Merge branch 'dev' of https://github.com/imClumsyPanda/langchain-ChatGLM into dev

.gitignore

@@ -175,3 +175,6 @@ embedding/*
 pyrightconfig.json
 loader/tmp_files
 flagged/*
+ptuning-v2/*.json
+ptuning-v2/*.bin
+

README.md

@@ -255,7 +255,7 @@ Web UI 可以实现如下功能：
 - [x] VUE 前端
 
 ## 项目交流群
-<img src="img/qr_code_43.jpg" alt="二维码" width="300" height="300" />
+<img src="img/qr_code_45.jpg" alt="二维码" width="300" height="300" />
 
 
 🎉 langchain-ChatGLM 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

README_en.md

@@ -87,6 +87,10 @@ $ conda create -p /your_path/env_name python=3.8
 # Activate the environment
 $ source activate /your_path/env_name
 
+# or, do not specify an env path, note that /your_path/env_name is to be replaced with env_name below
+$ conda create -n env_name python=3.8
+$ conda activate env_name # Activate the environment
+
 # Deactivate the environment
 $ source deactivate /your_path/env_name
 

api.py

@@ -9,8 +9,9 @@ import asyncio
 import nltk
 import pydantic
 import uvicorn
-from fastapi import Body, FastAPI, File, Form, Query, UploadFile, WebSocket
+from fastapi import Body, Request, FastAPI, File, Form, Query, UploadFile, WebSocket
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 from typing_extensions import Annotated
 from starlette.responses import RedirectResponse
@@ -55,7 +56,7 @@ class ListDocsResponse(BaseResponse):
 class ChatMessage(BaseModel):
     question: str = pydantic.Field(..., description="Question text")
     response: str = pydantic.Field(..., description="Response text")
-    history: List[List[str]] = pydantic.Field(..., description="History text")
+    history: List[List[Optional[str]]] = pydantic.Field(..., description="History text")
     source_documents: List[str] = pydantic.Field(
         ..., description="List of source documents and their scores"
     )
@@ -303,7 +304,8 @@ async def update_doc(
 async def local_doc_chat(
         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(
+        streaming: bool = Body(False, description="是否开启流式输出，默认false，有些模型可能不支持。"),
+        history: List[List[Optional[str]]] = Body(
             [],
             description="History of previous questions and answers",
             example=[
@@ -324,27 +326,39 @@ async def local_doc_chat(
             source_documents=[],
         )
     else:
-        for resp, history in local_doc_qa.get_knowledge_based_answer(
+        if (streaming):
-                query=question, vs_path=vs_path, chat_history=history, streaming=True
+            def generate_answer ():
-        ):
+                last_print_len = 0
-            pass
+                for resp, next_history in local_doc_qa.get_knowledge_based_answer(
-        source_documents = [
+                    query=question, vs_path=vs_path, chat_history=history, streaming=True
-            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"""
+                    yield resp["result"][last_print_len:]
-            for inum, doc in enumerate(resp["source_documents"])
+                    last_print_len=len(resp["result"])
-        ]
 
-        return ChatMessage(
+            return StreamingResponse(generate_answer())
-            question=question,
+        else:
-            response=resp["result"],
+            for resp, history in local_doc_qa.get_knowledge_based_answer(
-            history=history,
+                    query=question, vs_path=vs_path, chat_history=history, streaming=True
-            source_documents=source_documents,
+            ):
-        )
+                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,
+                response=resp["result"],
+                history=history,
+                source_documents=source_documents,
+            )
 
 
 async def bing_search_chat(
         question: str = Body(..., description="Question", example="工伤保险是什么？"),
-        history: Optional[List[List[str]]] = Body(
+        history: Optional[List[List[Optional[str]]]] = Body(
             [],
             description="History of previous questions and answers",
             example=[
@@ -374,7 +388,8 @@ async def bing_search_chat(
 
