diff --git a/README.md b/README.md
index b690ad0..991487b 100644
--- a/README.md
+++ b/README.md
@@ -126,6 +126,7 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 - [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh)
 - [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)
 - [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)
+- [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct)
 - [text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)
 - [text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)
 - [text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)
@@ -133,6 +134,7 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 - [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)
 - [nghuyong/ernie-3.0-nano-zh](https://huggingface.co/nghuyong/ernie-3.0-nano-zh)
 - [nghuyong/ernie-3.0-base-zh](https://huggingface.co/nghuyong/ernie-3.0-base-zh)
+- [OpenAI/text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings)
 
 ---
 
@@ -206,6 +208,7 @@ embedding_model_dict = {
                         "m3e-base": "/Users/xxx/Downloads/m3e-base",
                        }
 ```
+如果你选择使用OpenAI的Embedding模型，请将模型的```key```写入`embedding_model_dict`中。使用该模型，你需要鞥能够访问OpenAI官的API，或设置代理。
 
 ### 4. 知识库初始化与迁移
 
@@ -298,24 +301,13 @@ $ python server/llm_api_shutdown.py --serve all
 
 亦可单独停止一个 FastChat 服务模块，可选 [`all`, `controller`, `model_worker`, `openai_api_server`]
 
-##### 5.1.3 PEFT 加载
+##### 5.1.3 PEFT 加载(包括lora,p-tuning,prefix tuning, prompt tuning,ia等)
 
 本项目基于 FastChat 加载 LLM 服务，故需以 FastChat 加载 PEFT 路径，即保证路径名称里必须有 peft 这个词，配置文件的名字为 adapter_config.json，peft 路径下包含 model.bin 格式的 PEFT 权重。
+详细步骤参考[加载lora微调后模型失效](https://github.com/chatchat-space/Langchain-Chatchat/issues/1130#issuecomment-1685291822)
 
-示例代码如下：
+![image](https://github.com/chatchat-space/Langchain-Chatchat/assets/22924096/4e056c1c-5c4b-4865-a1af-859cd58a625d)
 
-```shell
-PEFT_SHARE_BASE_WEIGHTS=true python3 -m fastchat.serve.multi_model_worker \
-    --model-path /data/chris/peft-llama-dummy-1 \
-    --model-names peft-dummy-1 \
-    --model-path /data/chris/peft-llama-dummy-2 \
-    --model-names peft-dummy-2 \
-    --model-path /data/chris/peft-llama-dummy-3 \
-    --model-names peft-dummy-3 \
-    --num-gpus 2
-```
-
-详见 [FastChat 相关 PR](https://github.com/lm-sys/fastchat/pull/1905#issuecomment-1627801216)
 
 #### 5.2 启动 API 服务
 
@@ -441,6 +433,6 @@ $ python startup.py --all-webui --model-name Qwen-7B-Chat
 
 ## 项目交流群
 
-<img src="img/qr_code_54.jpg" alt="二维码" width="300" height="300" />
+<img src="img/qr_code_56.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。
diff --git a/common/__init__.py b/common/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/configs/model_config.py.example b/configs/model_config.py.example
index ccfd1b6..5b2574e 100644
--- a/configs/model_config.py.example
+++ b/configs/model_config.py.example
@@ -24,7 +24,9 @@ embedding_model_dict = {
     "m3e-large": "moka-ai/m3e-large",
     "bge-small-zh": "BAAI/bge-small-zh",
     "bge-base-zh": "BAAI/bge-base-zh",
-    "bge-large-zh": "BAAI/bge-large-zh"
+    "bge-large-zh": "BAAI/bge-large-zh",
+    "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
+    "text-embedding-ada-002": os.environ.get("OPENAI_API_KEY")
 }
 
 # 选用的 Embedding 名称
@@ -41,12 +43,6 @@ llm_model_dict = {
         "api_key": "EMPTY"
     },
 
-    "chatglm-6b-int4": {
-        "local_model_path": "THUDM/chatglm-6b-int4",
-        "api_base_url": "http://localhost:8888/v1",  # "name"修改为fastchat服务中的"api_base_url"
-        "api_key": "EMPTY"
-    },
-
     "chatglm2-6b": {
         "local_model_path": "THUDM/chatglm2-6b",
         "api_base_url": "http://localhost:8888/v1",  # URL需要与运行fastchat服务端的server_config.FSCHAT_OPENAI_API一致
@@ -59,12 +55,6 @@ llm_model_dict = {
         "api_key": "EMPTY"
     },
 
-    "vicuna-13b-hf": {
-        "local_model_path": "",
-        "api_base_url": "http://localhost:8888/v1",  # "name"修改为fastchat服务中的"api_base_url"
-        "api_key": "EMPTY"
-    },
-
     # 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
@@ -75,10 +65,15 @@ llm_model_dict = {
     # 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，需要切换为日本、新加坡等地
+
+    # 如果出现WARNING: Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in
+    # 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
+    # 需要添加代理访问(正常开的代理软件可能会拦截不上)需要设置配置openai_proxy 或者 使用环境遍历OPENAI_PROXY 进行设置
     "gpt-3.5-turbo": {
         "local_model_path": "gpt-3.5-turbo",
         "api_base_url": "https://api.openai.com/v1",
-        "api_key": os.environ.get("OPENAI_API_KEY")
+        "api_key": os.environ.get("OPENAI_API_KEY"),
+        "openai_proxy": os.environ.get("OPENAI_PROXY")
     },
 }
 
@@ -114,7 +109,7 @@ kbs_config = {
         "secure": False,
     },
     "pg": {
-        "connection_uri": "postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatglm",
+        "connection_uri": "postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatchat",
     }
 }
 
@@ -142,12 +137,12 @@ SEARCH_ENGINE_TOP_K = 5
 # nltk 模型存储路径
 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
 
-# 基于本地知识问答的提示词模版
-PROMPT_TEMPLATE = """【指令】根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用中文。 
+# 基于本地知识问答的提示词模版（使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
+PROMPT_TEMPLATE = """<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用中文。 </指令>
 
-【已知信息】{context} 
+<已知信息>{{ context }}</已知信息>
 
-【问题】{question}"""
+<问题>{{ question }}</问题>"""
 
 # API 是否开启跨域，默认为False，如果需要开启，请设置为True
 # is open cross domain
diff --git a/docs/FAQ.md b/docs/FAQ.md
index 62fe080..490eb25 100644
--- a/docs/FAQ.md
+++ b/docs/FAQ.md
@@ -170,3 +170,16 @@ A13: 疑为 chatglm 的 quantization 的问题或 torch 版本差异问题，针
 Q14: 修改配置中路径后，加载 text2vec-large-chinese 依然提示 `WARNING: No sentence-transformers model found with name text2vec-large-chinese. Creating a new one with MEAN pooling.`
 
 A14: 尝试更换 embedding，如 text2vec-base-chinese，请在 [configs/model_config.py](../configs/model_config.py) 文件中，修改 `text2vec-base`参数为本地路径，绝对路径或者相对路径均可
+
+
+---
+
+Q15: 使用pg向量库建表报错
+
+A15: 需要手动安装对应的vector扩展(连接pg执行 CREATE EXTENSION IF NOT EXISTS vector)
+
+---
+
+Q16: pymilvus 连接超时
+
+A16.pymilvus版本需要匹配和milvus对应否则会超时参考pymilvus==2.1.3
\ No newline at end of file
diff --git a/docs/docker/vector_db/pg/docker-compose.yml b/docs/docker/vector_db/pg/docker-compose.yml
index 8e8359c..b14296b 100644
--- a/docs/docker/vector_db/pg/docker-compose.yml
+++ b/docs/docker/vector_db/pg/docker-compose.yml
@@ -2,9 +2,9 @@ version: "3.8"
 services:
  postgresql:
   image: ankane/pgvector:v0.4.1
-  container_name: langchain-chatgml-pg-db
+  container_name: langchain_chatchat-pg-db
   environment:
-      POSTGRES_DB: langchain_chatgml
+      POSTGRES_DB: langchain_chatchat
       POSTGRES_USER: postgres
       POSTGRES_PASSWORD: postgres
   ports:
diff --git a/docs/向量库环境docker.md b/docs/向量库环境docker.md
index 162b0f0..dd5e2cb 100644
--- a/docs/向量库环境docker.md
+++ b/docs/向量库环境docker.md
@@ -5,3 +5,4 @@
 cd docs/docker/vector_db/milvus
 docker-compose up -d
 ```
+
diff --git a/img/qr_code_55.jpg b/img/qr_code_55.jpg
new file mode 100644
index 0000000..8ff046c
Binary files /dev/null and b/img/qr_code_55.jpg differ
diff --git a/img/qr_code_56.jpg b/img/qr_code_56.jpg
new file mode 100644
index 0000000..f17458d
Binary files /dev/null and b/img/qr_code_56.jpg differ
diff --git a/init_database.py b/init_database.py
index 61d00e1..7fc8494 100644
--- a/init_database.py
+++ b/init_database.py
@@ -2,6 +2,8 @@ from server.knowledge_base.migrate import create_tables, folder2db, recreate_all
 from configs.model_config import NLTK_DATA_PATH
 import nltk
 nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
+from startup import dump_server_info
+
 
