diff --git a/embeddings/add_embedding_keywords.py b/embeddings/add_embedding_keywords.py
index 622a4cac..f46dee29 100644
--- a/embeddings/add_embedding_keywords.py
+++ b/embeddings/add_embedding_keywords.py
@@ -7,31 +7,35 @@
 保存的模型的位置位于原本嵌入模型的目录下，模型的名称为原模型名称+Merge_Keywords_时间戳
 '''
 import sys
+
 sys.path.append("..")
+import os
+import torch
+
 from datetime import datetime
 from configs import (
     MODEL_PATH,
     EMBEDDING_MODEL,
     EMBEDDING_KEYWORD_FILE,
 )
-import os
-import torch
+
 from safetensors.torch import save_model
 from sentence_transformers import SentenceTransformer
+from langchain_core._api import deprecated
 
 
+@deprecated(
+        since="0.3.0",
+        message="自定义关键词 Langchain-Chatchat 0.3.x 重写, 0.2.x中相关功能将废弃",
+        removal="0.3.0"
+    )
 def get_keyword_embedding(bert_model, tokenizer, key_words):
     tokenizer_output = tokenizer(key_words, return_tensors="pt", padding=True, truncation=True)
-
-    # No need to manually convert to tensor as we've set return_tensors="pt"
     input_ids = tokenizer_output['input_ids']
-
-    # Remove the first and last token for each sequence in the batch
     input_ids = input_ids[:, 1:-1]
 
     keyword_embedding = bert_model.embeddings.word_embeddings(input_ids)
     keyword_embedding = torch.mean(keyword_embedding, 1)
-
     return keyword_embedding
 
 
@@ -47,14 +51,11 @@ def add_keyword_to_model(model_name=EMBEDDING_MODEL, keyword_file: str = "", out
     bert_model = word_embedding_model.auto_model
     tokenizer = word_embedding_model.tokenizer
     key_words_embedding = get_keyword_embedding(bert_model, tokenizer, key_words)
-    # key_words_embedding = st_model.encode(key_words)
 
     embedding_weight = bert_model.embeddings.word_embeddings.weight
     embedding_weight_len = len(embedding_weight)
     tokenizer.add_tokens(key_words)
     bert_model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=32)
-
-    # key_words_embedding_tensor = torch.from_numpy(key_words_embedding)
     embedding_weight = bert_model.embeddings.word_embeddings.weight
     with torch.no_grad():
         embedding_weight[embedding_weight_len:embedding_weight_len + key_words_len, :] = key_words_embedding
@@ -76,46 +77,3 @@ def add_keyword_to_embedding_model(path: str = EMBEDDING_KEYWORD_FILE):
     output_model_name = "{}_Merge_Keywords_{}".format(EMBEDDING_MODEL, current_time)
     output_model_path = os.path.join(model_parent_directory, output_model_name)
     add_keyword_to_model(model_name, keyword_file, output_model_path)
-
-
-if __name__ == '__main__':
-    add_keyword_to_embedding_model(EMBEDDING_KEYWORD_FILE)
-
-    # input_model_name = ""
-    # output_model_path = ""
-    # # 以下为加入关键字前后tokenizer的测试用例对比
-    # def print_token_ids(output, tokenizer, sentences):
-    #     for idx, ids in enumerate(output['input_ids']):
-    #         print(f'sentence={sentences[idx]}')
-    #         print(f'ids={ids}')
-    #         for id in ids:
-    #             decoded_id = tokenizer.decode(id)
-    #             print(f'    {decoded_id}->{id}')
-    #
-    # sentences = [
-    #     '数据科学与大数据技术',
-    #     'Langchain-Chatchat'
-    # ]
-    #
-    # st_no_keywords = SentenceTransformer(input_model_name)
-    # tokenizer_without_keywords = st_no_keywords.tokenizer
-    # print("===== tokenizer with no keywords added =====")
-    # output = tokenizer_without_keywords(sentences)
-    # print_token_ids(output, tokenizer_without_keywords, sentences)
-    # print(f'-------- embedding with no keywords added -----')
-    # embeddings = st_no_keywords.encode(sentences)
-    # print(embeddings)
-    #
-    # print("--------------------------------------------")
-    # print("--------------------------------------------")
-    # print("--------------------------------------------")
-    #
-    # st_with_keywords = SentenceTransformer(output_model_path)
-    # tokenizer_with_keywords = st_with_keywords.tokenizer
-    # print("===== tokenizer with keyword added =====")
-    # output = tokenizer_with_keywords(sentences)
-    # print_token_ids(output, tokenizer_with_keywords, sentences)
-    #
-    # print(f'-------- embedding with keywords added -----')
-    # embeddings = st_with_keywords.encode(sentences)
-    # print(embeddings)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index b041742e..5ab39657 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -38,13 +38,11 @@ numpy~=1.24.4
 pandas~=2.0.3
 einops>=0.7.0
 transformers_stream_generator==0.0.4
-vllm==0.2.6; sys_platform == "linux"
-httpx[brotli,http2,socks]==0.25.2
-llama-index
+vllm==0.2.7; sys_platform == "linux"
 
 # optional document loaders
 
-# rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
+#rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
 jq==1.6.0 # for .json and .jsonl files. suggest `conda install jq` on windows
 beautifulsoup4~=4.12.2 # for .mhtml files
 pysrt~=1.1.2
@@ -69,9 +67,11 @@ metaphor-python~=0.1.23
 
 # WebUI requirements
 
-streamlit~=1.29.0
-streamlit-option-menu>=0.3.6
+streamlit==1.30.0
+streamlit-option-menu==0.3.6
+streamlit-antd-components==0.3.1
 streamlit-chatbox==1.1.11
-streamlit-modal>=0.1.0
-streamlit-aggrid>=0.3.4.post3
-watchdog>=3.0.0
+streamlit-modal==0.1.0
+streamlit-aggrid==0.3.4.post3
+httpx==0.26.0
+watchdog==3.0.0
\ No newline at end of file
diff --git a/requirements_api.txt b/requirements_api.txt
index 5cd1048f..0e2a9eee 100644
--- a/requirements_api.txt
+++ b/requirements_api.txt
@@ -36,8 +36,8 @@ numpy~=1.24.4
 pandas~=2.0.3
 einops>=0.7.0
 transformers_stream_generator==0.0.4
-vllm==0.2.6; sys_platform == "linux"
-httpx[brotli,http2,socks]==0.25.2
+vllm==0.2.7; sys_platform == "linux"
+httpx==0.26.0
 llama-index
 
