diff --git a/config.py b/config.py index c9ede12f..b8b84de8 100644 --- a/config.py +++ b/config.py @@ -36,7 +36,7 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo", - "gemini-pro", "chatglm3" + "gemini-1.5-pro", "chatglm3" ] # --- --- --- --- # P.S. 其他可用的模型还包括 @@ -50,6 +50,7 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p # "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2", # "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama", # "deepseek-chat" ,"deepseek-coder", +# "gemini-1.5-flash", # "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview", # ] # --- --- --- --- diff --git a/request_llms/bridge_all.py b/request_llms/bridge_all.py index 036f64dd..f08aaf76 100644 --- a/request_llms/bridge_all.py +++ b/request_llms/bridge_all.py @@ -407,22 +407,46 @@ model_info = { "tokenizer": tokenizer_gpt35, "token_cnt": get_token_num_gpt35, }, + # Gemini + # Note: now gemini-pro is an alias of gemini-1.0-pro. + # Warning: gemini-pro-vision has been deprecated. + # Support for gemini-pro-vision has been removed. "gemini-pro": { "fn_with_ui": genai_ui, "fn_without_ui": genai_noui, "endpoint": gemini_endpoint, + "has_multimodal_capacity": False, "max_token": 1024 * 32, "tokenizer": tokenizer_gpt35, "token_cnt": get_token_num_gpt35, }, - "gemini-pro-vision": { + "gemini-1.0-pro": { "fn_with_ui": genai_ui, "fn_without_ui": genai_noui, "endpoint": gemini_endpoint, + "has_multimodal_capacity": False, "max_token": 1024 * 32, "tokenizer": tokenizer_gpt35, "token_cnt": get_token_num_gpt35, }, + "gemini-1.5-pro": { + "fn_with_ui": genai_ui, + "fn_without_ui": genai_noui, + "endpoint": gemini_endpoint, + "has_multimodal_capacity": True, + "max_token": 1024 * 204800, + "tokenizer": tokenizer_gpt35, + "token_cnt": get_token_num_gpt35, + }, + "gemini-1.5-flash": { + "fn_with_ui": genai_ui, + "fn_without_ui": genai_noui, + "endpoint": gemini_endpoint, + "has_multimodal_capacity": True, + "max_token": 1024 * 204800, + "tokenizer": tokenizer_gpt35, + "token_cnt": get_token_num_gpt35, + }, # cohere "cohere-command-r-plus": { diff --git a/request_llms/bridge_google_gemini.py b/request_llms/bridge_google_gemini.py index 9bf4e23a..3697b8fc 100644 --- a/request_llms/bridge_google_gemini.py +++ b/request_llms/bridge_google_gemini.py @@ -8,15 +8,15 @@ import os import time from request_llms.com_google import GoogleChatInit from toolbox import ChatBotWithCookies -from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat +from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat, encode_image proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY') timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \ '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。' -def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, - console_slience=False): +def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], + console_slience:bool=False): # 检查API_KEY if get_conf("GEMINI_API_KEY") == "": raise ValueError(f"请配置 GEMINI_API_KEY。") @@ -44,9 +44,20 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", raise RuntimeError(f'{gpt_replying_buffer} 对话错误') return gpt_replying_buffer +def make_media_input(inputs, image_paths): + image_base64_array = [] + for image_path in image_paths: + path = os.path.abspath(image_path) + inputs = inputs + f'

' + base64 = encode_image(path) + image_base64_array.append(base64) + return inputs, image_base64_array def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies, history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None): + + from .bridge_all import model_info + # 检查API_KEY if get_conf("GEMINI_API_KEY") == "": yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0) @@ -57,18 +68,17 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith from core_functional import handle_core_functionality inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) - if "vision" in llm_kwargs["llm_model"]: - have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot) - if not have_recent_file: - chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写")) - yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面 - return - def make_media_input(inputs, image_paths): - for image_path in image_paths: - inputs = inputs + f'

