实现Api和WEBUI的前后端分离 (#1772)

* update ApiRequest: 删除no_remote_api本地调用模式；支持同步/异步调用
* 实现API和WEBUI的分离：
- API运行服务器上的配置通过/llm_model/get_model_config、/server/configs接口提供，WEBUI运行机器上的配置项仅作为代码内部默认值使用
- 服务器可用的搜索引擎通过/server/list_search_engines提供
- WEBUI可选LLM列表中只列出在FSCHAT_MODEL_WORKERS中配置的模型
- 修改WEBUI中默认LLM_MODEL获取方式，改为从api端读取
- 删除knowledge_base_chat中`local_doc_url`参数

其它修改：
- 删除多余的kb_config.py.exmaple(名称错误)
- server_config中默认关闭vllm
- server_config中默认注释除智谱AI之外的在线模型
- 修改requests从系统获取的代理，避免model worker注册错误

* 修正：
- api.list_config_models返回模型原始配置
- api.list_config_models和api.get_model_config中过滤online api模型的敏感信息
- 将GPT等直接访问的模型列入WEBUI可选模型列表

其它：
- 指定langchain==0.3.313, fschat==0.2.30, langchain-experimental==0.0.30

configs/kb_config.py.exmaple

@@ -1,99 +0,0 @@
-import os
-
-
-# 默认向量库类型。可选：faiss, milvus, pg.
-DEFAULT_VS_TYPE = "faiss"
-
-# 缓存向量库数量（针对FAISS）
-CACHED_VS_NUM = 1
-
-# 知识库中单段文本长度(不适用MarkdownHeaderTextSplitter)
-CHUNK_SIZE = 250
-
-# 知识库中相邻文本重合长度(不适用MarkdownHeaderTextSplitter)
-OVERLAP_SIZE = 50
-
-# 知识库匹配向量数量
-VECTOR_SEARCH_TOP_K = 3
-
-# 知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右
-SCORE_THRESHOLD = 1
-
-# 搜索引擎匹配结题数量
-SEARCH_ENGINE_TOP_K = 3
-
-
-# Bing 搜索必备变量
-# 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search
-# 具体申请方式请见
-# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/create-bing-search-service-resource
-# 使用python创建bing api 搜索实例详见:
-# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/quickstarts/rest/python
-BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"
-# 注意不是bing Webmaster Tools的api key，
-
-# 此外，如果是在服务器上，报Failed to establish a new connection: [Errno 110] Connection timed out
-# 是因为服务器加了防火墙，需要联系管理员加白名单，如果公司的服务器的话，就别想了GG
-BING_SUBSCRIPTION_KEY = ""
-
-# 是否开启中文标题加强，以及标题增强的相关配置
-# 通过增加标题判断，判断哪些文本为标题，并在metadata中进行标记；
-# 然后将文本与往上一级的标题进行拼合，实现文本信息的增强。
-ZH_TITLE_ENHANCE = False
-
-
-# 通常情况下不需要更改以下内容
-
-# 知识库默认存储路径
-KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")
-if not os.path.exists(KB_ROOT_PATH):
-    os.mkdir(KB_ROOT_PATH)
-
-# 数据库默认存储路径。
-# 如果使用sqlite，可以直接修改DB_ROOT_PATH；如果使用其它数据库，请直接修改SQLALCHEMY_DATABASE_URI。
-DB_ROOT_PATH = os.path.join(KB_ROOT_PATH, "info.db")
-SQLALCHEMY_DATABASE_URI = f"sqlite:///{DB_ROOT_PATH}"
-
-# 可选向量库类型及对应配置
-kbs_config = {
-    "faiss": {
-    },
-    "milvus": {
-        "host": "127.0.0.1",
-        "port": "19530",
-        "user": "",
-        "password": "",
-        "secure": False,
-    },
-    "pg": {
-        "connection_uri": "postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatchat",
-    }
-}
-
-# TextSplitter配置项，如果你不明白其中的含义，就不要修改。
-text_splitter_dict = {
-    "ChineseRecursiveTextSplitter": {
-        "source": "huggingface",  ## 选择tiktoken则使用openai的方法
-        "tokenizer_name_or_path": "gpt2",
-    },
-    "SpacyTextSplitter": {
-        "source": "huggingface",
-        "tokenizer_name_or_path": "",
-    },
-    "RecursiveCharacterTextSplitter": {
-        "source": "tiktoken",
-        "tokenizer_name_or_path": "cl100k_base",
-    },
-    "MarkdownHeaderTextSplitter": {
-        "headers_to_split_on":
-            [
-                ("#", "head1"),
-                ("##", "head2"),
-                ("###", "head3"),
-                ("####", "head4"),
-            ]
-    },
-}
-
-# TEXT_SPLITTER 名称
-TEXT_SPLITTER_NAME = "ChineseRecursiveTextSplitter"

configs/server_config.py.example

@@ -32,6 +32,7 @@ FSCHAT_OPENAI_API = {
 # fastchat model_worker server
 # 这些模型必须是在model_config.MODEL_PATH或ONLINE_MODEL中正确配置的。
 # 在启动startup.py时，可用通过`--model-worker --model-name xxxx`指定模型，不指定则为LLM_MODEL
+# 必须在这里添加的模型才会出现在WEBUI中可选模型列表里（LLM_MODEL会自动添加）
 FSCHAT_MODEL_WORKERS = {
     # 所有模型共用的默认配置，可在模型专项配置中进行覆盖。
     "default": {
@@ -39,7 +40,8 @@ FSCHAT_MODEL_WORKERS = {
         "port": 20002,
         "device": LLM_DEVICE,
         # False,'vllm',使用的推理加速框架,使用vllm如果出现HuggingFace通信问题，参见doc/FAQ
-        "infer_turbo": "vllm" if sys.platform.startswith("linux") else False,
+        # vllm对一些模型支持还不成熟，暂时默认关闭
+        "infer_turbo": False,
 
         # model_worker多卡加载需要配置的参数
         # "gpus": None, # 使用的GPU，以str的格式指定，如"0,1"，如失效请使用CUDA_VISIBLE_DEVICES="0,1"等形式指定
@@ -97,24 +99,24 @@ FSCHAT_MODEL_WORKERS = {
     "zhipu-api": { # 请为每个要运行的在线API设置不同的端口
         "port": 21001,
     },
-    "minimax-api": {
+    # "minimax-api": {
-        "port": 21002,
+    #     "port": 21002,
-    },
+    # },
-    "xinghuo-api": {
+    # "xinghuo-api": {
-        "port": 21003,
+    #     "port": 21003,
-    },
+    # },
-    "qianfan-api": {
+    # "qianfan-api": {
-        "port": 21004,
+    #     "port": 21004,
-    },
+    # },
-    "fangzhou-api": {
+    # "fangzhou-api": {
-        "port": 21005,
+    #     "port": 21005,
-    },
+    # },
-    "qwen-api": {
+    # "qwen-api": {
-        "port": 21006,
+    #     "port": 21006,
-    },
+    # },
-    "baichuan-api": {
+    # "baichuan-api": {
-        "port": 21007,
+    #     "port": 21007,
-    },
+    # },
 }
 
