update cli_demo.py

chains/local_doc_qa.py

@@ -8,6 +8,7 @@ import sentence_transformers
 import os
 from configs.model_config import *
 import datetime
+from typing import List
 
 # return top-k text chunk from vector store
 VECTOR_SEARCH_TOP_K = 10
@@ -42,25 +43,35 @@ class LocalDocQA:
         self.top_k = top_k
 
     def init_knowledge_vector_store(self,
-                                    filepath: str):
+                                    filepath: str or List[str]):
-        if not os.path.exists(filepath):
+        if isinstance(filepath, str):
-            print("路径不存在")
+            if not os.path.exists(filepath):
-            return None
+                print("路径不存在")
-        elif os.path.isfile(filepath):
-            file = os.path.split(filepath)[-1]
-            try:
-                loader = UnstructuredFileLoader(filepath, mode="elements")
-                docs = loader.load()
-                print(f"{file} 已成功加载")
-            except:
-                print(f"{file} 未能成功加载")
                 return None
-        elif os.path.isdir(filepath):
+            elif os.path.isfile(filepath):
-            docs = []
+                file = os.path.split(filepath)[-1]
-            for file in os.listdir(filepath):
-                fullfilepath = os.path.join(filepath, file)
                 try:
-                    loader = UnstructuredFileLoader(fullfilepath, mode="elements")
+                    loader = UnstructuredFileLoader(filepath, mode="elements")
+                    docs = loader.load()
+                    print(f"{file} 已成功加载")
+                except:
+                    print(f"{file} 未能成功加载")
+                    return None
+            elif os.path.isdir(filepath):
+                docs = []
+                for file in os.listdir(filepath):
+                    fullfilepath = os.path.join(filepath, file)
+                    try:
+                        loader = UnstructuredFileLoader(fullfilepath, mode="elements")
+                        docs += loader.load()
+                        print(f"{file} 已成功加载")
+                    except:
+                        print(f"{file} 未能成功加载")
+        else:
+            docs = []
+            for file in filepath:
+                try:
+                    loader = UnstructuredFileLoader(file, mode="elements")
                     docs += loader.load()
                     print(f"{file} 已成功加载")
                 except:
@@ -74,7 +85,7 @@ class LocalDocQA:
     def get_knowledge_based_answer(self,
                                    query,
                                    vs_path,
-                                   chat_history=[],):
+                                   chat_history=[], ):
         prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
     如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
     

webui.py

@@ -1,7 +1,8 @@
 import gradio as gr
 import os
 import shutil
-import cli_demo as kb
+from chains.local_doc_qa import LocalDocQA
+from configs.model_config import *
 
 
 def get_file_list():
@@ -12,9 +13,11 @@ def get_file_list():
 
 file_list = get_file_list()
 
-embedding_model_dict_list = list(kb.embedding_model_dict.keys())
+embedding_model_dict_list = list(embedding_model_dict.keys())
 
-llm_model_dict_list = list(kb.llm_model_dict.keys())
+llm_model_dict_list = list(llm_model_dict.keys())
+
+local_doc_qa = LocalDocQA()
 
 
 def upload_file(file):
@@ -27,9 +30,9 @@ def upload_file(file):
     return gr.Dropdown.update(choices=file_list, value=filename)
 
 
-def get_answer(query, vector_store, history):
+def get_answer(query, vs_path, history):
-    resp, history = kb.get_knowledge_based_answer(
+    resp, history = local_doc_qa.get_knowledge_based_answer(
-        query=query, vector_store=vector_store, chat_history=history)
+        query=query, vs_path=vs_path, chat_history=history)
     return history, history
 
 
@@ -41,6 +44,25 @@ def get_file_status(history):
     return history + [[None, "文档已完成加载，请开始提问"]]
 