 if __name__ == "__main__":
     import argparse
@@ -21,6 +23,8 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
+    dump_server_info()
+
     create_tables()
     print("database talbes created")
 
diff --git a/server/chat/chat.py b/server/chat/chat.py
index 2bc21db..2e939f1 100644
--- a/server/chat/chat.py
+++ b/server/chat/chat.py
@@ -21,7 +21,7 @@ def chat(query: str = Body(..., description="用户输入", examples=["恼羞成
                                        ),
          stream: bool = Body(False, description="流式输出"),
          ):
-    history = [History(**h) if isinstance(h, dict) else h for h in history]
+    history = [History.from_data(h) for h in history]
 
     async def chat_iterator(query: str,
                             history: List[History] = [],
@@ -34,11 +34,13 @@ def chat(query: str = Body(..., description="用户输入", examples=["恼羞成
             callbacks=[callback],
             openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
             openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-            model_name=LLM_MODEL
+            model_name=LLM_MODEL,
+            openai_proxy=llm_model_dict[LLM_MODEL].get("openai_proxy")
         )
 
+        input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", "{input}")])
+            [i.to_msg_template() for i in history] + [input_msg])
         chain = LLMChain(prompt=chat_prompt, llm=model)
 
         # Begin a task that runs in the background.
diff --git a/server/chat/knowledge_base_chat.py b/server/chat/knowledge_base_chat.py
index 84c62f0..2774569 100644
--- a/server/chat/knowledge_base_chat.py
+++ b/server/chat/knowledge_base_chat.py
@@ -38,7 +38,7 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
     if kb is None:
         return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
 
-    history = [History(**h) if isinstance(h, dict) else h for h in history]
+    history = [History.from_data(h) for h in history]
 
     async def knowledge_base_chat_iterator(query: str,
                                            kb: KBService,
@@ -52,13 +52,15 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
             callbacks=[callback],
             openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
             openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-            model_name=LLM_MODEL
+            model_name=LLM_MODEL,
+            openai_proxy=llm_model_dict[LLM_MODEL].get("openai_proxy")
         )
         docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
         context = "\n".join([doc.page_content for doc in docs])
 
+        input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
+            [i.to_msg_template() for i in history] + [input_msg])
 
         chain = LLMChain(prompt=chat_prompt, llm=model)
 
diff --git a/server/chat/search_engine_chat.py b/server/chat/search_engine_chat.py
index 15834d0..032d06a 100644
--- a/server/chat/search_engine_chat.py
+++ b/server/chat/search_engine_chat.py
@@ -73,6 +73,8 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
     if search_engine_name not in SEARCH_ENGINES.keys():
         return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
 
+    history = [History.from_data(h) for h in history]
+
     async def search_engine_chat_iterator(query: str,
                                           search_engine_name: str,
                                           top_k: int,
@@ -85,14 +87,16 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
             callbacks=[callback],
             openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
             openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-            model_name=LLM_MODEL
+            model_name=LLM_MODEL,
+            openai_proxy=llm_model_dict[LLM_MODEL].get("openai_proxy")
         )
 
         docs = lookup_search_engine(query, search_engine_name, top_k)
         context = "\n".join([doc.page_content for doc in docs])
 
+        input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
+            [i.to_msg_template() for i in history] + [input_msg])
 
         chain = LLMChain(prompt=chat_prompt, llm=model)
 
@@ -117,7 +121,7 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
             answer = ""
             async for token in callback.aiter():
                 answer += token
-            yield json.dumps({"answer": token,
+            yield json.dumps({"answer": answer,
                               "docs": source_documents},
                              ensure_ascii=False)
         await task
diff --git a/server/chat/utils.py b/server/chat/utils.py
index f8afb10..2167f10 100644
--- a/server/chat/utils.py
+++ b/server/chat/utils.py
@@ -1,6 +1,7 @@
 import asyncio
-from typing import Awaitable
+from typing import Awaitable, List, Tuple, Dict, Union
 from pydantic import BaseModel, Field
+from langchain.prompts.chat import ChatMessagePromptTemplate
 
 
 async def wrap_done(fn: Awaitable, event: asyncio.Event):
@@ -28,3 +29,29 @@ class History(BaseModel):
 
     def to_msg_tuple(self):
         return "ai" if self.role=="assistant" else "human", self.content
+
+    def to_msg_template(self, is_raw=True) -> ChatMessagePromptTemplate:
+        role_maps = {
+            "ai": "assistant",
+            "human": "user",
+        }
+        role = role_maps.get(self.role, self.role)
+        if is_raw: # 当前默认历史消息都是没有input_variable的文本。
+            content = "{% raw %}" + self.content + "{% endraw %}"
+        else:
+            content = self.content
+
+        return ChatMessagePromptTemplate.from_template(
+            content,
+            "jinja2",
+            role=role,
+        )
+
+    @classmethod
+    def from_data(cls, h: Union[List, Tuple, Dict]) -> "History":
+        if isinstance(h, (list,tuple)) and len(h) >= 2:
+            h = cls(role=h[0], content=h[1])
+        elif isinstance(h, dict):
+            h = cls(**h)
+
+        return h
diff --git a/server/knowledge_base/kb_api.py b/server/knowledge_base/kb_api.py
index 4753ba4..b9151b8 100644
--- a/server/knowledge_base/kb_api.py
+++ b/server/knowledge_base/kb_api.py
@@ -15,7 +15,7 @@ async def list_kbs():
 async def create_kb(knowledge_base_name: str = Body(..., examples=["samples"]),
                     vector_store_type: str = Body("faiss"),
                     embed_model: str = Body(EMBEDDING_MODEL),
-                    ):
+                    ) -> BaseResponse:
     # Create selected knowledge base
     if not validate_kb_name(knowledge_base_name):
         return BaseResponse(code=403, msg="Don't attack me")
@@ -27,13 +27,18 @@ async def create_kb(knowledge_base_name: str = Body(..., examples=["samples"]),
         return BaseResponse(code=404, msg=f"已存在同名知识库 {knowledge_base_name}")
 
     kb = KBServiceFactory.get_service(knowledge_base_name, vector_store_type, embed_model)
-    kb.create_kb()
+    try:
+        kb.create_kb()
+    except Exception as e:
+        print(e)
+        return BaseResponse(code=500, msg=f"创建知识库出错： {e}")
+
     return BaseResponse(code=200, msg=f"已新增知识库 {knowledge_base_name}")
 
 
 async def delete_kb(
         knowledge_base_name: str = Body(..., examples=["samples"])
-    ):
+    ) -> BaseResponse:
     # Delete selected knowledge base
     if not validate_kb_name(knowledge_base_name):
         return BaseResponse(code=403, msg="Don't attack me")
@@ -51,5 +56,6 @@ async def delete_kb(
             return BaseResponse(code=200, msg=f"成功删除知识库 {knowledge_base_name}")
     except Exception as e:
         print(e)
+        return BaseResponse(code=500, msg=f"删除知识库时出现意外： {e}")
 
     return BaseResponse(code=500, msg=f"删除知识库失败 {knowledge_base_name}")
diff --git a/server/knowledge_base/kb_doc_api.py b/server/knowledge_base/kb_doc_api.py
index 0bf2cb7..ae027c1 100644
--- a/server/knowledge_base/kb_doc_api.py
+++ b/server/knowledge_base/kb_doc_api.py
@@ -22,7 +22,7 @@ def search_docs(query: str = Body(..., description="用户输入", examples=["
                 ) -> List[DocumentWithScore]:
     kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
     if kb is None:
-        return {"code": 404, "msg": f"未找到知识库 {knowledge_base_name}", "docs": []}
+        return []
     docs = kb.search_docs(query, top_k, score_threshold)
     data = [DocumentWithScore(**x[0].dict(), score=x[1]) for x in docs]
 