' - return inputs - if have_recent_file: - inputs = make_media_input(inputs, image_paths) + # multimodal capacity + # inspired by codes in bridge_chatgpt + has_multimodal_capacity = model_info[llm_kwargs['llm_model']].get('has_multimodal_capacity', False) + if has_multimodal_capacity: + has_recent_image_upload, image_paths = have_any_recent_upload_image_files(chatbot, pop=True) + else: + has_recent_image_upload, image_paths = False, [] + if has_recent_image_upload: + inputs, image_base64_array = make_media_input(inputs, image_paths) + else: + inputs, image_base64_array = inputs, [] chatbot.append((inputs, "")) yield from update_ui(chatbot=chatbot, history=history) @@ -76,7 +86,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith retry = 0 while True: try: - stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt) + stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity) break except Exception as e: retry += 1 @@ -112,7 +122,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith yield from update_ui(chatbot=chatbot, history=history) - if __name__ == '__main__': import sys llm_kwargs = {'llm_model': 'gemini-pro'} diff --git a/request_llms/com_google.py b/request_llms/com_google.py index 75f6b53b..9df3a997 100644 --- a/request_llms/com_google.py +++ b/request_llms/com_google.py @@ -7,7 +7,7 @@ import os import re import requests from typing import List, Dict, Tuple -from toolbox import get_conf, encode_image, get_pictures_list, to_markdown_tabs +from toolbox import get_conf, update_ui, encode_image, get_pictures_list, to_markdown_tabs proxies, TIMEOUT_SECONDS = get_conf("proxies", "TIMEOUT_SECONDS") @@ -112,6 +112,14 @@ def html_local_img(__file, layout="left", max_width=None, max_height=None, md=Tr return a +def reverse_base64_from_input(inputs): + pattern = re.compile(r'

]+base64="([^"]+)">
') + base64_strings = pattern.findall(inputs) + return base64_strings + +def contain_base64(inputs): + base64_strings = reverse_base64_from_input(inputs) + return len(base64_strings) > 0 class GoogleChatInit: def __init__(self, llm_kwargs): @@ -119,9 +127,9 @@ class GoogleChatInit: endpoint = model_info[llm_kwargs['llm_model']]['endpoint'] self.url_gemini = endpoint + "/%m:streamGenerateContent?key=%k" - def generate_chat(self, inputs, llm_kwargs, history, system_prompt): + def generate_chat(self, inputs, llm_kwargs, history, system_prompt, image_base64_array:list=[], has_multimodal_capacity:bool=False): headers, payload = self.generate_message_payload( - inputs, llm_kwargs, history, system_prompt + inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity ) response = requests.post( url=self.url_gemini, @@ -133,13 +141,16 @@ class GoogleChatInit: ) return response.iter_lines() - def __conversation_user(self, user_input, llm_kwargs): + def __conversation_user(self, user_input, llm_kwargs, enable_multimodal_capacity): what_i_have_asked = {"role": "user", "parts": []} - if "vision" not in self.url_gemini: + from .bridge_all import model_info + + if enable_multimodal_capacity: + input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs) + else: input_ = user_input encode_img = [] - else: - input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs) + what_i_have_asked["parts"].append({"text": input_}) if encode_img: for data in encode_img: @@ -153,12 +164,12 @@ class GoogleChatInit: ) return what_i_have_asked - def __conversation_history(self, history, llm_kwargs): + def __conversation_history(self, history, llm_kwargs, enable_multimodal_capacity): messages = [] conversation_cnt = len(history) // 2 if conversation_cnt: for index in range(0, 2 * conversation_cnt, 2): - what_i_have_asked = self.__conversation_user(history[index], llm_kwargs) + what_i_have_asked = self.__conversation_user(history[index], llm_kwargs, enable_multimodal_capacity) what_gpt_answer = { "role": "model", "parts": [{"text": history[index + 1]}], @@ -168,7 +179,7 @@ class GoogleChatInit: return messages def generate_message_payload( - self, inputs, llm_kwargs, history, system_prompt + self, inputs, llm_kwargs, history, system_prompt, image_base64_array:list=[], has_multimodal_capacity:bool=False ) -> Tuple[Dict, Dict]: messages = [ # {"role": "system", "parts": [{"text": system_prompt}]}, # gemini 不允许对话轮次为偶数，所以这个没有用，看后续支持吧。。。 @@ -179,21 +190,29 @@ class GoogleChatInit: "%m", llm_kwargs["llm_model"] ).replace("%k", get_conf("GEMINI_API_KEY")) header = {"Content-Type": "application/json"} - if "vision" not in self.url_gemini: # 不是vision 才处理history + + if has_multimodal_capacity: + enable_multimodal_capacity = (len(image_base64_array) > 0) or any([contain_base64(h) for h in history]) + else: + enable_multimodal_capacity = False + + if not enable_multimodal_capacity: messages.extend( - self.__conversation_history(history, llm_kwargs) + self.__conversation_history(history, llm_kwargs, enable_multimodal_capacity) ) # 处理 history - messages.append(self.__conversation_user(inputs, llm_kwargs)) # 处理用户对话 + + messages.append(self.__conversation_user(inputs, llm_kwargs, enable_multimodal_capacity)) # 处理用户对话 payload = { "contents": messages, "generationConfig": { - # "maxOutputTokens": 800, + # "maxOutputTokens": llm_kwargs.get("max_token", 1024), "stopSequences": str(llm_kwargs.get("stop", "")).split(" "), "temperature": llm_kwargs.get("temperature", 1), "topP": llm_kwargs.get("top_p", 0.8), "topK": 10, }, } + return header, payload