support claude api
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binary-husky
parent
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6f21ae8939
@ -71,7 +71,7 @@ MAX_RETRY = 2
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# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
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LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
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# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "newbing-free", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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# ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
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@ -89,9 +89,11 @@ CONCURRENT_COUNT = 100
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# 是否在提交时自动清空输入框
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AUTO_CLEAR_TXT = False
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# 色彩主体，可选 ["Default", "Chuanhu-Small-and-Beautiful"]
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THEME = "Default"
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# 加一个live2d装饰
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ADD_WAIFU = False
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@ -131,3 +133,7 @@ put your new bing cookies here
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ENABLE_AUDIO = False
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ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
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ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
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# Claude API KEY
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ANTHROPIC_API_KEY = ""
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@ -170,6 +170,29 @@ model_info = {
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AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
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AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]
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if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
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from .bridge_claude import predict_no_ui_long_connection as claude_noui
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from .bridge_claude import predict as claude_ui
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model_info.update({
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"claude-1-100k": {
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"fn_with_ui": claude_ui,
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"fn_without_ui": claude_noui,
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"endpoint": None,
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"max_token": 8196,
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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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model_info.update({
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"claude-2": {
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"fn_with_ui": claude_ui,
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"fn_without_ui": claude_noui,
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"endpoint": None,
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"max_token": 8196,
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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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if "jittorllms_rwkv" in AVAIL_LLM_MODELS:
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from .bridge_jittorllms_rwkv import predict_no_ui_long_connection as rwkv_noui
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from .bridge_jittorllms_rwkv import predict as rwkv_ui
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231
request_llm/bridge_claude.py
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231
request_llm/bridge_claude.py
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@ -0,0 +1,231 @@
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# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
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"""
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该文件中主要包含2个函数
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不具备多线程能力的函数：
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1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
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具备多线程调用能力的函数
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2. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
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"""
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import os
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import json
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import time
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import gradio as gr
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import logging
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import traceback
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import requests
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import importlib
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# config_private.py放自己的秘密如API和代理网址
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# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
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from toolbox import get_conf, update_ui, trimmed_format_exc, ProxyNetworkActivate
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proxies, TIMEOUT_SECONDS, MAX_RETRY, ANTHROPIC_API_KEY = \
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get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'ANTHROPIC_API_KEY')
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timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
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'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
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def get_full_error(chunk, stream_response):
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"""
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获取完整的从Openai返回的报错
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"""
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while True:
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try:
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chunk += next(stream_response)
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except:
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break
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return chunk
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
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"""
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发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
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inputs：
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是本次问询的输入
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sys_prompt:
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系统静默prompt
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llm_kwargs：
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chatGPT的内部调优参数
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history：
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是之前的对话列表
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observe_window = None：
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用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
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"""
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from anthropic import Anthropic
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watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
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prompt = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
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retry = 0
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if len(ANTHROPIC_API_KEY) == 0:
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raise RuntimeError("没有设置ANTHROPIC_API_KEY选项")
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while True:
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try:
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# make a POST request to the API endpoint, stream=False
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from .bridge_all import model_info
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anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
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# endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
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# with ProxyNetworkActivate()
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stream = anthropic.completions.create(
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prompt=prompt,
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max_tokens_to_sample=4096, # The maximum number of tokens to generate before stopping.
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model=llm_kwargs['llm_model'],
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stream=True,
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temperature = llm_kwargs['temperature']
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)
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break
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except Exception as e:
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retry += 1
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traceback.print_exc()
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if retry > MAX_RETRY: raise TimeoutError
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if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
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result = ''
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try:
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for completion in stream:
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result += completion.completion
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if not console_slience: print(completion.completion, end='')
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if observe_window is not None:
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# 观测窗，把已经获取的数据显示出去
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if len(observe_window) >= 1: observe_window[0] += completion.completion
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# 看门狗，如果超过期限没有喂狗，则终止
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if len(observe_window) >= 2:
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if (time.time()-observe_window[1]) > watch_dog_patience:
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raise RuntimeError("用户取消了程序。")
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except Exception as e:
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traceback.print_exc()
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return result
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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发送至chatGPT，流式获取输出。
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用于基础的对话功能。
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inputs 是本次问询的输入
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top_p, temperature是chatGPT的内部调优参数
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history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
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chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
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additional_fn代表点击的哪个按钮，按钮见functional.py
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"""
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from anthropic import Anthropic
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if len(ANTHROPIC_API_KEY) == 0:
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chatbot.append((inputs, "没有设置ANTHROPIC_API_KEY"))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
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return
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if additional_fn is not None:
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import core_functional
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importlib.reload(core_functional) # 热更新prompt
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core_functional = core_functional.get_core_functions()
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if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
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inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
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raw_input = inputs
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logging.info(f'[raw_input] {raw_input}')
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chatbot.append((inputs, ""))
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yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
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try:
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prompt = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
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except RuntimeError as e:
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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。")
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yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
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return
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history.append(inputs); history.append("")
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retry = 0
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while True:
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try:
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# make a POST request to the API endpoint, stream=True
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from .bridge_all import model_info
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anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
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# endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
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# with ProxyNetworkActivate()
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stream = anthropic.completions.create(
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prompt=prompt,
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max_tokens_to_sample=4096, # The maximum number of tokens to generate before stopping.
