feature@创建hf应用

README.md

@@ -50,7 +50,7 @@ cchardet
 ### 启动Gradio
 
 ```shell
-python app.py
+python main.py
 ```
 
 ## 🚀 特性

app.py

@@ -192,9 +192,9 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
 
 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
+    enable_queue=True,
+    inbrowser=True,
 )

clc/source_service.py

@@ -57,10 +57,10 @@ class SourceService(object):
 
     def search_web(self, query):
 
-        SESSION.proxies = {
-            "http": f"socks5h://localhost:7890",
-            "https": f"socks5h://localhost:7890"
-        }
+        # SESSION.proxies = {
+        #     "http": f"socks5h://localhost:7890",
+        #     "https": f"socks5h://localhost:7890"
+        # }
         results = ddg(query)
         web_content = ''
         if results:

main.py

@@ -0,0 +1,201 @@
+import os
+import shutil
+
+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',
+        '大规模金融研报知识图谱': '.cache/financial_research_reports',
+        '初始化知识库': '.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
+
+
+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:
+        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,
+            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 = ''
+    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']))
+    search_text = ''
+    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)
+            kg_name = gr.Radio(['中文维基百科',
+                                '大规模金融研报知识图谱',
+                                '初始化知识库'
+                                ],
+                               label="知识库",
+                               value='初始化知识库',
+                               interactive=True)
+            set_kg_btn = gr.Button("重新加载知识库")
+
+            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"
+                               )
+
+            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']
+                           )
+
+            file.upload(upload_file,
+                        inputs=file,
+                        outputs=None)
+        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')
+
+        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,
+
+                       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,
+                           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,
+)