feature@添加问答模式选择
This commit is contained in:
yanqiangmiffy
parent
57f12d9a21
commit
65d97b14b4
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@ -8,6 +8,7 @@ colorTo: yellow
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pinned: true
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app_file: app.py
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---
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# Chinese-LangChain
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> Chinese-LangChain：中文langchain项目，基于ChatGLM-6b+langchain实现本地化知识库检索与智能答案生成
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@ -55,6 +56,8 @@ python main.py
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## 🚀 特性
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- 📝 2023/04/20 支持模型问答与检索问答模式切换
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- 📝 2023/04/20 感谢HF官方提供免费算力，添加HuggingFace Spaces在线体验[[🤗 DEMO](https://huggingface.co/spaces/ChallengeHub/Chinese-LangChain)
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- 📝 2023/04/19 发布45万Wikipedia的文本预处理语料以及FAISS索引向量
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- 🐯 2023/04/19 引入ChuanhuChatGPT皮肤
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- 📱 2023/04/19 增加web search功能，需要确保网络畅通！(感谢[@wanghao07456](https://github.com/wanghao07456),提供的idea)
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@ -87,6 +90,7 @@ python main.py
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* [x] 支持加载不同知识库
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* [x] 支持检索结果与LLM生成结果对比
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* [ ] 支持检索生成结果与原始LLM生成结果对比
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* [ ] 支持模型问答与检索问答
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* [ ] 检索结果过滤与排序
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* [x] 互联网检索结果接入
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* [ ] 模型初始化有问题
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68
app.py
68
app.py
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@ -1,6 +1,7 @@
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import os
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import shutil
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from app_modules.overwrites import postprocess
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from app_modules.presets import *
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from clc.langchain_application import LangChainApplication
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@ -8,15 +9,16 @@ from clc.langchain_application import LangChainApplication
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# 修改成自己的配置！！！
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class LangChainCFG:
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llm_model_name = 'THUDM/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
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embedding_model_name = 'GanymedeNil/text2vec-base-chinese' # 检索模型文件 or huggingface远程仓库
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embedding_model_name = 'GanymedeNil/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
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vector_store_path = './cache'
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docs_path = './docs'
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kg_vector_stores = {
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'中文维基百科': './cache/zh_wikipedia',
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'大规模金融研报知识图谱': '.cache/financial_research_reports',
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'初始化知识库': '.cache',
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'大规模金融研报': './cache/financial_research_reports',
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'初始化': './cache',
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} # 可以替换成自己的知识库，如果没有需要设置为None
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# kg_vector_stores=None
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patterns = ['模型问答', '知识库问答'] #
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config = LangChainCFG()
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@ -61,6 +63,7 @@ def predict(input,
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embedding_model,
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top_k,
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use_web,
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use_pattern,
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history=None):
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# print(large_language_model, embedding_model)
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print(input)
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@ -71,6 +74,14 @@ def predict(input,
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web_content = application.source_service.search_web(query=input)
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else:
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web_content = ''
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search_text = ''
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if use_pattern == '模型问答':
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result = application.get_llm_answer(query=input, web_content=web_content)
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history.append((input, result))
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search_text += web_content
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return '', history, history, search_text
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else:
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resp = application.get_knowledge_based_answer(
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query=input,
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history_len=1,
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@ -81,7 +92,6 @@ def predict(input,
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chat_history=history
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)
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history.append((input, resp['result']))
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search_text = ''
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for idx, source in enumerate(resp['source_documents'][:4]):
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sep = f'----------【搜索结果{idx + 1}：】---------------\n'
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search_text += f'{sep}\n{source.page_content}\n\n'
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@ -121,28 +131,35 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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step=1,
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label="检索top-k文档",
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interactive=True)
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kg_name = gr.Radio(['中文维基百科',
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'大规模金融研报知识图谱',
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'初始化知识库'
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],
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label="知识库",
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value='初始化知识库',
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interactive=True)
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set_kg_btn = gr.Button("重新加载知识库")
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use_web = gr.Radio(["使用", "不使用"], label="web search",
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info="是否使用网络搜索，使用时确保网络通常",
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value="不使用"
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)
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use_pattern = gr.Radio(
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[
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'模型问答',
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'知识库问答',
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],
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label="模式",
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value='模型问答',
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interactive=True)
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kg_name = gr.Radio(['中文维基百科',
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'大规模金融研报知识图谱',
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'初始化知识库'
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],
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label="知识库",
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value=None,
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info="使用知识库问答，请加载知识库",
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interactive=True)
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set_kg_btn = gr.Button("加载知识库")
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file = gr.File(label="将文件上传到知识库库，内容要尽量匹配",
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visible=True,
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file_types=['.txt', '.md', '.docx', '.pdf']
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)
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file.upload(upload_file,
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inputs=file,
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outputs=None)
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with gr.Column(scale=4):
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with gr.Row():
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chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400)
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@ -159,6 +176,10 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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with gr.Column(scale=2):
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search = gr.Textbox(label='搜索结果')
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# ============= 触发动作=============
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file.upload(upload_file,
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inputs=file,
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outputs=None)
