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| import os | |
| import shutil | |
| from app_modules.overwrites import postprocess | |
| from app_modules.presets import * | |
| from clc.langchain_application import LangChainApplication | |
| # 修改成自己的配置！！！ | |
| class LangChainCFG: | |
| llm_model_name = 'THUDM/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库 | |
| embedding_model_name = 'GanymedeNil/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库 | |
| vector_store_path = './cache' | |
| docs_path = './docs' | |
| kg_vector_stores = { | |
| '中文维基百科': './cache/zh_wikipedia', | |
| '大规模金融研报': './cache/financial_research_reports', | |
| '初始化': './cache', | |
| } # 可以替换成自己的知识库，如果没有需要设置为None | |
| # kg_vector_stores=None | |
| patterns = ['模型问答', '知识库问答'] # | |
| config = LangChainCFG() | |
| application = LangChainApplication(config) | |
| def get_file_list(): | |
| if not os.path.exists("docs"): | |
| return [] | |
| return [f for f in os.listdir("docs")] | |
| file_list = get_file_list() | |
| def upload_file(file): | |
| if not os.path.exists("docs"): | |
| os.mkdir("docs") | |
| filename = os.path.basename(file.name) | |
| shutil.move(file.name, "docs/" + filename) | |
| # file_list首位插入新上传的文件 | |
| file_list.insert(0, filename) | |
| application.source_service.add_document("docs/" + filename) | |
| return gr.Dropdown.update(choices=file_list, value=filename) | |
| def set_knowledge(kg_name, history): | |
| try: | |
| application.source_service.load_vector_store(config.kg_vector_stores[kg_name]) | |
| msg_status = f'{kg_name}知识库已成功加载' | |
| except Exception as e: | |
| print(e) | |
| msg_status = f'{kg_name}知识库未成功加载' | |
| return history + [[None, msg_status]] | |
| def clear_session(): | |
| return '', None | |
| def predict(input, | |
| large_language_model, | |
| embedding_model, | |
| top_k, | |
| use_web, | |
| use_pattern, | |
| history=None): | |
| # print(large_language_model, embedding_model) | |
| print(input) | |
| if history == None: | |
| history = [] | |
| if use_web == '使用': | |
| web_content = application.source_service.search_web(query=input) | |
| else: | |
| web_content = '' | |
| search_text = '' | |
| if use_pattern == '模型问答': | |
| result = application.get_llm_answer(query=input, web_content=web_content) | |
| history.append((input, result)) | |
| search_text += web_content | |
| return '', history, history, search_text | |
| else: | |
| resp = application.get_knowledge_based_answer( | |
| query=input, | |
| history_len=1, | |
| temperature=0.1, | |
| top_p=0.9, | |
| top_k=top_k, | |
| web_content=web_content, | |
| chat_history=history | |
| ) | |
| history.append((input, resp['result'])) | |
| for idx, source in enumerate(resp['source_documents'][:4]): | |
| sep = f'----------【搜索结果{idx + 1}：】---------------\n' | |
| search_text += f'{sep}\n{source.page_content}\n\n' | |
| print(search_text) | |
| search_text += "----------【网络检索内容】-----------\n" | |
| search_text += web_content | |
| return '', history, history, search_text | |
| with open("assets/custom.css", "r", encoding="utf-8") as f: | |
| customCSS = f.read() | |
| with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo: | |
| gr.Markdown("""<h1><center>Chinese-LangChain</center></h1> | |
| <center><font size=3> | |
| </center></font> | |
| """) | |
| state = gr.State() | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| embedding_model = gr.Dropdown([ | |
| "text2vec-base" | |
| ], | |
| label="Embedding model", | |
| value="text2vec-base") | |
| large_language_model = gr.Dropdown( | |
| [ | |
| "ChatGLM-6B-int4", | |
| ], | |
| label="large language model", | |
| value="ChatGLM-6B-int4") | |
| top_k = gr.Slider(1, | |
| 20, | |
| value=4, | |
| step=1, | |
| label="检索top-k文档", | |
| interactive=True) | |
| use_web = gr.Radio(["使用", "不使用"], label="web search", | |
| info="是否使用网络搜索，使用时确保网络通常", | |
| value="不使用" | |
| ) | |
| use_pattern = gr.Radio( | |
| [ | |
| '模型问答', | |
| '知识库问答', | |
| ], | |
| label="模式", | |
| value='模型问答', | |
| interactive=True) | |
| kg_name = gr.Radio(list(config.kg_vector_stores.keys()), | |
| label="知识库", | |
| value=None, | |
| info="使用知识库问答，请加载知识库", | |
| interactive=True) | |
| set_kg_btn = gr.Button("加载知识库") | |
| file = gr.File(label="将文件上传到知识库库，内容要尽量匹配", | |
| visible=True, | |
| file_types=['.txt', '.md', '.docx', '.pdf'] | |
| ) | |
| with gr.Column(scale=4): | |
| with gr.Row(): | |
| chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400) | |
| with gr.Row(): | |
| message = gr.Textbox(label='请输入问题') | |
| with gr.Row(): | |
| clear_history = gr.Button("🧹 清除历史对话") | |
| send = gr.Button("🚀 发送") | |
| with gr.Row(): | |
| gr.Markdown("""提醒：<br> | |
| [Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) <br> | |
| 有任何使用问题[Github Issue区](https://github.com/yanqiangmiffy/Chinese-LangChain)进行反馈. <br> | |
| """) | |
| with gr.Column(scale=2): | |
| search = gr.Textbox(label='搜索结果') | |
| # ============= 触发动作============= | |
| file.upload(upload_file, | |
| inputs=file, | |
| outputs=None) | |
| set_kg_btn.click( | |
| set_knowledge, | |
| show_progress=True, | |
| inputs=[kg_name, chatbot], | |
| outputs=chatbot | |
| ) | |
| # 发送按钮 提交 | |
| send.click(predict, | |
| inputs=[ | |
| message, | |
| large_language_model, | |
| embedding_model, | |
| top_k, | |
| use_web, | |
| use_pattern, | |
| state | |
| ], | |
| outputs=[message, chatbot, state, search]) | |
| # 清空历史对话按钮 提交 | |
| clear_history.click(fn=clear_session, | |
| inputs=[], | |
| outputs=[chatbot, state], | |
| queue=False) | |
| # 输入框 回车 | |
| message.submit(predict, | |
| inputs=[ | |
| message, | |
| large_language_model, | |
| embedding_model, | |
| top_k, | |
| use_web, | |
| use_pattern, | |
| state | |
| ], | |
| outputs=[message, chatbot, state, search]) | |
| demo.queue(concurrency_count=2).launch( | |
| server_name='0.0.0.0', | |
| server_port=8888, | |
| share=False, | |
| show_error=True, | |
| debug=True, | |
| enable_queue=True, | |
| inbrowser=True, | |
| ) | |