request_llm/bridge_spark.py

import time
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe

model_name = '星火认知大模型'

def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
    """
        ⭐多线程方法
        函数的说明请见 request_llm/bridge_all.py
    """
    watch_dog_patience = 5
    response = ""

    from .com_sparkapi import SparkRequestInstance
    sri = SparkRequestInstance()
    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
        if len(observe_window) >= 1:
            observe_window[0] = response
        if len(observe_window) >= 2:
            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
    return response

def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
    """
        ⭐单线程方法
        函数的说明请见 request_llm/bridge_all.py
    """
    chatbot.append((inputs, ""))

    if additional_fn is not None:
        from core_functional import handle_core_functionality
        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)

    # 开始接收回复    
    from .com_sparkapi import SparkRequestInstance
    sri = SparkRequestInstance()
    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
        chatbot[-1] = (inputs, response)
        yield from update_ui(chatbot=chatbot, history=history)

    # 总结输出
    if response == f"[Local Message]: 等待{model_name}响应中 ...":
        response = f"[Local Message]: {model_name}响应异常 ..."
    history.extend([inputs, response])
    yield from update_ui(chatbot=chatbot, history=history)