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

@@ -170,36 +170,32 @@ 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:
- for resp, history in local_doc_qa.get_knowledge_based_answer(
- query=question, vs_path=vs_path, chat_history=history, streaming=False
- ):
- pass
- source_documents = [
- f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
- 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。")]
+ 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=True
+ ):
+ pass
+ source_documents = [
+ f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
+ f"""相关度：{doc.metadata['score']}\n\n"""
+ for inum, doc in enumerate(resp["source_documents"])
+ ]
 
  return ChatMessage(
  question=question,

textsplitter/chinese_text_splitter.py

@@ -3,56 +3,73 @@
 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}|$))') # del ：；
- sent_list = []
- for ele in sent_sep_pattern.split(text):
- if sent_sep_pattern.match(ele) and sent_list:
- sent_list[-1] += ele
- elif ele:
- sent_list.append(ele)
+ if use_document_segmentation:
+ result = p(documents=text)
+ sent_list = [i for i in result["text"].split("\n\t") if i]
+ else:
+ sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del ：；
+ sent_list = []
+ for ele in sent_sep_pattern.split(text):
+ if sent_sep_pattern.match(ele) and sent_list:
+ sent_list[-1] += ele
+ elif ele:
+ 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)
- 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
- text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
- # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
- text = text.rstrip() # 段尾如果有多余的\n就去掉它
- # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
- ls = [i for i in text.split("\n") if i]
- for ele in ls:
- if len(ele) > SENTENCE_SIZE:
- ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
- ele1_ls = ele1.split("\n")
- for ele_ele1 in ele1_ls:
- if len(ele_ele1) > SENTENCE_SIZE:
- ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
- ele2_ls = ele_ele2.split("\n")
- for ele_ele2 in ele2_ls:
- 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:]
- 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:]
- return ls
-
+ 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
+ text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
+ # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
+ text = text.rstrip() # 段尾如果有多余的\n就去掉它
+ # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
+ ls = [i for i in text.split("\n") if i]
+ for ele in ls:
+ if len(ele) > SENTENCE_SIZE:
+ ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
+ ele1_ls = ele1.split("\n")
+ for ele_ele1 in ele1_ls:
+ if len(ele_ele1) > SENTENCE_SIZE:
+ ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
+ ele2_ls = ele_ele2.split("\n")
+ for ele_ele2 in ele2_ls:
+ 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:]
+ 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:]
+ return ls