 async def chat(
         question: str = Body(..., description="Question", example="工伤保险是什么？"),
-        history: List[List[str]] = Body(
+        streaming: bool = Body(False, description="是否开启流式输出，默认false，有些模型可能不支持。"),
+        history: List[List[Optional[str]]] = Body(
             [],
             description="History of previous questions and answers",
             example=[
@@ -385,6 +400,30 @@ async def chat(
             ],
         ),
 ):
+    if (streaming):
+        def generate_answer ():
+            last_print_len = 0
+            answer_result_stream_result = local_doc_qa.llm_model_chain(
+                {"prompt": question, "history": history, "streaming": True})
+            for answer_result in answer_result_stream_result['answer_result_stream']:
+                yield answer_result.llm_output["answer"][last_print_len:]
+                last_print_len = len(answer_result.llm_output["answer"])
+            
+        return StreamingResponse(generate_answer())
+    else:
+        answer_result_stream_result = local_doc_qa.llm_model_chain(
+            {"prompt": question, "history": history, "streaming": True})
+        for answer_result in answer_result_stream_result['answer_result_stream']:
+            resp = answer_result.llm_output["answer"]
+            history = answer_result.history
+            pass
+
+        return ChatMessage(
+            question=question,
+            response=resp,
+            history=history,
+            source_documents=[],
+        )
     answer_result_stream_result = local_doc_qa.llm_model_chain(
         {"prompt": question, "history": history, "streaming": True})
 
@@ -392,7 +431,6 @@ async def chat(
         resp = answer_result.llm_output["answer"]
         history = answer_result.history
         pass
-
     return ChatMessage(
         question=question,
         response=resp,
@@ -545,7 +583,7 @@ if __name__ == "__main__":
     parser.add_argument("--ssl_keyfile", type=str)
     parser.add_argument("--ssl_certfile", type=str)
     # 初始化消息
-    args = None
+
     args = parser.parse_args()
     args_dict = vars(args)
     shared.loaderCheckPoint = LoaderCheckPoint(args_dict)

chains/local_doc_qa.py

@@ -8,7 +8,6 @@ from typing import List
 from utils import torch_gc
 from tqdm import tqdm
 from pypinyin import lazy_pinyin
-from loader import UnstructuredPaddleImageLoader, UnstructuredPaddlePDFLoader
 from models.base import (BaseAnswer,
                          AnswerResult)
 from models.loader.args import parser
@@ -59,6 +58,7 @@ def tree(filepath, ignore_dir_names=None, ignore_file_names=None):
 
 
 def load_file(filepath, sentence_size=SENTENCE_SIZE, using_zh_title_enhance=ZH_TITLE_ENHANCE):
+
     if filepath.lower().endswith(".md"):
         loader = UnstructuredFileLoader(filepath, mode="elements")
         docs = loader.load()
@@ -67,10 +67,14 @@ def load_file(filepath, sentence_size=SENTENCE_SIZE, using_zh_title_enhance=ZH_T
         textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
         docs = loader.load_and_split(textsplitter)
     elif filepath.lower().endswith(".pdf"):
+        # 暂且将paddle相关的loader改为动态加载，可以在不上传pdf/image知识文件的前提下使用protobuf=4.x
+        from loader import UnstructuredPaddlePDFLoader
         loader = UnstructuredPaddlePDFLoader(filepath)
         textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size)
         docs = loader.load_and_split(textsplitter)
     elif filepath.lower().endswith(".jpg") or filepath.lower().endswith(".png"):
+        # 暂且将paddle相关的loader改为动态加载，可以在不上传pdf/image知识文件的前提下使用protobuf=4.x
+        from loader import UnstructuredPaddleImageLoader
         loader = UnstructuredPaddleImageLoader(filepath, mode="elements")
         textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
         docs = loader.load_and_split(text_splitter=textsplitter)