 # optional document loaders
diff --git a/requirements_lite.txt b/requirements_lite.txt
index 584d0c75..db57273a 100644
--- a/requirements_lite.txt
+++ b/requirements_lite.txt
@@ -27,7 +27,7 @@ numpy~=1.24.4
 pandas~=2.0.3
 einops>=0.7.0
 transformers_stream_generator==0.0.4
-vllm==0.2.6; sys_platform == "linux"
+vllm==0.2.7; sys_platform == "linux"
 httpx[brotli,http2,socks]==0.25.2
 requests
 pathlib
@@ -54,11 +54,11 @@ metaphor-python~=0.1.23
 
 # WebUI requirements
 
-streamlit>=1.29.0
-streamlit-option-menu>=0.3.6
-streamlit-antd-components>=0.3.0
-streamlit-chatbox>=1.1.11
-streamlit-modal>=0.1.0
-streamlit-aggrid>=0.3.4.post3
-httpx[brotli,http2,socks]>=0.25.2
-watchdog>=3.0.0
\ No newline at end of file
+streamlit==1.30.0
+streamlit-option-menu==0.3.6
+streamlit-antd-components==0.3.1
+streamlit-chatbox==1.1.11
+streamlit-modal==0.1.0
+streamlit-aggrid==0.3.4.post3
+httpx==0.26.0
+watchdog==3.0.0
\ No newline at end of file
diff --git a/requirements_webui.txt b/requirements_webui.txt
index 28810274..111dedaa 100644
--- a/requirements_webui.txt
+++ b/requirements_webui.txt
@@ -1,10 +1,10 @@
 # WebUI requirements
 
-streamlit>=1.29.0
-streamlit-option-menu>=0.3.6
-streamlit-antd-components>=0.3.0
-streamlit-chatbox>=1.1.11
-streamlit-modal>=0.1.0
-streamlit-aggrid>=0.3.4.post3
-httpx[brotli,http2,socks]>=0.25.2
-watchdog>=3.0.0
\ No newline at end of file
+streamlit==1.30.0
+streamlit-option-menu==0.3.6
+streamlit-antd-components==0.3.1
+streamlit-chatbox==1.1.11
+streamlit-modal==0.1.0
+streamlit-aggrid==0.3.4.post3
+httpx==0.26.0
+watchdog==3.0.0
\ No newline at end of file
diff --git a/server/agent/custom_agent/ChatGLM3Agent.py b/server/agent/custom_agent/ChatGLM3Agent.py
index 92ddc28b..65f57567 100644
--- a/server/agent/custom_agent/ChatGLM3Agent.py
+++ b/server/agent/custom_agent/ChatGLM3Agent.py
@@ -1,22 +1,19 @@
 """
-This file is a modified version for ChatGLM3-6B the original ChatGLM3Agent.py file from the langchain repo.
+This file is a modified version for ChatGLM3-6B the original glm3_agent.py file from the langchain repo.
 """
 from __future__ import annotations
 
-import yaml
-from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
-from langchain.memory import ConversationBufferWindowMemory
-from typing import Any, List, Sequence, Tuple, Optional, Union
-import os
-from langchain.agents.agent import Agent
-from langchain.chains.llm import LLMChain
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate, MessagesPlaceholder,
-)
 import json
 import logging
+from typing import Any, List, Sequence, Tuple, Optional, Union
+from pydantic.schema import model_schema
+
+
+from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.agents.agent import Agent
+from langchain.chains.llm import LLMChain
+from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
 from langchain.agents.agent import AgentOutputParser
 from langchain.output_parsers import OutputFixingParser
 from langchain.pydantic_v1 import Field
@@ -43,12 +40,18 @@ class StructuredChatOutputParserWithRetries(AgentOutputParser):
         first_index = min([text.find(token) if token in text else len(text) for token in special_tokens])
         text = text[:first_index]
         if "tool_call" in text:
-            tool_name_end = text.find("```")
-            tool_name = text[:tool_name_end].strip()
-            input_para = text.split("='")[-1].split("'")[0]
+            action_end = text.find("```")
+            action = text[:action_end].strip()
+            params_str_start = text.find("(") + 1
+            params_str_end = text.rfind(")")
+            params_str = text[params_str_start:params_str_end]
+
+            params_pairs = [param.split("=") for param in params_str.split(",") if "=" in param]
+            params = {pair[0].strip(): pair[1].strip().strip("'\"") for pair in params_pairs}
+
             action_json = {
-                "action": tool_name,
-                "action_input": input_para
+                "action": action,
+                "action_input": params
             }
         else:
             action_json = {
@@ -109,10 +112,6 @@ class StructuredGLM3ChatAgent(Agent):
         else:
             return agent_scratchpad
 
-    @classmethod
-    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
-        pass
-
     @classmethod
     def _get_default_output_parser(
             cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
@@ -121,7 +120,7 @@ class StructuredGLM3ChatAgent(Agent):
 
     @property
     def _stop(self) -> List[str]:
-        return ["```<observation>"]
+        return ["<|observation|>"]
 
     @classmethod
     def create_prompt(
@@ -131,44 +130,25 @@ class StructuredGLM3ChatAgent(Agent):
             input_variables: Optional[List[str]] = None,
             memory_prompts: Optional[List[BasePromptTemplate]] = None,
     ) -> BasePromptTemplate:
-        def tool_config_from_file(tool_name, directory="server/agent/tools/"):
-            """search tool yaml and return simplified json format"""
-            file_path = os.path.join(directory, f"{tool_name.lower()}.yaml")
-            try:
-                with open(file_path, 'r', encoding='utf-8') as file:
-                    tool_config = yaml.safe_load(file)
-                    # Simplify the structure if needed
-                    simplified_config = {
-                        "name": tool_config.get("name", ""),
-                        "description": tool_config.get("description", ""),
-                        "parameters": tool_config.get("parameters", {})
-                    }
-                    return simplified_config
-            except FileNotFoundError:
-                logger.error(f"File not found: {file_path}")
-                return None
-            except Exception as e:
-                logger.error(f"An error occurred while reading {file_path}: {e}")
-                return None
-
         tools_json = []
         tool_names = []
         for tool in tools:
-            tool_config = tool_config_from_file(tool.name)
-            if tool_config:
-                tools_json.append(tool_config)
-                tool_names.append(tool.name)
-
-        # Format the tools for output
+            tool_schema = model_schema(tool.args_schema) if tool.args_schema else {}
+            simplified_config_langchain = {
+                "name": tool.name,
+                "description": tool.description,
+                "parameters": tool_schema.get("properties", {})
+            }
+            tools_json.append(simplified_config_langchain)
+            tool_names.append(tool.name)
         formatted_tools = "\n".join([
             f"{tool['name']}: {tool['description']}, args: {tool['parameters']}"
             for tool in tools_json
         ])
         formatted_tools = formatted_tools.replace("'", "\\'").replace("{", "{{").replace("}", "}}")
-
         template = prompt.format(tool_names=tool_names,
                                  tools=formatted_tools,
-                                 history="{history}",
+                                 history="None",
                                  input="{input}",
                                  agent_scratchpad="{agent_scratchpad}")
 