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set_kg_btn.click(
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set_knowledge,
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show_progress=True,
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@ -168,9 +189,12 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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# 发送按钮 提交
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send.click(predict,
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inputs=[
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message, large_language_model,
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embedding_model, top_k, use_web,
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message,
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large_language_model,
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embedding_model,
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top_k,
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use_web,
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use_pattern,
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state
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],
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outputs=[message, chatbot, state, search])
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@ -184,8 +208,12 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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# 输入框 回车
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message.submit(predict,
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inputs=[
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message, large_language_model,
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embedding_model, top_k, use_web,
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message,
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large_language_model,
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embedding_model,
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top_k,
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use_web,
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use_pattern,
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state
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],
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outputs=[message, chatbot, state, search])
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@ -1,5 +1,5 @@
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:root {
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--chatbot-color-light: #F3F3F3;
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--chatbot-color-light: rgba(255, 255, 255, 0.08);
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--chatbot-color-dark: #121111;
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}
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@ -40,7 +40,7 @@ ol:not(.options), ul:not(.options) {
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color: #000000 !important;
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}
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[data-testid = "bot"] {
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background-color: #FFFFFF !important;
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background-color: rgba(255, 255, 255, 0.08) !important;
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}
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[data-testid = "user"] {
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background-color: #95EC69 !important;
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@ -49,7 +49,7 @@ ol:not(.options), ul:not(.options) {
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/* Dark mode */
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.dark #chuanhu_chatbot {
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background-color: var(--chatbot-color-dark) !important;
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color: #FFFFFF !important;
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color: rgba(255, 255, 255, 0.08) !important;
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}
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.dark [data-testid = "bot"] {
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background-color: #2C2C2C !important;
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@ -12,7 +12,7 @@
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class LangChainCFG:
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llm_model_name = 'chatglm-6b' # 本地模型文件 or huggingface远程仓库
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embedding_model_name = 'text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
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llm_model_name = 'THUDM/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
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embedding_model_name = 'GanymedeNil/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
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vector_store_path = '.'
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docs_path = './docs'
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@ -9,10 +9,10 @@
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@software: PyCharm
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@description: coding..
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"""
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from langchain.chains import RetrievalQA
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from langchain.prompts.prompt import PromptTemplate
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from clc.config import LangChainCFG
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from clc.gpt_service import ChatGLMService
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from clc.source_service import SourceService
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@ -23,15 +23,16 @@ class LangChainApplication(object):
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self.llm_service = ChatGLMService()
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self.llm_service.load_model(model_name_or_path=self.config.llm_model_name)
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self.source_service = SourceService(config)
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if self.config.kg_vector_stores is None:
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print("init a source vector store")
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self.source_service.init_source_vector()
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else:
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print("load zh_wikipedia source vector store ")
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try:
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self.source_service.load_vector_store(self.config.kg_vector_stores['初始化知识库'])
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except Exception as e:
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self.source_service.init_source_vector()
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# if self.config.kg_vector_stores is None:
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# print("init a source vector store")
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# self.source_service.init_source_vector()
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# else:
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# print("load zh_wikipedia source vector store ")
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# try:
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# self.source_service.load_vector_store(self.config.kg_vector_stores['初始化知识库'])
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# except Exception as e:
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# self.source_service.init_source_vector()
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def get_knowledge_based_answer(self, query,
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history_len=5,
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@ -75,11 +76,22 @@ class LangChainApplication(object):
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result = knowledge_chain({"query": query})
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return result
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# if __name__ == '__main__':
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# config = LangChainCFG()
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# application = LangChainApplication(config)
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def get_llm_answer(self, query='', web_content=''):
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if web_content:
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prompt = f'基于网络检索内容：{web_content}，回答以下问题{query}'
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else:
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prompt = query
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result = self.llm_service._call(prompt)
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return result
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if __name__ == '__main__':
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config = LangChainCFG()
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application = LangChainApplication(config)
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# result = application.get_knowledge_based_answer('马保国是谁')
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# print(result)
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# application.source_service.add_document('/home/searchgpt/yq/Knowledge-ChatGLM/docs/added/马保国.txt')
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# result = application.get_knowledge_based_answer('马保国是谁')
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# print(result)
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result = application.get_llm_answer('马保国是谁')
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print(result)
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@ -13,7 +13,6 @@
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import os