@@ -31,7 +31,7 @@ def search_docs(query: str = Body(..., description="用户输入", examples=["
 
 async def list_docs(
     knowledge_base_name: str
-):
+) -> ListResponse:
     if not validate_kb_name(knowledge_base_name):
         return ListResponse(code=403, msg="Don't attack me", data=[])
 
@@ -41,13 +41,14 @@ async def list_docs(
         return ListResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}", data=[])
     else:
         all_doc_names = kb.list_docs()
-    return ListResponse(data=all_doc_names)
+        return ListResponse(data=all_doc_names)
 
 
 async def upload_doc(file: UploadFile = File(..., description="上传文件"),
                      knowledge_base_name: str = Form(..., description="知识库名称", examples=["kb1"]),
                      override: bool = Form(False, description="覆盖已有文件"),
-                     ):
+                     not_refresh_vs_cache: bool = Form(False, description="暂不保存向量库（用于FAISS）"),
+                     ) -> BaseResponse:
     if not validate_kb_name(knowledge_base_name):
         return BaseResponse(code=403, msg="Don't attack me")
 
@@ -57,31 +58,38 @@ async def upload_doc(file: UploadFile = File(..., description="上传文件"),
 
     file_content = await file.read()  # 读取上传文件的内容
 
-    kb_file = KnowledgeFile(filename=file.filename,
-                            knowledge_base_name=knowledge_base_name)
-
-    if (os.path.exists(kb_file.filepath)
-            and not override
-            and os.path.getsize(kb_file.filepath) == len(file_content)
-    ):
-        # TODO: filesize 不同后的处理
-        file_status = f"文件 {kb_file.filename} 已存在。"
-        return BaseResponse(code=404, msg=file_status)
-
     try:
+        kb_file = KnowledgeFile(filename=file.filename,
+                                knowledge_base_name=knowledge_base_name)
+
+        if (os.path.exists(kb_file.filepath)
+                and not override
+                and os.path.getsize(kb_file.filepath) == len(file_content)
+        ):
+            # TODO: filesize 不同后的处理
+            file_status = f"文件 {kb_file.filename} 已存在。"
+            return BaseResponse(code=404, msg=file_status)
+
         with open(kb_file.filepath, "wb") as f:
             f.write(file_content)
     except Exception as e:
+        print(e)
         return BaseResponse(code=500, msg=f"{kb_file.filename} 文件上传失败，报错信息为: {e}")
 
-    kb.add_doc(kb_file)
+    try:
+        kb.add_doc(kb_file, not_refresh_vs_cache=not_refresh_vs_cache)
+    except Exception as e:
+        print(e)
+        return BaseResponse(code=500, msg=f"{kb_file.filename} 文件向量化失败，报错信息为: {e}")
+
     return BaseResponse(code=200, msg=f"成功上传文件 {kb_file.filename}")
 
 
 async def delete_doc(knowledge_base_name: str = Body(..., examples=["samples"]),
                      doc_name: str = Body(..., examples=["file_name.md"]),
                      delete_content: bool = Body(False),
-                    ):
+                     not_refresh_vs_cache: bool = Body(False, description="暂不保存向量库（用于FAISS）"),
+                    ) -> BaseResponse:
     if not validate_kb_name(knowledge_base_name):
         return BaseResponse(code=403, msg="Don't attack me")
 
@@ -92,17 +100,23 @@ async def delete_doc(knowledge_base_name: str = Body(..., examples=["samples"]),
 
     if not kb.exist_doc(doc_name):
         return BaseResponse(code=404, msg=f"未找到文件 {doc_name}")
-    kb_file = KnowledgeFile(filename=doc_name,
-                            knowledge_base_name=knowledge_base_name)
-    kb.delete_doc(kb_file, delete_content)
+
+    try:
+        kb_file = KnowledgeFile(filename=doc_name,
+                                knowledge_base_name=knowledge_base_name)
+        kb.delete_doc(kb_file, delete_content, not_refresh_vs_cache=not_refresh_vs_cache)
+    except Exception as e:
+        print(e)
+        return BaseResponse(code=500, msg=f"{kb_file.filename} 文件删除失败，错误信息：{e}")
+
     return BaseResponse(code=200, msg=f"{kb_file.filename} 文件删除成功")
-    # return BaseResponse(code=500, msg=f"{kb_file.filename} 文件删除失败")
 
 
 async def update_doc(
         knowledge_base_name: str = Body(..., examples=["samples"]),
         file_name: str = Body(..., examples=["file_name"]),
-    ):
+        not_refresh_vs_cache: bool = Body(False, description="暂不保存向量库（用于FAISS）"),
+    ) -> BaseResponse:
     '''
     更新知识库文档
     '''
@@ -113,14 +127,17 @@ async def update_doc(
     if kb is None:
         return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
 
-    kb_file = KnowledgeFile(filename=file_name,
-                            knowledge_base_name=knowledge_base_name)
+    try:
+        kb_file = KnowledgeFile(filename=file_name,
+                                knowledge_base_name=knowledge_base_name)
+        if os.path.exists(kb_file.filepath):
+            kb.update_doc(kb_file, not_refresh_vs_cache=not_refresh_vs_cache)
+            return BaseResponse(code=200, msg=f"成功更新文件 {kb_file.filename}")
+    except Exception as e:
+        print(e)
+        return BaseResponse(code=500, msg=f"{kb_file.filename} 文件更新失败，错误信息是：{e}")
 
-    if os.path.exists(kb_file.filepath):
-        kb.update_doc(kb_file)
-        return BaseResponse(code=200, msg=f"成功更新文件 {kb_file.filename}")
-    else:
-        return BaseResponse(code=500, msg=f"{kb_file.filename} 文件更新失败")
+    return BaseResponse(code=500, msg=f"{kb_file.filename} 文件更新失败")
 
 
 async def download_doc(
@@ -137,18 +154,20 @@ async def download_doc(
     if kb is None:
         return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
 
-    kb_file = KnowledgeFile(filename=file_name,
-                            knowledge_base_name=knowledge_base_name)
-
-    if os.path.exists(kb_file.filepath):
-        return FileResponse(
-            path=kb_file.filepath,
-            filename=kb_file.filename,
-            media_type="multipart/form-data")
-    else:
-        return BaseResponse(code=500, msg=f"{kb_file.filename} 读取文件失败")
+    try:
+        kb_file = KnowledgeFile(filename=file_name,
+                                knowledge_base_name=knowledge_base_name)
 
+        if os.path.exists(kb_file.filepath):
+            return FileResponse(
+                path=kb_file.filepath,
+                filename=kb_file.filename,
+                media_type="multipart/form-data")
+    except Exception as e:
+        print(e)
+        return BaseResponse(code=500, msg=f"{kb_file.filename} 读取文件失败，错误信息是：{e}")
 
+    return BaseResponse(code=500, msg=f"{kb_file.filename} 读取文件失败")
 
 
 async def recreate_vector_store(
@@ -163,24 +182,35 @@ async def recreate_vector_store(
     by default, get_service_by_name only return knowledge base in the info.db and having document files in it.
     set allow_empty_kb to True make it applied on empty knowledge base which it not in the info.db or having no documents.
     '''
-    kb = KBServiceFactory.get_service(knowledge_base_name, vs_type, embed_model)
-    if not kb.exists() and not allow_empty_kb:
-        return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
 