 # fastchat multi model worker server

requirements.txt

@@ -1,5 +1,6 @@
-langchain>=0.0.310
+langchain==0.0.313
-fschat[model_worker]>=0.2.30
+langchain-experimental==0.0.30
+fschat[model_worker]==0.2.30
 openai
 sentence_transformers
 transformers>=4.34

requirements_api.txt

@@ -1,5 +1,6 @@
-langchain>=0.0.310
+langchain==0.0.313
-fschat[model_worker]>=0.2.30
+langchain-experimental==0.0.30
+fschat[model_worker]==0.2.30
 openai
 sentence_transformers>=2.2.2
 transformers>=4.34

server/api.py

@@ -17,8 +17,10 @@ from server.knowledge_base.kb_api import list_kbs, create_kb, delete_kb
 from server.knowledge_base.kb_doc_api import (list_files, upload_docs, delete_docs,
                                               update_docs, download_doc, recreate_vector_store,
                                               search_docs, DocumentWithScore)
-from server.llm_api import list_running_models, list_config_models, change_llm_model, stop_llm_model
+from server.llm_api import (list_running_models, list_config_models,
-from server.utils import BaseResponse, ListResponse, FastAPI, MakeFastAPIOffline
+                            change_llm_model, stop_llm_model,
+                            get_model_config, list_search_engines)
+from server.utils import BaseResponse, ListResponse, FastAPI, MakeFastAPIOffline, get_server_configs
 from typing import List
 
 nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
@@ -139,6 +141,11 @@ def create_app():
              summary="列出configs已配置的模型",
              )(list_config_models)
 
+    app.post("/llm_model/get_model_config",
+             tags=["LLM Model Management"],
+             summary="获取模型配置（合并后）",
+             )(get_model_config)
+
     app.post("/llm_model/stop",
              tags=["LLM Model Management"],
              summary="停止指定的LLM模型（Model Worker)",
@@ -149,6 +156,17 @@ def create_app():
              summary="切换指定的LLM模型（Model Worker)",
              )(change_llm_model)
 
+    # 服务器相关接口
+    app.post("/server/configs",
+             tags=["Server State"],
+             summary="获取服务器原始配置信息",
+             )(get_server_configs)
+
+    app.post("/server/list_search_engines",
+             tags=["Server State"],
+             summary="获取服务器支持的搜索引擎",
+             )(list_search_engines)
+
     return app
 
 

server/chat/knowledge_base_chat.py

@@ -33,7 +33,6 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
                             temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
                             max_tokens: int = Body(1024, description="限制LLM生成Token数量，当前默认为1024"), # TODO: fastchat更新后默认值设为None，自动使用LLM支持的最大值。
                             prompt_name: str = Body("knowledge_base_chat", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
-                            local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"),
                             request: Request = None,
                         ):
     kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
@@ -74,9 +73,6 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
         source_documents = []
         for inum, doc in enumerate(docs):
             filename = os.path.split(doc.metadata["source"])[-1]
-            if local_doc_url:
-                url = "file://" + doc.metadata["source"]
-            else:
             parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name":filename})
             url = f"{request.base_url}knowledge_base/download_doc?" + parameters
             text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n"""

server/llm_api.py

@@ -1,7 +1,7 @@
 from fastapi import Body
 from configs import logger, log_verbose, LLM_MODEL, HTTPX_DEFAULT_TIMEOUT
-from server.utils import BaseResponse, fschat_controller_address, list_llm_models, get_httpx_client
+from server.utils import (BaseResponse, fschat_controller_address, list_config_llm_models,
-
+                          get_httpx_client, get_model_worker_config)
 
 
 def list_running_models(
@@ -9,19 +9,21 @@ def list_running_models(
     placeholder: str = Body(None, description="该参数未使用，占位用"),
 ) -> BaseResponse:
     '''
-    从fastchat controller获取已加载模型列表
+    从fastchat controller获取已加载模型列表及其配置项
     '''
     try:
         controller_address = controller_address or fschat_controller_address()
         with get_httpx_client() as client:
             r = client.post(controller_address + "/list_models")
-            return BaseResponse(data=r.json()["models"])
+            models = r.json()["models"]
+            data = {m: get_model_worker_config(m) for m in models}
+            return BaseResponse(data=data)
     except Exception as e:
         logger.error(f'{e.__class__.__name__}: {e}',
                         exc_info=e if log_verbose else None)
         return BaseResponse(
             code=500,
-            data=[],
+            data={},
             msg=f"failed to get available models from controller: {controller_address}。错误信息是： {e}")
 
 
@@ -29,7 +31,38 @@ def list_config_models() -> BaseResponse:
     '''
     从本地获取configs中配置的模型列表
     '''
-    return BaseResponse(data=list_llm_models())
+    configs = list_config_llm_models()
+
+    # 删除ONLINE_MODEL配置中的敏感信息
+    for config in configs["online"].values():
+        del_keys = set(["worker_class"])
+        for k in config:
+            if "key" in k.lower() or "secret" in k.lower():
+                del_keys.add(k)
+        for k in del_keys:
+            config.pop(k, None)
+
+    return BaseResponse(data=configs)
+
+
+def get_model_config(
+    model_name: str = Body(description="配置中LLM模型的名称"),
+    placeholder: str = Body(description="占位用，无实际效果")
+) -> BaseResponse:
+    '''
+    获取LLM模型配置项（合并后的）
+    '''
+    config = get_model_worker_config(model_name=model_name)
+
+    # 删除ONLINE_MODEL配置中的敏感信息
+    del_keys = set(["worker_class"])
+    for k in config:
+        if "key" in k.lower() or "secret" in k.lower():
+            del_keys.add(k)
+    for k in del_keys:
+        config.pop(k, None)
+
+    return BaseResponse(data=config)
 
 
 def stop_llm_model(
@@ -79,3 +112,9 @@ def change_llm_model(
         return BaseResponse(
             code=500,
             msg=f"failed to switch LLM model from controller: {controller_address}。错误信息是： {e}")
+
+
+def list_search_engines() -> BaseResponse:
+    from server.chat.search_engine_chat import SEARCH_ENGINES
+
+    return BaseResponse(data=list(SEARCH_ENGINES))

server/utils.py

@@ -258,17 +258,18 @@ def list_embed_models() -> List[str]:
     return list(MODEL_PATH["embed_model"])
 