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from duckduckgo_search import ddg
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from duckduckgo_search.utils import SESSION
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from langchain.document_loaders import UnstructuredFileLoader
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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@ -61,12 +60,16 @@ class SourceService(object):
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# "http": f"socks5h://localhost:7890",
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# "https": f"socks5h://localhost:7890"
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# }
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try:
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results = ddg(query)
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web_content = ''
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if results:
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for result in results:
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web_content += result['body']
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return web_content
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except Exception as e:
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print(f"网络检索异常:{query}")
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return ''
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# if __name__ == '__main__':
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# config = LangChainCFG()
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# source_service = SourceService(config)
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66
main.py
66
main.py
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@ -1,6 +1,7 @@
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import os
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import shutil
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from app_modules.overwrites import postprocess
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from app_modules.presets import *
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from clc.langchain_application import LangChainApplication
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@ -13,10 +14,11 @@ class LangChainCFG:
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docs_path = './docs'
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kg_vector_stores = {
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'中文维基百科': './cache/zh_wikipedia',
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'大规模金融研报知识图谱': '.cache/financial_research_reports',
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'初始化知识库': '.cache',
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'大规模金融研报': './cache/financial_research_reports',
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'初始化': './cache',
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} # 可以替换成自己的知识库，如果没有需要设置为None
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# kg_vector_stores=None
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patterns = ['模型问答', '知识库问答'] #
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config = LangChainCFG()
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@ -61,6 +63,7 @@ def predict(input,
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embedding_model,
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top_k,
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use_web,
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use_pattern,
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history=None):
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# print(large_language_model, embedding_model)
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print(input)
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@ -71,6 +74,14 @@ def predict(input,
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web_content = application.source_service.search_web(query=input)
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else:
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web_content = ''
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search_text = ''
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if use_pattern == '模型问答':
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result = application.get_llm_answer(query=input, web_content=web_content)
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history.append((input, result))
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search_text += web_content
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return '', history, history, search_text
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else:
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resp = application.get_knowledge_based_answer(
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query=input,
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history_len=1,
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@ -81,7 +92,6 @@ def predict(input,
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chat_history=history
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)
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history.append((input, resp['result']))
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search_text = ''
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for idx, source in enumerate(resp['source_documents'][:4]):
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sep = f'----------【搜索结果{idx + 1}：】---------------\n'
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search_text += f'{sep}\n{source.page_content}\n\n'
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@ -121,28 +131,35 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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step=1,
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label="检索top-k文档",
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interactive=True)
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kg_name = gr.Radio(['中文维基百科',
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'大规模金融研报知识图谱',
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'初始化知识库'
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],
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label="知识库",
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value='初始化知识库',
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interactive=True)
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set_kg_btn = gr.Button("重新加载知识库")
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use_web = gr.Radio(["使用", "不使用"], label="web search",
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info="是否使用网络搜索，使用时确保网络通常",
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value="不使用"
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)
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use_pattern = gr.Radio(
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[
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'模型问答',
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'知识库问答',
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],
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label="模式",
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value='模型问答',
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interactive=True)
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kg_name = gr.Radio(['中文维基百科',
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'大规模金融研报知识图谱',
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'初始化知识库'
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],
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label="知识库",
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value=None,
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info="使用知识库问答，请加载知识库",
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interactive=True)
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set_kg_btn = gr.Button("加载知识库")
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file = gr.File(label="将文件上传到知识库库，内容要尽量匹配",
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visible=True,
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file_types=['.txt', '.md', '.docx', '.pdf']
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)
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file.upload(upload_file,
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inputs=file,
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outputs=None)
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with gr.Column(scale=4):
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with gr.Row():
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chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400)
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@ -159,6 +176,10 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
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with gr.Column(scale=2):
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search = gr.Textbox(label='搜索结果')
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# ============= 触发动作=============
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file.upload(upload_file,
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inputs=file,
|
||||
outputs=None)
|
||||
set_kg_btn.click(
|
||||
set_knowledge,
|
||||
show_progress=True,
|
||||
|
|
@ -168,9 +189,12 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
|
|||
# 发送按钮 提交
|
||||
send.click(predict,
|
||||
inputs=[
|
||||
message, large_language_model,
|
||||
embedding_model, top_k, use_web,
|
||||
|
||||
message,
|
||||
large_language_model,
|
||||
embedding_model,
|
||||
top_k,
|
||||
use_web,
|
||||
use_pattern,
|
||||
state
|
||||
],
|
||||
outputs=[message, chatbot, state, search])
|
||||
|
|
@ -184,8 +208,12 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
|
|||
# 输入框 回车
|
||||
message.submit(predict,
|
||||
inputs=[
|
||||
message, large_language_model,
|
||||
embedding_model, top_k, use_web,
|
||||
message,
|
||||
large_language_model,
|
||||
embedding_model,
|
||||
top_k,
|
||||
use_web,
|
||||
use_pattern,
|
||||
state
|
||||
],
|
||||
outputs=[message, chatbot, state, search])
|
||||
|
|
|
|||
Loading…
Reference in New Issue