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model=llm_kwargs['llm_model'],
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stream=True,
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temperature = llm_kwargs['temperature']
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)
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break
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except:
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retry += 1
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chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
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retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
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yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
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if retry > MAX_RETRY: raise TimeoutError
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gpt_replying_buffer = ""
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for completion in stream:
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try:
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gpt_replying_buffer = gpt_replying_buffer + completion.completion
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history[-1] = gpt_replying_buffer
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chatbot[-1] = (history[-2], history[-1])
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yield from update_ui(chatbot=chatbot, history=history, msg='正常') # 刷新界面
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except Exception as e:
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from toolbox import regular_txt_to_markdown
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tb_str = '```\n' + trimmed_format_exc() + '```'
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str}")
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yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面
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return
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# https://github.com/jtsang4/claude-to-chatgpt/blob/main/claude_to_chatgpt/adapter.py
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def convert_messages_to_prompt(messages):
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prompt = ""
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role_map = {
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"system": "Human",
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"user": "Human",
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"assistant": "Assistant",
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}
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for message in messages:
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role = message["role"]
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content = message["content"]
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transformed_role = role_map[role]
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prompt += f"\n\n{transformed_role.capitalize()}: {content}"
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prompt += "\n\nAssistant: "
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return prompt
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def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
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"""
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整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
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"""
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from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
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conversation_cnt = len(history) // 2
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messages = [{"role": "system", "content": system_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "": continue
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if what_gpt_answer["content"] == timeout_bot_msg: continue
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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messages.append(what_i_ask_now)
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prompt = convert_messages_to_prompt(messages)
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return prompt
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@ -10,10 +10,11 @@ def validate_path():
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validate_path() # validate path so you can run from base directory
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if __name__ == "__main__":
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from request_llm.bridge_newbingfree import predict_no_ui_long_connection
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# from request_llm.bridge_newbingfree import predict_no_ui_long_connection
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# from request_llm.bridge_moss import predict_no_ui_long_connection
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# from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
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# from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
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from request_llm.bridge_claude import predict_no_ui_long_connection
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llm_kwargs = {
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'max_length': 512,
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@ -28,17 +29,6 @@ if __name__ == "__main__":
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print('final result:', result)
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result = predict_no_ui_long_connection(inputs="what is a hero?",
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llm_kwargs=llm_kwargs,
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history=["hello world"],
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sys_prompt="")
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print('final result:', result)
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result = predict_no_ui_long_connection(inputs="如何理解传奇?",
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llm_kwargs=llm_kwargs,
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history=[],
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sys_prompt="")
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print('final result:', result)
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# # print(result)
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# from multiprocessing import Process, Pipe
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@ -56,7 +46,6 @@ if __name__ == "__main__":
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# os.chdir(root_dir_assume + '/request_llm/jittorllms')
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# sys.path.append(root_dir_assume + '/request_llm/jittorllms')
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# validate_path() # validate path so you can run from base directory
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# jittorllms_model = None
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# import types
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# try:
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@ -70,7 +59,6 @@ if __name__ == "__main__":
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# except:
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# # self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
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# raise RuntimeError("不能正常加载jittorllms的参数！")
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# x = GetGLMHandle()
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# x.start()
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@ -9,6 +9,7 @@ prompt_toolkit
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latex2mathml
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python-docx
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mdtex2html
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anthropic
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colorama
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Markdown
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pygments
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