-    async def output(kb):
-        kb.create_kb()
-        kb.clear_vs()
-        docs = list_docs_from_folder(knowledge_base_name)
-        for i, doc in enumerate(docs):
-            try:
-                kb_file = KnowledgeFile(doc, knowledge_base_name)
-                yield json.dumps({
-                    "total": len(docs),
-                    "finished": i,
-                    "doc": doc,
-                }, ensure_ascii=False)
-                kb.add_doc(kb_file)
-            except Exception as e:
-                print(e)
+    async def output():
+        kb = KBServiceFactory.get_service(knowledge_base_name, vs_type, embed_model)
+        if not kb.exists() and not allow_empty_kb:
+            yield {"code": 404, "msg": f"未找到知识库 ‘{knowledge_base_name}’"}
+        else:
+            kb.create_kb()
+            kb.clear_vs()
+            docs = list_docs_from_folder(knowledge_base_name)
+            for i, doc in enumerate(docs):
+                try:
+                    kb_file = KnowledgeFile(doc, knowledge_base_name)
+                    yield json.dumps({
+                        "code": 200,
+                        "msg": f"({i + 1} / {len(docs)}): {doc}",
+                        "total": len(docs),
+                        "finished": i,
+                        "doc": doc,
+                    }, ensure_ascii=False)
+                    if i == len(docs) - 1:
+                        not_refresh_vs_cache = False
+                    else:
+                        not_refresh_vs_cache = True
+                    kb.add_doc(kb_file, not_refresh_vs_cache=not_refresh_vs_cache)
+                except Exception as e:
+                    print(e)
+                    yield json.dumps({
+                        "code": 500,
+                        "msg": f"添加文件‘{doc}’到知识库‘{knowledge_base_name}’时出错：{e}。已跳过。",
+                    })
 
-    return StreamingResponse(output(kb), media_type="text/event-stream")
+    return StreamingResponse(output(), media_type="text/event-stream")
diff --git a/server/knowledge_base/kb_service/base.py b/server/knowledge_base/kb_service/base.py
index d506f63..9af5b0e 100644
--- a/server/knowledge_base/kb_service/base.py
+++ b/server/knowledge_base/kb_service/base.py
@@ -71,36 +71,37 @@ class KBService(ABC):
         status = delete_kb_from_db(self.kb_name)
         return status
 
-    def add_doc(self, kb_file: KnowledgeFile):
+    def add_doc(self, kb_file: KnowledgeFile, **kwargs):
         """
         向知识库添加文件
         """
         docs = kb_file.file2text()
         if docs:
+            self.delete_doc(kb_file)
             embeddings = self._load_embeddings()
-            self.do_add_doc(docs, embeddings)
+            self.do_add_doc(docs, embeddings, **kwargs)
             status = add_doc_to_db(kb_file)
         else:
             status = False
         return status
 
-    def delete_doc(self, kb_file: KnowledgeFile, delete_content: bool = False):
+    def delete_doc(self, kb_file: KnowledgeFile, delete_content: bool = False, **kwargs):
         """
         从知识库删除文件
         """
-        self.do_delete_doc(kb_file)
+        self.do_delete_doc(kb_file, **kwargs)
         status = delete_file_from_db(kb_file)
         if delete_content and os.path.exists(kb_file.filepath):
             os.remove(kb_file.filepath)
         return status
 
-    def update_doc(self, kb_file: KnowledgeFile):
+    def update_doc(self, kb_file: KnowledgeFile, **kwargs):
         """
         使用content中的文件更新向量库
         """
         if os.path.exists(kb_file.filepath):
-            self.delete_doc(kb_file)
-            return self.add_doc(kb_file)
+            self.delete_doc(kb_file, **kwargs)
+            return self.add_doc(kb_file, **kwargs)
         
     def exist_doc(self, file_name: str):
         return doc_exists(KnowledgeFile(knowledge_base_name=self.kb_name,
diff --git a/server/knowledge_base/kb_service/faiss_kb_service.py b/server/knowledge_base/kb_service/faiss_kb_service.py
index 5c8376f..b3f5439 100644
--- a/server/knowledge_base/kb_service/faiss_kb_service.py
+++ b/server/knowledge_base/kb_service/faiss_kb_service.py
@@ -13,7 +13,8 @@ from functools import lru_cache
 from server.knowledge_base.utils import get_vs_path, load_embeddings, KnowledgeFile
 from langchain.vectorstores import FAISS
 from langchain.embeddings.base import Embeddings
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings,HuggingFaceBgeEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
 from typing import List
 from langchain.docstore.document import Document
 from server.utils import torch_gc
@@ -21,10 +22,19 @@ from server.utils import torch_gc
 
 # make HuggingFaceEmbeddings hashable
 def _embeddings_hash(self):
-    return hash(self.model_name)
-
+    if isinstance(self, HuggingFaceEmbeddings):
+        return hash(self.model_name)
+    elif isinstance(self, HuggingFaceBgeEmbeddings):
+        return hash(self.model_name)
+    elif isinstance(self, OpenAIEmbeddings):
+        return hash(self.model)
 
 HuggingFaceEmbeddings.__hash__ = _embeddings_hash
+OpenAIEmbeddings.__hash__ = _embeddings_hash
+HuggingFaceBgeEmbeddings.__hash__ = _embeddings_hash
+
+_VECTOR_STORE_TICKS = {}
+
 
 _VECTOR_STORE_TICKS = {}
 
@@ -41,7 +51,23 @@ def load_vector_store(
     vs_path = get_vs_path(knowledge_base_name)
     if embeddings is None:
         embeddings = load_embeddings(embed_model, embed_device)
-    search_index = FAISS.load_local(vs_path, embeddings, normalize_L2=True)
+
+    if not os.path.exists(vs_path):
+        os.makedirs(vs_path)
+
+    if "index.faiss" in os.listdir(vs_path):
+        search_index = FAISS.load_local(vs_path, embeddings, normalize_L2=True)
+    else:
+        # create an empty vector store
+        doc = Document(page_content="init", metadata={})
+        search_index = FAISS.from_documents([doc], embeddings, normalize_L2=True)
+        ids = [k for k, v in search_index.docstore._dict.items()]
+        search_index.delete(ids)
+        search_index.save_local(vs_path)
+    
+    if tick == 0: # vector store is loaded first time
+        _VECTOR_STORE_TICKS[knowledge_base_name] = 0
+
     return search_index
 
 
@@ -50,6 +76,7 @@ def refresh_vs_cache(kb_name: str):
     make vector store cache refreshed when next loading
     """
     _VECTOR_STORE_TICKS[kb_name] = _VECTOR_STORE_TICKS.get(kb_name, 0) + 1
+    print(f"知识库 {kb_name} 缓存刷新：{_VECTOR_STORE_TICKS[kb_name]}")
 
 
 class FaissKBService(KBService):
@@ -74,8 +101,10 @@ class FaissKBService(KBService):
     def do_create_kb(self):
         if not os.path.exists(self.vs_path):
             os.makedirs(self.vs_path)
+        load_vector_store(self.kb_name)
 
     def do_drop_kb(self):
+        self.clear_vs()
         shutil.rmtree(self.kb_path)
 
     def do_search(self,
@@ -93,38 +122,40 @@ class FaissKBService(KBService):
     def do_add_doc(self,
                    docs: List[Document],
                    embeddings: Embeddings,
+                   **kwargs,
                    ):
-        if os.path.exists(self.vs_path) and "index.faiss" in os.listdir(self.vs_path):
-            vector_store = FAISS.load_local(self.vs_path, embeddings, normalize_L2=True)
-            vector_store.add_documents(docs)
-            torch_gc()
-        else:
-            if not os.path.exists(self.vs_path):
-                os.makedirs(self.vs_path)
-            vector_store = FAISS.from_documents(
-                docs, embeddings, normalize_L2=True)  # docs 为Document列表
-            torch_gc()
-        vector_store.save_local(self.vs_path)
-        refresh_vs_cache(self.kb_name)
-
-    def do_delete_doc(self,
-                      kb_file: KnowledgeFile):
-        embeddings = self._load_embeddings()
-        if os.path.exists(self.vs_path) and "index.faiss" in os.listdir(self.vs_path):
-            vector_store = FAISS.load_local(self.vs_path, embeddings, normalize_L2=True)
-            ids = [k for k, v in vector_store.docstore._dict.items() if v.metadata["source"] == kb_file.filepath]
-            if len(ids) == 0:
-                return None
-            vector_store.delete(ids)
+        vector_store = load_vector_store(self.kb_name,
+                                         embeddings=embeddings,
+                                         tick=_VECTOR_STORE_TICKS.get(self.kb_name, 0))
+        vector_store.add_documents(docs)
+        torch_gc()
+        if not kwargs.get("not_refresh_vs_cache"):
             vector_store.save_local(self.vs_path)
             refresh_vs_cache(self.kb_name)
-            return True
-        else:
+
+    def do_delete_doc(self,
+                      kb_file: KnowledgeFile,
+                      **kwargs):
+        embeddings = self._load_embeddings()
+        vector_store = load_vector_store(self.kb_name,
+                                         embeddings=embeddings,
+                                         tick=_VECTOR_STORE_TICKS.get(self.kb_name, 0))
+
+        ids = [k for k, v in vector_store.docstore._dict.items() if v.metadata["source"] == kb_file.filepath]
+        if len(ids) == 0:
             return None
 
