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import time |
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import threading |
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import importlib |
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from toolbox import update_ui, get_conf, update_ui_lastest_msg |
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from multiprocessing import Process, Pipe |
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model_name = '星火认知大模型' |
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def validate_key(): |
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XFYUN_APPID, = get_conf('XFYUN_APPID', ) |
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if XFYUN_APPID == '00000000' or XFYUN_APPID == '': |
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return False |
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return True |
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): |
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""" |
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⭐多线程方法 |
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函数的说明请见 request_llm/bridge_all.py |
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""" |
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watch_dog_patience = 5 |
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response = "" |
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if validate_key() is False: |
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raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET') |
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from .com_sparkapi import SparkRequestInstance |
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sri = SparkRequestInstance() |
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for response in sri.generate(inputs, llm_kwargs, history, sys_prompt): |
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if len(observe_window) >= 1: |
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observe_window[0] = response |
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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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return response |
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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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⭐单线程方法 |
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函数的说明请见 request_llm/bridge_all.py |
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""" |
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chatbot.append((inputs, "")) |
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yield from update_ui(chatbot=chatbot, history=history) |
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if validate_key() is False: |
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yield from update_ui_lastest_msg(lastmsg="[Local Message]: 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0) |
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return |
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if additional_fn is not None: |
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from core_functional import handle_core_functionality |
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) |
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from .com_sparkapi import SparkRequestInstance |
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sri = SparkRequestInstance() |
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for response in sri.generate(inputs, llm_kwargs, history, system_prompt): |
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chatbot[-1] = (inputs, response) |
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yield from update_ui(chatbot=chatbot, history=history) |
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if response == f"[Local Message]: 等待{model_name}响应中 ...": |
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response = f"[Local Message]: {model_name}响应异常 ..." |
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history.extend([inputs, response]) |
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yield from update_ui(chatbot=chatbot, history=history) |