@@ -225,7 +205,6 @@ def initialize_glm3_agent(
         tools: Sequence[BaseTool],
         llm: BaseLanguageModel,
         prompt: str = None,
-        callback_manager: Optional[BaseCallbackManager] = None,
         memory: Optional[ConversationBufferWindowMemory] = None,
         agent_kwargs: Optional[dict] = None,
         *,
@@ -238,14 +217,12 @@ def initialize_glm3_agent(
         llm=llm,
         tools=tools,
         prompt=prompt,
-        callback_manager=callback_manager, **agent_kwargs
+        **agent_kwargs
     )
     return AgentExecutor.from_agent_and_tools(
         agent=agent_obj,
         tools=tools,
-        callback_manager=callback_manager,
         memory=memory,
         tags=tags_,
         **kwargs,
-    )
-
+    )
\ No newline at end of file
diff --git a/server/agent/model_contain.py b/server/agent/model_contain.py
index 1927c88f..0141ad03 100644
--- a/server/agent/model_contain.py
+++ b/server/agent/model_contain.py
@@ -1,5 +1,3 @@
-
-## 由于工具类无法传参，所以使用全局变量来传递模型和对应的知识库介绍
 class ModelContainer:
     def __init__(self):
         self.MODEL = None
diff --git a/server/agent/tools/__init__.py b/server/agent/tools/__init__.py
index 43d57343..21dfd973 100644
--- a/server/agent/tools/__init__.py
+++ b/server/agent/tools/__init__.py
@@ -3,7 +3,7 @@ from .search_knowledgebase_simple import search_knowledgebase_simple
 from .search_knowledgebase_once import search_knowledgebase_once, KnowledgeSearchInput
 from .search_knowledgebase_complex import search_knowledgebase_complex, KnowledgeSearchInput
 from .calculate import calculate, CalculatorInput
-from .weather_check import weathercheck, WhetherSchema
+from .weather_check import weathercheck, WeatherInput
 from .shell import shell, ShellInput
 from .search_internet import search_internet, SearchInternetInput
 from .wolfram import wolfram, WolframInput
diff --git a/server/agent/tools/arxiv.yaml b/server/agent/tools/arxiv.yaml
deleted file mode 100644
index 5da8a9f9..00000000
--- a/server/agent/tools/arxiv.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: arxiv
-description: A wrapper around Arxiv.org for searching and retrieving scientific articles in various fields.
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: The search query title
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/calculate.yaml b/server/agent/tools/calculate.yaml
deleted file mode 100644
index 2976c011..00000000
--- a/server/agent/tools/calculate.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: calculate
-description: Useful for when you need to answer questions about simple calculations
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: The formula to be calculated
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/search_internet.yaml b/server/agent/tools/search_internet.yaml
deleted file mode 100644
index 57608e7e..00000000
--- a/server/agent/tools/search_internet.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: search_internet
-description: Use this tool to surf internet and get information
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: Query for Internet search
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/search_knowledgebase_complex.yaml b/server/agent/tools/search_knowledgebase_complex.yaml
deleted file mode 100644
index bccbfd8c..00000000
--- a/server/agent/tools/search_knowledgebase_complex.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: search_knowledgebase_complex
-description: Use this tool to search local knowledgebase and get information
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: The query to be searched
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/search_youtube.yaml b/server/agent/tools/search_youtube.yaml
deleted file mode 100644
index ce446068..00000000
--- a/server/agent/tools/search_youtube.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: search_youtube
-description: Use this tools to search youtube videos
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: Query for Videos search
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/shell.yaml b/server/agent/tools/shell.yaml
deleted file mode 100644
index 22b94180..00000000
--- a/server/agent/tools/shell.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: shell
-description: Use Linux Shell to execute Linux commands
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: The command to execute
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/weather_check.py b/server/agent/tools/weather_check.py
index db88b874..7e55c7cb 100644
--- a/server/agent/tools/weather_check.py
+++ b/server/agent/tools/weather_check.py
@@ -1,338 +1,25 @@
-from __future__ import annotations
-
-## 单独运行的时候需要添加
-import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-import re
-import warnings
-from typing import Dict
-
-from langchain.callbacks.manager import (
-    AsyncCallbackManagerForChainRun,
-    CallbackManagerForChainRun,
-)
-from langchain.chains.base import Chain
-from langchain.chains.llm import LLMChain
-from langchain.pydantic_v1 import Extra, root_validator
-from langchain.schema import BasePromptTemplate
-from langchain.schema.language_model import BaseLanguageModel
-import requests
-from typing import List, Any, Optional
-from datetime import datetime
-from langchain.prompts import PromptTemplate
-from server.agent import model_container
-from pydantic import BaseModel, Field
-
-## 使用和风天气API查询天气
-KEY = "ac880e5a877042809ac7ffdd19d95b0d"
-# key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key
-
-
-_PROMPT_TEMPLATE = """
-用户会提出一个关于天气的问题，你的目标是拆分出用户问题中的区，市 并按照我提供的工具回答。
-例如 用户提出的问题是: 上海浦东未来1小时天气情况？
-则 提取的市和区是: 上海 浦东
-如果用户提出的问题是: 上海未来1小时天气情况？
-则 提取的市和区是: 上海 None
-请注意以下内容:
-1. 如果你没有找到区的内容,则一定要使用 None 替代，否则程序无法运行
-2. 如果用户没有指定市 则直接返回缺少信息
-
-问题: ${{用户的问题}}
-
-你的回答格式应该按照下面的内容，请注意，格式内的```text 等标记都必须输出，这是我用来提取答案的标记。
-```text
-
-${{拆分的市和区，中间用空格隔开}}
-```
-... weathercheck(市 区)...
-```output
-
-${{提取后的答案}}
-```
-答案: ${{答案}}
-
-
-
-这是一个例子：
-问题: 上海浦东未来1小时天气情况？
-
-
-```text
-上海 浦东
-```
-...weathercheck(上海 浦东)...
-
-```output
-预报时间: 1小时后
-具体时间: 今天 18:00
-温度: 24°C
-天气: 多云
-风向: 西南风
-风速: 7级
-湿度: 88%
-降水概率: 16%
-
-Answer: 上海浦东一小时后的天气是多云。
-
-现在，这是我的问题：
-
-问题: {question}
 """
-PROMPT = PromptTemplate(
-    input_variables=["question"],
-    template=_PROMPT_TEMPLATE,
-)
+更简单的单参数输入工具实现，用于查询现在天气的情况
+"""
+from pydantic import BaseModel, Field
+import requests
+
+def weather(location: str, api_key: str):
+    url = f"https://api.seniverse.com/v3/weather/now.json?key={api_key}&location={location}&language=zh-Hans&unit=c"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        weather = {
+            "temperature": data["results"][0]["now"]["temperature"],
+            "description": data["results"][0]["now"]["text"],
+        }
+        return weather
+    else:
+        raise Exception(
+            f"Failed to retrieve weather: {response.status_code}")
 