+        vector_store.delete(ids)
+        if not kwargs.get("not_refresh_vs_cache"):
+            vector_store.save_local(self.vs_path)
+            refresh_vs_cache(self.kb_name)
+
+        return True
+
     def do_clear_vs(self):
         shutil.rmtree(self.vs_path)
         os.makedirs(self.vs_path)
+        refresh_vs_cache(self.kb_name)
 
     def exist_doc(self, file_name: str):
         if super().exist_doc(file_name):
diff --git a/server/knowledge_base/kb_service/milvus_kb_service.py b/server/knowledge_base/kb_service/milvus_kb_service.py
index f9c40c0..ba74c14 100644
--- a/server/knowledge_base/kb_service/milvus_kb_service.py
+++ b/server/knowledge_base/kb_service/milvus_kb_service.py
@@ -45,12 +45,12 @@ class MilvusKBService(KBService):
     def do_drop_kb(self):
         self.milvus.col.drop()
 
-    def do_search(self, query: str, top_k: int, score_threshold: float, embeddings: Embeddings) -> List[Document]:
+    def do_search(self, query: str, top_k: int, embeddings: Embeddings):
         # todo: support score threshold
         self._load_milvus(embeddings=embeddings)
-        return self.milvus.similarity_search(query, top_k, score_threshold=SCORE_THRESHOLD)
+        return self.milvus.similarity_search_with_score(query, top_k)
 
-    def add_doc(self, kb_file: KnowledgeFile):
+    def add_doc(self, kb_file: KnowledgeFile, **kwargs):
         """
         向知识库添加文件
         """
@@ -60,10 +60,10 @@ class MilvusKBService(KBService):
         status = add_doc_to_db(kb_file)
         return status
 
-    def do_add_doc(self, docs: List[Document], embeddings: Embeddings):
+    def do_add_doc(self, docs: List[Document], embeddings: Embeddings, **kwargs):
         pass
 
-    def do_delete_doc(self, kb_file: KnowledgeFile):
+    def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs):
         filepath = kb_file.filepath.replace('\\', '\\\\')
         delete_list = [item.get("pk") for item in
                        self.milvus.col.query(expr=f'source == "{filepath}"', output_fields=["pk"])]
@@ -76,6 +76,7 @@ class MilvusKBService(KBService):
 if __name__ == '__main__':
     # 测试建表使用
     from server.db.base import Base, engine
+
     Base.metadata.create_all(bind=engine)
     milvusService = MilvusKBService("test")
     milvusService.add_doc(KnowledgeFile("README.md", "test"))
diff --git a/server/knowledge_base/kb_service/pg_kb_service.py b/server/knowledge_base/kb_service/pg_kb_service.py
index a3126ec..38d8065 100644
--- a/server/knowledge_base/kb_service/pg_kb_service.py
+++ b/server/knowledge_base/kb_service/pg_kb_service.py
@@ -43,12 +43,12 @@ class PGKBService(KBService):
             '''))
             connect.commit()
 
-    def do_search(self, query: str, top_k: int, score_threshold: float, embeddings: Embeddings) -> List[Document]:
+    def do_search(self, query: str, top_k: int, embeddings: Embeddings):
         # todo: support score threshold
         self._load_pg_vector(embeddings=embeddings)
-        return self.pg_vector.similarity_search(query, top_k)
+        return self.pg_vector.similarity_search_with_score(query, top_k)
 
-    def add_doc(self, kb_file: KnowledgeFile):
+    def add_doc(self, kb_file: KnowledgeFile, **kwargs):
         """
         向知识库添加文件
         """
@@ -58,10 +58,10 @@ class PGKBService(KBService):
         status = add_doc_to_db(kb_file)
         return status
 
-    def do_add_doc(self, docs: List[Document], embeddings: Embeddings):
+    def do_add_doc(self, docs: List[Document], embeddings: Embeddings, **kwargs):
         pass
 
-    def do_delete_doc(self, kb_file: KnowledgeFile):
+    def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs):
         with self.pg_vector.connect() as connect:
             filepath = kb_file.filepath.replace('\\', '\\\\')
             connect.execute(
@@ -76,6 +76,7 @@ class PGKBService(KBService):
 
 if __name__ == '__main__':
     from server.db.base import Base, engine
+
     Base.metadata.create_all(bind=engine)
     pGKBService = PGKBService("test")
     pGKBService.create_kb()
diff --git a/server/knowledge_base/migrate.py b/server/knowledge_base/migrate.py
index 1c023fa..c96d386 100644
--- a/server/knowledge_base/migrate.py
+++ b/server/knowledge_base/migrate.py
@@ -43,7 +43,11 @@ def folder2db(
                 kb_file = KnowledgeFile(doc, kb_name)
                 if callable(callback_before):
                     callback_before(kb_file, i, docs)
-                kb.add_doc(kb_file)
+                if i == len(docs) - 1:
+                    not_refresh_vs_cache = False
+                else:
+                    not_refresh_vs_cache = True
+                kb.add_doc(kb_file, not_refresh_vs_cache=not_refresh_vs_cache)
                 if callable(callback_after):
                     callback_after(kb_file, i, docs)
             except Exception as e:
@@ -67,7 +71,11 @@ def folder2db(
                 kb_file = KnowledgeFile(doc, kb_name)
                 if callable(callback_before):
                     callback_before(kb_file, i, docs)
-                kb.update_doc(kb_file)
+                if i == len(docs) - 1:
+                    not_refresh_vs_cache = False
+                else:
+                    not_refresh_vs_cache = True
+                kb.update_doc(kb_file, not_refresh_vs_cache=not_refresh_vs_cache)
                 if callable(callback_after):
                     callback_after(kb_file, i, docs)
             except Exception as e:
@@ -81,7 +89,11 @@ def folder2db(
                 kb_file = KnowledgeFile(doc, kb_name)
                 if callable(callback_before):
                     callback_before(kb_file, i, docs)
-                kb.add_doc(kb_file)
+                if i == len(docs) - 1:
+                    not_refresh_vs_cache = False
+                else:
+                    not_refresh_vs_cache = True
+                kb.add_doc(kb_file, not_refresh_vs_cache=not_refresh_vs_cache)
                 if callable(callback_after):
                     callback_after(kb_file, i, docs)
             except Exception as e:
diff --git a/server/knowledge_base/utils.py b/server/knowledge_base/utils.py
index 4f0bad9..da53049 100644
--- a/server/knowledge_base/utils.py
+++ b/server/knowledge_base/utils.py
@@ -1,5 +1,7 @@
 import os
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import HuggingFaceBgeEmbeddings
 from configs.model_config import (
     embedding_model_dict,
     KB_ROOT_PATH,
@@ -41,11 +43,20 @@ def list_docs_from_folder(kb_name: str):
 
