update cli_demo.py
This commit is contained in:
imClumsyPanda
parent
5d2055c6e5
commit
3187423ed4
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@ -8,6 +8,7 @@ import sentence_transformers
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import os
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from configs.model_config import *
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import datetime
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from typing import List
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# return top-k text chunk from vector store
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VECTOR_SEARCH_TOP_K = 10
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@ -42,7 +43,8 @@ class LocalDocQA:
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self.top_k = top_k
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def init_knowledge_vector_store(self,
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filepath: str):
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filepath: str or List[str]):
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if isinstance(filepath, str):
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if not os.path.exists(filepath):
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print("路径不存在")
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return None
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@ -65,6 +67,15 @@ class LocalDocQA:
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print(f"{file} 已成功加载")
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except:
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print(f"{file} 未能成功加载")
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else:
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docs = []
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for file in filepath:
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try:
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loader = UnstructuredFileLoader(file, mode="elements")
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docs += loader.load()
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print(f"{file} 已成功加载")
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except:
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print(f"{file} 未能成功加载")
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vector_store = FAISS.from_documents(docs, self.embeddings)
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vs_path = f"""./vector_store/{os.path.splitext(file)[0]}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}"""
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127
webui.py
127
webui.py
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@ -1,7 +1,8 @@
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import gradio as gr
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import os
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import shutil
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import cli_demo as kb
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from chains.local_doc_qa import LocalDocQA
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from configs.model_config import *
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def get_file_list():
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@ -12,9 +13,11 @@ def get_file_list():
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file_list = get_file_list()
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embedding_model_dict_list = list(kb.embedding_model_dict.keys())
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embedding_model_dict_list = list(embedding_model_dict.keys())
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llm_model_dict_list = list(kb.llm_model_dict.keys())
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llm_model_dict_list = list(llm_model_dict.keys())
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local_doc_qa = LocalDocQA()
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def upload_file(file):
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@ -27,9 +30,9 @@ def upload_file(file):
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return gr.Dropdown.update(choices=file_list, value=filename)
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def get_answer(query, vector_store, history):
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resp, history = kb.get_knowledge_based_answer(
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query=query, vector_store=vector_store, chat_history=history)
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def get_answer(query, vs_path, history):
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resp, history = local_doc_qa.get_knowledge_based_answer(
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query=query, vs_path=vs_path, chat_history=history)
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return history, history
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@ -41,6 +44,25 @@ def get_file_status(history):
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return history + [[None, "文档已完成加载，请开始提问"]]
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def init_model():
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try:
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local_doc_qa.init_cfg()
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return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
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except:
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return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
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def reinit_model(llm_model, embedding_model, llm_history_len, top_k):
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local_doc_qa.init_cfg(llm_model=llm_model,
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embedding_model=embedding_model,
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llm_history_len=llm_history_len,
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top_k=top_k),
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model_status = gr.State()
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history = gr.State([])
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vs_path = gr.State()
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model_status = init_model()
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with gr.Blocks(css="""
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.importantButton {
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background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
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@ -63,44 +85,41 @@ with gr.Blocks(css="""
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with gr.Row():
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with gr.Column(scale=2):
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chatbot = gr.Chatbot([[None, """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请依次如下 3 个步骤：
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1. 选择语言模型、Embedding 模型及相关参数后点击"step.1: setting"，并等待加载完成提示
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2. 上传或选择已有文件作为本地知识文档输入后点击"step.2 loading"，并等待加载完成提示
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3. 输入要提交的问题后点击"step.3 asking" """]],
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1. 选择语言模型、Embedding 模型及相关参数后点击"重新加载模型"，并等待加载完成提示
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2. 上传或选择已有文件作为本地知识文档输入后点击"重新加载文档"，并等待加载完成提示
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3. 输入要提交的问题后，点击回车提交 """], [None, str(model_status)]],
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elem_id="chat-box",
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show_label=False).style(height=600)
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query = gr.Textbox(show_label=False,
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placeholder="请提问",
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lines=1,
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value="用200字总结一下"
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).style(container=False)
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with gr.Column(scale=1):
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with gr.Column():
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llm_model = gr.Radio(llm_model_dict_list,
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label="llm model",
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label="LLM 模型",
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value="chatglm-6b",
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interactive=True)
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LLM_HISTORY_LEN = gr.Slider(0,
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llm_history_len = gr.Slider(0,
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10,
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value=3,
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step=1,
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label="LLM history len",
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interactive=True)
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embedding_model = gr.Radio(embedding_model_dict_list,
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label="embedding model",
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label="Embedding 模型",
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value="text2vec",
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interactive=True)
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VECTOR_SEARCH_TOP_K = gr.Slider(1,
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top_k = gr.Slider(1,
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20,
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value=6,
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step=1,
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label="vector search top k",
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label="向量匹配 top k",
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interactive=True)
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load_model_button = gr.Button("step.1：setting")
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load_model_button.click(lambda *args:
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kb.init_cfg(args[0], args[1], args[2], args[3]),
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show_progress=True,
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api_name="init_cfg",
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inputs=[llm_model, embedding_model, LLM_HISTORY_LEN,VECTOR_SEARCH_TOP_K]
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).then(
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get_model_status, chatbot, chatbot
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)
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load_model_button = gr.Button("重新加载模型")
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with gr.Column():
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# with gr.Column():
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with gr.Tab("select"):
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selectFile = gr.Dropdown(file_list,
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label="content file",
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@ -109,43 +128,35 @@ with gr.Blocks(css="""
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with gr.Tab("upload"):
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file = gr.File(label="content file",
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file_types=['.txt', '.md', '.docx', '.pdf']
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).style(height=100)
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) # .style(height=100)
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load_button = gr.Button("重新加载文件")
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load_model_button.click(reinit_model,
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show_progress=True,
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api_name="init_cfg",
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inputs=[llm_model, embedding_model, llm_history_len, top_k]
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).then(
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get_model_status, chatbot, chatbot
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)
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# 将上传的文件保存到content文件夹下,并更新下拉框
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file.upload(upload_file,
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inputs=file,
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outputs=selectFile)
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history = gr.State([])
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vector_store = gr.State()
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load_button = gr.Button("step.2：loading")
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load_button.click(lambda fileName:
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kb.init_knowledge_vector_store(
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"content/" + fileName),
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show_progress=True,
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api_name="init_knowledge_vector_store",
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inputs=selectFile,
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outputs=vector_store
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).then(
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get_file_status,
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chatbot,
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chatbot,
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show_progress=True,
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)
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with gr.Row():
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with gr.Column(scale=2):
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query = gr.Textbox(show_label=False,
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placeholder="Prompts",
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lines=1,
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value="用200字总结一下"
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).style(container=False)
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with gr.Column(scale=1):
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generate_button = gr.Button("step.3：asking",
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elem_classes="importantButton")
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generate_button.click(get_answer,
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[query, vector_store, chatbot],
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[chatbot, history],
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api_name="get_knowledge_based_answer"
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)
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# load_button.click(local_doc_qa.init_knowledge_vector_store,
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# show_progress=True,
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# api_name="init_knowledge_vector_store",
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# inputs=selectFile,
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# outputs=vs_path
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# ).then(
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# get_file_status,
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# chatbot,
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# chatbot,
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# show_progress=True,
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# )
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# query.submit(get_answer,
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# [query, vs_path, chatbot],
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# [chatbot, history],
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# api_name="get_knowledge_based_answer"
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# )
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demo.queue(concurrency_count=3).launch(
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server_name='0.0.0.0', share=False, inbrowser=False)
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