 
-def list_llm_models() -> Dict[str, List[str]]:
+def list_config_llm_models() -> Dict[str, Dict]:
     '''
-    get names of configured llm models with different types.
+    get configured llm models with different types.
     return [(model_name, config_type), ...]
     '''
     workers = list(FSCHAT_MODEL_WORKERS)
-    if "default" in workers:
+    if LLM_MODEL not in workers:
-        workers.remove("default")
+        workers.insert(0, LLM_MODEL)
+
     return {
-        "local": list(MODEL_PATH["llm_model"]),
+        "local": MODEL_PATH["llm_model"],
-        "online": list(ONLINE_LLM_MODEL),
+        "online": ONLINE_LLM_MODEL,
         "worker": workers,
     }
 
@@ -306,7 +307,7 @@ def get_model_worker_config(model_name: str = None) -> dict:
     加载model worker的配置项。
     优先级:FSCHAT_MODEL_WORKERS[model_name] > ONLINE_LLM_MODEL[model_name] > FSCHAT_MODEL_WORKERS["default"]
     '''
-    from configs.model_config import ONLINE_LLM_MODEL
+    from configs.model_config import ONLINE_LLM_MODEL, MODEL_PATH
     from configs.server_config import FSCHAT_MODEL_WORKERS
     from server import model_workers
 
@@ -324,7 +325,8 @@ def get_model_worker_config(model_name: str = None) -> dict:
                 msg = f"在线模型 ‘{model_name}’ 的provider没有正确配置"
                 logger.error(f'{e.__class__.__name__}: {msg}',
                              exc_info=e if log_verbose else None)
-
+    # 本地模型
+    if model_name in MODEL_PATH["llm_model"]:
         config["model_path"] = get_model_path(model_name)
         config["device"] = llm_device(config.get("device"))
     return config
@@ -449,11 +451,11 @@ def set_httpx_config(
 
     # TODO: 简单的清除系统代理不是个好的选择，影响太多。似乎修改代理服务器的bypass列表更好。
     # patch requests to use custom proxies instead of system settings
-    # def _get_proxies():
+    def _get_proxies():
-    #     return {}
+        return proxies
 
-    # import urllib.request
+    import urllib.request
-    # urllib.request.getproxies = _get_proxies
+    urllib.request.getproxies = _get_proxies
 
 
 # 自动检查torch可用的设备。分布式部署时，不运行LLM的机器上可以不装torch
@@ -557,3 +559,35 @@ def get_httpx_client(
         return httpx.AsyncClient(**kwargs)
     else:
         return httpx.Client(**kwargs)
+
+
+def get_server_configs() -> Dict:
+    '''
+    获取configs中的原始配置项，供前端使用
+    '''
+    from configs.kb_config import (
+        DEFAULT_VS_TYPE,
+        CHUNK_SIZE,
+        OVERLAP_SIZE,
+        SCORE_THRESHOLD,
+        VECTOR_SEARCH_TOP_K,
+        SEARCH_ENGINE_TOP_K,
+        ZH_TITLE_ENHANCE,
+        text_splitter_dict,
+        TEXT_SPLITTER_NAME,
+    )
+    from configs.model_config import (
+        LLM_MODEL,
+        EMBEDDING_MODEL,
+        HISTORY_LEN,
+        TEMPERATURE,
+    )
+    from configs.prompt_config import PROMPT_TEMPLATES
+
+    _custom = {
+        "controller_address": fschat_controller_address(),
+        "openai_api_address": fschat_openai_api_address(),
+        "api_address": api_address(),
+    }
+
+    return {**{k: v for k, v in locals().items() if k[0] != "_"}, **_custom}

tests/api/test_kb_api_request.py

@@ -14,7 +14,7 @@ from pprint import pprint
 
 
 api_base_url = api_address()
-api: ApiRequest = ApiRequest(api_base_url, no_remote_api=False)
+api: ApiRequest = ApiRequest(api_base_url)
 
 
 kb = "kb_for_api_test"

tests/api/test_llm_api.py

@@ -32,7 +32,7 @@ def get_running_models(api="/llm_model/list_models"):
     return []
 
 
-def test_running_models(api="/llm_model/list_models"):
+def test_running_models(api="/llm_model/list_running_models"):
     url = api_base_url + api
     r = requests.post(url)
     assert r.status_code == 200
@@ -48,7 +48,7 @@ def test_running_models(api="/llm_model/list_models"):
 #     r = requests.post(url, json={""})
 
 
-def test_change_model(api="/llm_model/change"):
+def test_change_model(api="/llm_model/change_model"):
     url = api_base_url + api
 
     running_models = get_running_models()

webui.py

@@ -22,9 +22,10 @@ if __name__ == "__main__":
     )
 
     if not chat_box.chat_inited:
+        running_models = api.list_running_models()
         st.toast(
             f"欢迎使用 [`Langchain-Chatchat`](https://github.com/chatchat-space/Langchain-Chatchat) ! \n\n"
-            f"当前使用模型`{LLM_MODEL}`, 您可以开始提问了."
+            f"当前运行中的模型`{running_models}`, 您可以开始提问了."
         )
 
     pages = {

webui_pages/dialogue/dialogue.py

@@ -2,11 +2,11 @@ import streamlit as st
 from webui_pages.utils import *
 from streamlit_chatbox import *
 from datetime import datetime
-from server.chat.search_engine_chat import SEARCH_ENGINES
 import os
-from configs import LLM_MODEL, TEMPERATURE
+from configs import LLM_MODEL, TEMPERATURE, HISTORY_LEN
-from server.utils import get_model_worker_config
 from typing import List, Dict
+
+
 chat_box = ChatBox(
     assistant_avatar=os.path.join(
         "img",
@@ -15,9 +15,6 @@ chat_box = ChatBox(
 )
 
 
-
-
-
 def get_messages_history(history_len: int, content_in_expander: bool = False) -> List[Dict]:
     '''
     返回消息历史。
@@ -38,6 +35,26 @@ def get_messages_history(history_len: int, content_in_expander: bool = False) ->
     return chat_box.filter_history(history_len=history_len, filter=filter)
 
 
+def get_default_llm_model(api: ApiRequest) -> (str, bool):
+    '''
+    从服务器上获取当前运行的LLM模型，如果本机配置的LLM_MODEL属于本地模型且在其中，则优先返回
+    返回类型为（model_name, is_local_model）
+    '''
+    running_models = api.list_running_models()
+
+    if not running_models:
+        return "", False
+
+    if LLM_MODEL in running_models:
+        return LLM_MODEL, True
+    
+    local_models = [k for k, v in running_models.items() if not v.get("online_api")]
+    if local_models:
+        return local_models[0], True
+    
+    return running_models[0], False
+
+
 def dialogue_page(api: ApiRequest):
     chat_box.init_session()
 