 
-def get_city_info(location, adm, key):
-    base_url = 'https://geoapi.qweather.com/v2/city/lookup?'
-    params = {'location': location, 'adm': adm, 'key': key}
-    response = requests.get(base_url, params=params)
-    data = response.json()
-    return data
-
-
-def format_weather_data(data, place):
-    hourly_forecast = data['hourly']
-    formatted_data = f"\n 这是查询到的关于{place}未来24小时的天气信息: \n"
-    for forecast in hourly_forecast:
-        # 将预报时间转换为datetime对象
-        forecast_time = datetime.strptime(forecast['fxTime'], '%Y-%m-%dT%H:%M%z')
-        # 获取预报时间的时区
-        forecast_tz = forecast_time.tzinfo
-        # 获取当前时间（使用预报时间的时区）
-        now = datetime.now(forecast_tz)
-        # 计算预报日期与当前日期的差值
-        days_diff = (forecast_time.date() - now.date()).days
-        if days_diff == 0:
-            forecast_date_str = '今天'
-        elif days_diff == 1:
-            forecast_date_str = '明天'
-        elif days_diff == 2:
-            forecast_date_str = '后天'
-        else:
-            forecast_date_str = str(days_diff) + '天后'
-        forecast_time_str = forecast_date_str + ' ' + forecast_time.strftime('%H:%M')
-        # 计算预报时间与当前时间的差值
-        time_diff = forecast_time - now
-        # 将差值转换为小时
-        hours_diff = time_diff.total_seconds() // 3600
-        if hours_diff < 1:
-            hours_diff_str = '1小时后'
-        elif hours_diff >= 24:
-            # 如果超过24小时，转换为天数
-            days_diff = hours_diff // 24
-            hours_diff_str = str(int(days_diff)) + '天'
-        else:
-            hours_diff_str = str(int(hours_diff)) + '小时'
-        # 将预报时间和当前时间的差值添加到输出中
-        formatted_data += '预报时间: ' + forecast_time_str + '  距离现在有: ' + hours_diff_str + '\n'
-        formatted_data += '温度: ' + forecast['temp'] + '°C\n'
-        formatted_data += '天气: ' + forecast['text'] + '\n'
-        formatted_data += '风向: ' + forecast['windDir'] + '\n'
-        formatted_data += '风速: ' + forecast['windSpeed'] + '级\n'
-        formatted_data += '湿度: ' + forecast['humidity'] + '%\n'
-        formatted_data += '降水概率: ' + forecast['pop'] + '%\n'
-        # formatted_data += '降水量: ' + forecast['precip'] + 'mm\n'
-        formatted_data += '\n'
-    return formatted_data
-
-
-def get_weather(key, location_id, place):
-    url = "https://devapi.qweather.com/v7/weather/24h?"
-    params = {
-        'location': location_id,
-        'key': key,
-    }
-    response = requests.get(url, params=params)
-    data = response.json()
-    return format_weather_data(data, place)
-
-
-def split_query(query):
-    parts = query.split()
-    adm = parts[0]
-    if len(parts) == 1:
-        return adm, adm
-    location = parts[1] if parts[1] != 'None' else adm
-    return location, adm
-
-
-def weather(query):
-    location, adm = split_query(query)
-    key = KEY
-    if key == "":
-        return "请先在代码中填入和风天气API Key"
-    try:
-        city_info = get_city_info(location=location, adm=adm, key=key)
-        location_id = city_info['location'][0]['id']
-        place = adm + "市" + location + "区"
-
-        weather_data = get_weather(key=key, location_id=location_id, place=place)
-        return weather_data + "以上是查询到的天气信息，请你查收\n"
-    except KeyError:
-        try:
-            city_info = get_city_info(location=adm, adm=adm, key=key)
-            location_id = city_info['location'][0]['id']
-            place = adm + "市"
-            weather_data = get_weather(key=key, location_id=location_id, place=place)
-            return weather_data + "重要提醒：用户提供的市和区中，区的信息不存在，或者出现错别字，因此该信息是关于市的天气，请你查收\n"
-        except KeyError:
-            return "输入的地区不存在，无法提供天气预报"
-
-
-class LLMWeatherChain(Chain):
-    llm_chain: LLMChain
-    llm: Optional[BaseLanguageModel] = None
-    """[Deprecated] LLM wrapper to use."""
-    prompt: BasePromptTemplate = PROMPT
-    """[Deprecated] Prompt to use to translate to python if necessary."""
-    input_key: str = "question"  #: :meta private:
-    output_key: str = "answer"  #: :meta private:
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-        arbitrary_types_allowed = True
-
-    @root_validator(pre=True)
-    def raise_deprecation(cls, values: Dict) -> Dict:
-        if "llm" in values:
-            warnings.warn(
-                "Directly instantiating an LLMWeatherChain with an llm is deprecated. "
-                "Please instantiate with llm_chain argument or using the from_llm "
-                "class method."
-            )
-            if "llm_chain" not in values and values["llm"] is not None:
-                prompt = values.get("prompt", PROMPT)
-                values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
-        return values
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Expect input key.
-
-        :meta private:
-        """
-        return [self.input_key]
-
-    @property
-    def output_keys(self) -> List[str]:
-        """Expect output key.
-
-        :meta private:
-        """
-        return [self.output_key]
-
-    def _evaluate_expression(self, expression: str) -> str:
-        try:
-            output = weather(expression)
-        except Exception as e:
-            output = "输入的信息有误，请再次尝试"
-        return output
-
-    def _process_llm_result(
-            self, llm_output: str, run_manager: CallbackManagerForChainRun
-    ) -> Dict[str, str]:
-
-        run_manager.on_text(llm_output, color="green", verbose=self.verbose)
-
-        llm_output = llm_output.strip()
-        text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
-        if text_match:
-            expression = text_match.group(1)
-            output = self._evaluate_expression(expression)
-            run_manager.on_text("\nAnswer: ", verbose=self.verbose)
-            run_manager.on_text(output, color="yellow", verbose=self.verbose)
-            answer = "Answer: " + output
-        elif llm_output.startswith("Answer:"):
-            answer = llm_output
-        elif "Answer:" in llm_output:
-            answer = "Answer: " + llm_output.split("Answer:")[-1]
-        else:
-            return {self.output_key: f"输入的格式不对: {llm_output},应该输入 (市 区)的组合"}
-        return {self.output_key: answer}
-
-    async def _aprocess_llm_result(
-            self,
-            llm_output: str,
-            run_manager: AsyncCallbackManagerForChainRun,
-    ) -> Dict[str, str]:
-        await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
-        llm_output = llm_output.strip()
-        text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
-
-        if text_match:
-            expression = text_match.group(1)
-            output = self._evaluate_expression(expression)
-            await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
-            await run_manager.on_text(output, color="yellow", verbose=self.verbose)
-            answer = "Answer: " + output
-        elif llm_output.startswith("Answer:"):
-            answer = llm_output
-        elif "Answer:" in llm_output:
-            answer = "Answer: " + llm_output.split("Answer:")[-1]
-        else:
-            raise ValueError(f"unknown format from LLM: {llm_output}")
-        return {self.output_key: answer}
-
-    def _call(
-            self,
-            inputs: Dict[str, str],
-            run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, str]:
-        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
-        _run_manager.on_text(inputs[self.input_key])
-        llm_output = self.llm_chain.predict(
-            question=inputs[self.input_key],
-            stop=["```output"],
-            callbacks=_run_manager.get_child(),
-        )
-        return self._process_llm_result(llm_output, _run_manager)
-
-    async def _acall(
-            self,
-            inputs: Dict[str, str],
-            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
-    ) -> Dict[str, str]:
-        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
-        await _run_manager.on_text(inputs[self.input_key])
-        llm_output = await self.llm_chain.apredict(
-            question=inputs[self.input_key],
-            stop=["```output"],
-            callbacks=_run_manager.get_child(),
-        )
-        return await self._aprocess_llm_result(llm_output, _run_manager)
-
-    @property
-    def _chain_type(self) -> str:
-        return "llm_weather_chain"
-
-    @classmethod
-    def from_llm(
-            cls,
-            llm: BaseLanguageModel,
-            prompt: BasePromptTemplate = PROMPT,
-            **kwargs: Any,
-    ) -> LLMWeatherChain:
-        llm_chain = LLMChain(llm=llm, prompt=prompt)
-        return cls(llm_chain=llm_chain, **kwargs)
-
-
-def weathercheck(query: str):
-    model = model_container.MODEL
-    llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT)
-    ans = llm_weather.run(query)
-    return ans
-
-
-class WhetherSchema(BaseModel):
-    location: str = Field(description="应该是一个地区的名称，用空格隔开，例如：上海 浦东，如果没有区的信息，可以只输入上海")
-
-if __name__ == '__main__':
-    result = weathercheck("苏州姑苏区今晚热不热？")
+def weathercheck(location: str):
+    return weather(location, "S8vrB4U_-c5mvAMiK")
+class WeatherInput(BaseModel):
+    location: str = Field(description="City name,include city and county,like '厦门'")
diff --git a/server/agent/tools/weather_check.yaml b/server/agent/tools/weather_check.yaml
deleted file mode 100644
index e72676ac..00000000
--- a/server/agent/tools/weather_check.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: weather_check
-description: Use Weather API to get weather information
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: City name,include city and county,like "厦门市思明区"
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools/wolfram.yaml b/server/agent/tools/wolfram.yaml
deleted file mode 100644
index 532f8248..00000000
--- a/server/agent/tools/wolfram.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: wolfram
-description: Useful for when you need to calculate difficult math formulas
-parameters:
-  type: object
-  properties:
-    query:
-      type: string
-      description: The formula to be calculated
-required:
-  - query
\ No newline at end of file
diff --git a/server/agent/tools_select.py b/server/agent/tools_select.py
index 821d324e..237c20b6 100644
--- a/server/agent/tools_select.py
+++ b/server/agent/tools_select.py
@@ -1,8 +1,6 @@
 from langchain.tools import Tool
 from server.agent.tools import *
 
-## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本。
-
 tools = [
     Tool.from_function(
         func=calculate,
@@ -20,7 +18,7 @@ tools = [
         func=weathercheck,
         name="weather_check",
         description="",
-        args_schema=WhetherSchema,
+        args_schema=WeatherInput,
     ),
     Tool.from_function(
         func=shell,
diff --git a/server/callback_handler/conversation_callback_handler.py b/server/callback_handler/conversation_callback_handler.py
index 6642c1e2..8f09b40d 100644
--- a/server/callback_handler/conversation_callback_handler.py
+++ b/server/callback_handler/conversation_callback_handler.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, List
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema import LLMResult
diff --git a/server/chat/agent_chat.py b/server/chat/agent_chat.py
index 03c0c2b9..f47958a0 100644
--- a/server/chat/agent_chat.py
+++ b/server/chat/agent_chat.py
@@ -1,23 +1,23 @@
-from langchain.memory import ConversationBufferWindowMemory
+import json
+import asyncio
 
-from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
-from server.agent.tools_select import tools, tool_names
-from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
-from langchain.agents import LLMSingleActionAgent, AgentExecutor
-from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
 from fastapi import Body
 from sse_starlette.sse import EventSourceResponse
 from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
-from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
-from langchain.chains import LLMChain
-from typing import AsyncIterable, Optional
-import asyncio
-from typing import List
-from server.chat.utils import History
-import json
-from server.agent import model_container
-from server.knowledge_base.kb_service.base import get_kb_details
 
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.agents import LLMSingleActionAgent, AgentExecutor
+from typing import AsyncIterable, Optional, List
+
+from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
+from server.knowledge_base.kb_service.base import get_kb_details
+from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
+from server.agent.tools_select import tools, tool_names
+from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
+from server.chat.utils import History
+from server.agent import model_container
+from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
 
 async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
                      history: List[History] = Body([],
@@ -33,7 +33,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                      max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
                      prompt_name: str = Body("default",
                                              description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
-                     # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
                      ):
     history = [History.from_data(h) for h in history]
 
@@ -55,12 +54,10 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
             callbacks=[callback],
         )
 