configs/model_config.py

@@ -31,7 +31,7 @@ EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backe
 # llm_model_dict 处理了loader的一些预设行为，如加载位置，模型名称，模型处理器实例
 # 在以下字典中修改属性值，以指定本地 LLM 模型存储位置
 # 如将 "chatglm-6b" 的 "local_model_path" 由 None 修改为 "User/Downloads/chatglm-6b"
-# 此处请写绝对路径
+# 此处请写绝对路径,且路径中必须包含repo-id的模型名称，因为FastChat是以模型名匹配的
 llm_model_dict = {
     "chatglm-6b-int4-qe": {
         "name": "chatglm-6b-int4-qe",
@@ -154,6 +154,15 @@ llm_model_dict = {
         "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
         "api_base_url": "http://localhost:8000/v1",  # "name"修改为fastchat服务中的"api_base_url"
         "api_key": "EMPTY"
+    },
+        # 通过 fastchat 调用的模型请参考如下格式
+    "fastchat-chatglm-6b-int4": {
+        "name": "chatglm-6b-int4",  # "name"修改为fastchat服务中的"model_name"
+        "pretrained_model_name": "chatglm-6b-int4",
+        "local_model_path": None,
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
+        "api_base_url": "http://localhost:8001/v1",  # "name"修改为fastchat服务中的"api_base_url"
+        "api_key": "EMPTY"
     },
     "fastchat-chatglm2-6b": {
         "name": "chatglm2-6b",  # "name"修改为fastchat服务中的"model_name"
@@ -175,11 +184,13 @@ llm_model_dict = {
     # 调用chatgpt时如果报出： urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.openai.com', port=443):
     #  Max retries exceeded with url: /v1/chat/completions
     # 则需要将urllib3版本修改为1.25.11
+    # 如果依然报urllib3.exceptions.MaxRetryError: HTTPSConnectionPool，则将https改为http
+    # 参考https://zhuanlan.zhihu.com/p/350015032
 
     # 如果报出：raise NewConnectionError(
     # urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001FE4BDB85E0>:
     # Failed to establish a new connection: [WinError 10060]
-    # 则是因为内地和香港的IP都被OPENAI封了，需要挂切换为日本、新加坡等地
+    # 则是因为内地和香港的IP都被OPENAI封了，需要切换为日本、新加坡等地
     "openai-chatgpt-3.5": {
         "name": "gpt-3.5-turbo",
         "pretrained_model_name": "gpt-3.5-turbo",
@@ -200,20 +211,17 @@ BF16 = False
 # 本地lora存放的位置
 LORA_DIR = "loras/"
 
-# LLM lora path，默认为空，如果有请直接指定文件夹路径
+# LORA的名称，如有请指定为列表
-LLM_LORA_PATH = ""
+
-USE_LORA = True if LLM_LORA_PATH else False
+LORA_NAME = ""
+USE_LORA = True if LORA_NAME else False
 
 # LLM streaming reponse
 STREAMING = True
 
 # Use p-tuning-v2 PrefixEncoder
 USE_PTUNING_V2 = False
-PTUNING_DIR='./ptuing-v2'
+PTUNING_DIR='./ptuning-v2'
-<<<<<<< HEAD
-=======
-
->>>>>>> f68d347c25b4bdd07f293c65a6e44a673a11f614
 # LLM running device
 LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 

docs/FAQ.md

@@ -180,9 +180,9 @@ Q14 调用api中的 `bing_search_chat`接口时，报出 `Failed to establish a
 
 ---
 
-Q15 加载chatglm-6b-int8或chatglm-6b-int4抛出`RuntimeError: Only Tensors of floating point andcomplex dtype can require gradients`
+Q15 加载chatglm-6b-int8或chatglm-6b-int4抛出 `RuntimeError: Only Tensors of floating point andcomplex dtype can require gradients`
 
-疑为chatglm的quantization的问题或torch版本差异问题，针对已经变为Parameter的torch.zeros矩阵也执行Parameter操作，从而抛出`RuntimeError: Only Tensors of floating point andcomplex dtype can require gradients`。解决办法是在chatglm-项目的原始文件中的quantization.py文件374行改为：
+疑为chatglm的quantization的问题或torch版本差异问题，针对已经变为Parameter的torch.zeros矩阵也执行Parameter操作，从而抛出 `RuntimeError: Only Tensors of floating point andcomplex dtype can require gradients`。解决办法是在chatglm-项目的原始文件中的quantization.py文件374行改为：
 
 ```
     try:
@@ -191,6 +191,8 @@ Q15 加载chatglm-6b-int8或chatglm-6b-int4抛出`RuntimeError: Only Tensors of
         pass
 ```
 
-    注：虽然模型可以顺利加载但在cpu上仍存在推理失败的可能：即针对每个问题，模型一直输出gugugugu。
+    如果上述方式不起作用，则在.cache/hugggingface/modules/目录下针对chatglm项目的原始文件中的quantization.py文件执行上述操作，若软链接不止一个，按照错误提示选择正确的路径。
+
+注：虽然模型可以顺利加载但在cpu上仍存在推理失败的可能：即针对每个问题，模型一直输出gugugugu。
 