@@ -51,7 +68,6 @@ def dialogue_page(api: ApiRequest):
                 if cur_kb:
                     text = f"{text} 当前知识库： `{cur_kb}`。"
             st.toast(text)
-            # sac.alert(text, description="descp", type="success", closable=True, banner=True)
 
         dialogue_mode = st.selectbox("请选择对话模式：",
                                      ["LLM 对话",
@@ -65,7 +81,7 @@ def dialogue_page(api: ApiRequest):
                                      )
 
         def on_llm_change():
-            config = get_model_worker_config(llm_model)
+            config = api.get_model_config(llm_model)
             if not config.get("online_api"):  # 只有本地model_worker可以切换模型
                 st.session_state["prev_llm_model"] = llm_model
             st.session_state["cur_llm_model"] = st.session_state.llm_model
@@ -75,15 +91,20 @@ def dialogue_page(api: ApiRequest):
                 return f"{x} (Running)"
             return x
 
-        running_models = api.list_running_models()
+        running_models = list(api.list_running_models())
         available_models = []
         config_models = api.list_config_models()
-        for models in config_models.values():
+        worker_models = list(config_models.get("worker", {})) # 仅列出在FSCHAT_MODEL_WORKERS中配置的模型
-            for m in models:
+        for m in worker_models:
-                if m not in running_models:
+            if m not in running_models and m != "default":
                 available_models.append(m)
+        for k, v in config_models.get("online", {}).items(): # 列出ONLINE_MODELS中直接访问的模型（如GPT）
+            if not v.get("provider") and k not in running_models:
+                print(k, v)
+                available_models.append(k)
+
         llm_models = running_models + available_models
-        index = llm_models.index(st.session_state.get("cur_llm_model", LLM_MODEL))
+        index = llm_models.index(st.session_state.get("cur_llm_model", get_default_llm_model(api)[0]))
         llm_model = st.selectbox("选择LLM模型：",
                                  llm_models,
                                  index,
@@ -92,7 +113,7 @@ def dialogue_page(api: ApiRequest):
                                  key="llm_model",
                                  )
         if (st.session_state.get("prev_llm_model") != llm_model
-                and not get_model_worker_config(llm_model).get("online_api")
+                and not api.get_model_config(llm_model).get("online_api")
                 and llm_model not in running_models):
             with st.spinner(f"正在加载模型： {llm_model}，请勿进行操作或刷新页面"):
                 prev_model = st.session_state.get("prev_llm_model")
@@ -114,7 +135,7 @@ def dialogue_page(api: ApiRequest):
 
         if dialogue_mode == "知识库问答":
             with st.expander("知识库配置", True):
-                kb_list = api.list_knowledge_bases(no_remote_api=True)
+                kb_list = api.list_knowledge_bases()
                 selected_kb = st.selectbox(
                     "请选择知识库：",
                     kb_list,
@@ -126,7 +147,7 @@ def dialogue_page(api: ApiRequest):
                 # chunk_content = st.checkbox("关联上下文", False, disabled=True)
                 # chunk_size = st.slider("关联长度：", 0, 500, 250, disabled=True)
         elif dialogue_mode == "搜索引擎问答":
-            search_engine_list = list(SEARCH_ENGINES.keys())
+            search_engine_list = api.list_search_engines()
             with st.expander("搜索引擎配置", True):
                 search_engine = st.selectbox(
                     label="请选择搜索引擎",

webui_pages/utils.py

@@ -1,12 +1,14 @@
-# 该文件包含webui通用工具，可以被不同的webui使用
+# 该文件封装了对api.py的请求，可以被不同的webui使用
+# 通过ApiRequest和AsyncApiRequest支持同步/异步调用
+
+
 from typing import *
 from pathlib import Path
+# 此处导入的配置为发起请求（如WEBUI）机器上的配置，主要用于为前端设置默认值。分布式部署时可以与服务器上的不同
 from configs import (
     EMBEDDING_MODEL,
     DEFAULT_VS_TYPE,
-    KB_ROOT_PATH,
     LLM_MODEL,
-    HISTORY_LEN,
     TEMPERATURE,
     SCORE_THRESHOLD,
     CHUNK_SIZE,
@@ -14,59 +16,44 @@ from configs import (
     ZH_TITLE_ENHANCE,
     VECTOR_SEARCH_TOP_K,
     SEARCH_ENGINE_TOP_K,
-    FSCHAT_MODEL_WORKERS,
     HTTPX_DEFAULT_TIMEOUT,
     logger, log_verbose,
 )
 import httpx
-import asyncio
 from server.chat.openai_chat import OpenAiChatMsgIn
-from fastapi.responses import StreamingResponse
 import contextlib
 import json
 import os
 from io import BytesIO
-from server.utils import run_async, iter_over_async, set_httpx_config, api_address, get_httpx_client
+from server.utils import run_async, set_httpx_config, api_address, get_httpx_client
 
-from configs.model_config import NLTK_DATA_PATH
-import nltk
-nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
 from pprint import pprint
 
 
-KB_ROOT_PATH = Path(KB_ROOT_PATH)
 set_httpx_config()
 
 
 class ApiRequest:
     '''
-    api.py调用的封装,主要实现:
+    api.py调用的封装（同步模式）,简化api调用方式
-    1. 简化api调用方式
-    2. 实现无api调用(直接运行server.chat.*中的视图函数获取结果),无需启动api.py
     '''
     def __init__(
         self,
         base_url: str = api_address(),
         timeout: float = HTTPX_DEFAULT_TIMEOUT,
-        no_remote_api: bool = False,   # call api view function directly
     ):
         self.base_url = base_url
         self.timeout = timeout
-        self.no_remote_api = no_remote_api
+        self._use_async = False
-        self._client = get_httpx_client()
+        self._client = None
-        self._aclient = get_httpx_client(use_async=True)
-        if no_remote_api:
-            logger.warn("将来可能取消对no_remote_api的支持，更新版本时请注意。")
 
-    def _parse_url(self, url: str) -> str:
+    @property
-        if (not url.startswith("http")
+    def client(self):
-                    and self.base_url
+        if self._client is None or self._client.is_closed:
-                ):
+            self._client = get_httpx_client(base_url=self.base_url,
-            part1 = self.base_url.strip(" /")
+                                            use_async=self._use_async,
-            part2 = url.strip(" /")
+                                            timeout=self.timeout)
-            return f"{part1}/{part2}"
+        return self._client
-        else:
-            return url
 
     def get(
         self,
@@ -75,44 +62,19 @@ class ApiRequest:
         retry: int = 3,
         stream: bool = False,
         **kwargs: Any,
-    ) -> Union[httpx.Response, None]:
+    ) -> Union[httpx.Response, Iterator[httpx.Response], None]:
-        url = self._parse_url(url)
-        kwargs.setdefault("timeout", self.timeout)
         while retry > 0:
             try:
                 if stream:
-                    return self._client.stream("GET", url, params=params, **kwargs)
+                    return self.client.stream("GET", url, params=params, **kwargs)
                 else:
-                    return self._client.get(url, params=params, **kwargs)
+                    return self.client.get(url, params=params, **kwargs)
             except Exception as e:
                 msg = f"error when get {url}: {e}"
                 logger.error(f'{e.__class__.__name__}: {msg}',
                              exc_info=e if log_verbose else None)
                 retry -= 1
 
