适配 google gemini 优化为从用户input中提取文件 (#1419)
适配 google gemini 优化为从用户input中提取文件
This commit is contained in:
XIao
qingxu fu
parent
a96f842b3a
commit
a7c960dcb0
17
config.py
17
config.py
@ -89,12 +89,14 @@ DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
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LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview","gpt-4-vision-preview",
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"gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
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"api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
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"gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
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"chatglm3", "moss", "claude-2"]
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# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random"
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"gemini-pro", "chatglm3", "moss", "claude-2"]
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# P.S. 其他可用的模型还包括 [
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# "qwen-turbo", "qwen-plus", "qwen-max"
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# "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613",
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# "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
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# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"
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# “qwen-turbo", "qwen-plus", "qwen-max"]
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# ]
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# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
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@ -204,6 +206,10 @@ ANTHROPIC_API_KEY = ""
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CUSTOM_API_KEY_PATTERN = ""
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# Google Gemini API-Key
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GEMINI_API_KEY = ''
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# HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
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HUGGINGFACE_ACCESS_TOKEN = "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"
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@ -292,6 +298,9 @@ NUM_CUSTOM_BASIC_BTN = 4
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├── "qwen-turbo" 等通义千问大模型
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│ └── DASHSCOPE_API_KEY
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│
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├── "Gemini"
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│ └── GEMINI_API_KEY
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│
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└── "newbing" Newbing接口不再稳定，不推荐使用
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├── NEWBING_STYLE
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└── NEWBING_COOKIES
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@ -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
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101
request_llms/bridge_google_gemini.py
Normal file
@ -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
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198
request_llms/com_google.py
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@ -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()
|
||||
# print(gootle.generate_message_payload('你好呀', {},
|
||||
# ['123123', '3123123'], ''))
|
||||
# gootle.input_encode_handle('123123[123123](./123123), ')
|
||||
232
toolbox.py
232
toolbox.py
@ -11,8 +11,10 @@ import glob
|
||||
import math
|
||||
from latex2mathml.converter import convert as tex2mathml
|
||||
from functools import wraps, lru_cache
|
||||
|
||||
pj = os.path.join
|
||||
default_user_name = 'default_user'
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
第一部分
|
||||
@ -26,6 +28,7 @@ default_user_name = 'default_user'
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
|
||||
class ChatBotWithCookies(list):
|
||||
def __init__(self, cookie):
|
||||
"""
|
||||
@ -67,18 +70,18 @@ def ArgsGeneralWrapper(f):
|
||||
else:
|
||||
user_name = default_user_name
|
||||
cookies.update({
|
||||
'top_p':top_p,
|
||||
'top_p': top_p,
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': llm_model,
|
||||
'temperature':temperature,
|
||||
'temperature': temperature,
|
||||
'user_name': user_name,
|
||||
})
|
||||
llm_kwargs = {
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': llm_model,
|
||||
'top_p':top_p,
|
||||
'top_p': top_p,
|
||||
'max_length': max_length,
|
||||
'temperature':temperature,
|
||||
'temperature': temperature,
|
||||
'client_ip': request.client.host,
|
||||
'most_recent_uploaded': cookies.get('most_recent_uploaded')
|
||||
}
|
||||
@ -87,7 +90,7 @@ def ArgsGeneralWrapper(f):
|
||||
}
|
||||
chatbot_with_cookie = ChatBotWithCookies(cookies)
|
||||
chatbot_with_cookie.write_list(chatbot)
|
||||
|
||||
|
||||
if cookies.get('lock_plugin', None) is None:
|
||||
# 正常状态
|
||||
if len(args) == 0: # 插件通道
|
||||
@ -103,8 +106,10 @@ def ArgsGeneralWrapper(f):
|
||||
final_cookies = chatbot_with_cookie.get_cookies()
|
||||
# len(args) != 0 代表“提交”键对话通道，或者基础功能通道
|
||||
if len(args) != 0 and 'files_to_promote' in final_cookies and len(final_cookies['files_to_promote']) > 0:
|
||||
chatbot_with_cookie.append(["检测到**滞留的缓存文档**，请及时处理。", "请及时点击“**保存当前对话**”获取所有滞留文档。"])
|
||||
chatbot_with_cookie.append(
|
||||
["检测到**滞留的缓存文档**，请及时处理。", "请及时点击“**保存当前对话**”获取所有滞留文档。"])
|
||||
yield from update_ui(chatbot_with_cookie, final_cookies['history'], msg="检测到被滞留的缓存文档")
|
||||
|
||||
return decorated
|
||||
|
||||
|
||||
@ -129,6 +134,7 @@ def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
|
||||
|
||||
yield cookies, chatbot_gr, history, msg
|
||||
|
||||
|
||||
def update_ui_lastest_msg(lastmsg, chatbot, history, delay=1): # 刷新界面
|
||||
"""
|
||||
刷新用户界面
|
||||
@ -147,6 +153,7 @@ def trimmed_format_exc():
|
||||
replace_path = "."