-        ## 传入全局变量来实现agent调用
         kb_list = {x["kb_name"]: x for x in get_kb_details()}
         model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()}
 
         if Agent_MODEL:
-            ## 如果有指定使用Agent模型来完成任务
             model_agent = get_ChatOpenAI(
                 model_name=Agent_MODEL,
                 temperature=temperature,
@@ -79,15 +76,11 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
         )
         output_parser = CustomOutputParser()
         llm_chain = LLMChain(llm=model, prompt=prompt_template_agent)
-        # 把history转成agent的memory
         memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2)
         for message in history:
-            # 检查消息的角色
             if message.role == 'user':
-                # 添加用户消息
                 memory.chat_memory.add_user_message(message.content)
             else:
-                # 添加AI消息
                 memory.chat_memory.add_ai_message(message.content)
 
         if "chatglm3" in model_container.MODEL.model_name:
@@ -95,7 +88,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                 llm=model,
                 tools=tools,
                 callback_manager=None,
-                # Langchain Prompt is not constructed directly here, it is constructed inside the GLM3 agent.
                 prompt=prompt_template,
                 input_variables=["input", "intermediate_steps", "history"],
                 memory=memory,
@@ -155,7 +147,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
             answer = ""
             final_answer = ""
             async for chunk in callback.aiter():
-                # Use server-sent-events to stream the response
                 data = json.loads(chunk)
                 if data["status"] == Status.start or data["status"] == Status.complete:
                     continue
@@ -181,7 +172,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
         await task
 
     return EventSourceResponse(agent_chat_iterator(query=query,
-                                                 history=history,
-                                                 model_name=model_name,
-                                                 prompt_name=prompt_name),
-                             )
+                                                   history=history,
+                                                   model_name=model_name,
+                                                   prompt_name=prompt_name),
+                               )
diff --git a/server/chat/search_engine_chat.py b/server/chat/search_engine_chat.py
index db00dfa4..42bef3c2 100644
--- a/server/chat/search_engine_chat.py
+++ b/server/chat/search_engine_chat.py
@@ -1,23 +1,23 @@
 from langchain.utilities.bing_search import BingSearchAPIWrapper
 from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
 from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
-                     LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE,
-                     TEXT_SPLITTER_NAME, OVERLAP_SIZE)
-from fastapi import Body
-from sse_starlette import EventSourceResponse
-from fastapi.concurrency import run_in_threadpool
-from server.utils import wrap_done, get_ChatOpenAI
-from server.utils import BaseResponse, get_prompt_template
+                     LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE)
 from langchain.chains import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
-from typing import AsyncIterable
-import asyncio
+
 from langchain.prompts.chat import ChatPromptTemplate
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from typing import List, Optional, Dict
-from server.chat.utils import History
 from langchain.docstore.document import Document
+from fastapi import Body
+from fastapi.concurrency import run_in_threadpool
+from sse_starlette import EventSourceResponse
+from server.utils import wrap_done, get_ChatOpenAI
+from server.utils import BaseResponse, get_prompt_template
+from server.chat.utils import History
+from typing import AsyncIterable
+import asyncio
 import json
+from typing import List, Optional, Dict
 from strsimpy.normalized_levenshtein import NormalizedLevenshtein
 from markdownify import markdownify
 
@@ -38,11 +38,11 @@ def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs):
 
 
 def metaphor_search(
-    text: str,
-    result_len: int = SEARCH_ENGINE_TOP_K,
-    split_result: bool = False,
-    chunk_size: int = 500,
-    chunk_overlap: int = OVERLAP_SIZE,
+        text: str,
+        result_len: int = SEARCH_ENGINE_TOP_K,
+        split_result: bool = False,
+        chunk_size: int = 500,
+        chunk_overlap: int = OVERLAP_SIZE,
 ) -> List[Dict]:
     from metaphor_python import Metaphor
 
@@ -58,13 +58,13 @@ def metaphor_search(
     # metaphor 返回的内容都是长文本，需要分词再检索
     if split_result:
         docs = [Document(page_content=x.extract,
-                        metadata={"link": x.url, "title": x.title})
+                         metadata={"link": x.url, "title": x.title})
                 for x in contents]
         text_splitter = RecursiveCharacterTextSplitter(["\n\n", "\n", ".", " "],
                                                        chunk_size=chunk_size,
                                                        chunk_overlap=chunk_overlap)
         splitted_docs = text_splitter.split_documents(docs)
-        
+
         # 将切分好的文档放入临时向量库，重新筛选出TOP_K个文档
         if len(splitted_docs) > result_len:
             normal = NormalizedLevenshtein()
@@ -74,13 +74,13 @@ def metaphor_search(
             splitted_docs = splitted_docs[:result_len]
 
         docs = [{"snippet": x.page_content,
-                "link": x.metadata["link"],
-                "title": x.metadata["title"]}
+                 "link": x.metadata["link"],
+                 "title": x.metadata["title"]}
                 for x in splitted_docs]
     else:
         docs = [{"snippet": x.extract,
-                "link": x.url,
-                "title": x.title}
+                 "link": x.url,
+                 "title": x.title}
                 for x in contents]
 
     return docs
@@ -113,25 +113,27 @@ async def lookup_search_engine(
     docs = search_result2docs(results)
     return docs
 
-
 async def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
-                            search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
-                            top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
-                            history: List[History] = Body([],
-                                                            description="历史对话",
-                                                            examples=[[
-                                                                {"role": "user",
+                             search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
+                             top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
+                             history: List[History] = Body([],
+                                                           description="历史对话",
+                                                           examples=[[
+                                                               {"role": "user",
                                                                 "content": "我们来玩成语接龙，我先来，生龙活虎"},
-                                                                {"role": "assistant",
+                                                               {"role": "assistant",
                                                                 "content": "虎头虎脑"}]]
-                                                            ),
-                            stream: bool = Body(False, description="流式输出"),
-                            model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
-                            temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
-                            max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
-                            prompt_name: str = Body("default",description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
-                            split_result: bool = Body(False, description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）")
-                       ):
+                                                           ),
+                             stream: bool = Body(False, description="流式输出"),
+                             model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
+                             temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
+                             max_tokens: Optional[int] = Body(None,
+                                                              description="限制LLM生成Token数量，默认None代表模型最大值"),
+                             prompt_name: str = Body("default",
+                                                     description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
+                             split_result: bool = Body(False,
+                                                       description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）")
+                             ):
     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}")
 
