merge master

README.md

@@ -178,6 +178,6 @@ Web UI 可以实现如下功能：
   - [ ] 实现调用 API 的 Web UI Demo
 
 ## 项目交流群
-![二维码](img/qr_code_11.jpg)
+![二维码](img/qr_code_12.jpg)
 
 🎉 langchain-ChatGLM 项目交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

api.py

@@ -170,24 +170,25 @@ async def delete_docs(
 
 
 async def chat(
-        knowledge_base_id: str = Body(..., description="知识库名字", example="kb1"),
-        question: str = Body(..., description="问题", example="工伤保险是什么？"),
+        knowledge_base_id: str = Body(..., description="Knowledge Base Name", example="kb1"),
+        question: str = Body(..., description="Question", example="工伤保险是什么？"),
         history: List[List[str]] = Body(
             [],
-            description="问题及答案的历史记录",
+            description="History of previous questions and answers",
             example=[
                 [
-                    "这里是问题，如：工伤保险是什么？",
-                    "答案：工伤保险是指用人单位按照国家规定，为本单位的职工和用人单位的其他人员，缴纳工伤保险费，由保险机构按照国家规定的标准，给予工伤保险待遇的社会保险制度。",
+                    "工伤保险是什么？",
+                    "工伤保险是指用人单位按照国家规定，为本单位的职工和用人单位的其他人员，缴纳工伤保险费，由保险机构按照国家规定的标准，给予工伤保险待遇的社会保险制度。",
                 ]
             ],
         ),
 ):
     vs_path = os.path.join(VS_ROOT_PATH, knowledge_base_id)
-    resp = {}
-    if os.path.exists(vs_path) and knowledge_base_id:
+    if not os.path.exists(vs_path):
+        raise ValueError(f"Knowledge base {knowledge_base_id} not found")
+
     for resp, history in local_doc_qa.get_knowledge_based_answer(
-                query=question, vs_path=vs_path, chat_history=history, streaming=False
+            query=question, vs_path=vs_path, chat_history=history, streaming=True
     ):
         pass
     source_documents = [
@@ -195,11 +196,6 @@ async def chat(
         f"""相关度：{doc.metadata['score']}\n\n"""
         for inum, doc in enumerate(resp["source_documents"])
     ]
-    else:
-        for resp_s, history in local_doc_qa.llm._call(prompt=question, history=history, streaming=False):
-            pass
-        resp["result"] = resp_s
-        source_documents =[("当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。")]
 
     return ChatMessage(
         question=question,

textsplitter/chinese_text_splitter.py

@@ -3,17 +3,25 @@ import re
 from typing import List
 from configs.model_config import SENTENCE_SIZE
 
+
 class ChineseTextSplitter(CharacterTextSplitter):
     def __init__(self, pdf: bool = False, **kwargs):
         super().__init__(**kwargs)
         self.pdf = pdf
 
-    def split_text1(self, text: str) -> List[str]:
+    def split_text1(self, text: str, use_document_segmentation: bool = False) -> List[str]:
+        # use_document_segmentation参数指定是否用语义切分文档，此处采取的文档语义分割模型为达摩院开源的nlp_bert_document-segmentation_chinese-base，论文见https://arxiv.org/abs/2107.09278
+        # 如果使用模型进行文档语义切分，那么需要安装modelscope[nlp]：pip install "modelscope[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+        # 考虑到使用了三个模型，可能对于低配置gpu不太友好，因此这里将模型load进cpu计算，有需要的话可以替换device为自己的显卡id
         if self.pdf:
             text = re.sub(r"\n{3,}", "\n", text)
             text = re.sub('\s', ' ', text)
             text = text.replace("\n\n", "")
-        sent_sep_pattern = re.compile('([﹒﹔；﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')  # del ：；
+        if use_document_segmentation:
+            result = p(documents=text)
+            sent_list = [i for i in result["text"].split("\n\t") if i]
+        else:
+            sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')  # del ：；
             sent_list = []
             for ele in sent_sep_pattern.split(text):
                 if sent_sep_pattern.match(ele) and sent_list:
@@ -22,11 +30,21 @@ class ChineseTextSplitter(CharacterTextSplitter):
                     sent_list.append(ele)
         return sent_list
 
-    def split_text(self, text: str) -> List[str]:
+    def split_text(self, text: str, use_document_segmentation: bool = False) -> List[str]:
         if self.pdf:
             text = re.sub(r"\n{3,}", r"\n", text)
             text = re.sub('\s', " ", text)
             text = re.sub("\n\n", "", text)
+        if use_document_segmentation:
+            from modelscope.pipelines import pipeline
+            p = pipeline(
+                task="document-segmentation",
+                model='damo/nlp_bert_document-segmentation_chinese-base',
+                device="cpu")
+            result = p(documents=text)
+            sent_list = [i for i in result["text"].split("\n\t") if i]
+            return sent_list
+        else:
             text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text)  # 单字符断句符
             text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text)  # 英文省略号
             text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text)  # 中文省略号
@@ -47,12 +65,11 @@ class ChineseTextSplitter(CharacterTextSplitter):
                                 if len(ele_ele2) > SENTENCE_SIZE:
                                     ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
                                     ele2_id = ele2_ls.index(ele_ele2)
-                                ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[ele2_id + 1:]
+                                    ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
+                                                                                                           ele2_id + 1:]
                             ele_id = ele1_ls.index(ele_ele1)
                             ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]
 
                     id = ls.index(ele)
-                ls = ls[:id] + [i for i in ele1_ls if i] + ls[id+1:]
+                    ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]
             return ls
-
-