|
||||
return str.replace(current_path, replace_path)
|
||||
|
||||
|
||||
def CatchException(f):
|
||||
"""
|
||||
装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
|
||||
@ -164,9 +171,9 @@ def CatchException(f):
|
||||
if len(chatbot_with_cookie) == 0:
|
||||
chatbot_with_cookie.clear()
|
||||
chatbot_with_cookie.append(["插件调度异常", "异常原因"])
|
||||
chatbot_with_cookie[-1] = (chatbot_with_cookie[-1][0],
|
||||
f"[Local Message] 插件调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
|
||||
yield from update_ui(chatbot=chatbot_with_cookie, history=history, msg=f'异常 {e}') # 刷新界面
|
||||
chatbot_with_cookie[-1] = (chatbot_with_cookie[-1][0], f"[Local Message] 插件调用出错: \n\n{tb_str} \n")
|
||||
yield from update_ui(chatbot=chatbot_with_cookie, history=history, msg=f'异常 {e}') # 刷新界面
|
||||
|
||||
return decorated
|
||||
|
||||
|
||||
@ -209,6 +216,7 @@ def HotReload(f):
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
|
||||
def get_reduce_token_percent(text):
|
||||
"""
|
||||
* 此函数未来将被弃用
|
||||
@ -220,9 +228,9 @@ def get_reduce_token_percent(text):
|
||||
EXCEED_ALLO = 500 # 稍微留一点余地，否则在回复时会因余量太少出问题
|
||||
max_limit = float(match[0]) - EXCEED_ALLO
|
||||
current_tokens = float(match[1])
|
||||
ratio = max_limit/current_tokens
|
||||
ratio = max_limit / current_tokens
|
||||
assert ratio > 0 and ratio < 1
|
||||
return ratio, str(int(current_tokens-max_limit))
|
||||
return ratio, str(int(current_tokens - max_limit))
|
||||
except:
|
||||
return 0.5, '不详'
|
||||
|
||||
@ -242,7 +250,7 @@ def write_history_to_file(history, file_basename=None, file_fullname=None, auto_
|
||||
with open(file_fullname, 'w', encoding='utf8') as f:
|
||||
f.write('# GPT-Academic Report\n')
|
||||
for i, content in enumerate(history):
|
||||
try:
|
||||
try:
|
||||
if type(content) != str: content = str(content)
|
||||
except:
|
||||
continue
|
||||
@ -268,8 +276,6 @@ def regular_txt_to_markdown(text):
|
||||
return text
|
||||
|
||||
|
||||
|
||||
|
||||
def report_exception(chatbot, history, a, b):
|
||||
"""
|
||||
向chatbot中添加错误信息
|
||||
@ -286,7 +292,7 @@ def text_divide_paragraph(text):
|
||||
suf = '</div>'
|
||||
if text.startswith(pre) and text.endswith(suf):
|
||||
return text
|
||||
|
||||
|
||||
if '```' in text:
|
||||
# careful input
|
||||
return text
|
||||
@ -312,7 +318,7 @@ def markdown_convertion(txt):
|
||||
if txt.startswith(pre) and txt.endswith(suf):
|
||||
# print('警告，输入了已经经过转化的字符串，二次转化可能出问题')
|
||||
return txt # 已经被转化过，不需要再次转化
|
||||
|
||||
|
||||
markdown_extension_configs = {
|
||||
'mdx_math': {
|
||||
'enable_dollar_delimiter': True,
|
||||
@ -352,7 +358,8 @@ def markdown_convertion(txt):
|
||||
"""
|
||||
解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
|
||||
"""
|
||||
content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
|
||||
content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">',
|
||||
'<script type="math/tex; mode=display">')
|
||||
content = content.replace('</script>\n</script>', '</script>')
|
||||
return content
|
||||
|
||||
@ -363,16 +370,16 @@ def markdown_convertion(txt):
|
||||
if '```' in txt and '```reference' not in txt: return False
|
||||
if '$' not in txt and '\\[' not in txt: return False
|
||||
mathpatterns = {
|
||||
r'(?<!\\|\$)(\$)([^\$]+)(\$)': {'allow_multi_lines': False}, # $...$
|
||||
r'(?<!\\)(\$\$)([^\$]+)(\$\$)': {'allow_multi_lines': True}, # $$...$$
|
||||
r'(?<!\\)(\\\[)(.+?)(\\\])': {'allow_multi_lines': False}, # \[...\]
|
||||
# r'(?<!\\)(\\\()(.+?)(\\\))': {'allow_multi_lines': False}, # \(...\)
|
||||
# r'(?<!\\)(\\begin{([a-z]+?\*?)})(.+?)(\\end{\2})': {'allow_multi_lines': True}, # \begin...\end
|
||||