@@ -198,9 +200,9 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
         await task
 
     return EventSourceResponse(search_engine_chat_iterator(query=query,
-                                                         search_engine_name=search_engine_name,
-                                                         top_k=top_k,
-                                                         history=history,
-                                                         model_name=model_name,
-                                                         prompt_name=prompt_name),
-                             )
+                                                           search_engine_name=search_engine_name,
+                                                           top_k=top_k,
+                                                           history=history,
+                                                           model_name=model_name,
+                                                           prompt_name=prompt_name),
+                               )
diff --git a/webui_pages/utils.py b/webui_pages/utils.py
index 4426d7d3..5a73e628 100644
--- a/webui_pages/utils.py
+++ b/webui_pages/utils.py
@@ -1,7 +1,6 @@
 # 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
@@ -27,7 +26,7 @@ from io import BytesIO
 from server.utils import set_httpx_config, api_address, get_httpx_client
 
 from pprint import pprint
-
+from langchain_core._api import deprecated
 
 set_httpx_config()
 
@@ -36,10 +35,11 @@ class ApiRequest:
     '''
     api.py调用的封装（同步模式）,简化api调用方式
     '''
+
     def __init__(
-        self,
-        base_url: str = api_address(),
-        timeout: float = HTTPX_DEFAULT_TIMEOUT,
+            self,
+            base_url: str = api_address(),
+            timeout: float = HTTPX_DEFAULT_TIMEOUT,
     ):
         self.base_url = base_url
         self.timeout = timeout
@@ -55,12 +55,12 @@ class ApiRequest:
         return self._client
 
     def get(
-        self,
-        url: str,
-        params: Union[Dict, List[Tuple], bytes] = None,
-        retry: int = 3,
-        stream: bool = False,
-        **kwargs: Any,
+            self,
+            url: str,
+            params: Union[Dict, List[Tuple], bytes] = None,
+            retry: int = 3,
+            stream: bool = False,
+            **kwargs: Any,
     ) -> Union[httpx.Response, Iterator[httpx.Response], None]:
         while retry > 0:
             try:
@@ -75,13 +75,13 @@ class ApiRequest:
                 retry -= 1
 
     def post(
-        self,
-        url: str,
-        data: Dict = None,
-        json: Dict = None,
-        retry: int = 3,
-        stream: bool = False,
-        **kwargs: Any
+            self,
+            url: str,
+            data: Dict = None,
+            json: Dict = None,
+            retry: int = 3,
+            stream: bool = False,
+            **kwargs: Any
     ) -> Union[httpx.Response, Iterator[httpx.Response], None]:
         while retry > 0:
             try:
@@ -97,13 +97,13 @@ class ApiRequest:
                 retry -= 1
 
     def delete(
-        self,
-        url: str,
-        data: Dict = None,
-        json: Dict = None,
-        retry: int = 3,
-        stream: bool = False,
-        **kwargs: Any
+            self,
+            url: str,
+            data: Dict = None,
+            json: Dict = None,
+            retry: int = 3,
+            stream: bool = False,
+            **kwargs: Any
     ) -> Union[httpx.Response, Iterator[httpx.Response], None]:
         while retry > 0:
             try:
@@ -118,24 +118,25 @@ class ApiRequest:
                 retry -= 1
 
     def _httpx_stream2generator(
-        self,
-        response: contextlib._GeneratorContextManager,
-        as_json: bool = False,
+            self,
+            response: contextlib._GeneratorContextManager,
+            as_json: bool = False,
     ):
         '''
         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
+                        if not chunk:  # fastchat api yield empty bytes on start and end
                             continue
                         if as_json:
                             try:
                                 if chunk.startswith("data: "):
                                     data = json.loads(chunk[6:-2])
-                                elif chunk.startswith(":"): # skip sse comment line
+                                elif chunk.startswith(":"):  # skip sse comment line
                                     continue
                                 else:
                                     data = json.loads(chunk)
@@ -143,7 +144,7 @@ class ApiRequest:
                             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)
+                                             exc_info=e if log_verbose else None)
                         else:
                             # print(chunk, end="", flush=True)
                             yield chunk
@@ -158,20 +159,20 @@ class ApiRequest:
             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)
+                             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):
-                        if not chunk: # fastchat api yield empty bytes on start and end
+                        if not chunk:  # fastchat api yield empty bytes on start and end
                             continue
                         if as_json:
                             try:
                                 if chunk.startswith("data: "):
                                     data = json.loads(chunk[6:-2])
-                                elif chunk.startswith(":"): # skip sse comment line
+                                elif chunk.startswith(":"):  # skip sse comment line
                                     continue
                                 else:
                                     data = json.loads(chunk)
@@ -179,7 +180,7 @@ class ApiRequest:
                             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)
+                                             exc_info=e if log_verbose else None)
                         else:
                             # print(chunk, end="", flush=True)
                             yield chunk
@@ -194,7 +195,7 @@ class ApiRequest:
             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)
+                             exc_info=e if log_verbose else None)
                 yield {"code": 500, "msg": msg}
 
         if self._use_async:
@@ -203,16 +204,17 @@ class ApiRequest:
             return ret_sync(response, as_json)
 
     def _get_response_value(
-        self,
-        response: httpx.Response,
-        as_json: bool = False,
-        value_func: Callable = None,
+            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()
@@ -220,7 +222,7 @@ class ApiRequest:
                 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)
+                                 exc_info=e if log_verbose else None)
                 return {"code": 500, "msg": msg, "data": None}
 
         if value_func is None:
@@ -250,10 +252,10 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True, value_func=lambda r: r["data"])
 
     def get_prompt_template(
-        self,
-        type: str = "llm_chat",
-        name: str = "default",
-        **kwargs,
+            self,
+            type: str = "llm_chat",
+            name: str = "default",
+            **kwargs,
     ) -> str:
         data = {
             "type": type,
@@ -297,15 +299,19 @@ class ApiRequest:
         response = self.post("/chat/chat", json=data, stream=True, **kwargs)
         return self._httpx_stream2generator(response, as_json=True)
 
+    @deprecated(
+        since="0.3.0",
+        message="自定义Agent问答将于 Langchain-Chatchat 0.3.x重写, 0.2.x中相关功能将废弃",
+        removal="0.3.0")
     def agent_chat(
-        self,
-        query: str,
-        history: List[Dict] = [],
-        stream: bool = True,
-        model: str = LLM_MODELS[0],
-        temperature: float = TEMPERATURE,
-        max_tokens: int = None,
-        prompt_name: str = "default",
+            self,
+            query: str,
+            history: List[Dict] = [],
+            stream: bool = True,
+            model: str = LLM_MODELS[0],
+            temperature: float = TEMPERATURE,
+            max_tokens: int = None,
+            prompt_name: str = "default",
     ):
         '''
         对应api.py/chat/agent_chat 接口
@@ -327,17 +333,17 @@ class ApiRequest:
         return self._httpx_stream2generator(response, as_json=True)
 
