镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-07 15:06:48 +00:00
XIao
@@ -28,6 +28,9 @@ from .bridge_chatglm3 import predict as chatglm3_ui
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from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
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from .bridge_qianfan import predict as qianfan_ui
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from .bridge_google_gemini import predict as genai_ui
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from .bridge_google_gemini import predict_no_ui_long_connection as genai_noui
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colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
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class LazyloadTiktoken(object):
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@@ -246,6 +249,22 @@ model_info = {
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gemini-pro": {
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"fn_with_ui": genai_ui,
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"fn_without_ui": genai_noui,
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"endpoint": None,
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"max_token": 1024 * 32,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"gemini-pro-vision": {
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"fn_with_ui": genai_ui,
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"fn_without_ui": genai_noui,
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"endpoint": None,
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"max_token": 1024 * 32,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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}
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# -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
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101
request_llms/bridge_google_gemini.py
普通文件
101
request_llms/bridge_google_gemini.py
普通文件
@@ -0,0 +1,101 @@
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# encoding: utf-8
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# @Time : 2023/12/21
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# @Author : Spike
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# @Descr :
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import json
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import re
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import time
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from request_llms.com_google import GoogleChatInit
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from toolbox import get_conf, update_ui, update_ui_lastest_msg
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proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
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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 predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
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console_slience=False):
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# 检查API_KEY
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if get_conf("GEMINI_API_KEY") == "":
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raise ValueError(f"请配置 GEMINI_API_KEY。")
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genai = GoogleChatInit()
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watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
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gpt_replying_buffer = ''
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stream_response = genai.generate_chat(inputs, llm_kwargs, history, sys_prompt)
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for response in stream_response:
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results = response.decode()
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match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
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error_match = re.search(r'\"message\":\s*\"(.*?)\"', results, flags=re.DOTALL)
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if match:
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try:
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paraphrase = json.loads('{"text": "%s"}' % match.group(1))
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except:
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raise ValueError(f"解析GEMINI消息出错。")
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buffer = paraphrase['text']
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gpt_replying_buffer += buffer
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if len(observe_window) >= 1:
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observe_window[0] = gpt_replying_buffer
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if len(observe_window) >= 2:
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if (time.time() - observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
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if error_match:
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raise RuntimeError(f'{gpt_replying_buffer} 对话错误')
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return gpt_replying_buffer
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
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# 检查API_KEY
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if get_conf("GEMINI_API_KEY") == "":
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yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
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return
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chatbot.append((inputs, ""))
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yield from update_ui(chatbot=chatbot, history=history)
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genai = GoogleChatInit()
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retry = 0
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while True:
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try:
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stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt)
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break
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except Exception as e:
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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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gpt_security_policy = ""
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history.extend([inputs, ''])
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for response in stream_response:
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results = response.decode("utf-8") # 被这个解码给耍了。。
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gpt_security_policy += results
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match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
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error_match = re.search(r'\"message\":\s*\"(.*)\"', results, flags=re.DOTALL)
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if match:
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try:
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paraphrase = json.loads('{"text": "%s"}' % match.group(1))
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except:
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raise ValueError(f"解析GEMINI消息出错。")
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gpt_replying_buffer += paraphrase['text'] # 使用 json 解析库进行处理
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chatbot[-1] = (inputs, gpt_replying_buffer)
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history[-1] = gpt_replying_buffer
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yield from update_ui(chatbot=chatbot, history=history)
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if error_match:
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history = history[-2] # 错误的不纳入对话
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chatbot[-1] = (inputs, gpt_replying_buffer + f"对话错误，请查看message\n\n```\n{error_match.group(1)}\n```")
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yield from update_ui(chatbot=chatbot, history=history)
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raise RuntimeError('对话错误')
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if not gpt_replying_buffer:
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history = history[-2] # 错误的不纳入对话
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chatbot[-1] = (inputs, gpt_replying_buffer + f"触发了Google的安全访问策略，没有回答\n\n```\n{gpt_security_policy}\n```")
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yield from update_ui(chatbot=chatbot, history=history)
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if __name__ == '__main__':
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import sys
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llm_kwargs = {'llm_model': 'gemini-pro'}
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result = predict('Write long a story about a magic backpack.', llm_kwargs, llm_kwargs, [])
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for i in result:
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print(i)
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198
request_llms/com_google.py
普通文件
198
request_llms/com_google.py
普通文件
@@ -0,0 +1,198 @@
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# encoding: utf-8
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# @Time : 2023/12/25
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# @Author : Spike
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# @Descr :
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import json
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import os
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import re
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import requests
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from typing import List, Dict, Tuple
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from toolbox import get_conf, encode_image
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proxies, TIMEOUT_SECONDS = get_conf('proxies', 'TIMEOUT_SECONDS')
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"""
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========================================================================
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第五部分 一些文件处理方法
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files_filter_handler 根据type过滤文件
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input_encode_handler 提取input中的文件，并解析
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file_manifest_filter_html 根据type过滤文件, 并解析为html or md 文本
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link_mtime_to_md 文件增加本地时间参数，避免下载到缓存文件
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html_view_blank 超链接
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html_local_file 本地文件取相对路径
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to_markdown_tabs 文件list 转换为 md tab
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"""
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def files_filter_handler(file_list):
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new_list = []
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filter_ = ['png', 'jpg', 'jpeg', 'bmp', 'svg', 'webp', 'ico', 'tif', 'tiff', 'raw', 'eps']
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for file in file_list:
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file = str(file).replace('file=', '')
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if os.path.exists(file):
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if str(os.path.basename(file)).split('.')[-1] in filter_:
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new_list.append(file)
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return new_list