 @lru_cache(1)
 def load_embeddings(model: str, device: str):
-    embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
-                                       model_kwargs={'device': device})
+    if model == "text-embedding-ada-002":  # openai text-embedding-ada-002
+        embeddings = OpenAIEmbeddings(openai_api_key=embedding_model_dict[model], chunk_size=CHUNK_SIZE)
+    elif 'bge-' in model:
+        embeddings = HuggingFaceBgeEmbeddings(model_name=embedding_model_dict[model],
+                                              model_kwargs={'device': device},
+                                              query_instruction="为这个句子生成表示以用于检索相关文章：")
+        if model == "bge-large-zh-noinstruct":  # bge large -noinstruct embedding
+            embeddings.query_instruction = ""
+    else:
+        embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model], model_kwargs={'device': device})
     return embeddings
 
 
+
 LOADER_DICT = {"UnstructuredFileLoader": ['.eml', '.html', '.json', '.md', '.msg', '.rst',
                                           '.rtf', '.txt', '.xml',
                                           '.doc', '.docx', '.epub', '.odt', '.pdf',
diff --git a/server/utils.py b/server/utils.py
index c0f11a5..4a88722 100644
--- a/server/utils.py
+++ b/server/utils.py
@@ -9,8 +9,8 @@ from typing import Any, Optional
 
 
 class BaseResponse(BaseModel):
-    code: int = pydantic.Field(200, description="HTTP status code")
-    msg: str = pydantic.Field("success", description="HTTP status message")
+    code: int = pydantic.Field(200, description="API status code")
+    msg: str = pydantic.Field("success", description="API status message")
 
     class Config:
         schema_extra = {
diff --git a/startup.py b/startup.py
index 66814ad..df00851 100644
--- a/startup.py
+++ b/startup.py
@@ -5,6 +5,14 @@ import sys
 import os
 from pprint import pprint
 
+# 设置numexpr最大线程数，默认为CPU核心数
+try:
+    import numexpr
+    n_cores = numexpr.utils.detect_number_of_cores()
+    os.environ["NUMEXPR_MAX_THREADS"] = str(n_cores)
+except:
+    pass
+
 sys.path.append(os.path.dirname(os.path.dirname(__file__)))
 from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL, llm_model_dict, LLM_MODEL, LLM_DEVICE, LOG_PATH, \
     logger
@@ -252,35 +260,36 @@ def run_webui(q: Queue, run_seq: int = 5):
 def parse_args() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
     parser.add_argument(
+        "-a",
         "--all-webui",
         action="store_true",
-        help="run fastchat's controller/model_worker/openai_api servers, run api.py and webui.py",
+        help="run fastchat's controller/openai_api/model_worker servers, run api.py and webui.py",
         dest="all_webui",
     )
     parser.add_argument(
         "--all-api",
         action="store_true",
-        help="run fastchat's controller/model_worker/openai_api servers, run api.py and webui.py",
+        help="run fastchat's controller/openai_api/model_worker servers, run api.py",
         dest="all_api",
     )
     parser.add_argument(
         "--llm-api",
         action="store_true",
-        help="run fastchat's controller/model_worker/openai_api servers, run api.py and webui.py",
+        help="run fastchat's controller/openai_api/model_worker servers",
         dest="llm_api",
     )
     parser.add_argument(
         "-o",
         "--openai-api",
         action="store_true",
-        help="run fastchat controller/openai_api servers",
+        help="run fastchat's controller/openai_api servers",
         dest="openai_api",
     )
     parser.add_argument(
         "-m",
         "--model-worker",
         action="store_true",
-        help="run fastchat model_worker server with specified model name. specify --model-name if not using default LLM_MODEL",
+        help="run fastchat's model_worker server with specified model name. specify --model-name if not using default LLM_MODEL",
         dest="model_worker",
     )
     parser.add_argument(
@@ -315,13 +324,39 @@ def parse_args() -> argparse.ArgumentParser:
     return args
 
 
-if __name__ == "__main__":
+def dump_server_info(after_start=False):
     import platform
-    import time
     import langchain
     import fastchat
     from configs.server_config import api_address, webui_address
 
+    print("\n\n")
+    print("=" * 30 + "Langchain-Chatchat Configuration" + "=" * 30)
+    print(f"操作系统：{platform.platform()}.")
+    print(f"python版本：{sys.version}")
+    print(f"项目版本：{VERSION}")
+    print(f"langchain版本：{langchain.__version__}. fastchat版本：{fastchat.__version__}")
+    print("\n")
+    print(f"当前LLM模型：{LLM_MODEL} @ {LLM_DEVICE}")
+    pprint(llm_model_dict[LLM_MODEL])
+    print(f"当前Embbedings模型： {EMBEDDING_MODEL} @ {EMBEDDING_DEVICE}")
+    if after_start:
+        print("\n")
+        print(f"服务端运行信息：")
+        if args.openai_api:
+            print(f"    OpenAI API Server: {fschat_openai_api_address()}/v1")
+            print("     (请确认llm_model_dict中配置的api_base_url与上面地址一致。)")
+        if args.api:
+            print(f"    Chatchat  API  Server: {api_address()}")
+        if args.webui:
+            print(f"    Chatchat WEBUI Server: {webui_address()}")
+    print("=" * 30 + "Langchain-Chatchat Configuration" + "=" * 30)
+    print("\n\n")
+
+
+if __name__ == "__main__":
+    import time
+
     mp.set_start_method("spawn")
     queue = Queue()
     args = parse_args()
@@ -343,6 +378,7 @@ if __name__ == "__main__":
         args.api = False
         args.webui = False
 
+    dump_server_info()
     logger.info(f"正在启动服务：")
     logger.info(f"如需查看 llm_api 日志，请前往 {LOG_PATH}")
 