-    async def aget(
-        self,
-        url: str,
-        params: Union[Dict, List[Tuple], bytes] = None,
-        retry: int = 3,
-        stream: bool = False,
-        **kwargs: Any,
-    ) -> Union[httpx.Response, None]:
-        url = self._parse_url(url)
-        kwargs.setdefault("timeout", self.timeout)
-
-        while retry > 0:
-            try:
-                if stream:
-                    return await self._aclient.stream("GET", url, params=params, **kwargs)
-                else:
-                    return await self._aclient.get(url, params=params, **kwargs)
-            except Exception as e:
-                msg = f"error when aget {url}: {e}"
-                logger.error(f'{e.__class__.__name__}: {msg}',
-                                exc_info=e if log_verbose else None)
-                retry -= 1
-
     def post(
         self,
         url: str,
@@ -121,45 +83,19 @@ class ApiRequest:
         retry: int = 3,
         stream: bool = False,
         **kwargs: Any
-    ) -> Union[httpx.Response, None]:
+    ) -> Union[httpx.Response, Iterator[httpx.Response], None]:
-        url = self._parse_url(url)
-        kwargs.setdefault("timeout", self.timeout)
         while retry > 0:
             try:
                 if stream:
-                    return self._client.stream("POST", url, data=data, json=json, **kwargs)
+                    return self.client.stream("POST", url, data=data, json=json, **kwargs)
                 else:
-                    return self._client.post(url, data=data, json=json, **kwargs)
+                    return self.client.post(url, data=data, json=json, **kwargs)
             except Exception as e:
                 msg = f"error when post {url}: {e}"
                 logger.error(f'{e.__class__.__name__}: {msg}',
                              exc_info=e if log_verbose else None)
                 retry -= 1
 
-    async def apost(
-        self,
-        url: str,
-        data: Dict = None,
-        json: Dict = None,
-        retry: int = 3,
-        stream: bool = False,
-        **kwargs: Any
-    ) -> Union[httpx.Response, None]:
-        url = self._parse_url(url)
-        kwargs.setdefault("timeout", self.timeout)
-
-        while retry > 0:
-            try:
-                if stream:
-                    return await self._client.stream("POST", url, data=data, json=json, **kwargs)
-                else:
-                    return await self._client.post(url, data=data, json=json, **kwargs)
-            except Exception as e:
-                msg = f"error when apost {url}: {e}"
-                logger.error(f'{e.__class__.__name__}: {msg}',
-                                exc_info=e if log_verbose else None)
-                retry -= 1
-
     def delete(
         self,
         url: str,
@@ -168,65 +104,19 @@ class ApiRequest:
         retry: int = 3,
         stream: bool = False,
         **kwargs: Any
-    ) -> Union[httpx.Response, None]:
+    ) -> Union[httpx.Response, Iterator[httpx.Response], None]:
-        url = self._parse_url(url)
-        kwargs.setdefault("timeout", self.timeout)
         while retry > 0:
             try:
                 if stream:
-                    return self._client.stream("DELETE", url, data=data, json=json, **kwargs)
+                    return self.client.stream("DELETE", url, data=data, json=json, **kwargs)
                 else:
-                    return self._client.delete(url, data=data, json=json, **kwargs)
+                    return self.client.delete(url, data=data, json=json, **kwargs)
             except Exception as e:
                 msg = f"error when delete {url}: {e}"
                 logger.error(f'{e.__class__.__name__}: {msg}',
                              exc_info=e if log_verbose else None)
                 retry -= 1
 
-    async def adelete(
-        self,
-        url: str,
-        data: Dict = None,
-        json: Dict = None,
-        retry: int = 3,
-        stream: bool = False,
-        **kwargs: Any
-    ) -> Union[httpx.Response, None]:
-        url = self._parse_url(url)
-        kwargs.setdefault("timeout", self.timeout)
-
-        while retry > 0:
-            try:
-                if stream:
-                    return await self._aclient.stream("DELETE", url, data=data, json=json, **kwargs)
-                else:
-                    return await self._aclient.delete(url, data=data, json=json, **kwargs)
-            except Exception as e:
-                msg = f"error when adelete {url}: {e}"
-                logger.error(f'{e.__class__.__name__}: {msg}',
-                                exc_info=e if log_verbose else None)
-                retry -= 1
-
-    def _fastapi_stream2generator(self, response: StreamingResponse, as_json: bool =False):
-        '''
-        将api.py中视图函数返回的StreamingResponse转化为同步生成器
-        '''
-        try:
-            loop = asyncio.get_event_loop()
-        except:
-            loop = asyncio.new_event_loop()
-
-        try:
-            for chunk in  iter_over_async(response.body_iterator, loop):
-                if as_json and chunk:
-                    yield json.loads(chunk)
-                elif chunk.strip():
-                    yield chunk
-        except Exception as e:
-            msg = f"error when run fastapi router: {e}"
-            logger.error(f'{e.__class__.__name__}: {msg}',
-                         exc_info=e if log_verbose else None)
-
     def _httpx_stream2generator(
         self,
         response: contextlib._GeneratorContextManager,
@@ -235,6 +125,39 @@ class ApiRequest:
         '''
         将httpx.stream返回的GeneratorContextManager转化为普通生成器
         '''
+        async def ret_async(response, as_json):
+            try:
+                async with response as r:
+                    async for chunk in r.aiter_text(None):
+                        if not chunk: # fastchat api yield empty bytes on start and end
+                            continue
+                        if as_json:
+                            try:
+                                data = json.loads(chunk)
+                                pprint(data, depth=1)
+                                yield data
+                            except Exception as e:
+                                msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
+                                logger.error(f'{e.__class__.__name__}: {msg}',
+                                            exc_info=e if log_verbose else None)
+                        else:
+                            # print(chunk, end="", flush=True)
+                            yield chunk
+            except httpx.ConnectError as e:
+                msg = f"无法连接API服务器，请确认 ‘api.py’ 已正常启动。({e})"
+                logger.error(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Wiki '5. 启动 API 服务或 Web UI'）。（{e}）"
+                logger.error(msg)
+                yield {"code": 500, "msg": msg}
+            except Exception as e:
+                msg = f"API通信遇到错误：{e}"
+                logger.error(f'{e.__class__.__name__}: {msg}',
+                            exc_info=e if log_verbose else None)
+                yield {"code": 500, "msg": msg}
+
+        def ret_sync(response, as_json):
             try:
                 with response as r:
                     for chunk in r.iter_text(None):
@@ -255,10 +178,9 @@ class ApiRequest:
             except httpx.ConnectError as e:
                 msg = f"无法连接API服务器，请确认 ‘api.py’ 已正常启动。({e})"
                 logger.error(msg)
-            logger.error(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'）。（{e}）"
+                msg = f"API通信超时，请确认已启动FastChat与API服务（详见Wiki '5. 启动 API 服务或 Web UI'）。（{e}）"
                 logger.error(msg)
                 yield {"code": 500, "msg": msg}
             except Exception as e:
@@ -267,6 +189,54 @@ class ApiRequest:
                             exc_info=e if log_verbose else None)
                 yield {"code": 500, "msg": msg}
 