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def input_encode_handler(inputs):
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md_encode = []
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pattern_md_file = r"(!?\[[^\]]+\]\([^\)]+\))"
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matches_path = re.findall(pattern_md_file, inputs)
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for md_path in matches_path:
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pattern_file = r"\((file=.*)\)"
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matches_path = re.findall(pattern_file, md_path)
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encode_file = files_filter_handler(file_list=matches_path)
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if encode_file:
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md_encode.extend([{
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"data": encode_image(i),
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"type": os.path.splitext(i)[1].replace('.', '')
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} for i in encode_file])
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inputs = inputs.replace(md_path, '')
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return inputs, md_encode
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def file_manifest_filter_html(file_list, filter_: list = None, md_type=False):
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new_list = []
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if not filter_:
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filter_ = ['png', 'jpg', 'jpeg', 'bmp', 'svg', 'webp', 'ico', 'tif', 'tiff', 'raw', 'eps']
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for file in file_list:
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if str(os.path.basename(file)).split('.')[-1] in filter_:
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new_list.append(html_local_img(file, md=md_type))
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elif os.path.exists(file):
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new_list.append(link_mtime_to_md(file))
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else:
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new_list.append(file)
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return new_list
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def link_mtime_to_md(file):
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link_local = html_local_file(file)
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link_name = os.path.basename(file)
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a = f"[{link_name}]({link_local}?{os.path.getmtime(file)})"
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return a
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def html_local_file(file):
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base_path = os.path.dirname(__file__) # 项目目录
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if os.path.exists(str(file)):
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file = f'file={file.replace(base_path, ".")}'
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return file
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def html_local_img(__file, layout='left', max_width=None, max_height=None, md=True):
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style = ''
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if max_width is not None:
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style += f"max-width: {max_width};"
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if max_height is not None:
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style += f"max-height: {max_height};"
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__file = html_local_file(__file)
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a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
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if md:
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a = f''
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return a
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def to_markdown_tabs(head: list, tabs: list, alignment=':---:', column=False):
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"""
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Args:
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head: 表头：[]
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tabs: 表值：[[列1], [列2], [列3], [列4]]
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alignment: :--- 左对齐， :---: 居中对齐， ---: 右对齐
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column: True to keep data in columns, False to keep data in rows (default).
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Returns:
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A string representation of the markdown table.
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"""
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if column:
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transposed_tabs = list(map(list, zip(*tabs)))
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else:
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transposed_tabs = tabs
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# Find the maximum length among the columns
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max_len = max(len(column) for column in transposed_tabs)
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tab_format = "| %s "
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tabs_list = "".join([tab_format % i for i in head]) + '|\n'
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tabs_list += "".join([tab_format % alignment for i in head]) + '|\n'
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for i in range(max_len):
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row_data = [tab[i] if i < len(tab) else '' for tab in transposed_tabs]
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row_data = file_manifest_filter_html(row_data, filter_=None)
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tabs_list += "".join([tab_format % i for i in row_data]) + '|\n'
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return tabs_list
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class GoogleChatInit:
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def __init__(self):
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self.url_gemini = 'https://generativelanguage.googleapis.com/v1beta/models/%m:streamGenerateContent?key=%k'
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def __conversation_user(self, user_input):
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what_i_have_asked = {"role": "user", "parts": []}
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if 'vision' not in self.url_gemini:
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input_ = user_input
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encode_img = []
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else:
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input_, encode_img = input_encode_handler(user_input)
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what_i_have_asked['parts'].append({'text': input_})
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if encode_img:
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for data in encode_img:
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what_i_have_asked['parts'].append(
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{'inline_data': {
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"mime_type": f"image/{data['type']}",
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"data": data['data']
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}})
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return what_i_have_asked
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def __conversation_history(self, history):
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messages = []
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conversation_cnt = len(history) // 2
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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 = self.__conversation_user(history[index])
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what_gpt_answer = {
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"role": "model",
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"parts": [{"text": history[index + 1]}]
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}
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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return messages
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def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
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headers, payload = self.generate_message_payload(inputs, llm_kwargs, history, system_prompt)
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response = requests.post(url=self.url_gemini, headers=headers, data=json.dumps(payload),
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stream=True, proxies=proxies, timeout=TIMEOUT_SECONDS)
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return response.iter_lines()
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def generate_message_payload(self, inputs, llm_kwargs, history, system_prompt) -> Tuple[Dict, Dict]:
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messages = [
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# {"role": "system", "parts": [{"text": system_prompt}]}, # gemini 不允许对话轮次为偶数，所以这个没有用，看后续支持吧。。。
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# {"role": "user", "parts": [{"text": ""}]},
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# {"role": "model", "parts": [{"text": ""}]}
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]
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self.url_gemini = self.url_gemini.replace(
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'%m', llm_kwargs['llm_model']).replace(
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'%k', get_conf('GEMINI_API_KEY')
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)
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header = {'Content-Type': 'application/json'}
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if 'vision' not in self.url_gemini: # 不是vision 才处理history
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messages.extend(self.__conversation_history(history)) # 处理 history
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messages.append(self.__conversation_user(inputs)) # 处理用户对话
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payload = {
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"contents": messages,
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"generationConfig": {
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"stopSequences": str(llm_kwargs.get('stop', '')).split(' '),
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"temperature": llm_kwargs.get('temperature', 1),
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# "maxOutputTokens": 800,
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"topP": llm_kwargs.get('top_p', 0.8),
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"topK": 10
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}
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}
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return header, payload
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if __name__ == '__main__':
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google = GoogleChatInit()
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# print(gootle.generate_message_payload('你好呀', {},
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# ['123123', '3123123'], ''))
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# gootle.input_encode_handle('123123[123123](./123123), ')
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在新工单中引用
屏蔽一个用户