229 lines
9.1 KiB
Python
229 lines
9.1 KiB
Python
# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
|
||
|
||
"""
|
||
该文件中主要包含2个函数
|
||
|
||
不具备多线程能力的函数：
|
||
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
|
||
|
||
具备多线程调用能力的函数
|
||
2. predict_no_ui_long_connection：支持多线程
|
||
"""
|
||
|
||
import os
|
||
import json
|
||
import time
|
||
import gradio as gr
|
||
import logging
|
||
import traceback
|
||
import requests
|
||
import importlib
|
||
|
||
# config_private.py放自己的秘密如API和代理网址
|
||
# 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
|
||
from toolbox import get_conf, update_ui, trimmed_format_exc, ProxyNetworkActivate
|
||
proxies, TIMEOUT_SECONDS, MAX_RETRY, ANTHROPIC_API_KEY = \
|
||
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'ANTHROPIC_API_KEY')
|
||
|
||
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
|
||
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
|
||
|
||
def get_full_error(chunk, stream_response):
|
||
"""
|
||
获取完整的从Openai返回的报错
|
||
"""
|
||
while True:
|
||
try:
|
||
chunk += next(stream_response)
|
||
except:
|
||
break
|
||
return chunk
|
||
|
||
|
||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||
"""
|
||
发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
|
||
inputs：
|
||
是本次问询的输入
|
||
sys_prompt:
|
||
系统静默prompt
|
||
llm_kwargs：
|
||
chatGPT的内部调优参数
|
||
history：
|
||
是之前的对话列表
|
||
observe_window = None：
|
||
用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
|
||
"""
|
||
from anthropic import Anthropic
|
||
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
|
||
prompt = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
|
||
retry = 0
|
||
if len(ANTHROPIC_API_KEY) == 0:
|
||
raise RuntimeError("没有设置ANTHROPIC_API_KEY选项")
|
||
|
||
while True:
|
||
try:
|
||
# make a POST request to the API endpoint, stream=False
|
||
from .bridge_all import model_info
|
||
anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
|
||
# endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||
# with ProxyNetworkActivate()
|
||
stream = anthropic.completions.create(
|
||
prompt=prompt,
|
||
max_tokens_to_sample=4096, # The maximum number of tokens to generate before stopping.
|
||
model=llm_kwargs['llm_model'],
|
||
stream=True,
|
||
temperature = llm_kwargs['temperature']
|
||
)
|
||
break
|
||
except Exception as e:
|
||
retry += 1
|
||
traceback.print_exc()
|
||
if retry > MAX_RETRY: raise TimeoutError
|
||
if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
|
||
result = ''
|
||
try:
|
||
for completion in stream:
|
||
result += completion.completion
|
||
if not console_slience: print(completion.completion, end='')
|
||
if observe_window is not None:
|
||
# 观测窗，把已经获取的数据显示出去
|
||
if len(observe_window) >= 1: observe_window[0] += completion.completion
|
||
# 看门狗，如果超过期限没有喂狗，则终止
|
||
if len(observe_window) >= 2:
|
||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||
raise RuntimeError("用户取消了程序。")
|
||
except Exception as e:
|
||
traceback.print_exc()
|
||
|
||
return result
|
||
|
||
|
||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||
"""
|
||
发送至chatGPT，流式获取输出。
|
||
用于基础的对话功能。
|
||
inputs 是本次问询的输入
|
||
top_p, temperature是chatGPT的内部调优参数
|
||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
|
||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||
"""
|
||
from anthropic import Anthropic
|
||
if len(ANTHROPIC_API_KEY) == 0:
|
||
chatbot.append((inputs, "没有设置ANTHROPIC_API_KEY"))
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
|
||
return
|
||
|
||
if additional_fn is not None:
|
||
from core_functional import handle_core_functionality
|
||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||
|
||
raw_input = inputs
|
||
logging.info(f'[raw_input] {raw_input}')
|
||
chatbot.append((inputs, ""))
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
|
||
|
||
try:
|
||
prompt = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
|
||
except RuntimeError as e:
|
||
chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
|
||
return
|
||
|
||
history.append(inputs); history.append("")
|
||
|
||
retry = 0
|
||
while True:
|
||
try:
|
||
# make a POST request to the API endpoint, stream=True
|
||
from .bridge_all import model_info
|
||
anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
|
||
# endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||
# with ProxyNetworkActivate()
|
||
stream = anthropic.completions.create(
|
||
prompt=prompt,
|
||
max_tokens_to_sample=4096, # The maximum number of tokens to generate before stopping.
|
||
model=llm_kwargs['llm_model'],
|
||
stream=True,
|
||
temperature = llm_kwargs['temperature']
|
||
)
|
||
|
||
break
|
||
except:
|
||
retry += 1
|
||
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
|
||
retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
|
||
if retry > MAX_RETRY: raise TimeoutError
|
||
|
||
gpt_replying_buffer = ""
|
||
|
||
for completion in stream:
|
||
try:
|
||
gpt_replying_buffer = gpt_replying_buffer + completion.completion
|
||
history[-1] = gpt_replying_buffer
|
||
chatbot[-1] = (history[-2], history[-1])
|
||
yield from update_ui(chatbot=chatbot, history=history, msg='正常') # 刷新界面
|
||
|
||
except Exception as e:
|
||
from toolbox import regular_txt_to_markdown
|
||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str}")
|
||
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面
|
||
return
|
||
|
||
|
||
|
||
|
||
# https://github.com/jtsang4/claude-to-chatgpt/blob/main/claude_to_chatgpt/adapter.py
|
||
def convert_messages_to_prompt(messages):
|
||
prompt = ""
|
||
role_map = {
|
||
"system": "Human",
|
||
"user": "Human",
|
||
"assistant": "Assistant",
|
||
}
|
||
for message in messages:
|
||
role = message["role"]
|
||
content = message["content"]
|
||
transformed_role = role_map[role]
|
||
prompt += f"\n\n{transformed_role.capitalize()}: {content}"
|
||
prompt += "\n\nAssistant: "
|
||
return prompt
|
||
|
||
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||
"""
|
||
整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
|
||
"""
|
||
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
|
||
|
||
conversation_cnt = len(history) // 2
|
||
|
||
messages = [{"role": "system", "content": system_prompt}]
|
||
if conversation_cnt:
|
||
for index in range(0, 2*conversation_cnt, 2):
|
||
what_i_have_asked = {}
|
||
what_i_have_asked["role"] = "user"
|
||
what_i_have_asked["content"] = history[index]
|
||
what_gpt_answer = {}
|
||
what_gpt_answer["role"] = "assistant"
|
||
what_gpt_answer["content"] = history[index+1]
|
||
if what_i_have_asked["content"] != "":
|
||
if what_gpt_answer["content"] == "": continue
|
||
if what_gpt_answer["content"] == timeout_bot_msg: continue
|
||
messages.append(what_i_have_asked)
|
||
messages.append(what_gpt_answer)
|
||
else:
|
||
messages[-1]['content'] = what_gpt_answer['content']
|
||
|
||
what_i_ask_now = {}
|
||
what_i_ask_now["role"] = "user"
|
||
what_i_ask_now["content"] = inputs
|
||
messages.append(what_i_ask_now)
|
||
prompt = convert_messages_to_prompt(messages)
|
||
|
||
return prompt
|
||
|
||
|