# r'(?<!\\)(\$`)([^`]+)(`\$)': {'allow_multi_lines': False}, # $`...`$
|
||||
r'(?<!\\|\$)(\$)([^\$]+)(\$)': {'allow_multi_lines': False}, # $...$
|
||||
r'(?<!\\)(\$\$)([^\$]+)(\$\$)': {'allow_multi_lines': True}, # $$...$$
|
||||
r'(?<!\\)(\\\[)(.+?)(\\\])': {'allow_multi_lines': False}, # \[...\]
|
||||
# r'(?<!\\)(\\\()(.+?)(\\\))': {'allow_multi_lines': False}, # \(...\)
|
||||
# r'(?<!\\)(\\begin{([a-z]+?\*?)})(.+?)(\\end{\2})': {'allow_multi_lines': True}, # \begin...\end
|
||||
# r'(?<!\\)(\$`)([^`]+)(`\$)': {'allow_multi_lines': False}, # $`...`$
|
||||
}
|
||||
matches = []
|
||||
for pattern, property in mathpatterns.items():
|
||||
flags = re.ASCII|re.DOTALL if property['allow_multi_lines'] else re.ASCII
|
||||
flags = re.ASCII | re.DOTALL if property['allow_multi_lines'] else re.ASCII
|
||||
matches.extend(re.findall(pattern, txt, flags))
|
||||
if len(matches) == 0: return False
|
||||
contain_any_eq = False
|
||||
@ -380,16 +387,16 @@ def markdown_convertion(txt):
|
||||
for match in matches:
|
||||
if len(match) != 3: return False
|
||||
eq_canidate = match[1]
|
||||
if illegal_pattern.search(eq_canidate):
|
||||
if illegal_pattern.search(eq_canidate):
|
||||
return False
|
||||
else:
|
||||
else:
|
||||
contain_any_eq = True
|
||||
return contain_any_eq
|
||||
|
||||
def fix_markdown_indent(txt):
|
||||
# fix markdown indent
|
||||
if (' - ' not in txt) or ('. ' not in txt):
|
||||
return txt # do not need to fix, fast escape
|
||||
if (' - ' not in txt) or ('. ' not in txt):
|
||||
return txt # do not need to fix, fast escape
|
||||
# walk through the lines and fix non-standard indentation
|
||||
lines = txt.split("\n")
|
||||
pattern = re.compile(r'^\s+-')
|
||||
@ -401,7 +408,7 @@ def markdown_convertion(txt):
|
||||
stripped_string = line.lstrip()
|
||||
num_spaces = len(line) - len(stripped_string)
|
||||
if (num_spaces % 4) == 3:
|
||||
num_spaces_should_be = math.ceil(num_spaces/4) * 4
|
||||
num_spaces_should_be = math.ceil(num_spaces / 4) * 4
|
||||
lines[i] = ' ' * num_spaces_should_be + stripped_string
|
||||
return '\n'.join(lines)
|
||||
|
||||
@ -409,7 +416,8 @@ def markdown_convertion(txt):
|
||||
if is_equation(txt): # 有$标识的公式符号，且没有代码段```的标识
|
||||
# convert everything to html format
|
||||
split = markdown.markdown(text='---')
|
||||
convert_stage_1 = markdown.markdown(text=txt, extensions=['sane_lists', 'tables', 'mdx_math', 'fenced_code'], extension_configs=markdown_extension_configs)
|
||||
convert_stage_1 = markdown.markdown(text=txt, extensions=['sane_lists', 'tables', 'mdx_math', 'fenced_code'],
|
||||
extension_configs=markdown_extension_configs)
|
||||
convert_stage_1 = markdown_bug_hunt(convert_stage_1)
|
||||
# 1. convert to easy-to-copy tex (do not render math)
|
||||
convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
|
||||
@ -441,8 +449,7 @@ def close_up_code_segment_during_stream(gpt_reply):
|
||||
segments = gpt_reply.split('```')
|
||||
n_mark = len(segments) - 1
|
||||
if n_mark % 2 == 1:
|
||||
# print('输出代码片段中！')
|
||||
return gpt_reply+'\n```'
|
||||
return gpt_reply + '\n```' # 输出代码片段中！
|
||||
else:
|
||||
return gpt_reply
|
||||
|
||||
@ -533,7 +540,7 @@ def find_recent_files(directory):
|
||||
current_time = time.time()
|
||||
one_minute_ago = current_time - 60
|
||||
recent_files = []
|
||||
if not os.path.exists(directory):
|
||||
if not os.path.exists(directory):
|
||||
os.makedirs(directory, exist_ok=True)
|
||||
for filename in os.listdir(directory):
|
||||
file_path = pj(directory, filename)
|
||||
@ -559,6 +566,7 @@ def file_already_in_downloadzone(file, user_path):
|
||||
except:
|
||||
return False
|
||||
|
||||
|
||||
def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