+        if self._use_async:
+            return ret_async(response, as_json)
+        else:
+            return ret_sync(response, as_json)
+
+    def _get_response_value(
+        self,
+        response: httpx.Response,
+        as_json: bool = False,
+        value_func: Callable = None,
+    ):
+        '''
+        转换同步或异步请求返回的响应
+        `as_json`: 返回json
+        `value_func`: 用户可以自定义返回值，该函数接受response或json
+        '''
+        def to_json(r):
+            try:
+                return r.json()
+            except Exception as e:
+                msg = "API未能返回正确的JSON。" + str(e)
+                if log_verbose:
+                    logger.error(f'{e.__class__.__name__}: {msg}',
+                                exc_info=e if log_verbose else None)
+                return {"code": 500, "msg": msg}
+
+        if value_func is None:
+            value_func = (lambda r: r)
+
+        async def ret_async(response):
+            if as_json:
+                return value_func(to_json(await response))
+            else:
+                return value_func(await response)
+
+        if self._use_async:
+            return ret_async(response)
+        else:
+            if as_json:
+                return value_func(to_json(response))
+            else:
+                return value_func(response)
+
+    # 服务器信息
+    def get_server_configs(self, **kwargs):
+        response = self.post("/server/configs", **kwargs)
+        return self._get_response_value(response, lambda r: r.json())
+
     # 对话相关操作
 
     def chat_fastchat(
@@ -275,15 +245,12 @@ class ApiRequest:
         stream: bool = True,
         model: str = LLM_MODEL,
         temperature: float = TEMPERATURE,
-        max_tokens: int = 1024, # TODO:根据message内容自动计算max_tokens
+        max_tokens: int = 1024,
-        no_remote_api: bool = None,
         **kwargs: Any,
     ):
         '''
         对应api.py/chat/fastchat接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
         msg = OpenAiChatMsgIn(**{
             "messages": messages,
             "stream": stream,
@@ -293,11 +260,6 @@ class ApiRequest:
             **kwargs,
         })
 
-        if no_remote_api:
-            from server.chat.openai_chat import openai_chat
-            response = run_async(openai_chat(msg))
-            return self._fastapi_stream2generator(response)
-        else:
         data = msg.dict(exclude_unset=True, exclude_none=True)
         print(f"received input message:")
         pprint(data)
@@ -318,14 +280,11 @@ class ApiRequest:
         temperature: float = TEMPERATURE,
         max_tokens: int = 1024,
         prompt_name: str = "llm_chat",
-        no_remote_api: bool = None,
+        **kwargs,
     ):
         '''
-        对应api.py/chat/chat接口
+        对应api.py/chat/chat接口 #TODO: 考虑是否返回json
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "query": query,
             "history": history,
@@ -339,12 +298,7 @@ class ApiRequest:
         print(f"received input message:")
         pprint(data)
 
-        if no_remote_api:
+        response = self.post("/chat/chat", json=data, stream=True, **kwargs)
-            from server.chat.chat import chat
-            response = run_async(chat(**data))
-            return self._fastapi_stream2generator(response)
-        else:
-            response = self.post("/chat/chat", json=data, stream=True)
         return self._httpx_stream2generator(response)
 
     def agent_chat(
@@ -355,14 +309,10 @@ class ApiRequest:
         model: str = LLM_MODEL,
         temperature: float = TEMPERATURE,
         max_tokens: int = 1024,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/chat/agent_chat 接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "query": query,
             "history": history,
@@ -375,11 +325,6 @@ class ApiRequest:
         print(f"received input message:")
         pprint(data)
 
-        if no_remote_api:
-            from server.chat.agent_chat import agent_chat
-            response = run_async(agent_chat(**data))
-            return self._fastapi_stream2generator(response)
-        else:
         response = self.post("/chat/agent_chat", json=data, stream=True)
         return self._httpx_stream2generator(response)
 
@@ -395,14 +340,10 @@ class ApiRequest:
         temperature: float = TEMPERATURE,
         max_tokens: int = 1024,
         prompt_name: str = "knowledge_base_chat",
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/chat/knowledge_base_chat接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "query": query,
             "knowledge_base_name": knowledge_base_name,
@@ -413,18 +354,12 @@ class ApiRequest:
             "model_name": model,
             "temperature": temperature,
             "max_tokens": max_tokens,
-            "local_doc_url": no_remote_api,
             "prompt_name": prompt_name,
         }
 
         print(f"received input message:")
         pprint(data)
 
-        if no_remote_api:
-            from server.chat.knowledge_base_chat import knowledge_base_chat
-            response = run_async(knowledge_base_chat(**data))
-            return self._fastapi_stream2generator(response, as_json=True)
-        else:
         response = self.post(
             "/chat/knowledge_base_chat",
             json=data,
@@ -443,14 +378,10 @@ class ApiRequest:
         temperature: float = TEMPERATURE,
         max_tokens: int = 1024,
         prompt_name: str = "knowledge_base_chat",
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/chat/search_engine_chat接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "query": query,
             "search_engine_name": search_engine_name,
@@ -466,11 +397,6 @@ class ApiRequest:
         print(f"received input message:")
         pprint(data)
 
-        if no_remote_api:
-            from server.chat.search_engine_chat import search_engine_chat
-            response = run_async(search_engine_chat(**data))
-            return self._fastapi_stream2generator(response, as_json=True)
-        else:
         response = self.post(
             "/chat/search_engine_chat",
             json=data,
@@ -480,116 +406,65 @@ class ApiRequest:
 
