"""
该文件中主要包含2个函数，是所有LLM的通用接口，它们会继续向下调用更底层的LLM模型，处理多模型并行等细节
不具备多线程能力的函数：正常对话时使用，具备完备的交互功能，不可多线程
1. predict(...)
具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁
2. predict_no_ui_long_connection(...)
"""
import tiktoken, copy
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor
from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask
from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
from .bridge_chatgpt import predict as chatgpt_ui
from .bridge_chatgpt_vision import predict_no_ui_long_connection as chatgpt_vision_noui
from .bridge_chatgpt_vision import predict as chatgpt_vision_ui
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
from .bridge_chatglm import predict as chatglm_ui
from .bridge_chatglm3 import predict_no_ui_long_connection as chatglm3_noui
from .bridge_chatglm3 import predict as chatglm3_ui
from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
from .bridge_qianfan import predict as qianfan_ui
from .bridge_google_gemini import predict as genai_ui
from .bridge_google_gemini import predict_no_ui_long_connection as genai_noui
from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
from .bridge_zhipu import predict as zhipu_ui
colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
class LazyloadTiktoken(object):
def __init__(self, model):
self.model = model
@staticmethod
@lru_cache(maxsize=128)
def get_encoder(model):
print('正在加载tokenizer，如果是第一次运行，可能需要一点时间下载参数')
tmp = tiktoken.encoding_for_model(model)
print('加载tokenizer完毕')
return tmp
def encode(self, *args, **kwargs):
encoder = self.get_encoder(self.model)
return encoder.encode(*args, **kwargs)
def decode(self, *args, **kwargs):
encoder = self.get_encoder(self.model)
return encoder.decode(*args, **kwargs)
# Endpoint 重定向
API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
openai_endpoint = "https://api.openai.com/v1/chat/completions"
api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
# 兼容旧版的配置
try:
API_URL = get_conf("API_URL")
if API_URL != "https://api.openai.com/v1/chat/completions":
openai_endpoint = API_URL
print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
except:
pass
# 新版配置
if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
if api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]
if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]
# 获取tokenizer
tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
tokenizer_gpt4 = LazyloadTiktoken("gpt-4")
get_token_num_gpt35 = lambda txt: len(tokenizer_gpt35.encode(txt, disallowed_special=()))
get_token_num_gpt4 = lambda txt: len(tokenizer_gpt4.encode(txt, disallowed_special=()))
# 开始初始化模型
AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]
# -=-=-=-=-=-=- 以下这部分是最早加入的最稳定的模型 -=-=-=-=-=-=-
model_info = {
# openai
"gpt-3.5-turbo": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-3.5-turbo-16k": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 16385,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-3.5-turbo-0613": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-3.5-turbo-16k-0613": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 16385,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-3.5-turbo-1106": {#16k
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 16385,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gpt-4": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 8192,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-4-32k": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 32768,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-4-turbo-preview": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 128000,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-4-1106-preview": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 128000,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-4-0125-preview": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 128000,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-3.5-random": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": openai_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
"gpt-4-vision-preview": {
"fn_with_ui": chatgpt_vision_ui,
"fn_without_ui": chatgpt_vision_noui,
"endpoint": openai_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
# azure openai
"azure-gpt-3.5":{
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": azure_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"azure-gpt-4":{
