108 lines
6.2 KiB
Python
108 lines
6.2 KiB
Python
from toolbox import CatchException, update_ui, ProxyNetworkActivate
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
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@CatchException
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def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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"""
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txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
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llm_kwargs gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
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plugin_kwargs 插件模型的参数，暂时没有用武之地
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chatbot 聊天显示框的句柄，用于显示给用户
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history 聊天历史，前情提要
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system_prompt 给gpt的静默提醒
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web_port 当前软件运行的端口号
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"""
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history = [] # 清空历史，以免输入溢出
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chatbot.append(("这是什么功能？", "[Local Message] 从一批文件(txt, md, tex)中读取数据构建知识库, 然后进行问答。"))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# resolve deps
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try:
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from zh_langchain import construct_vector_store
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from .crazy_utils import knowledge_archive_interface
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except Exception as e:
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chatbot.append(
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["依赖不足",
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"导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."]
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)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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from .crazy_utils import try_install_deps
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try_install_deps(['zh_langchain==0.2.1', 'pypinyin'])
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# < --------------------读取参数--------------- >
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if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
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kai_id = plugin_kwargs.get("advanced_arg", 'default')
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# < --------------------读取文件--------------- >
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file_manifest = []
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spl = ["txt", "doc", "docx", "email", "epub", "html", "json", "md", "msg", "pdf", "ppt", "pptx", "rtf"]
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for sp in spl:
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_, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}')
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file_manifest += file_manifest_tmp
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if len(file_manifest) == 0:
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chatbot.append(["没有找到任何可读取文件", "当前支持的格式包括: txt, md, docx, pptx, pdf, json等"])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# < -------------------预热文本向量化模组--------------- >
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chatbot.append(['<br/>'.join(file_manifest), "正在预热文本向量化模组, 如果是第一次运行, 将消耗较长时间下载中文向量化模型..."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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print('Checking Text2vec ...')
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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with ProxyNetworkActivate(): # 临时地激活代理网络
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HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
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# < -------------------构建知识库--------------- >
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chatbot.append(['<br/>'.join(file_manifest), "正在构建知识库..."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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print('Establishing knowledge archive ...')
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with ProxyNetworkActivate(): # 临时地激活代理网络
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kai = knowledge_archive_interface()
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kai.feed_archive(file_manifest=file_manifest, id=kai_id)
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kai_files = kai.get_loaded_file()
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kai_files = '<br/>'.join(kai_files)
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# chatbot.append(['知识库构建成功', "正在将知识库存储至cookie中"])
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# yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# chatbot._cookies['langchain_plugin_embedding'] = kai.get_current_archive_id()
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# chatbot._cookies['lock_plugin'] = 'crazy_functions.Langchain知识库->读取知识库作答'
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# chatbot.append(['完成', "“根据知识库作答”函数插件已经接管问答系统, 提问吧! 但注意, 您接下来不能再使用其他插件了，刷新页面即可以退出知识库问答模式。"])
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chatbot.append(['构建完成', f"当前知识库内的有效文件：\n\n---\n\n{kai_files}\n\n---\n\n请切换至“知识库问答”插件进行知识库访问, 或者使用此插件继续上传更多文件。"])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
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@CatchException
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def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port=-1):
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# resolve deps
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try:
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from zh_langchain import construct_vector_store
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from .crazy_utils import knowledge_archive_interface
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except Exception as e:
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chatbot.append(["依赖不足", "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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from .crazy_utils import try_install_deps
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try_install_deps(['zh_langchain==0.2.1'])
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# < ------------------- --------------- >
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kai = knowledge_archive_interface()
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if 'langchain_plugin_embedding' in chatbot._cookies:
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resp, prompt = kai.answer_with_archive_by_id(txt, chatbot._cookies['langchain_plugin_embedding'])
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else:
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if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
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kai_id = plugin_kwargs.get("advanced_arg", 'default')
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resp, prompt = kai.answer_with_archive_by_id(txt, kai_id)
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chatbot.append((txt, '[Local Message] ' + prompt))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=prompt, inputs_show_user=txt,
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
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sys_prompt=system_prompt
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)
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history.extend((prompt, gpt_say))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
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