镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
version 3.75 (#1702)
* Update version to 3.74 * Add support for Yi Model API (#1635) * 更新以支持零一万物模型 * 删除newbing * 修改config --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * Refactor function signatures in bridge files * fix qwen api change * rename and ref functions * rename and move some cookie functions * 增加haiku模型，新增endpoint配置说明 (#1626) * haiku added * 新增haiku，新增endpoint配置说明 * Haiku added * 将说明同步至最新Endpoint --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * private_upload目录下进行文件鉴权 (#1596) * private_upload目录下进行文件鉴权 * minor fastapi adjustment * Add logging functionality to enable saving conversation records * waiting to fix username retrieve * support 2rd web path * allow accessing default user dir --------- Co-authored-by: binary-husky <qingxu.fu@outlook.com> * remove yaml deps * fix favicon * fix abs path auth problem * forget to write a return * add `dashscope` to deps * fix GHSA-v9q9-xj86-953p * 用户名重叠越权访问patch (#1681) * add cohere model api access * cohere + can_multi_thread * fix block user access(fail) * fix fastapi bug * change cohere api endpoint * explain version * # fix com_zhipuglm.py illegal temperature problem (#1687) * Update com_zhipuglm.py # fix 用户在使用 zhipuai 界面时遇到了关于温度参数的非法参数错误 * allow store lm model dropdown * add a btn to reverse previous reset * remove extra fns * Add support for glm-4v model (#1700) * 修改chatglm3量化加载方式 (#1688) Co-authored-by: zym9804 <ren990603@gmail.com> * save chat stage 1 * consider null cookie situation * 在点击复制按钮时激活语音 * miss some parts * move all to js * done first stage * add edge tts * bug fix * bug fix * remove console log * bug fix * bug fix * bug fix * audio switch * update tts readme * remove tempfile when done * disable auto audio follow * avoid play queue update after shut up * feat: minimizing common.js * improve tts functionality * deterine whether the cached model is in choices * Add support for Ollama (#1740) * print err when doc2x not successful * add icon * adjust url for doc2x key version * prepare merge --------- Co-authored-by: Menghuan1918 <menghuan2003@outlook.com> Co-authored-by: Skyzayre <120616113+Skyzayre@users.noreply.github.com> Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com> Co-authored-by: Yuki <903728862@qq.com> Co-authored-by: zyren123 <91042213+zyren123@users.noreply.github.com> Co-authored-by: zym9804 <ren990603@gmail.com>
这个提交包含在：
binary-husky
272
request_llms/bridge_ollama.py
普通文件
272
request_llms/bridge_ollama.py
普通文件
@@ -0,0 +1,272 @@
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# 借鉴自同目录下的bridge_chatgpt.py
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"""
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该文件中主要包含三个函数
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不具备多线程能力的函数：
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1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
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具备多线程调用能力的函数
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2. predict_no_ui_long_connection：支持多线程
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"""
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import json
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import time
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import gradio as gr
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import logging
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import traceback
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import requests
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import importlib
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import random
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# config_private.py放自己的秘密如API和代理网址
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# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
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from toolbox import get_conf, update_ui, trimmed_format_exc, is_the_upload_folder, read_one_api_model_name
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proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
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"proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
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)
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timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
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'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。'
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def get_full_error(chunk, stream_response):
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"""
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获取完整的从Openai返回的报错
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"""
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while True:
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try:
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chunk += next(stream_response)
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except:
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break
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return chunk
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def decode_chunk(chunk):
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# 提前读取一些信息（用于判断异常）
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chunk_decoded = chunk.decode()
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chunkjson = None
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is_last_chunk = False
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try:
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chunkjson = json.loads(chunk_decoded)
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is_last_chunk = chunkjson.get("done", False)
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except:
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pass
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return chunk_decoded, chunkjson, is_last_chunk
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
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"""
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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。
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inputs：
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是本次问询的输入
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sys_prompt:
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系统静默prompt
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llm_kwargs：
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chatGPT的内部调优参数
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history：
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是之前的对话列表
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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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watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
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if inputs == "": inputs = "空空如也的输入栏"
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headers, payload = generate_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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# make a POST request to the API endpoint, stream=False
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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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response = requests.post(endpoint, headers=headers, proxies=proxies,
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json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
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except requests.exceptions.ReadTimeout as e:
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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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stream_response = response.iter_lines()
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result = ''
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while True:
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try: chunk = next(stream_response)
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except StopIteration:
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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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chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
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if chunk:
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try:
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if is_last_chunk:
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# 判定为数据流的结束，gpt_replying_buffer也写完了
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logging.info(f'[response] {result}')
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break
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result += chunkjson['message']["content"]
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if not console_slience: print(chunkjson['message']["content"], end='')
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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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observe_window[0] += chunkjson['message']["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 (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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except Exception as e:
