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_14.jpg)
+![二维码](img/qr_code_15.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。

cli_demo.py

@@ -31,7 +31,7 @@ if __name__ == "__main__":
                                                                      chat_history=history,
                                                                      streaming=STREAMING):
             if STREAMING:
-                logger.info(resp["result"][last_print_len:], end="", flush=True)
+                logger.info(resp["result"][last_print_len:])
                 last_print_len = len(resp["result"])
             else:
                 logger.info(resp["result"])

configs/model_config.py

@@ -69,6 +69,9 @@ LLM_HISTORY_LEN = 3
 # return top-k text chunk from vector store
 VECTOR_SEARCH_TOP_K = 5
 
+# 如果为0，则不生效，经测试小于500值的结果更精准
+VECTOR_SEARCH_SCORE_THRESHOLD = 0
+
 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
 
 FLAG_USER_NAME = uuid.uuid4().hex
@@ -79,4 +82,4 @@ llm device: {LLM_DEVICE}
 embedding device: {EMBEDDING_DEVICE}
 dir: {os.path.dirname(os.path.dirname(__file__))}
 flagging username: {FLAG_USER_NAME}
-""")
\ No newline at end of file
+""")

models/chatglm_llm.py

@@ -125,7 +125,7 @@ class ChatGLM(LLM):
                 prefix_encoder_file.close()
                 model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
                 model_config.prefix_projection = prefix_encoder_config['prefix_projection']
-            except Exception as e:                
+            except Exception as e:
                 logger.error(f"加载PrefixEncoder config.json失败: {e}")
         self.model = AutoModel.from_pretrained(model_name_or_path, config=model_config, trust_remote_code=True,
                                                **kwargs)
@@ -163,7 +163,7 @@ class ChatGLM(LLM):
                         new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
                 self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
                 self.model.transformer.prefix_encoder.float()
-            except Exception as e:              
+            except Exception as e:
                 logger.error(f"加载PrefixEncoder模型参数失败:{e}")
 
         self.model = self.model.eval()

textsplitter/chinese_text_splitter.py

@@ -5,9 +5,10 @@ from configs.model_config import SENTENCE_SIZE
 
 
 class ChineseTextSplitter(CharacterTextSplitter):
-    def __init__(self, pdf: bool = False, **kwargs):
+    def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
         super().__init__(**kwargs)
         self.pdf = pdf
+        self.sentence_size = sentence_size
 
     def split_text1(self, text: str) -> List[str]:
         if self.pdf:
@@ -23,7 +24,7 @@ class ChineseTextSplitter(CharacterTextSplitter):
                 sent_list.append(ele)
         return sent_list
 
-    def split_text(self, text: str) -> List[str]:
+    def split_text(self, text: str) -> List[str]:   ##此处需要进一步优化逻辑
         if self.pdf:
             text = re.sub(r"\n{3,}", r"\n", text)
             text = re.sub('\s', " ", text)
@@ -38,15 +39,15 @@ class ChineseTextSplitter(CharacterTextSplitter):
         # 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:
+            if len(ele) > self.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:
+                    if len(ele_ele1) > self.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:
+                            if len(ele_ele2) > self.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[