     因此，最好不要试图用cpu加载量化模型，原因可能是目前python主流量化包的量化操作是在gpu上执行的,会天然地存在gap。

docs/INSTALL.md

@@ -12,6 +12,12 @@ $ conda create -p /your_path/env_name python=3.8
 
 # 激活环境
 $ source activate /your_path/env_name
+
+# 或，conda安装，不指定路径, 注意以下，都将/your_path/env_name替换为env_name
+$ conda create -n env_name python=3.8
+$ conda activate env_name # Activate the environment
+
+# 更新py库
 $ pip3 install --upgrade pip
 
 # 关闭环境
@@ -49,7 +55,7 @@ $ python loader/image_loader.py
 
 ## llama-cpp模型调用的说明
 
-1. 首先从huggingface hub中下载对应的模型，如https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/的[ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)，建议使用huggingface_hub库的snapshot_download下载。
+1. 首先从huggingface hub中下载对应的模型，如 [https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/) 的 [ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)，建议使用huggingface_hub库的snapshot_download下载。
 2. 将下载的模型重命名。通过huggingface_hub下载的模型会被重命名为随机序列，因此需要重命名为原始文件名，如[ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)。
 3. 基于下载模型的ggml的加载时间，推测对应的llama-cpp版本，下载对应的llama-cpp-python库的wheel文件，实测[ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)与llama-cpp-python库兼容,然后手动安装wheel文件。
 4. 将下载的模型信息写入configs/model_config.py文件里 `llm_model_dict`中，注意保证参数的兼容性，一些参数组合可能会报错.

models/chatglm_llm.py

@@ -2,14 +2,14 @@ from abc import ABC
 from langchain.chains.base import Chain
 from typing import Any, Dict, List, Optional, Generator
 from langchain.callbacks.manager import CallbackManagerForChainRun
-from transformers.generation.logits_process import LogitsProcessor
+# from transformers.generation.logits_process import LogitsProcessor
-from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
+# from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
 from models.loader import LoaderCheckPoint
 from models.base import (BaseAnswer,
                          AnswerResult,
                          AnswerResultStream,
                          AnswerResultQueueSentinelTokenListenerQueue)
-import torch
+# import torch
 import transformers
 
 

models/fastchat_openai_llm.py

@@ -245,7 +245,7 @@ if __name__ == "__main__":
 
     chain = FastChatOpenAILLMChain()
 
-    chain.set_api_key("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")
+    chain.set_api_key("EMPTY")
     # chain.set_api_base_url("https://api.openai.com/v1")
     # chain.call_model_name("gpt-3.5-turbo")
 

models/loader/args.py

@@ -42,9 +42,10 @@ parser.add_argument('--no-remote-model', action='store_true', help='remote in th
                                                                    'model to add the ` '
                                                                    '--no-remote-model`')
 parser.add_argument('--model-name', type=str, default=LLM_MODEL, help='Name of the model to load by default.')
-parser.add_argument('--lora', type=str, help='Name of the LoRA to apply to the model by default.')
+parser.add_argument("--use-lora",type=bool,default=USE_LORA,help="use lora or not")
+parser.add_argument('--lora', type=str, default=LORA_NAME,help='Name of the LoRA to apply to the model by default.')
 parser.add_argument("--lora-dir", type=str, default=LORA_DIR, help="Path to directory with all the loras")
-parser.add_argument('--use-ptuning-v2',type=str,default=False,help="whether use ptuning-v2 checkpoint")
+parser.add_argument('--use-ptuning-v2',action=USE_PTUNING_V2,help="whether use ptuning-v2 checkpoint")
 parser.add_argument("--ptuning-dir",type=str,default=PTUNING_DIR,help="the dir of ptuning-v2 checkpoint")
 # Accelerate/transformers
 parser.add_argument('--load-in-8bit', action='store_true', default=LOAD_IN_8BIT,