# 将文件复制一份到下载区
|
||||
import shutil
|
||||
@ -581,8 +589,10 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
if not os.path.exists(new_path): shutil.copyfile(file, new_path)
|
||||
# 将文件添加到chatbot cookie中
|
||||
if chatbot is not None:
|
||||
if 'files_to_promote' in chatbot._cookies: current = chatbot._cookies['files_to_promote']
|
||||
else: current = []
|
||||
if 'files_to_promote' in chatbot._cookies:
|
||||
current = chatbot._cookies['files_to_promote']
|
||||
else:
|
||||
current = []
|
||||
if new_path not in current: # 避免把同一个文件添加多次
|
||||
chatbot._cookies.update({'files_to_promote': [new_path] + current})
|
||||
return new_path
|
||||
@ -605,8 +615,10 @@ def del_outdated_uploads(outdate_time_seconds, target_path_base=None):
|
||||
for subdirectory in glob.glob(f'{user_upload_dir}/*'):
|
||||
subdirectory_time = os.path.getmtime(subdirectory)
|
||||
if subdirectory_time < one_hour_ago:
|
||||
try: shutil.rmtree(subdirectory)
|
||||
except: pass
|
||||
try:
|
||||
shutil.rmtree(subdirectory)
|
||||
except:
|
||||
pass
|
||||
return
|
||||
|
||||
|
||||
@ -679,9 +691,9 @@ def on_file_uploaded(request: gradio.Request, files, chatbot, txt, txt2, checkbo
|
||||
time_tag = gen_time_str()
|
||||
target_path_base = get_upload_folder(user_name, tag=time_tag)
|
||||
os.makedirs(target_path_base, exist_ok=True)
|
||||
|
||||
|
||||
# 移除过时的旧文件从而节省空间&保护隐私
|
||||
outdate_time_seconds = 3600 # 一小时
|
||||
outdate_time_seconds = 3600 # 一小时
|
||||
del_outdated_uploads(outdate_time_seconds, get_upload_folder(user_name))
|
||||
|
||||
# 逐个文件转移到目标路径
|
||||
@ -690,21 +702,20 @@ def on_file_uploaded(request: gradio.Request, files, chatbot, txt, txt2, checkbo
|
||||
file_origin_name = os.path.basename(file.orig_name)
|
||||
this_file_path = pj(target_path_base, file_origin_name)
|
||||
shutil.move(file.name, this_file_path)
|
||||
upload_msg += extract_archive(file_path=this_file_path, dest_dir=this_file_path+'.extract')
|
||||
|
||||
if "浮动输入区" in checkboxes:
|
||||
txt, txt2 = "", target_path_base
|
||||
else:
|
||||
txt, txt2 = target_path_base, ""
|
||||
upload_msg += extract_archive(file_path=this_file_path, dest_dir=this_file_path + '.extract')
|
||||
|
||||
# 整理文件集合 输出消息
|
||||
moved_files = [fp for fp in glob.glob(f'{target_path_base}/**/*', recursive=True)]
|
||||
moved_files_str = to_markdown_tabs(head=['文件'], tabs=[moved_files])
|
||||
chatbot.append(['我上传了文件，请查收',
|
||||
chatbot.append(['我上传了文件，请查收',
|
||||
f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
|
||||
f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
|
||||
f'\n\n现在您点击任意函数插件时，以上文件将被作为输入参数'+upload_msg])
|
||||
|
||||
f'\n\n现在您点击任意函数插件时，以上文件将被作为输入参数' + upload_msg])
|
||||
|
||||
txt, txt2 = target_path_base, ""
|
||||
if "浮动输入区" in checkboxes:
|
||||
txt, txt2 = txt2, txt
|
||||
|
||||
# 记录近期文件
|
||||
cookies.update({
|
||||
'most_recent_uploaded': {
|
||||
@ -732,34 +743,40 @@ def on_report_generated(cookies, files, chatbot):
|
||||
chatbot.append(['报告如何远程获取？', f'报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。{file_links}'])
|
||||
return cookies, report_files, chatbot
|
||||
|
||||
|
||||
def load_chat_cookies():
|
||||
API_KEY, LLM_MODEL, AZURE_API_KEY = get_conf('API_KEY', 'LLM_MODEL', 'AZURE_API_KEY')
|
||||
AZURE_CFG_ARRAY, NUM_CUSTOM_BASIC_BTN = get_conf('AZURE_CFG_ARRAY', 'NUM_CUSTOM_BASIC_BTN')
|
||||
|
||||
# deal with azure openai key
|
||||
if is_any_api_key(AZURE_API_KEY):
|
||||
if is_any_api_key(API_KEY): API_KEY = API_KEY + ',' + AZURE_API_KEY
|
||||
else: API_KEY = AZURE_API_KEY
|
||||
if is_any_api_key(API_KEY):
|
||||
API_KEY = API_KEY + ',' + AZURE_API_KEY
|
||||
else:
|
||||