     # 知识库相关操作
 
-    def _check_httpx_json_response(
-            self,
-            response: httpx.Response,
-            errorMsg: str = f"无法连接API服务器，请确认 ‘api.py’ 已正常启动。",
-        ) -> Dict:
-        '''
-        check whether httpx returns correct data with normal Response.
-        error in api with streaming support was checked in _httpx_stream2enerator
-        '''
-        try:
-            return response.json()
-        except Exception as e:
-            msg = "API未能返回正确的JSON。" + (errorMsg or str(e))
-            logger.error(f'{e.__class__.__name__}: {msg}',
-                         exc_info=e if log_verbose else None)
-            return {"code": 500, "msg": msg}
-
     def list_knowledge_bases(
         self,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/list_knowledge_bases接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
-        if no_remote_api:
-            from server.knowledge_base.kb_api import list_kbs
-            response = list_kbs()
-            return response.data
-        else:
         response = self.get("/knowledge_base/list_knowledge_bases")
-            data = self._check_httpx_json_response(response)
+        return self._get_response_value(response,
-            return data.get("data", [])
+                                        as_json=True,
+                                        value_func=lambda r: r.get("data", []))
 
     def create_knowledge_base(
         self,
         knowledge_base_name: str,
-        vector_store_type: str = "faiss",
+        vector_store_type: str = DEFAULT_VS_TYPE,
         embed_model: str = EMBEDDING_MODEL,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/create_knowledge_base接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "knowledge_base_name": knowledge_base_name,
             "vector_store_type": vector_store_type,
             "embed_model": embed_model,
         }
 
-        if no_remote_api:
-            from server.knowledge_base.kb_api import create_kb
-            response = create_kb(**data)
-            return response.dict()
-        else:
         response = self.post(
             "/knowledge_base/create_knowledge_base",
             json=data,
         )
-            return self._check_httpx_json_response(response)
+        return self._get_response_value(response, as_json=True)
 
     def delete_knowledge_base(
         self,
         knowledge_base_name: str,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/delete_knowledge_base接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
-        if no_remote_api:
-            from server.knowledge_base.kb_api import delete_kb
-            response = delete_kb(knowledge_base_name)
-            return response.dict()
-        else:
         response = self.post(
             "/knowledge_base/delete_knowledge_base",
             json=f"{knowledge_base_name}",
         )
-            return self._check_httpx_json_response(response)
+        return self._get_response_value(response, as_json=True)
 
     def list_kb_docs(
         self,
         knowledge_base_name: str,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/list_files接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
-        if no_remote_api:
-            from server.knowledge_base.kb_doc_api import list_files
-            response = list_files(knowledge_base_name)
-            return response.data
-        else:
         response = self.get(
             "/knowledge_base/list_files",
             params={"knowledge_base_name": knowledge_base_name}
         )
-            data = self._check_httpx_json_response(response)
+        return self._get_response_value(response,
-            return data.get("data", [])
+                                        as_json=True,
+                                        value_func=lambda r: r.get("data", []))
 
     def search_kb_docs(
         self,
@@ -597,14 +472,10 @@ class ApiRequest:
         knowledge_base_name: str,
         top_k: int = VECTOR_SEARCH_TOP_K,
         score_threshold: int = SCORE_THRESHOLD,
-        no_remote_api: bool = None,
     ) -> List:
         '''
         对应api.py/knowledge_base/search_docs接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "query": query,
             "knowledge_base_name": knowledge_base_name,
@@ -612,16 +483,11 @@ class ApiRequest:
             "score_threshold": score_threshold,
         }
 
-        if no_remote_api:
-            from server.knowledge_base.kb_doc_api import search_docs
-            return search_docs(**data)
-        else:
         response = self.post(
             "/knowledge_base/search_docs",
             json=data,
         )
-            data = self._check_httpx_json_response(response)
+        return self._get_response_value(response, as_json=True)
-            return data
 
     def upload_kb_docs(
         self,
@@ -634,14 +500,10 @@ class ApiRequest:
         zh_title_enhance=ZH_TITLE_ENHANCE,
         docs: Dict = {},
         not_refresh_vs_cache: bool = False,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/upload_docs接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         def convert_file(file, filename=None):
             if isinstance(file, bytes): # raw bytes
                 file = BytesIO(file)
@@ -664,21 +526,6 @@ class ApiRequest:
             "not_refresh_vs_cache": not_refresh_vs_cache,
         }
 
-        if no_remote_api:
-            from server.knowledge_base.kb_doc_api import upload_docs
-            from fastapi import UploadFile
-            from tempfile import SpooledTemporaryFile
-
-            upload_files = []
-            for filename, file in files:
-                temp_file = SpooledTemporaryFile(max_size=10 * 1024 * 1024)
-                temp_file.write(file.read())
-                temp_file.seek(0)
-                upload_files.append(UploadFile(file=temp_file, filename=filename))
-
-            response = upload_docs(upload_files, **data)
-            return response.dict()
-        else:
         if isinstance(data["docs"], dict):
             data["docs"] = json.dumps(data["docs"], ensure_ascii=False)
         response = self.post(
@@ -686,7 +533,7 @@ class ApiRequest:
             data=data,
             files=[("files", (filename, file)) for filename, file in files],
         )
-            return self._check_httpx_json_response(response)
+        return self._get_response_value(response, as_json=True)
 
     def delete_kb_docs(
         self,
@@ -694,14 +541,10 @@ class ApiRequest:
         file_names: List[str],
         delete_content: bool = False,
         not_refresh_vs_cache: bool = False,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/delete_docs接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "knowledge_base_name": knowledge_base_name,
             "file_names": file_names,
@@ -709,16 +552,11 @@ class ApiRequest:
             "not_refresh_vs_cache": not_refresh_vs_cache,
         }
 
-        if no_remote_api:
-            from server.knowledge_base.kb_doc_api import delete_docs
-            response = delete_docs(**data)
-            return response.dict()
-        else:
         response = self.post(
             "/knowledge_base/delete_docs",
             json=data,
         )
-            return self._check_httpx_json_response(response)
+        return self._get_response_value(response, as_json=True)
 
     def update_kb_docs(
         self,
@@ -730,14 +568,10 @@ class ApiRequest:
         zh_title_enhance=ZH_TITLE_ENHANCE,
         docs: Dict = {},
         not_refresh_vs_cache: bool = False,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/update_docs接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "knowledge_base_name": knowledge_base_name,
             "file_names": file_names,
@@ -748,18 +582,15 @@ class ApiRequest:
             "docs": docs,
             "not_refresh_vs_cache": not_refresh_vs_cache,
         }
-        if no_remote_api:
+
-            from server.knowledge_base.kb_doc_api import update_docs
-            response = update_docs(**data)
-            return response.dict()
-        else:
         if isinstance(data["docs"], dict):
             data["docs"] = json.dumps(data["docs"], ensure_ascii=False)
+
         response = self.post(
             "/knowledge_base/update_docs",
             json=data,
         )
-            return self._check_httpx_json_response(response)
+        return self._get_response_value(response, as_json=True)
 
     def recreate_vector_store(
         self,
@@ -770,14 +601,10 @@ class ApiRequest:
         chunk_size=CHUNK_SIZE,
         chunk_overlap=OVERLAP_SIZE,
         zh_title_enhance=ZH_TITLE_ENHANCE,
-        no_remote_api: bool = None,
     ):
         '''
         对应api.py/knowledge_base/recreate_vector_store接口
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "knowledge_base_name": knowledge_base_name,
             "allow_empty_kb": allow_empty_kb,
@@ -788,11 +615,6 @@ class ApiRequest:
             "zh_title_enhance": zh_title_enhance,
         }
 
