_buffer = ""
|
||
|
||
stream_response = response.iter_lines()
|
||
wait_counter = 0
|
||
while True:
|
||
try:
|
||
chunk = next(stream_response)
|
||
except StopIteration:
|
||
if wait_counter != 0 and gpt_replying_buffer == "":
|
||
yield from update_ui_latest_msg(lastmsg="模型调用失败 ...", chatbot=chatbot, history=history, msg="failed")
|
||
break
|
||
except requests.exceptions.ConnectionError:
|
||
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
|
||
response_text, reasoning_content, finish_reason, decoded_chunk = decode_chunk(chunk)
|
||
if decoded_chunk == ': keep-alive':
|
||
wait_counter += 1
|
||
yield from update_ui_latest_msg(lastmsg="等待中 " + "".join(["."] * (wait_counter%10)), chatbot=chatbot, history=history, msg="waiting ...")
|
||
continue
|
||
# 返回的数据流第一次为空，继续等待
|
||
if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
|
||
status_text = f"finish_reason: {finish_reason}"
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg=status_text
|
||
)
|
||
continue
|
||
if chunk:
|
||
try:
|
||
if response_text == "API_ERROR" and (
|
||
finish_reason != "False" or finish_reason != "stop"
|
||
):
|
||
chunk = get_full_error(chunk, stream_response)
|
||
chunk_decoded = chunk.decode()
|
||
chatbot[-1] = (
|
||
chatbot[-1][0],
|
||
f"[Local Message] {finish_reason}, 获得以下报错信息：\n"
|
||
+ chunk_decoded,
|
||
)
|
||
yield from update_ui(
|
||
chatbot=chatbot,
|
||
history=history,
|
||
msg="API异常:" + chunk_decoded,
|
||
) # 刷新界面
|
||
logger.error(chunk_decoded)
|
||
return
|
||
|
||
if finish_reason == "stop":
|
||
logger.info(f"[response] {gpt_replying_buffer}")
|
||
break
|
||
status_text = f"finish_reason: {finish_reason}"
|
||
if reasoning:
|
||
gpt_replying_buffer += response_text
|
||
gpt_reasoning_buffer += reasoning_content
|
||
paragraphs = ''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in gpt_reasoning_buffer.split('\n')])
|
||
history[-1] = f'<div class="reasoning_process">{paragraphs}</div>\n\n---\n\n' + gpt_replying_buffer
|
||
else:
|
||
gpt_replying_buffer += response_text
|
||
# 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
|
||
history[-1] = gpt_replying_buffer
|
||
chatbot[-1] = (history[-2], history[-1])
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg=status_text
|
||
) # 刷新界面
|
||
except Exception as e:
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg="Json解析不合常规"
|
||
) # 刷新界面
|
||
chunk = get_full_error(chunk, stream_response)
|
||
chunk_decoded = chunk.decode()
|
||
chatbot[-1] = (
|
||
chatbot[-1][0],
|
||
"[Local Message] 解析错误,获得以下报错信息：\n" + chunk_decoded,
|
||
)
|
||
yield from update_ui(
|
||
chatbot=chatbot, history=history, msg="Json异常" + chunk_decoded
|
||
) # 刷新界面
|
||
logger.error(chunk_decoded)
|
||
return
|
||
|
||
return predict_no_ui_long_connection, predict
|