API_KEY = AZURE_API_KEY
|
||||
if len(AZURE_CFG_ARRAY) > 0:
|
||||
for azure_model_name, azure_cfg_dict in AZURE_CFG_ARRAY.items():
|
||||
if not azure_model_name.startswith('azure'):
|
||||
if not azure_model_name.startswith('azure'):
|
||||
raise ValueError("AZURE_CFG_ARRAY中配置的模型必须以azure开头")
|
||||
AZURE_API_KEY_ = azure_cfg_dict["AZURE_API_KEY"]
|
||||
if is_any_api_key(AZURE_API_KEY_):
|
||||
if is_any_api_key(API_KEY): API_KEY = API_KEY + ',' + AZURE_API_KEY_
|
||||
else: API_KEY = AZURE_API_KEY_
|
||||
if is_any_api_key(API_KEY):
|
||||
API_KEY = API_KEY + ',' + AZURE_API_KEY_
|
||||
else:
|
||||
API_KEY = AZURE_API_KEY_
|
||||
|
||||
customize_fn_overwrite_ = {}
|
||||
for k in range(NUM_CUSTOM_BASIC_BTN):
|
||||
customize_fn_overwrite_.update({
|
||||
customize_fn_overwrite_.update({
|
||||
"自定义按钮" + str(k+1):{
|
||||
"Title": r"",
|
||||
"Prefix": r"请在自定义菜单中定义提示词前缀.",
|
||||
"Suffix": r"请在自定义菜单中定义提示词后缀",
|
||||
"Title": r"",
|
||||
"Prefix": r"请在自定义菜单中定义提示词前缀.",
|
||||
"Suffix": r"请在自定义菜单中定义提示词后缀",
|
||||
}
|
||||
})
|
||||
return {'api_key': API_KEY, 'llm_model': LLM_MODEL, 'customize_fn_overwrite': customize_fn_overwrite_}
|
||||
|
||||
|
||||
def is_openai_api_key(key):
|
||||
CUSTOM_API_KEY_PATTERN = get_conf('CUSTOM_API_KEY_PATTERN')
|
||||
if len(CUSTOM_API_KEY_PATTERN) != 0:
|
||||
@ -768,14 +785,17 @@ def is_openai_api_key(key):
|
||||
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
|
||||
return bool(API_MATCH_ORIGINAL)
|
||||
|
||||
|
||||
def is_azure_api_key(key):
|
||||
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
|
||||
return bool(API_MATCH_AZURE)
|
||||
|
||||
|
||||
def is_api2d_key(key):
|
||||
API_MATCH_API2D = re.match(r"fk[a-zA-Z0-9]{6}-[a-zA-Z0-9]{32}$", key)
|
||||
return bool(API_MATCH_API2D)
|
||||
|
||||
|
||||
def is_any_api_key(key):
|
||||
if ',' in key:
|
||||
keys = key.split(',')
|
||||
@ -785,24 +805,26 @@ def is_any_api_key(key):
|
||||
else:
|
||||
return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
|
||||
|
||||
|
||||
def what_keys(keys):
|
||||
avail_key_list = {'OpenAI Key':0, "Azure Key":0, "API2D Key":0}
|
||||
avail_key_list = {'OpenAI Key': 0, "Azure Key": 0, "API2D Key": 0}
|
||||
key_list = keys.split(',')
|
||||
|
||||
for k in key_list:
|
||||
if is_openai_api_key(k):
|
||||
if is_openai_api_key(k):
|
||||
avail_key_list['OpenAI Key'] += 1
|
||||
|
||||
for k in key_list:
|
||||
if is_api2d_key(k):
|
||||
if is_api2d_key(k):
|
||||
avail_key_list['API2D Key'] += 1
|
||||
|
||||
for k in key_list:
|
||||
if is_azure_api_key(k):
|
||||
if is_azure_api_key(k):
|
||||
avail_key_list['Azure Key'] += 1
|
||||
|
||||
return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个, Azure Key {avail_key_list['Azure Key']} 个, API2D Key {avail_key_list['API2D Key']} 个"
|
||||
|
||||
|
||||
def select_api_key(keys, llm_model):
|
||||
import random
|
||||
avail_key_list = []
|
||||
@ -826,6 +848,7 @@ def select_api_key(keys, llm_model):
|
||||
api_key = random.choice(avail_key_list) # 随机负载均衡
|
||||
return api_key
|
||||
|
||||
|
||||
def read_env_variable(arg, default_value):
|
||||
"""
|
||||
环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先)，也可以直接是`CONFIG`
|
||||
@ -843,10 +866,10 @@ def read_env_variable(arg, default_value):
|
||||
set GPT_ACADEMIC_AUTHENTICATION=[("username", "password"), ("username2", "password2")]
|
||||
"""
|
||||
from colorful import print亮红, print亮绿
|
||||
arg_with_prefix = "GPT_ACADEMIC_" + arg
|
||||
if arg_with_prefix in os.environ:
|
||||
arg_with_prefix = "GPT_ACADEMIC_" + arg
|
||||
if arg_with_prefix in os.environ:
|
||||
env_arg = os.environ[arg_with_prefix]
|
||||
elif arg in os.environ:
|
||||
elif arg in os.environ:
|
||||
env_arg = os.environ[arg]
|
||||
else:
|
||||
raise KeyError
|
||||
@ -856,7 +879,7 @@ def read_env_variable(arg, default_value):
|
||||
env_arg = env_arg.strip()
|
||||
if env_arg == 'True': r = True
|
||||
elif env_arg == 'False': r = False
|
||||
else: print('enter True or False, but have:', env_arg); r = default_value
|
||||
else: print('Enter True or False, but have:', env_arg); r = default_value
|
||||
elif isinstance(default_value, int):
|
||||
r = int(env_arg)
|
||||
elif isinstance(default_value, float):
|
||||
@ -880,13 +903,14 @@ def read_env_variable(arg, default_value):
|
||||
print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
|
||||
return r
|
||||
|
||||
|
||||
@lru_cache(maxsize=128)
|
||||
def read_single_conf_with_lru_cache(arg):
|
||||
from colorful import print亮红, print亮绿, print亮蓝
|
||||
try:
|
||||
# 优先级1. 获取环境变量作为配置
|
||||
default_ref = getattr(importlib.import_module('config'), arg) # 读取默认值作为数据类型转换的参考
|
||||
r = read_env_variable(arg, default_ref)
|
||||
default_ref = getattr(importlib.import_module('config'), arg) # 读取默认值作为数据类型转换的参考
|
||||
r = read_env_variable(arg, default_ref)
|
||||
except:
|
||||
try:
|
||||
# 优先级2. 获取config_private中的配置
|
||||
@ -899,7 +923,7 @@ def read_single_conf_with_lru_cache(arg):
|
||||
if arg == 'API_URL_REDIRECT':
|
||||
oai_rd = r.get("https://api.openai.com/v1/chat/completions", None) # API_URL_REDIRECT填写格式是错误的，请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`
|
||||
if oai_rd and not oai_rd.endswith('/completions'):
|
||||
print亮红( "\n\n[API_URL_REDIRECT] API_URL_REDIRECT填错了。请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`。如果您确信自己没填错，无视此消息即可。")
|
||||
print亮红("\n\n[API_URL_REDIRECT] API_URL_REDIRECT填错了。请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`。如果您确信自己没填错，无视此消息即可。")
|
||||
time.sleep(5)
|
||||
if arg == 'API_KEY':
|
||||
print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和Azure的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,azure-key3\"")
|
||||
@ -907,9 +931,9 @@ def read_single_conf_with_lru_cache(arg):
|
||||
if is_any_api_key(r):
|
||||
print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
|
||||
else:
|
||||
print亮红( "[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
|
||||
print亮红("[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
|
||||
if arg == 'proxies':
|
||||
if not read_single_conf_with_lru_cache('USE_PROXY'): r = None # 检查USE_PROXY，防止proxies单独起作用
|
||||
if not read_single_conf_with_lru_cache('USE_PROXY'): r = None # 检查USE_PROXY，防止proxies单独起作用
|
||||
if r is None:
|
||||
print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
|
||||
else:
|
||||
@ -953,17 +977,20 @@ class DummyWith():
|
||||
在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
|
||||
而在上下文执行结束时，__exit__()方法则会被调用。
|
||||
"""
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
return
|
||||
|
||||
|
||||
def run_gradio_in_subpath(demo, auth, port, custom_path):
|
||||
"""
|
||||
把gradio的运行地址更改到指定的二次路径上
|
||||
"""
|
||||
def is_path_legal(path: str)->bool:
|
||||
|
||||
def is_path_legal(path: str) -> bool:
|
||||
'''
|
||||
check path for sub url
|
||||
path: path to check
|
||||
@ -988,7 +1015,7 @@ def run_gradio_in_subpath(demo, auth, port, custom_path):
|
||||
app = FastAPI()
|
||||
if custom_path != "/":
|
||||
@app.get("/")
|
||||
def read_main():
|
||||
def read_main():
|
||||
return {"message": f"Gradio is running at: {custom_path}"}
|
||||
app = gr.mount_gradio_app(app, demo, path=custom_path)
|
||||
uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth
|
||||
@ -999,13 +1026,13 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
reduce the length of history by clipping.
