feature@添加问答模式选择

README.md

@@ -8,6 +8,7 @@ colorTo: yellow
 pinned: true
 app_file: app.py
 ---
+
 # Chinese-LangChain
 
 > 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
@@ -55,6 +56,8 @@ python main.py
 
 ## 🚀 特性
 
+- 📝 2023/04/20 支持模型问答与检索问答模式切换
+- 📝 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)
 - 📝 2023/04/19 发布45万Wikipedia的文本预处理语料以及FAISS索引向量
 - 🐯 2023/04/19 引入ChuanhuChatGPT皮肤
 - 📱 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)
@@ -87,6 +90,7 @@ python main.py
 * [x] 支持加载不同知识库
 * [x] 支持检索结果与LLM生成结果对比
 * [ ] 支持检索生成结果与原始LLM生成结果对比
+* [ ] 支持模型问答与检索问答
 * [ ] 检索结果过滤与排序
 * [x] 互联网检索结果接入
 * [ ] 模型初始化有问题

app.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 
+from app_modules.overwrites import postprocess
 from app_modules.presets import *
 from clc.langchain_application import LangChainApplication
 
@@ -8,15 +9,16 @@ 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-base-chinese'  # 检索模型文件 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',
+        '大规模金融研报': './cache/financial_research_reports',
-        '初始化知识库': '.cache',
+        '初始化': './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
+    patterns = ['模型问答', '知识库问答']  #
 
 
 config = LangChainCFG()
@@ -61,6 +63,7 @@ def predict(input,
             embedding_model,
             top_k,
             use_web,
+            use_pattern,
             history=None):
     # print(large_language_model, embedding_model)
     print(input)
@@ -71,24 +74,31 @@ def predict(input,
         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]):
+    if use_pattern == '模型问答':
-        sep = f'----------【搜索结果{idx + 1}：】---------------\n'
+        result = application.get_llm_answer(query=input, web_content=web_content)
-        search_text += f'{sep}\n{source.page_content}\n\n'
+        history.append((input, result))
-    print(search_text)
+        search_text += web_content
-    search_text += "----------【网络检索内容】-----------\n"
+        return '', history, history, search_text
-    search_text += web_content
+
-    return '', history, history, search_text
+    else:
+        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']))
+        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:
@@ -121,28 +131,35 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
                               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"
                                )
+            use_pattern = gr.Radio(
+                [
+                    '模型问答',
+                    '知识库问答',
+                ],
+                label="模式",
+                value='模型问答',
+                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=None,
+                               info="使用知识库问答，请加载知识库",
+                               interactive=True)
+            set_kg_btn = gr.Button("加载知识库")
 
             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)
@@ -159,6 +176,10 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
         with gr.Column(scale=2):
             search = gr.Textbox(label='搜索结果')
 
+        # ============= 触发动作=============
+        file.upload(upload_file,
+                    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,
+                       message,
-                       embedding_model, top_k, use_web,
+                       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,
+                           message,
-                           embedding_model, top_k, use_web,
+                           large_language_model,
+                           embedding_model,
+                           top_k,
+                           use_web,
+                           use_pattern,
                            state
                        ],
                        outputs=[message, chatbot, state, search])

assets/custom.css

@@ -1,5 +1,5 @@
 :root {
-    --chatbot-color-light: #F3F3F3;
+    --chatbot-color-light: rgba(255, 255, 255, 0.08);
     --chatbot-color-dark: #121111;
 }
 
@@ -40,7 +40,7 @@ ol:not(.options), ul:not(.options) {
     color: #000000 !important;
 }
 [data-testid = "bot"] {
-    background-color: #FFFFFF !important;
+    background-color: rgba(255, 255, 255, 0.08) !important;
 }
 [data-testid = "user"] {
     background-color: #95EC69 !important;
@@ -49,7 +49,7 @@ ol:not(.options), ul:not(.options) {
 /* Dark mode */
 .dark #chuanhu_chatbot {
     background-color: var(--chatbot-color-dark) !important;
-    color: #FFFFFF !important;
+    color: rgba(255, 255, 255, 0.08) !important;
 }
 .dark [data-testid = "bot"] {
     background-color: #2C2C2C !important;

clc/config.py

@@ -12,7 +12,7 @@
 
 
 class LangChainCFG:
-    llm_model_name = 'chatglm-6b'  # 本地模型文件 or huggingface远程仓库
+    llm_model_name = 'THUDM/chatglm-6b-int4-qe'  # 本地模型文件 or huggingface远程仓库
-    embedding_model_name = 'text2vec-large-chinese'  # 检索模型文件 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 = '.'
     docs_path = './docs'

clc/langchain_application.py

@@ -9,10 +9,10 @@
 @software: PyCharm
 @description: coding..
 """
-
 from langchain.chains import RetrievalQA
 from langchain.prompts.prompt import PromptTemplate
 
+from clc.config import LangChainCFG
 from clc.gpt_service import ChatGLMService
 from clc.source_service import SourceService
 