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": azure_endpoint,
"max_token": 8192,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
# 智谱AI
"glm-4": {
"fn_with_ui": zhipu_ui,
"fn_without_ui": zhipu_noui,
"endpoint": None,
"max_token": 10124 * 8,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"glm-3-turbo": {
"fn_with_ui": zhipu_ui,
"fn_without_ui": zhipu_noui,
"endpoint": None,
"max_token": 10124 * 4,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# api_2d (此后不需要在此处添加api2d的接口了，因为下面的代码会自动添加)
"api2d-gpt-4": {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": api2d_endpoint,
"max_token": 8192,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
},
# 将 chatglm 直接对齐到 chatglm2
"chatglm": {
"fn_with_ui": chatglm_ui,
"fn_without_ui": chatglm_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"chatglm2": {
"fn_with_ui": chatglm_ui,
"fn_without_ui": chatglm_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"chatglm3": {
"fn_with_ui": chatglm3_ui,
"fn_without_ui": chatglm3_noui,
"endpoint": None,
"max_token": 8192,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"qianfan": {
"fn_with_ui": qianfan_ui,
"fn_without_ui": qianfan_noui,
"endpoint": None,
"max_token": 2000,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gemini-pro": {
"fn_with_ui": genai_ui,
"fn_without_ui": genai_noui,
"endpoint": None,
"max_token": 1024 * 32,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gemini-pro-vision": {
"fn_with_ui": genai_ui,
"fn_without_ui": genai_noui,
"endpoint": None,
"max_token": 1024 * 32,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
}
# -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
for model in AVAIL_LLM_MODELS:
if model.startswith('api2d-') and (model.replace('api2d-','') in model_info.keys()):
mi = copy.deepcopy(model_info[model.replace('api2d-','')])
mi.update({"endpoint": api2d_endpoint})
model_info.update({model: mi})
# -=-=-=-=-=-=- azure 对齐支持 -=-=-=-=-=-=-
for model in AVAIL_LLM_MODELS:
if model.startswith('azure-') and (model.replace('azure-','') in model_info.keys()):
mi = copy.deepcopy(model_info[model.replace('azure-','')])
mi.update({"endpoint": azure_endpoint})
model_info.update({model: mi})
# -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
from .bridge_claude import predict_no_ui_long_connection as claude_noui
from .bridge_claude import predict as claude_ui
model_info.update({
"claude-1-100k": {
"fn_with_ui": claude_ui,
"fn_without_ui": claude_noui,
"endpoint": None,
"max_token": 8196,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
model_info.update({
"claude-2": {
"fn_with_ui": claude_ui,
"fn_without_ui": claude_noui,
"endpoint": None,
"max_token": 8196,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
if "jittorllms_rwkv" in AVAIL_LLM_MODELS:
from .bridge_jittorllms_rwkv import predict_no_ui_long_connection as rwkv_noui
from .bridge_jittorllms_rwkv import predict as rwkv_ui
model_info.update({
"jittorllms_rwkv": {
"fn_with_ui": rwkv_ui,
"fn_without_ui": rwkv_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
if "jittorllms_llama" in AVAIL_LLM_MODELS:
from .bridge_jittorllms_llama import predict_no_ui_long_connection as llama_noui
from .bridge_jittorllms_llama import predict as llama_ui
model_info.update({
"jittorllms_llama": {
"fn_with_ui": llama_ui,
"fn_without_ui": llama_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
if "jittorllms_pangualpha" in AVAIL_LLM_MODELS:
from .bridge_jittorllms_pangualpha import predict_no_ui_long_connection as pangualpha_noui
from .bridge_jittorllms_pangualpha import predict as pangualpha_ui
model_info.update({
"jittorllms_pangualpha": {
"fn_with_ui": pangualpha_ui,
"fn_without_ui": pangualpha_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
if "moss" in AVAIL_LLM_MODELS:
from .bridge_moss import predict_no_ui_long_connection as moss_noui
from .bridge_moss import predict as moss_ui
model_info.update({
"moss": {
"fn_with_ui": moss_ui,
"fn_without_ui": moss_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
if "stack-claude" in AVAIL_LLM_MODELS:
from .bridge_stackclaude import predict_no_ui_long_connection as claude_noui
from .bridge_stackclaude import predict as claude_ui
model_info.update({
"stack-claude": {
"fn_with_ui": claude_ui,
"fn_without_ui": claude_noui,