@@ -403,27 +439,7 @@ if __name__ == "__main__":
             no = queue.get()
             if no == len(processes):
                 time.sleep(0.5)
-                print("\n\n")
-                print("=" * 30 + "Langchain-Chatchat Configuration" + "=" * 30)
-                print(f"操作系统：{platform.platform()}.")
-                print(f"python版本：{sys.version}")
-                print(f"项目版本：{VERSION}")
-                print(f"langchain版本：{langchain.__version__}. fastchat版本：{fastchat.__version__}")
-                print("\n")
-                print(f"当前LLM模型：{LLM_MODEL} @ {LLM_DEVICE}")
-                pprint(llm_model_dict[LLM_MODEL])
-                print(f"当前Embbedings模型： {EMBEDDING_MODEL} @ {EMBEDDING_DEVICE}")
-                print("\n")
-                print(f"服务端运行信息：")
-                if args.openai_api:
-                    print(f"    OpenAI API Server: {fschat_openai_api_address()}/v1")
-                    print("请确认llm_model_dict中配置的api_base_url与上面地址一致。")
-                if args.api:
-                    print(f"    Chatchat  API  Server: {api_address()}")
-                if args.webui:
-                    print(f"    Chatchat WEBUI Server: {webui_address()}")
-                print("=" * 30 + "Langchain-Chatchat Configuration" + "=" * 30)
-                print("\n\n")
+                dump_server_info(True)
                 break
             else:
                 queue.put(no)
diff --git a/tests/api/test_kb_api.py b/tests/api/test_kb_api.py
new file mode 100644
index 0000000..5a8b97d
--- /dev/null
+++ b/tests/api/test_kb_api.py
@@ -0,0 +1,204 @@
+from doctest import testfile
+import requests
+import json
+import sys
+from pathlib import Path
+
+root_path = Path(__file__).parent.parent.parent
+sys.path.append(str(root_path))
+from configs.server_config import api_address
+from configs.model_config import VECTOR_SEARCH_TOP_K
+from server.knowledge_base.utils import get_kb_path
+
+from pprint import pprint
+
+
+api_base_url = api_address()
+
+kb = "kb_for_api_test"
+test_files = {
+    "README.MD": str(root_path / "README.MD"),
+    "FAQ.MD": str(root_path / "docs" / "FAQ.MD")
+}
+
+
+def test_delete_kb_before(api="/knowledge_base/delete_knowledge_base"):
+    if not Path(get_kb_path(kb)).exists():
+        return
+
+    url = api_base_url + api
+    print("\n测试知识库存在，需要删除")
+    r = requests.post(url, json=kb)
+    data = r.json()
+    pprint(data)
+
+    # check kb not exists anymore
+    url = api_base_url + "/knowledge_base/list_knowledge_bases"
+    print("\n获取知识库列表：")
+    r = requests.get(url)
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 200
+    assert isinstance(data["data"], list) and len(data["data"]) > 0
+    assert kb not in data["data"]
+
+
+def test_create_kb(api="/knowledge_base/create_knowledge_base"):
+    url = api_base_url + api
+
+    print(f"\n尝试用空名称创建知识库：")
+    r = requests.post(url, json={"knowledge_base_name": " "})
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 404
+    assert data["msg"] == "知识库名称不能为空，请重新填写知识库名称"
+
+    print(f"\n创建新知识库： {kb}")
+    r = requests.post(url, json={"knowledge_base_name": kb})
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 200
+    assert data["msg"] == f"已新增知识库 {kb}"
+
+    print(f"\n尝试创建同名知识库： {kb}")
+    r = requests.post(url, json={"knowledge_base_name": kb})
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 404
+    assert data["msg"] == f"已存在同名知识库 {kb}"
+
+
+def test_list_kbs(api="/knowledge_base/list_knowledge_bases"):
+    url = api_base_url + api
+    print("\n获取知识库列表：")
+    r = requests.get(url)
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 200
+    assert isinstance(data["data"], list) and len(data["data"]) > 0
+    assert kb in data["data"]
+
+
+def test_upload_doc(api="/knowledge_base/upload_doc"):
+    url = api_base_url + api
+    for name, path in test_files.items():
+        print(f"\n上传知识文件： {name}")
+        data = {"knowledge_base_name": kb, "override": True}
+        files = {"file": (name, open(path, "rb"))}
+        r = requests.post(url, data=data, files=files)
+        data = r.json()
+        pprint(data)
+        assert data["code"] == 200
+        assert data["msg"] == f"成功上传文件 {name}"
+
+    for name, path in test_files.items():
+        print(f"\n尝试重新上传知识文件： {name}， 不覆盖")
+        data = {"knowledge_base_name": kb, "override": False}
+        files = {"file": (name, open(path, "rb"))}
+        r = requests.post(url, data=data, files=files)
+        data = r.json()
+        pprint(data)
+        assert data["code"] == 404
+        assert data["msg"] == f"文件 {name} 已存在。"
+
+    for name, path in test_files.items():
+        print(f"\n尝试重新上传知识文件： {name}， 覆盖")
+        data = {"knowledge_base_name": kb, "override": True}
+        files = {"file": (name, open(path, "rb"))}
+        r = requests.post(url, data=data, files=files)
+        data = r.json()
+        pprint(data)
+        assert data["code"] == 200
+        assert data["msg"] == f"成功上传文件 {name}"
+
+
+def test_list_docs(api="/knowledge_base/list_docs"):
+    url = api_base_url + api
+    print("\n获取知识库中文件列表：")
+    r = requests.get(url, params={"knowledge_base_name": kb})
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 200
+    assert isinstance(data["data"], list)
+    for name in test_files:
+        assert name in data["data"]
+
+
+def test_search_docs(api="/knowledge_base/search_docs"):
+    url = api_base_url + api
+    query = "介绍一下langchain-chatchat项目"
+    print("\n检索知识库：")
+    print(query)
+    r = requests.post(url, json={"knowledge_base_name": kb, "query": query})
+    data = r.json()
+    pprint(data)
+    assert isinstance(data, list) and len(data) == VECTOR_SEARCH_TOP_K
+
+
+def test_update_doc(api="/knowledge_base/update_doc"):
+    url = api_base_url + api
+    for name, path in test_files.items():
+        print(f"\n更新知识文件： {name}")
+        r = requests.post(url, json={"knowledge_base_name": kb, "file_name": name})
+        data = r.json()
+        pprint(data)
+        assert data["code"] == 200
+        assert data["msg"] == f"成功更新文件 {name}"
+
+
+def test_delete_doc(api="/knowledge_base/delete_doc"):
+    url = api_base_url + api
+    for name, path in test_files.items():
+        print(f"\n删除知识文件： {name}")
+        r = requests.post(url, json={"knowledge_base_name": kb, "doc_name": name})
+        data = r.json()
+        pprint(data)
+        assert data["code"] == 200
+        assert data["msg"] == f"{name} 文件删除成功"
+
+    url = api_base_url + "/knowledge_base/search_docs"
+    query = "介绍一下langchain-chatchat项目"
+    print("\n尝试检索删除后的检索知识库：")
+    print(query)
+    r = requests.post(url, json={"knowledge_base_name": kb, "query": query})
+    data = r.json()
+    pprint(data)
+    assert isinstance(data, list) and len(data) == 0
+
+
+def test_recreate_vs(api="/knowledge_base/recreate_vector_store"):
+    url = api_base_url + api
+    print("\n重建知识库：")
+    r = requests.post(url, json={"knowledge_base_name": kb}, stream=True)
+    for chunk in r.iter_content(None):
+        data = json.loads(chunk)
+        assert isinstance(data, dict)
+        assert data["code"] == 200
+        print(data["msg"])
+
+    url = api_base_url + "/knowledge_base/search_docs"
+    query = "本项目支持哪些文件格式?"
+    print("\n尝试检索重建后的检索知识库：")
+    print(query)
+    r = requests.post(url, json={"knowledge_base_name": kb, "query": query})
+    data = r.json()
+    pprint(data)
+    assert isinstance(data, list) and len(data) == VECTOR_SEARCH_TOP_K
+
+
+def test_delete_kb_after(api="/knowledge_base/delete_knowledge_base"):
+    url = api_base_url + api
+    print("\n删除知识库")
+    r = requests.post(url, json=kb)
+    data = r.json()
+    pprint(data)
+
+    # check kb not exists anymore
+    url = api_base_url + "/knowledge_base/list_knowledge_bases"
+    print("\n获取知识库列表：")
+    r = requests.get(url)
+    data = r.json()
+    pprint(data)
+    assert data["code"] == 200
+    assert isinstance(data["data"], list) and len(data["data"]) > 0
+    assert kb not in data["data"]
diff --git a/tests/api/test_stream_chat_api.py b/tests/api/test_stream_chat_api.py
new file mode 100644
index 0000000..56d3237
--- /dev/null
+++ b/tests/api/test_stream_chat_api.py
@@ -0,0 +1,108 @@
+import requests
+import json
+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).parent.parent.parent))
+from configs.server_config import API_SERVER, api_address
+
+from pprint import pprint
+
+
+api_base_url = api_address()
+
+
+def dump_input(d, title):
+    print("\n")
+    print("=" * 30 + title + "  input " + "="*30)
+    pprint(d)
+
+
+def dump_output(r, title):
+    print("\n")
+    print("=" * 30 + title + "  output" + "="*30)
+    for line in r.iter_content(None, decode_unicode=True):
+        print(line, end="", flush=True)
+
+
+headers = {
+    'accept': 'application/json',
+    'Content-Type': 'application/json',
+}
+
+data = {
+    "query": "请用100字左右的文字介绍自己",
+    "history": [
+        {
+            "role": "user",
+            "content": "你好"
+        },
+        {
+            "role": "assistant",
+            "content": "你好，我是 ChatGLM"
+        }
+    ],
+    "stream": True
+}
+
+
+
+def test_chat_fastchat(api="/chat/fastchat"):
+    url = f"{api_base_url}{api}"
+    data2 = {
+        "stream": True,
+        "messages": data["history"] + [{"role": "user", "content": "推荐一部科幻电影"}]
+    }
+    dump_input(data2, api)
+    response = requests.post(url, headers=headers, json=data2, stream=True)
+    dump_output(response, api)
+    assert response.status_code == 200
+
+
+def test_chat_chat(api="/chat/chat"):
+    url = f"{api_base_url}{api}"
+    dump_input(data, api)
+    response = requests.post(url, headers=headers, json=data, stream=True)
+    dump_output(response, api)
+    assert response.status_code == 200
+
+
+def test_knowledge_chat(api="/chat/knowledge_base_chat"):
+    url = f"{api_base_url}{api}"
+    data = {
+        "query": "如何提问以获得高质量答案",
+        "knowledge_base_name": "samples",
+        "history": [
+            {
+                "role": "user",
+                "content": "你好"
+            },
+            {
+                "role": "assistant",
+                "content": "你好，我是 ChatGLM"
+            }
+        ],
+        "stream": True
+    }
+    dump_input(data, api)
+    response = requests.post(url, headers=headers, json=data, stream=True)
+    print("\n")
+    print("=" * 30 + api + "  output" + "="*30)
+    first = True
+    for line in response.iter_content(None, decode_unicode=True):
+        data = json.loads(line)
+        if first:
+            for doc in data["docs"]:
+                print(doc)
+            first = False
+        print(data["answer"], end="", flush=True)
+    assert response.status_code == 200
+
+
+def test_search_engine_chat(api="/chat/search_engine_chat"):
+    url = f"{api_base_url}{api}"
+    for se in ["bing", "duckduckgo"]:
+        dump_input(data, api)
+        response = requests.post(url, json=data, stream=True)
+        dump_output(response, api)
+        assert response.status_code == 200
diff --git a/webui_pages/knowledge_base/knowledge_base.py b/webui_pages/knowledge_base/knowledge_base.py
index f059475..4351e95 100644
--- a/webui_pages/knowledge_base/knowledge_base.py
+++ b/webui_pages/knowledge_base/knowledge_base.py
@@ -118,7 +118,7 @@ def knowledge_base_page(api: ApiRequest):
                     vector_store_type=vs_type,
                     embed_model=embed_model,
                 )
-                st.toast(ret["msg"])
+                st.toast(ret.get("msg", " "))
                 st.session_state["selected_kb_name"] = kb_name
                 st.experimental_rerun()
 