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chunk = get_full_error(chunk, stream_response)
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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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print(error_msg)
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raise RuntimeError("Json解析不合常规")
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return result
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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发送至chatGPT，流式获取输出。
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用于基础的对话功能。
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inputs 是本次问询的输入
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top_p, temperature是chatGPT的内部调优参数
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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）
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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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if inputs == "": inputs = "空空如也的输入栏"
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user_input = inputs
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if additional_fn is not None:
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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
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raw_input = inputs
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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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# check mis-behavior
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if is_the_upload_folder(user_input):
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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。")
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yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
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time.sleep(2)
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headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
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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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history.append(inputs); history.append("")
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retry = 0
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while True:
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try:
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# make a POST request to the API endpoint, stream=True
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response = requests.post(endpoint, headers=headers, proxies=proxies,
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json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
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except:
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retry += 1
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chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
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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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if retry > MAX_RETRY: raise TimeoutError
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gpt_replying_buffer = ""
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if stream:
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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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except StopIteration:
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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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# 提前读取一些信息 （用于判断异常）
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chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
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if chunk:
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try:
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if is_last_chunk:
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# 判定为数据流的结束，gpt_replying_buffer也写完了
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logging.info(f'[response] {gpt_replying_buffer}')
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break
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# 处理数据流的主体
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try:
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status_text = f"finish_reason: {chunkjson['error'].get('message', 'null')}"
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except:
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status_text = "finish_reason: null"
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gpt_replying_buffer = gpt_replying_buffer + chunkjson['message']["content"]
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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(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
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except Exception as e:
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yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
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chunk = get_full_error(chunk, stream_response)
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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
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yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
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print(error_msg)
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return
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def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
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from .bridge_all import model_info
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if "bad_request" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] 已经超过了模型的最大上下文或是模型格式错误,请尝试削减单次输入的文本量。")
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elif "authentication_error" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. 请确保API key有效。")
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elif "not_found" in error_msg:
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] {llm_kwargs['llm_model']} 无效，请确保使用小写的模型名称。")
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elif "rate_limit" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] 遇到了控制请求速率限制，请一分钟后重试。")
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elif "system_busy" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] 系统繁忙，请一分钟后重试。")
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else:
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from toolbox import regular_txt_to_markdown
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
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return chatbot, history
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def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
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"""
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整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
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"""
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headers = {
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"Content-Type": "application/json",
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}
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conversation_cnt = len(history) // 2
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messages = [{"role": "system", "content": system_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "": continue
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if what_gpt_answer["content"] == timeout_bot_msg: continue
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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messages.append(what_i_ask_now)
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model = llm_kwargs['llm_model']
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if llm_kwargs['llm_model'].startswith('ollama-'):
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model = llm_kwargs['llm_model'][len('ollama-'):]
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model, _ = read_one_api_model_name(model)
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options = {"temperature": llm_kwargs['temperature']}
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payload = {
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"model": model,
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"messages": messages,
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"options": options,
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}
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try:
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print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
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except:
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print('输入中可能存在乱码。')
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return headers,payload
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在新工单中引用
屏蔽一个用户