models/loader/loader.py

@@ -148,7 +148,7 @@ class LoaderCheckPoint:
                                                         trust_remote_code=True).half()
                     # 可传入device_map自定义每张卡的部署情况
                     if self.device_map is None:
-                        if 'chatglm' in self.model_name.lower():
+                        if 'chatglm' in self.model_name.lower() and not "chatglm2" in self.model_name.lower():
                             self.device_map = self.chatglm_auto_configure_device_map(num_gpus)
                         elif 'moss' in self.model_name.lower():
                             self.device_map = self.moss_auto_configure_device_map(num_gpus, checkpoint)
@@ -164,13 +164,6 @@ class LoaderCheckPoint:
                                                                     dtype=torch.float16 if not self.load_in_8bit else torch.int8,
                                                                     max_memory=max_memory,
                                                                     no_split_module_classes=model._no_split_modules)
-                            # 对于chaglm和moss意外的模型应使用自动指定，而非调用chatglm的配置方式
-                            # 其他模型定义的层类几乎不可能与chatglm和moss一致，使用chatglm_auto_configure_device_map
-                            # 百分百会报错，使用infer_auto_device_map虽然可能导致负载不均衡，但至少不会报错
-                            # 实测在bloom模型上如此
-                    #                             self.device_map = infer_auto_device_map(model,
-                    #                                                                     dtype=torch.int8,
-                    #                                                                     no_split_module_classes=model._no_split_modules)
 
                     model = dispatch_model(model, device_map=self.device_map)
             else:
@@ -185,6 +178,13 @@ class LoaderCheckPoint:
 
         elif self.is_llamacpp:
             
+            # 要调用llama-cpp模型，如vicuma-13b量化模型需要安装llama-cpp-python库
+            # but!!! 实测pip install 不好使，需要手动从ttps://github.com/abetlen/llama-cpp-python/releases/下载
+            # 而且注意不同时期的ggml格式并不！兼！容!!!因此需要安装的llama-cpp-python版本也不一致，需要手动测试才能确定
+            # 实测ggml-vicuna-13b-1.1在llama-cpp-python 0.1.63上可正常兼容
+            # 不过！！！本项目模型加载的方式控制的比较严格，与llama-cpp-python的兼容性较差，很多参数设定不能使用，
+            # 建议如非必要还是不要使用llama-cpp
+
             try:
                 from llama_cpp import Llama
 
@@ -448,7 +448,7 @@ class LoaderCheckPoint:
 
         if self.use_ptuning_v2:
             try:
-                prefix_encoder_file = open(Path(f'{self.ptuning_dir}/config.json'), 'r')
+                prefix_encoder_file = open(Path(f'{os.path.abspath(self.ptuning_dir)}/config.json'), 'r')
                 prefix_encoder_config = json.loads(prefix_encoder_file.read())
                 prefix_encoder_file.close()
                 self.model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
@@ -464,13 +464,14 @@ class LoaderCheckPoint:
 
         if self.use_ptuning_v2:
             try:
-                prefix_state_dict = torch.load(Path(f'{self.ptuning_dir}/pytorch_model.bin'))
+                prefix_state_dict = torch.load(Path(f'{os.path.abspath(self.ptuning_dir)}/pytorch_model.bin'))
                 new_prefix_state_dict = {}
                 for k, v in prefix_state_dict.items():
                     if k.startswith("transformer.prefix_encoder."):
                         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()
+                print("加载ptuning检查点成功！")
             except Exception as e:
                 print(e)
                 print("加载PrefixEncoder模型参数失败")

requirements.txt

@@ -23,13 +23,6 @@ openai
 #accelerate~=0.18.0
 #peft~=0.3.0
 #bitsandbytes; platform_system != "Windows"
-
-# 要调用llama-cpp模型，如vicuma-13b量化模型需要安装llama-cpp-python库
-# but!!! 实测pip install 不好使，需要手动从ttps://github.com/abetlen/llama-cpp-python/releases/下载
-# 而且注意不同时期的ggml格式并不！兼！容!!!因此需要安装的llama-cpp-python版本也不一致，需要手动测试才能确定
-# 实测ggml-vicuna-13b-1.1在llama-cpp-python 0.1.63上可正常兼容
-# 不过！！！本项目模型加载的方式控制的比较严格，与llama-cpp-python的兼容性较差，很多参数设定不能使用，
-# 建议如非必要还是不要使用llama-cpp
 torch~=2.0.0
 pydantic~=1.10.7
 starlette~=0.26.1

webui.py

@@ -104,6 +104,7 @@ def init_model():
     args_dict = vars(args)
     shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
     llm_model_ins = shared.loaderLLM()
+    llm_model_ins.history_len = LLM_HISTORY_LEN
     try:
         local_doc_qa.init_cfg(llm_model=llm_model_ins)
         answer_result_stream_result = local_doc_qa.llm_model_chain(