|
||||
this function search for the longest entries to clip, little by little,
|
||||
until the number of token of history is reduced under threshold.
|
||||
通过裁剪来缩短历史记录的长度。
|
||||
通过裁剪来缩短历史记录的长度。
|
||||
此函数逐渐地搜索最长的条目进行剪辑，
|
||||
直到历史记录的标记数量降低到阈值以下。
|
||||
"""
|
||||
import numpy as np
|
||||
from request_llms.bridge_all import model_info
|
||||
def get_token_num(txt):
|
||||
def get_token_num(txt):
|
||||
return len(tokenizer.encode(txt, disallowed_special=()))
|
||||
input_token_num = get_token_num(inputs)
|
||||
|
||||
@ -1039,14 +1066,15 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
while n_token > max_token_limit:
|
||||
where = np.argmax(everything_token)
|
||||
encoded = tokenizer.encode(everything[where], disallowed_special=())
|
||||
clipped_encoded = encoded[:len(encoded)-delta]
|
||||
everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
|
||||
clipped_encoded = encoded[:len(encoded) - delta]
|
||||
everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
|
||||
everything_token[where] = get_token_num(everything[where])
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
|
||||
history = everything[1:]
|
||||
return history
|
||||
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
第三部分
|
||||
@ -1058,6 +1086,7 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
|
||||
def zip_folder(source_folder, dest_folder, zip_name):
|
||||
import zipfile
|
||||
import os
|
||||
@ -1089,15 +1118,18 @@ def zip_folder(source_folder, dest_folder, zip_name):
|
||||
|
||||
print(f"Zip file created at {zip_file}")
|
||||
|
||||
|
||||
def zip_result(folder):
|
||||
t = gen_time_str()
|
||||
zip_folder(folder, get_log_folder(), f'{t}-result.zip')
|
||||
return pj(get_log_folder(), f'{t}-result.zip')
|
||||
|
||||
|
||||
def gen_time_str():
|
||||
import time
|
||||
return time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
|
||||
|
||||
|
||||
def get_log_folder(user=default_user_name, plugin_name='shared'):
|
||||
if user is None: user = default_user_name
|
||||
PATH_LOGGING = get_conf('PATH_LOGGING')
|
||||
@ -1108,29 +1140,36 @@ def get_log_folder(user=default_user_name, plugin_name='shared'):
|
||||
if not os.path.exists(_dir): os.makedirs(_dir)
|
||||
return _dir
|
||||
|
||||
|
||||
def get_upload_folder(user=default_user_name, tag=None):
|
||||
PATH_PRIVATE_UPLOAD = get_conf('PATH_PRIVATE_UPLOAD')
|
||||
if user is None: user = default_user_name
|
||||
if tag is None or len(tag)==0:
|
||||
if tag is None or len(tag) == 0:
|
||||
target_path_base = pj(PATH_PRIVATE_UPLOAD, user)
|
||||
else:
|
||||
target_path_base = pj(PATH_PRIVATE_UPLOAD, user, tag)
|
||||
return target_path_base
|
||||
|
||||
|
||||
def is_the_upload_folder(string):
|
||||
PATH_PRIVATE_UPLOAD = get_conf('PATH_PRIVATE_UPLOAD')
|
||||
pattern = r'^PATH_PRIVATE_UPLOAD[\\/][A-Za-z0-9_-]+[\\/]\d{4}-\d{2}-\d{2}-\d{2}-\d{2}-\d{2}$'
|
||||
pattern = pattern.replace('PATH_PRIVATE_UPLOAD', PATH_PRIVATE_UPLOAD)
|
||||
if re.match(pattern, string): return True
|
||||
else: return False
|
||||
if re.match(pattern, string):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
|
||||
def get_user(chatbotwithcookies):
|
||||
return chatbotwithcookies._cookies.get('user_name', default_user_name)
|
||||
|
||||
|
||||
class ProxyNetworkActivate():
|
||||
"""
|
||||
这段代码定义了一个名为ProxyNetworkActivate的空上下文管理器, 用于给一小段代码上代理
|
||||
"""
|
||||
|
||||
def __init__(self, task=None) -> None:
|
||||
self.task = task
|
||||
if not task:
|
||||
@ -1158,32 +1197,36 @@ class ProxyNetworkActivate():
|
||||
if 'HTTPS_PROXY' in os.environ: os.environ.pop('HTTPS_PROXY')
|
||||
return
|
||||
|
||||
|
||||
def objdump(obj, file='objdump.tmp'):
|
||||
import pickle
|
||||
with open(file, 'wb+') as f:
|
||||
pickle.dump(obj, f)
|
||||
return
|
||||
|
||||
|
||||
def objload(file='objdump.tmp'):
|
||||
import pickle, os
|
||||
if not os.path.exists(file):
|
||||