@@ -23,15 +23,16 @@ class LangChainApplication(object):
         self.llm_service = ChatGLMService()
         self.llm_service.load_model(model_name_or_path=self.config.llm_model_name)
         self.source_service = SourceService(config)
-        if self.config.kg_vector_stores is None:
+
-            print("init a source vector store")
+        # if self.config.kg_vector_stores is None:
-            self.source_service.init_source_vector()
+        #     print("init a source vector store")
-        else:
+        #     self.source_service.init_source_vector()
-            print("load zh_wikipedia source vector store ")
+        # else:
-            try:
+        #     print("load zh_wikipedia source vector store ")
-                self.source_service.load_vector_store(self.config.kg_vector_stores['初始化知识库'])
+        #     try:
-            except Exception as e:
+        #         self.source_service.load_vector_store(self.config.kg_vector_stores['初始化知识库'])
-                self.source_service.init_source_vector()
+        #     except Exception as e:
+        #         self.source_service.init_source_vector()
 
     def get_knowledge_based_answer(self, query,
                                    history_len=5,
@@ -75,11 +76,22 @@ class LangChainApplication(object):
         result = knowledge_chain({"query": query})
         return result
 
-# if __name__ == '__main__':
+    def get_llm_answer(self, query='', web_content=''):
-#     config = LangChainCFG()
+        if web_content:
-#     application = LangChainApplication(config)
+            prompt = f'基于网络检索内容：{web_content}，回答以下问题{query}'
-#     result = application.get_knowledge_based_answer('马保国是谁')
+        else:
-#     print(result)
+            prompt = query
-#     application.source_service.add_document('/home/searchgpt/yq/Knowledge-ChatGLM/docs/added/马保国.txt')
+        result = self.llm_service._call(prompt)
-#     result = application.get_knowledge_based_answer('马保国是谁')
+        return result
-#     print(result)
+
+
+if __name__ == '__main__':
+    config = LangChainCFG()
+    application = LangChainApplication(config)
+    # result = application.get_knowledge_based_answer('马保国是谁')
+    # print(result)
+    # application.source_service.add_document('/home/searchgpt/yq/Knowledge-ChatGLM/docs/added/马保国.txt')
+    # result = application.get_knowledge_based_answer('马保国是谁')
+    # print(result)
+    result = application.get_llm_answer('马保国是谁')
+    print(result)

clc/source_service.py

@@ -13,7 +13,6 @@
 import os
 
 from duckduckgo_search import ddg
-from duckduckgo_search.utils import SESSION
 from langchain.document_loaders import UnstructuredFileLoader
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
@@ -61,12 +60,16 @@ class SourceService(object):
         #     "http": f"socks5h://localhost:7890",
         #     "https": f"socks5h://localhost:7890"
         # }
-        results = ddg(query)
+        try:
-        web_content = ''
+            results = ddg(query)
-        if results:
+            web_content = ''
-            for result in results:
+            if results:
-                web_content += result['body']
+                for result in results:
-        return web_content
+                    web_content += result['body']
+            return web_content
+        except Exception as e:
+            print(f"网络检索异常:{query}")
+            return ''
 # if __name__ == '__main__':
 #     config = LangChainCFG()
 #     source_service = SourceService(config)

main.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 
+from app_modules.overwrites import postprocess
 from app_modules.presets import *
 from clc.langchain_application import LangChainApplication
 
@@ -13,10 +14,11 @@ class LangChainCFG:
     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',
+        '大规模金融研报': './cache/financial_research_reports',
-        '初始化知识库': '.cache',
+        '初始化': './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
+    patterns = ['模型问答', '知识库问答']  #
 
 
 config = LangChainCFG()
@@ -61,6 +63,7 @@ def predict(input,
             embedding_model,
             top_k,
             use_web,
+            use_pattern,
             history=None):
     # print(large_language_model, embedding_model)
     print(input)
@@ -71,24 +74,31 @@ def predict(input,
         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]):
+    if use_pattern == '模型问答':
-        sep = f'----------【搜索结果{idx + 1}：】---------------\n'
+        result = application.get_llm_answer(query=input, web_content=web_content)
-        search_text += f'{sep}\n{source.page_content}\n\n'
+        history.append((input, result))
-    print(search_text)
+        search_text += web_content
-    search_text += "----------【网络检索内容】-----------\n"
+        return '', history, history, search_text
-    search_text += web_content
+
-    return '', history, history, search_text
+    else:
+        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']))
+        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:
@@ -121,28 +131,35 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
                               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"
                                )
+            use_pattern = gr.Radio(
+                [
+                    '模型问答',
+                    '知识库问答',
+                ],
+                label="模式",
+                value='模型问答',
+                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=None,
+                               info="使用知识库问答，请加载知识库",
+                               interactive=True)
+            set_kg_btn = gr.Button("加载知识库")
 
             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)
@@ -159,6 +176,10 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
         with gr.Column(scale=2):
             search = gr.Textbox(label='搜索结果')
 
+        # ============= 触发动作=============
+        file.upload(upload_file,
+                    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,
+                       message,
-                       embedding_model, top_k, use_web,
+                       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,
+                           message,
-                           embedding_model, top_k, use_web,
+                           large_language_model,
+                           embedding_model,
+                           top_k,
+                           use_web,
+                           use_pattern,
                            state
                        ],
                        outputs=[message, chatbot, state, search])