 
+def init_model():
+    try:
+        local_doc_qa.init_cfg()
+        return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
+    except:
+        return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
+
+
+def reinit_model(llm_model, embedding_model, llm_history_len, top_k):
+    local_doc_qa.init_cfg(llm_model=llm_model,
+                          embedding_model=embedding_model,
+                          llm_history_len=llm_history_len,
+                          top_k=top_k),
+
+
+model_status = gr.State()
+history = gr.State([])
+vs_path = gr.State()
+model_status = init_model()
 with gr.Blocks(css="""
 .importantButton {
     background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
@@ -63,89 +85,78 @@ with gr.Blocks(css="""
     with gr.Row():
         with gr.Column(scale=2):
             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：
-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
+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
-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
+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
-3. 输入要提交的问题后点击"step.3 asking" """]],
+3. 输入要提交的问题后，点击回车提交 """], [None, str(model_status)]],
                                  elem_id="chat-box",
                                  show_label=False).style(height=600)
-        with gr.Column(scale=1):
-            with gr.Column():
-                llm_model = gr.Radio(llm_model_dict_list,
-                                     label="llm model",
-                                     value="chatglm-6b",
-                                     interactive=True)
-                LLM_HISTORY_LEN = gr.Slider(0,
-                                            10,
-                                            value=3,
-                                            step=1,
-                                            label="LLM history len",
-                                            interactive=True)
-                embedding_model = gr.Radio(embedding_model_dict_list,
-                                           label="embedding model",
-                                           value="text2vec",
-                                           interactive=True)
-                VECTOR_SEARCH_TOP_K = gr.Slider(1,
-                                                20,
-                                                value=6,
-                                                step=1,
-                                                label="vector search top k",
-                                                interactive=True)
-                load_model_button = gr.Button("step.1：setting")
-                load_model_button.click(lambda *args:
-                                        kb.init_cfg(args[0], args[1], args[2], args[3]),
-                                        show_progress=True,
-                                        api_name="init_cfg",
-                                        inputs=[llm_model, embedding_model, LLM_HISTORY_LEN,VECTOR_SEARCH_TOP_K]
-                                        ).then(
-                    get_model_status, chatbot, chatbot
-                )
-
-            with gr.Column():
-                with gr.Tab("select"):
-                    selectFile = gr.Dropdown(file_list,
-                                             label="content file",
-                                             interactive=True,
-                                             value=file_list[0] if len(file_list) > 0 else None)
-                with gr.Tab("upload"):
-                    file = gr.File(label="content file",
-                                   file_types=['.txt', '.md', '.docx', '.pdf']
-                                   ).style(height=100)
-                    # 将上传的文件保存到content文件夹下,并更新下拉框
-                    file.upload(upload_file,
-                                inputs=file,
-                                outputs=selectFile)
-                history = gr.State([])
-                vector_store = gr.State()
-                load_button = gr.Button("step.2：loading")
-                load_button.click(lambda fileName:
-                                  kb.init_knowledge_vector_store(
-                                      "content/" + fileName),
-                                  show_progress=True,
-                                  api_name="init_knowledge_vector_store",
-                                  inputs=selectFile,
-                                  outputs=vector_store
-                                  ).then(
-                    get_file_status,
-                    chatbot,
-                    chatbot,
-                    show_progress=True,
-                )
-
-    with gr.Row():
-        with gr.Column(scale=2):
             query = gr.Textbox(show_label=False,
-                               placeholder="Prompts",
+                               placeholder="请提问",
                                lines=1,
                                value="用200字总结一下"
                                ).style(container=False)
+
         with gr.Column(scale=1):
-            generate_button = gr.Button("step.3：asking",
+            llm_model = gr.Radio(llm_model_dict_list,
-                                        elem_classes="importantButton")
+                                 label="LLM 模型",
-            generate_button.click(get_answer,
+                                 value="chatglm-6b",
-                                  [query, vector_store, chatbot],
+                                 interactive=True)
-                                  [chatbot, history],
+            llm_history_len = gr.Slider(0,
-                                  api_name="get_knowledge_based_answer"
+                                        10,
-                                  )
+                                        value=3,
+                                        step=1,
+                                        label="LLM history len",
+                                        interactive=True)
+            embedding_model = gr.Radio(embedding_model_dict_list,
+                                       label="Embedding 模型",
+                                       value="text2vec",
+                                       interactive=True)
+            top_k = gr.Slider(1,
+                              20,
+                              value=6,
+                              step=1,
+                              label="向量匹配 top k",
+                              interactive=True)
+            load_model_button = gr.Button("重新加载模型")
+
+            # with gr.Column():
+            with gr.Tab("select"):
+                selectFile = gr.Dropdown(file_list,
+                                         label="content file",
+                                         interactive=True,
+                                         value=file_list[0] if len(file_list) > 0 else None)
+            with gr.Tab("upload"):
+                file = gr.File(label="content file",
+                               file_types=['.txt', '.md', '.docx', '.pdf']
+                               )  # .style(height=100)
+            load_button = gr.Button("重新加载文件")
+    load_model_button.click(reinit_model,
+                            show_progress=True,
+                            api_name="init_cfg",
+                            inputs=[llm_model, embedding_model, llm_history_len, top_k]
+                            ).then(
+        get_model_status, chatbot, chatbot
+    )
+    # 将上传的文件保存到content文件夹下,并更新下拉框
+    file.upload(upload_file,
+                inputs=file,
+                outputs=selectFile)
+    # load_button.click(local_doc_qa.init_knowledge_vector_store,
+    #                   show_progress=True,
+    #                   api_name="init_knowledge_vector_store",
+    #                   inputs=selectFile,
+    #                   outputs=vs_path
+    #                   ).then(
+    #     get_file_status,
+    #     chatbot,
+    #     chatbot,
+    #     show_progress=True,
+    # )
+    # query.submit(get_answer,
+    #              [query, vs_path, chatbot],
+    #              [chatbot, history],
+    #              api_name="get_knowledge_based_answer"
+    #              )
 
 demo.queue(concurrency_count=3).launch(
     server_name='0.0.0.0', share=False, inbrowser=False)