优化chatgpt对话的截断策略
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binary-husky
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0b89673ee9
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@ -98,7 +98,8 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
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history.extend([ "第一批", gpt_say ])
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meta_paper_info_list = meta_paper_info_list[10:]
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chatbot.append(["状态？", "已经全部完成"])
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chatbot.append(["状态？",
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"已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
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msg = '正常'
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yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
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res = write_results_to_file(history)
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@ -21,7 +21,7 @@ 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, is_any_api_key, select_api_key, what_keys
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from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
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proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
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get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
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@ -145,7 +145,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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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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history.append(inputs); history.append("")
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retry = 0
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while True:
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@ -198,14 +198,17 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
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chunk_decoded = chunk.decode()
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error_msg = chunk_decoded
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if "reduce the length" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长，或历史数据过长. 历史缓存数据现已释放，您可以请再次尝试.")
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history = [] # 清除历史
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if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
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history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
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max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])//2) # history至少释放二分之一
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
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# history = [] # 清除历史
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elif "does not exist" in error_msg:
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在，或者您没有获得体验资格.")
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chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
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elif "Incorrect API key" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由，拒绝服务.")
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务.")
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elif "exceeded your current quota" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由，拒绝服务.")
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chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务.")
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elif "bad forward key" in error_msg:
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chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
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elif "Not enough point" in error_msg:
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46
toolbox.py
46
toolbox.py
@ -551,3 +551,49 @@ def run_gradio_in_subpath(demo, auth, port, custom_path):
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return {"message": f"Gradio is running at: {custom_path}"}
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app = gr.mount_gradio_app(app, demo, path=custom_path)
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uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth
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def clip_history(inputs, history, tokenizer, max_token_limit):
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"""
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reduce the length of input/history by clipping.
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this function search for the longest entries to clip, little by little,
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until the number of token of input/history is reduced under threshold.
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通过剪辑来缩短输入/历史记录的长度。
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此函数逐渐地搜索最长的条目进行剪辑，
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直到输入/历史记录的标记数量降低到阈值以下。
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"""
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import numpy as np
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from request_llm.bridge_all import model_info
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def get_token_num(txt):
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return len(tokenizer.encode(txt, disallowed_special=()))
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input_token_num = get_token_num(inputs)
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if input_token_num < max_token_limit * 3 / 4:
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# 当输入部分的token占比小于限制的3/4时，在裁剪时把input的余量留出来
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max_token_limit = max_token_limit - input_token_num
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if max_token_limit < 128:
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# 余量太小了，直接清除历史
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history = []
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return history
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else:
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# 当输入部分的token占比 > 限制的3/4时，直接清除历史
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history = []
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return history
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everything = ['']
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everything.extend(history)
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n_token = get_token_num('\n'.join(everything))
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everything_token = [get_token_num(e) for e in everything]
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# 截断时的颗粒度
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delta = max(everything_token) // 16
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while n_token > max_token_limit:
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where = np.argmax(everything_token)
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encoded = tokenizer.encode(everything[where], disallowed_special=())
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clipped_encoded = encoded[:len(encoded)-delta]
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everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
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everything_token[where] = get_token_num(everything[where])
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n_token = get_token_num('\n'.join(everything))
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history = everything[1:]
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return history
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