if not os.path.exists(file):
|
||||
return
|
||||
with open(file, 'rb') as f:
|
||||
return pickle.load(f)
|
||||
|
||||
|
||||
|
||||
def Singleton(cls):
|
||||
"""
|
||||
一个单实例装饰器
|
||||
"""
|
||||
_instance = {}
|
||||
|
||||
|
||||
def _singleton(*args, **kargs):
|
||||
if cls not in _instance:
|
||||
_instance[cls] = cls(*args, **kargs)
|
||||
return _instance[cls]
|
||||
|
||||
|
||||
return _singleton
|
||||
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
第四部分
|
||||
@ -1197,6 +1240,7 @@ def Singleton(cls):
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
|
||||
def set_conf(key, value):
|
||||
from toolbox import read_single_conf_with_lru_cache, get_conf
|
||||
read_single_conf_with_lru_cache.cache_clear()
|
||||
@ -1205,10 +1249,12 @@ def set_conf(key, value):
|
||||
altered = get_conf(key)
|
||||
return altered
|
||||
|
||||
|
||||
def set_multi_conf(dic):
|
||||
for k, v in dic.items(): set_conf(k, v)
|
||||
return
|
||||
|
||||
|
||||
def get_plugin_handle(plugin_name):
|
||||
"""
|
||||
e.g. plugin_name = 'crazy_functions.批量Markdown翻译->Markdown翻译指定语言'
|
||||
@ -1220,12 +1266,14 @@ def get_plugin_handle(plugin_name):
|
||||
f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)
|
||||
return f_hot_reload
|
||||
|
||||
|
||||
def get_chat_handle():
|
||||
"""
|
||||
"""
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
return predict_no_ui_long_connection
|
||||
|
||||
|
||||
def get_plugin_default_kwargs():
|
||||
"""
|
||||
"""
|
||||
@ -1234,9 +1282,9 @@ def get_plugin_default_kwargs():
|
||||
llm_kwargs = {
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': cookies['llm_model'],
|
||||
'top_p':1.0,
|
||||
'top_p': 1.0,
|
||||
'max_length': None,
|
||||
'temperature':1.0,
|
||||
'temperature': 1.0,
|
||||
}
|
||||
chatbot = ChatBotWithCookies(llm_kwargs)
|
||||
|
||||
@ -1247,11 +1295,12 @@ def get_plugin_default_kwargs():
|
||||
"plugin_kwargs": {},
|
||||
"chatbot_with_cookie": chatbot,
|
||||
"history": [],
|
||||
"system_prompt": "You are a good AI.",
|
||||
"system_prompt": "You are a good AI.",
|
||||
"web_port": None
|
||||
}
|
||||
return DEFAULT_FN_GROUPS_kwargs
|
||||
|
||||
|
||||
def get_chat_default_kwargs():
|
||||
"""
|
||||
"""
|
||||
@ -1259,9 +1308,9 @@ def get_chat_default_kwargs():
|
||||
llm_kwargs = {
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': cookies['llm_model'],
|
||||
'top_p':1.0,
|
||||
'top_p': 1.0,
|
||||
'max_length': None,
|
||||
'temperature':1.0,
|
||||
'temperature': 1.0,
|
||||
}
|
||||
default_chat_kwargs = {
|
||||
"inputs": "Hello there, are you ready?",
|
||||
@ -1284,15 +1333,15 @@ def get_pictures_list(path):
|
||||
|
||||
def have_any_recent_upload_image_files(chatbot):
|
||||
_5min = 5 * 60
|
||||
if chatbot is None: return False, None # chatbot is None
|
||||
if chatbot is None: return False, None # chatbot is None
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
if not most_recent_uploaded: return False, None # most_recent_uploaded is None
|
||||
if not most_recent_uploaded: return False, None # most_recent_uploaded is None
|
||||
if time.time() - most_recent_uploaded["time"] < _5min:
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
path = most_recent_uploaded['path']
|
||||
file_manifest = get_pictures_list(path)
|
||||
if len(file_manifest) == 0: return False, None
|
||||
return True, file_manifest # most_recent_uploaded is new
|
||||
return True, file_manifest # most_recent_uploaded is new
|
||||
else:
|
||||
return False, None # most_recent_uploaded is too old
|
||||
|
||||
@ -1307,6 +1356,7 @@ def get_max_token(llm_kwargs):
|
||||
from request_llms.bridge_all import model_info
|
||||
return model_info[llm_kwargs['llm_model']]['max_token']
|
||||
|
||||
|
||||
def check_packages(packages=[]):
|
||||
import importlib.util
|
||||
for p in packages:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user