@@ -138,12 +138,14 @@ def knowledge_base_page(api: ApiRequest):
                 # use_container_width=True,
                 disabled=len(files) == 0,
         ):
-            for f in files:
-                ret = api.upload_kb_doc(f, kb)
-                if ret["code"] == 200:
-                    st.toast(ret["msg"], icon="✔")
-                else:
-                    st.toast(ret["msg"], icon="✖")
+            data = [{"file": f, "knowledge_base_name": kb, "not_refresh_vs_cache": True} for f in files]
+            data[-1]["not_refresh_vs_cache"]=False
+            for k in data:
+                ret = api.upload_kb_doc(**k)
+                if msg := check_success_msg(ret):
+                    st.toast(msg, icon="✔")
+                elif msg := check_error_msg(ret):
+                    st.toast(msg, icon="✖")
             st.session_state.files = []
 
         st.divider()
@@ -235,7 +237,7 @@ def knowledge_base_page(api: ApiRequest):
             ):
                 for row in selected_rows:
                     ret = api.delete_kb_doc(kb, row["file_name"], True)
-                    st.toast(ret["msg"])
+                    st.toast(ret.get("msg", " "))
                 st.experimental_rerun()
 
         st.divider()
@@ -249,12 +251,14 @@ def knowledge_base_page(api: ApiRequest):
                 use_container_width=True,
                 type="primary",
         ):
-            with st.spinner("向量库重构中"):
+            with st.spinner("向量库重构中，请耐心等待，勿刷新或关闭页面。"):
                 empty = st.empty()
                 empty.progress(0.0, "")
                 for d in api.recreate_vector_store(kb):
-                    print(d)
-                    empty.progress(d["finished"] / d["total"], f"正在处理： {d['doc']}")
+                    if msg := check_error_msg(d):
+                        st.toast(msg)
+                    else:
+                        empty.progress(d["finished"] / d["total"], f"正在处理： {d['doc']}")
                 st.experimental_rerun()
 
         if cols[2].button(
@@ -262,6 +266,6 @@ def knowledge_base_page(api: ApiRequest):
                 use_container_width=True,
         ):
             ret = api.delete_knowledge_base(kb)
-            st.toast(ret["msg"])
+            st.toast(ret.get("msg", " "))
             time.sleep(1)
             st.experimental_rerun()
diff --git a/webui_pages/utils.py b/webui_pages/utils.py
index 3e67ed7..c666d45 100644
--- a/webui_pages/utils.py
+++ b/webui_pages/utils.py
@@ -229,18 +229,18 @@ class ApiRequest:
                     elif chunk.strip():
                         yield chunk
         except httpx.ConnectError as e:
-            msg = f"无法连接API服务器，请确认已执行python server\\api.py"
+            msg = f"无法连接API服务器，请确认 ‘api.py’ 已正常启动。"
             logger.error(msg)
             logger.error(e)
-            yield {"code": 500, "errorMsg": msg}
+            yield {"code": 500, "msg": msg}
         except httpx.ReadTimeout as e:
             msg = f"API通信超时，请确认已启动FastChat与API服务（详见RADME '5. 启动 API 服务或 Web UI'）"
             logger.error(msg)
             logger.error(e)
-            yield {"code": 500, "errorMsg": msg}
+            yield {"code": 500, "msg": msg}
         except Exception as e:
             logger.error(e)
-            yield {"code": 500, "errorMsg": str(e)}
+            yield {"code": 500, "msg": str(e)}
 
     # 对话相关操作
 
@@ -394,7 +394,7 @@ class ApiRequest:
             return response.json()
         except Exception as e:
             logger.error(e)
-            return {"code": 500, "errorMsg": errorMsg or str(e)}
+            return {"code": 500, "msg": errorMsg or str(e)}
 
     def list_knowledge_bases(
         self,
@@ -496,6 +496,7 @@ class ApiRequest:
         knowledge_base_name: str,
         filename: str = None,
         override: bool = False,
+        not_refresh_vs_cache: bool = False,
         no_remote_api: bool = None,
     ):
         '''
@@ -529,7 +530,11 @@ class ApiRequest:
         else:
             response = self.post(
                 "/knowledge_base/upload_doc",
-                data={"knowledge_base_name": knowledge_base_name, "override": override},
+                data={
+                    "knowledge_base_name": knowledge_base_name,
+                    "override": override,
+                    "not_refresh_vs_cache": not_refresh_vs_cache,
+                },
                 files={"file": (filename, file)},
             )
             return self._check_httpx_json_response(response)
@@ -539,6 +544,7 @@ class ApiRequest:
         knowledge_base_name: str,
         doc_name: str,
         delete_content: bool = False,
+        not_refresh_vs_cache: bool = False,
         no_remote_api: bool = None,
     ):
         '''
@@ -551,6 +557,7 @@ class ApiRequest:
             "knowledge_base_name": knowledge_base_name,
             "doc_name": doc_name,
             "delete_content": delete_content,
+            "not_refresh_vs_cache": not_refresh_vs_cache,
         }
 
         if no_remote_api:
@@ -568,6 +575,7 @@ class ApiRequest:
         self,
         knowledge_base_name: str,
         file_name: str,
+        not_refresh_vs_cache: bool = False,
         no_remote_api: bool = None,
     ):
         '''
@@ -583,7 +591,11 @@ class ApiRequest:
         else:
             response = self.post(
                 "/knowledge_base/update_doc",
-                json={"knowledge_base_name": knowledge_base_name, "file_name": file_name},
+                json={
+                    "knowledge_base_name": knowledge_base_name,
+                    "file_name": file_name,
+                    "not_refresh_vs_cache": not_refresh_vs_cache,
+                },
             )
             return self._check_httpx_json_response(response)
 
@@ -617,7 +629,7 @@ class ApiRequest:
                 "/knowledge_base/recreate_vector_store",
                 json=data,
                 stream=True,
-                timeout=False,
+                timeout=None,
             )
             return self._httpx_stream2generator(response, as_json=True)
 
@@ -626,7 +638,22 @@ def check_error_msg(data: Union[str, dict, list], key: str = "errorMsg") -> str:
     '''
     return error message if error occured when requests API
     '''
-    if isinstance(data, dict) and key in data:
+    if isinstance(data, dict):
+        if key in data:
+            return data[key]
+        if "code" in data and data["code"] != 200:
+            return data["msg"]
+    return ""
+
+
+def check_success_msg(data: Union[str, dict, list], key: str = "msg") -> str:
+    '''
+    return error message if error occured when requests API
+    '''
+    if (isinstance(data, dict)
+        and key in data
+        and "code" in data
+        and data["code"] == 200):
         return data[key]
     return ""