webui_st.py

@@ -1,6 +1,8 @@
 import streamlit as st
 from streamlit_chatbox import st_chatbox
 import tempfile
+from pathlib import Path
+
 ###### 从webui借用的代码 #####
 ######   做了少量修改    #####
 import os
@@ -23,6 +25,7 @@ def get_vs_list():
     if not os.path.exists(KB_ROOT_PATH):
         return lst_default
     lst = os.listdir(KB_ROOT_PATH)
+    lst = [x for x in lst if os.path.isdir(os.path.join(KB_ROOT_PATH, x))]
     if not lst:
         return lst_default
     lst.sort()
@@ -100,23 +103,23 @@ def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCOR
 
 
 def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
-    vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
+    vs_path = Path(KB_ROOT_PATH) / vs_id / "vector_store"
-    filelist = []
+    con_path = Path(KB_ROOT_PATH) / vs_id / "content"
-    if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_id, "content")):
+    con_path.mkdir(parents=True, exist_ok=True)
-        os.makedirs(os.path.join(KB_ROOT_PATH, vs_id, "content"))
+
     qa = st.session_state.local_doc_qa
     if qa.llm_model_chain and qa.embeddings:
+        filelist = []
         if isinstance(files, list):
             for file in files:
                 filename = os.path.split(file.name)[-1]
-                shutil.move(file.name, os.path.join(
+                target = con_path / filename
-                    KB_ROOT_PATH, vs_id, "content", filename))
+                shutil.move(file.name, target)
-                filelist.append(os.path.join(
+                filelist.append(str(target))
-                    KB_ROOT_PATH, vs_id, "content", filename))
             vs_path, loaded_files = qa.init_knowledge_vector_store(
-                filelist, vs_path, sentence_size)
+                filelist, str(vs_path), sentence_size)
         else:
-            vs_path, loaded_files = qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation,
+            vs_path, loaded_files = qa.one_knowledge_add(str(vs_path), files, one_conent, one_content_segmentation,
                                                                    sentence_size)
         if len(loaded_files):
             file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
@@ -283,20 +286,24 @@ with st.sidebar:
 
         def on_new_kb():
             name = st.session_state.kb_name
-            if name in vs_list:
+            if not name:
-                st.error(f'名为“{name}”的知识库已存在。')
+                st.sidebar.error(f'新建知识库名称不能为空!')
+            elif name in vs_list:
+                st.sidebar.error(f'名为“{name}”的知识库已存在。')
             else:
-                vs_list.append(name)
                 st.session_state.vs_path = name
+                st.session_state.kb_name = ''
+                new_kb_dir = os.path.join(KB_ROOT_PATH, name)
+                if not os.path.exists(new_kb_dir):
+                    os.makedirs(new_kb_dir)
+                st.sidebar.success(f'名为“{name}”的知识库创建成功，您可以开始添加文件。')
 
         def on_vs_change():
             chat_box.robot_say(f'已加载知识库： {st.session_state.vs_path}')
         with st.expander('知识库配置', True):
             cols = st.columns([12, 10])
             kb_name = cols[0].text_input(
-                '新知识库名称', placeholder='新知识库名称', label_visibility='collapsed')
+                '新知识库名称', placeholder='新知识库名称', label_visibility='collapsed', key='kb_name')
-            if 'kb_name' not in st.session_state:
-                st.session_state.kb_name = kb_name
             cols[1].button('新建知识库', on_click=on_new_kb)
             index = 0
             try:
@@ -317,7 +324,8 @@ with st.sidebar:
             sentence_size = st.slider('文本入库分句长度限制', 1, 1000, SENTENCE_SIZE)
             files = st.file_uploader('上传知识文件',
                                      ['docx', 'txt', 'md', 'csv', 'xlsx', 'pdf'],
-                                     accept_multiple_files=True)
+                                     accept_multiple_files=True,
+                                     )
             if st.button('添加文件到知识库'):
                 temp_dir = tempfile.mkdtemp()
                 file_list = []
@@ -326,8 +334,8 @@ with st.sidebar:
                     with open(file, 'wb') as fp:
                         fp.write(f.getvalue())
                     file_list.append(TempFile(file))
-                    _, _, history = get_vector_store(
+                _, _, history = get_vector_store(
-                    vs_path, file_list, sentence_size, [], None, None)
+                vs_path, file_list, sentence_size, [], None, None)
                 st.session_state.files = []