之前我的文章有介绍如何快速搭建本地大语言模型：
• 快速搭建本地大语言模型和知识库• 本地中文大语言模型搭建和对比本地搭建了很多开源大模型，同时也有一些闭源付费大模型，如：ChatGPT、Claude3等，需要使用不同的方式去调用，不是很方便，所以我准备按照这样的思路搭建一个ChatBot UI界面去方便我后续AIGC的研究，下面是我的草图构思：
image-20240413121015084
第一阶段先实现多model的切换，当前包含：闭源模型，如：ChatGPT系列、Ollma本地大语言模型、本地量化模型（ChatGLM3-6B）等，后续也可加入更多的模型。说干就干！
实现首先梳理python项目的结果，参考如图：
image-20240413121550968
编写完访问LLM代码后，使用Gradio快速搭建Chatbot UI界面，对于不怎么熟悉Python的人也能快速上手，源码如下：
import gradio as grfrom llm.base import LLMfrom llm.message import Messagefrom llm.openai.chat import OpenAIChatfrom llm.chatglm.chat import ChatGLMChatfrom llm.ollama.chat import OllamaChatimport osfrom dotenv import load_dotenv, find_dotenvfrom typing import Optional, List, Iterator, Dict, Any, Mapping, Union# read local .env file_ = load_dotenv(find_dotenv()) api_key = os.getenv('OPENAI_API_KEY')api_base = os.getenv('OPENAI_API_BASE')# select modelsllm_clients: Dict[str, LLM] = {}chatgpt_models = ["gpt-3.5-turbo", "gpt-4-vision-preview", "gpt-4"]chatglm_models = ["chatglm3-6b"]ollama_models = ["openhermes", "qwen:7b", "llama2:7b", "mistral:7b", "gemma:7b"]models = chatgpt_models + chatglm_models + ollama_modelsdef switch_model(model_name): print("switch to model: " + model_name) return []def get_llm_by_name(model_name): llm: LLM = None if model_name in llm_clients: llm = llm_clients[model_name] else: if model_name in chatgpt_models: llm = OpenAIChat(model=model_name, api_key=api_key, base_url=api_base) elif model_name in chatglm_models: llm = ChatGLMChat() elif model_name in ollama_models: llm = OllamaChat(model=model_name) # update dict if model_name is not None and llm is not None: llm_clients[model_name] = llm print(llm_clients) return llmdef predict(input, model_name, chatbot, messages, max_length, top_p, temperature): llm = get_llm_by_name(model_name) if llm is None: raise Exception("LLM not set") chatbot.append((input, "")) user_prompt_message = Message(role="user", content=input) if input else None messages.append(user_prompt_message) request_kwargs = dict( max_tokens=max_length, top_p=top_p, temperature=temperature ) response = "" for chunk in llm.response_stream(messages, **request_kwargs): response += chunk chatbot[-1] = (chatbot[-1][0], response) yield chatbot, messages return chatbot, messages def reset_user_input(): return gr.update(value="")def reset_state(): return [], []with gr.Blocks() as demo: gr.HTML("""<h1 align="center">Eric's ChatBot</h1>""") with gr.Row(): with gr.Column(scale=1): model_selection = gr.Dropdown(models, label="select models:", value="gpt-3.5-turbo") with gr.Column(scale=4): chatbot = gr.Chatbot() with gr.Row(): with gr.Column(scale=4): user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=8) submitBtn = gr.Button("Submit", variant="primary") with gr.Column(scale=1): max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum Length", interactive=True) top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True) temperature = gr.Slider(0, 1, value=0.7, step=0.01, label="Temperature", interactive=True) emptyBtn = gr.Button("Clear History") messages = gr.State([]) model_selection.change(switch_model, inputs=model_selection, outputs=[messages]) submitBtn.click( predict, [user_input, model_selection, chatbot, messages, max_length, top_p, temperature], [chatbot, messages], show_progress=True, ) submitBtn.click(reset_user_input, [], [user_input]) emptyBtn.click(reset_state, outputs=[chatbot, messages], show_progress=True)demo.queue().launch(share=False, inbrowser=True)结果验证
eric chatboot demo1
eric chatbot demo2