-        if no_remote_api:
-            from server.knowledge_base.kb_doc_api import recreate_vector_store
-            response = recreate_vector_store(**data)
-            return self._fastapi_stream2generator(response, as_json=True)
-        else:
         response = self.post(
             "/knowledge_base/recreate_vector_store",
             json=data,
@@ -805,88 +627,89 @@ class ApiRequest:
     def list_running_models(
         self,
         controller_address: str = None,
-        no_remote_api: bool = None,
     ):
         '''
         获取Fastchat中正运行的模型列表
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "controller_address": controller_address,
         }
-        if no_remote_api:
+
-            from server.llm_api import list_running_models
+        response = self.post(
-            return list_running_models(**data).data
-        else:
-            r = self.post(
             "/llm_model/list_running_models",
             json=data,
         )
-            return r.json().get("data", [])
+        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", []))
 
-    def list_config_models(self, no_remote_api: bool = None) -> Dict[str, List[str]]:
+    def list_config_models(self) -> Dict[str, List[str]]:
         '''
-        获取configs中配置的模型列表，返回形式为{"type": [model_name1, model_name2, ...], ...}。
+        获取服务器configs中配置的模型列表，返回形式为{"type": [model_name1, model_name2, ...], ...}。
-        如果no_remote_api=True, 从运行ApiRequest的机器上获取；否则从运行api.py的机器上获取。
         '''
-        if no_remote_api is None:
+        response = self.post(
-            no_remote_api = self.no_remote_api
-
-        if no_remote_api:
-            from server.llm_api import list_config_models
-            return list_config_models().data
-        else:
-            r = self.post(
             "/llm_model/list_config_models",
         )
-            return r.json().get("data", {})
+        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
+
+    def get_model_config(
+        self,
+        model_name: str,
+    ) -> Dict:
+        '''
+        获取服务器上模型配置
+        '''
+        data={
+            "model_name": model_name,
+        }
+        response = self.post(
+            "/llm_model/get_model_config",
+        )
+        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
+
+    def list_search_engines(self) -> List[str]:
+        '''
+        获取服务器支持的搜索引擎
+        '''
+        response = self.post(
+            "/server/list_search_engines",
+        )
+        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
 
     def stop_llm_model(
         self,
         model_name: str,
         controller_address: str = None,
-        no_remote_api: bool = None,
     ):
         '''
         停止某个LLM模型。
         注意：由于Fastchat的实现方式，实际上是把LLM模型所在的model_worker停掉。
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         data = {
             "model_name": model_name,
             "controller_address": controller_address,
         }
 
-        if no_remote_api:
+        response = self.post(
-            from server.llm_api import stop_llm_model
-            return stop_llm_model(**data).dict()
-        else:
-            r = self.post(
             "/llm_model/stop",
             json=data,
         )
-            return r.json()
+        return self._get_response_value(response, as_json=True)
 
     def change_llm_model(
         self,
         model_name: str,
         new_model_name: str,
         controller_address: str = None,
-        no_remote_api: bool = None,
     ):
         '''
         向fastchat controller请求切换LLM模型。
         '''
-        if no_remote_api is None:
-            no_remote_api = self.no_remote_api
-
         if not model_name or not new_model_name:
-            return
+            return {
+                "code": 500,
+                "msg": f"未指定模型名称"
+            }
 
+        def ret_sync():
             running_models = self.list_running_models()
             if new_model_name == model_name or new_model_name in running_models:
                 return {
@@ -901,7 +724,7 @@ class ApiRequest:
                 }
 
             config_models = self.list_config_models()
-        if new_model_name not in config_models.get("local", []):
+            if new_model_name not in config_models.get("local", {}):
                 return {
                     "code": 500,
                     "msg": f"要切换的模型'{new_model_name}'在configs中没有配置。"
@@ -913,16 +736,55 @@ class ApiRequest:
                 "controller_address": controller_address,
             }
 
-        if no_remote_api:
+            response = self.post(
-            from server.llm_api import change_llm_model
-            return change_llm_model(**data).dict()
-        else:
-            r = self.post(
                 "/llm_model/change",
                 json=data,
-                timeout=HTTPX_DEFAULT_TIMEOUT, # wait for new worker_model
             )
-            return r.json()
+            return self._get_response_value(response, as_json=True)
+
+        async def ret_async():
+            running_models = await self.list_running_models()
+            if new_model_name == model_name or new_model_name in running_models:
+                return {
+                    "code": 200,
+                    "msg": "无需切换"
+                }
+
+            if model_name not in running_models:
+                return {
+                    "code": 500,
+                    "msg": f"指定的模型'{model_name}'没有运行。当前运行模型：{running_models}"
+                }
+
+            config_models = await self.list_config_models()
+            if new_model_name not in config_models.get("local", {}):
+                return {
+                    "code": 500,
+                    "msg": f"要切换的模型'{new_model_name}'在configs中没有配置。"
+                }
+
+            data = {
+                "model_name": model_name,
+                "new_model_name": new_model_name,
+                "controller_address": controller_address,
+            }
+
+            response = self.post(
+                "/llm_model/change",
+                json=data,
+            )
+            return self._get_response_value(response, as_json=True)
+
+        if self._use_async:
+            return ret_async()
+        else:
+            return ret_sync()
+
+
+class AsyncApiRequest(ApiRequest):
+    def __init__(self, base_url: str = api_address(), timeout: float = HTTPX_DEFAULT_TIMEOUT):
+        super().__init__(base_url, timeout)
+        self._use_async = True
 
 
 def check_error_msg(data: Union[str, dict, list], key: str = "errorMsg") -> str:
@@ -950,7 +812,8 @@ def check_success_msg(data: Union[str, dict, list], key: str = "msg") -> str:
 
 
 if __name__ == "__main__":
-    api = ApiRequest(no_remote_api=True)
+    api = ApiRequest()
+    aapi = AsyncApiRequest()
 
     # print(api.chat_fastchat(
     #     messages=[{"role": "user", "content": "hello"}]