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| """ | |
| 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 | |
| from functools import lru_cache | |
| from concurrent.futures import ThreadPoolExecutor | |
| from toolbox import get_conf, trimmed_format_exc | |
| from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui | |
| from .bridge_chatgpt import predict as chatgpt_ui | |
| from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui | |
| from .bridge_chatglm import predict as chatglm_ui | |
| from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui | |
| from .bridge_chatglm import predict as chatglm_ui | |
| from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui | |
| from .bridge_qianfan import predict as qianfan_ui | |
| colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044'] | |
| class LazyloadTiktoken(object): | |
| def __init__(self, model): | |
| self.model = model | |
| 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" | |
| 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": 1024*16, | |
| "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": 1024 * 16, | |
| "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, | |
| }, | |
| # 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, | |
| }, | |
| # api_2d | |
| "api2d-gpt-3.5-turbo": { | |
| "fn_with_ui": chatgpt_ui, | |
| "fn_without_ui": chatgpt_noui, | |
| "endpoint": api2d_endpoint, | |
| "max_token": 4096, | |
| "tokenizer": tokenizer_gpt35, | |
| "token_cnt": get_token_num_gpt35, | |
| }, | |
| "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, | |
| }, | |
| "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, | |
| }, | |
| } | |
| # -=-=-=-=-=-=- 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" in AVAIL_LLM_MODELS: | |
| 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": { | |
| "fn_with_ui": qwen_ui, | |
| "fn_without_ui": qwen_noui, | |
| "endpoint": None, | |
| "max_token": 4096, | |
| "tokenizer": tokenizer_gpt35, | |
| "token_cnt": get_token_num_gpt35, | |
| } | |
| }) | |
| except: | |
| print(trimmed_format_exc()) | |
| if "chatgpt_website" in AVAIL_LLM_MODELS: # 接入一些逆向工程https://github.com/acheong08/ChatGPT-to-API/ | |
| try: | |
| from .bridge_chatgpt_website import predict_no_ui_long_connection as chatgpt_website_noui | |
| from .bridge_chatgpt_website import predict as chatgpt_website_ui | |
| model_info.update({ | |
| "chatgpt_website": { | |
| "fn_with_ui": chatgpt_website_ui, | |
| "fn_without_ui": chatgpt_website_noui, | |
| "endpoint": openai_endpoint, | |
| "max_token": 4096, | |
| "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 "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()) | |
| 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 | |
| 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】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" ) | |
| res = '<br/><br/>\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】: <font color=\"{colors[i]}\"> {future.result()} </font>" ) | |
| window_mutex[-1] = False # stop mutex thread | |
| res = '<br/><br/>\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 | |
| """ | |
| 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) | |