"endpoint": None,
"max_token": 8192,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
if "newbing-free" in AVAIL_LLM_MODELS:
try:
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
from .bridge_newbingfree import predict as newbingfree_ui
model_info.update({
"newbing-free": {
"fn_with_ui": newbingfree_ui,
"fn_without_ui": newbingfree_noui,
"endpoint": newbing_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "newbing" in AVAIL_LLM_MODELS: # same with newbing-free
try:
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
from .bridge_newbingfree import predict as newbingfree_ui
model_info.update({
"newbing": {
"fn_with_ui": newbingfree_ui,
"fn_without_ui": newbingfree_noui,
"endpoint": newbing_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "chatglmft" in AVAIL_LLM_MODELS: # same with newbing-free
try:
from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
from .bridge_chatglmft import predict as chatglmft_ui
model_info.update({
"chatglmft": {
"fn_with_ui": chatglmft_ui,
"fn_without_ui": chatglmft_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "internlm" in AVAIL_LLM_MODELS:
try:
from .bridge_internlm import predict_no_ui_long_connection as internlm_noui
from .bridge_internlm import predict as internlm_ui
model_info.update({
"internlm": {
"fn_with_ui": internlm_ui,
"fn_without_ui": internlm_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "chatglm_onnx" in AVAIL_LLM_MODELS:
try:
from .bridge_chatglmonnx import predict_no_ui_long_connection as chatglm_onnx_noui
from .bridge_chatglmonnx import predict as chatglm_onnx_ui
model_info.update({
"chatglm_onnx": {
"fn_with_ui": chatglm_onnx_ui,
"fn_without_ui": chatglm_onnx_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "qwen-local" in AVAIL_LLM_MODELS:
try:
from .bridge_qwen_local import predict_no_ui_long_connection as qwen_local_noui
from .bridge_qwen_local import predict as qwen_local_ui
model_info.update({
"qwen-local": {
"fn_with_ui": qwen_local_ui,
"fn_without_ui": qwen_local_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS: # zhipuai
try:
from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
from .bridge_qwen import predict as qwen_ui
model_info.update({
"qwen-turbo": {
"fn_with_ui": qwen_ui,
"fn_without_ui": qwen_noui,
"endpoint": None,
"max_token": 6144,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"qwen-plus": {
"fn_with_ui": qwen_ui,
"fn_without_ui": qwen_noui,
"endpoint": None,
"max_token": 30720,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"qwen-max": {
"fn_with_ui": qwen_ui,
"fn_without_ui": qwen_noui,
"endpoint": None,
"max_token": 28672,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "spark" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
try:
from .bridge_spark import predict_no_ui_long_connection as spark_noui
from .bridge_spark import predict as spark_ui
model_info.update({
"spark": {
"fn_with_ui": spark_ui,
"fn_without_ui": spark_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "sparkv2" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
try:
from .bridge_spark import predict_no_ui_long_connection as spark_noui
from .bridge_spark import predict as spark_ui
model_info.update({
"sparkv2": {
"fn_with_ui": spark_ui,
"fn_without_ui": spark_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
try:
from .bridge_spark import predict_no_ui_long_connection as spark_noui
from .bridge_spark import predict as spark_ui
model_info.update({
"sparkv3": {
"fn_with_ui": spark_ui,
"fn_without_ui": spark_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"sparkv3.5": {
"fn_with_ui": spark_ui,
"fn_without_ui": spark_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "llama2" in AVAIL_LLM_MODELS: # llama2
try:
from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
from .bridge_llama2 import predict as llama2_ui
model_info.update({
"llama2": {
"fn_with_ui": llama2_ui,
"fn_without_ui": llama2_noui,
"endpoint": None,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
if "zhipuai" in AVAIL_LLM_MODELS: # zhipuai 是glm-4的别名，向后兼容配置
try:
model_info.update({
"zhipuai": {
"fn_with_ui": zhipu_ui,
"fn_without_ui": zhipu_noui,
"endpoint": None,
"max_token": 10124 * 8,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
except:
print(trimmed_format_exc())
if "deepseekcoder" in AVAIL_LLM_MODELS: # deepseekcoder
try:
from .bridge_deepseekcoder import predict_no_ui_long_connection as deepseekcoder_noui
from .bridge_deepseekcoder import predict as deepseekcoder_ui
model_info.update({
"deepseekcoder": {
"fn_with_ui": deepseekcoder_ui,
"fn_without_ui": deepseekcoder_noui,
"endpoint": None,
"max_token": 2048,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
}
})
except:
print(trimmed_format_exc())
# if "skylark" in AVAIL_LLM_MODELS:
# try:
# from .bridge_skylark2 import predict_no_ui_long_connection as skylark_noui
# from .bridge_skylark2 import predict as skylark_ui
# model_info.update({
# "skylark": {
# "fn_with_ui": skylark_ui,
# "fn_without_ui": skylark_noui,
# "endpoint": None,
# "max_token": 4096,
# "tokenizer": tokenizer_gpt35,
# "token_cnt": get_token_num_gpt35,
# }
# })
# except:
# print(trimmed_format_exc())
# <-- 用于定义和切换多个azure模型 -->
AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")
if len(AZURE_CFG_ARRAY) > 0:
for azure_model_name, azure_cfg_dict in AZURE_CFG_ARRAY.items():
# 可能会覆盖之前的配置，但这是意料之中的
if not azure_model_name.startswith('azure'):
raise ValueError("AZURE_CFG_ARRAY中配置的模型必须以azure开头")
endpoint_ = azure_cfg_dict["AZURE_ENDPOINT"] + \
f'openai/deployments/{azure_cfg_dict["AZURE_ENGINE"]}/chat/completions?api-version=2023-05-15'
model_info.update({
azure_model_name: {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": endpoint_,
"azure_api_key": azure_cfg_dict["AZURE_API_KEY"],
"max_token": azure_cfg_dict["AZURE_MODEL_MAX_TOKEN"],
"tokenizer": tokenizer_gpt35, # tokenizer只用于粗估token数量
"token_cnt": get_token_num_gpt35,
}
})
if azure_model_name not in AVAIL_LLM_MODELS:
AVAIL_LLM_MODELS += [azure_model_name]
def LLM_CATCH_EXCEPTION(f):
"""
装饰器函数，将错误显示出来
"""
def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
try:
return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
except Exception as e:
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
observe_window[0] = tb_str
return tb_str
return decorated
def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window=[], console_slience=False):
"""
发送至LLM，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：
LLM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
"""
import threading, time, copy
inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")
model = llm_kwargs['llm_model']
n_model = 1
if '&' not in model:
assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
# 如果只询问1个大语言模型：
method = model_info[model]["fn_without_ui"]
return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
else:
# 如果同时询问多个大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
executor = ThreadPoolExecutor(max_workers=4)
models = model.split('&')
n_model = len(models)
window_len = len(observe_window)
assert window_len==3
window_mutex = [["", time.time(), ""] for _ in range(n_model)] + [True]
futures = []
for i in range(n_model):
model = models[i]
method = model_info[model]["fn_without_ui"]
llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
llm_kwargs_feedin['llm_model'] = model
future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
futures.append(future)
def mutex_manager(window_mutex, observe_window):
while True:
time.sleep(0.25)
if not window_mutex[-1]: break
# 看门狗（watchdog）
for i in range(n_model):
window_mutex[i][1] = observe_window[1]
# 观察窗（window）
chat_string = []
for i in range(n_model):
chat_string.append( f"【{str(models[i])} 说】: {window_mutex[i][0]} " )
res = '
\n\n---\n\n'.join(chat_string)
# # # # # # # # # # #
observe_window[0] = res
t_model = threading.Thread(target=mutex_manager, args=(window_mutex, observe_window), daemon=True)
t_model.start()
return_string_collect = []
while True:
worker_done = [h.done() for h in futures]
if all(worker_done):
executor.shutdown()
break
time.sleep(1)
for i, future in enumerate(futures): # wait and get
return_string_collect.append( f"【{str(models[i])} 说】: {future.result()} " )
window_mutex[-1] = False # stop mutex thread
res = '
\n\n---\n\n'.join(return_string_collect)
return res
def predict(inputs, llm_kwargs, *args, **kwargs):
"""
发送至LLM，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LLM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
"""
inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")
method = model_info[llm_kwargs['llm_model']]["fn_with_ui"] # 如果这里报错，检查config中的AVAIL_LLM_MODELS选项
yield from method(inputs, llm_kwargs, *args, **kwargs)