     def knowledge_base_chat(
-        self,
-        query: str,
-        knowledge_base_name: str,
-        top_k: int = VECTOR_SEARCH_TOP_K,
-        score_threshold: float = SCORE_THRESHOLD,
-        history: List[Dict] = [],
-        stream: bool = True,
-        model: str = LLM_MODELS[0],
-        temperature: float = TEMPERATURE,
-        max_tokens: int = None,
-        prompt_name: str = "default",
+            self,
+            query: str,
+            knowledge_base_name: str,
+            top_k: int = VECTOR_SEARCH_TOP_K,
+            score_threshold: float = SCORE_THRESHOLD,
+            history: List[Dict] = [],
+            stream: bool = True,
+            model: str = LLM_MODELS[0],
+            temperature: float = TEMPERATURE,
+            max_tokens: int = None,
+            prompt_name: str = "default",
     ):
         '''
         对应api.py/chat/knowledge_base_chat接口
@@ -366,28 +372,29 @@ class ApiRequest:
         return self._httpx_stream2generator(response, as_json=True)
 
     def upload_temp_docs(
-        self,
-        files: List[Union[str, Path, bytes]],
-        knowledge_id: str = None,
-        chunk_size=CHUNK_SIZE,
-        chunk_overlap=OVERLAP_SIZE,
-        zh_title_enhance=ZH_TITLE_ENHANCE,
+            self,
+            files: List[Union[str, Path, bytes]],
+            knowledge_id: str = None,
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=OVERLAP_SIZE,
+            zh_title_enhance=ZH_TITLE_ENHANCE,
     ):
         '''
         对应api.py/knowledge_base/upload_tmep_docs接口
         '''
+
         def convert_file(file, filename=None):
-            if isinstance(file, bytes): # raw bytes
+            if isinstance(file, bytes):  # raw bytes
                 file = BytesIO(file)
-            elif hasattr(file, "read"): # a file io like object
+            elif hasattr(file, "read"):  # a file io like object
                 filename = filename or file.name
-            else: # a local path
+            else:  # a local path
                 file = Path(file).absolute().open("rb")
                 filename = filename or os.path.split(file.name)[-1]
             return filename, file
 
         files = [convert_file(file) for file in files]
-        data={
+        data = {
             "knowledge_id": knowledge_id,
             "chunk_size": chunk_size,
             "chunk_overlap": chunk_overlap,
@@ -402,17 +409,17 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def file_chat(
-        self,
-        query: str,
-        knowledge_id: str,
-        top_k: int = VECTOR_SEARCH_TOP_K,
-        score_threshold: float = SCORE_THRESHOLD,
-        history: List[Dict] = [],
-        stream: bool = True,
-        model: str = LLM_MODELS[0],
-        temperature: float = TEMPERATURE,
-        max_tokens: int = None,
-        prompt_name: str = "default",
+            self,
+            query: str,
+            knowledge_id: str,
+            top_k: int = VECTOR_SEARCH_TOP_K,
+            score_threshold: float = SCORE_THRESHOLD,
+            history: List[Dict] = [],
+            stream: bool = True,
+            model: str = LLM_MODELS[0],
+            temperature: float = TEMPERATURE,
+            max_tokens: int = None,
+            prompt_name: str = "default",
     ):
         '''
         对应api.py/chat/file_chat接口
@@ -440,18 +447,23 @@ class ApiRequest:
         )
         return self._httpx_stream2generator(response, as_json=True)
 
+    @deprecated(
+        since="0.3.0",
+        message="搜索引擎问答将于 Langchain-Chatchat 0.3.x重写, 0.2.x中相关功能将废弃",
+        removal="0.3.0"
+    )
     def search_engine_chat(
-        self,
-        query: str,
-        search_engine_name: str,
-        top_k: int = SEARCH_ENGINE_TOP_K,
-        history: List[Dict] = [],
-        stream: bool = True,
-        model: str = LLM_MODELS[0],
-        temperature: float = TEMPERATURE,
-        max_tokens: int = None,
-        prompt_name: str = "default",
-        split_result: bool = False,
+            self,
+            query: str,
+            search_engine_name: str,
+            top_k: int = SEARCH_ENGINE_TOP_K,
+            history: List[Dict] = [],
+            stream: bool = True,
+            model: str = LLM_MODELS[0],
+            temperature: float = TEMPERATURE,
+            max_tokens: int = None,
+            prompt_name: str = "default",
+            split_result: bool = False,
     ):
         '''
         对应api.py/chat/search_engine_chat接口
@@ -482,7 +494,7 @@ class ApiRequest:
     # 知识库相关操作
 
     def list_knowledge_bases(
-        self,
+            self,
     ):
         '''
         对应api.py/knowledge_base/list_knowledge_bases接口
@@ -493,10 +505,10 @@ class ApiRequest:
                                         value_func=lambda r: r.get("data", []))
 
     def create_knowledge_base(
-        self,
-        knowledge_base_name: str,
-        vector_store_type: str = DEFAULT_VS_TYPE,
-        embed_model: str = EMBEDDING_MODEL,
+            self,
+            knowledge_base_name: str,
+            vector_store_type: str = DEFAULT_VS_TYPE,
+            embed_model: str = EMBEDDING_MODEL,
     ):
         '''
         对应api.py/knowledge_base/create_knowledge_base接口
@@ -514,8 +526,8 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def delete_knowledge_base(
-        self,
-        knowledge_base_name: str,
+            self,
+            knowledge_base_name: str,
     ):
         '''
         对应api.py/knowledge_base/delete_knowledge_base接口
@@ -527,8 +539,8 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def list_kb_docs(
-        self,
-        knowledge_base_name: str,
+            self,
+            knowledge_base_name: str,
     ):
         '''
         对应api.py/knowledge_base/list_files接口
@@ -542,13 +554,13 @@ class ApiRequest:
                                         value_func=lambda r: r.get("data", []))
 
     def search_kb_docs(
-        self,
-        knowledge_base_name: str,
-        query: str = "",
-        top_k: int = VECTOR_SEARCH_TOP_K,
-        score_threshold: int = SCORE_THRESHOLD,
-        file_name: str = "",
-        metadata: dict = {},
+            self,
+            knowledge_base_name: str,
+            query: str = "",
+            top_k: int = VECTOR_SEARCH_TOP_K,
+            score_threshold: int = SCORE_THRESHOLD,
+            file_name: str = "",
+            metadata: dict = {},
     ) -> List:
         '''
         对应api.py/knowledge_base/search_docs接口
@@ -569,9 +581,9 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def update_docs_by_id(
-        self,
-        knowledge_base_name: str,
-        docs: Dict[str, Dict],
+            self,
+            knowledge_base_name: str,
+            docs: Dict[str, Dict],
     ) -> bool:
         '''
         对应api.py/knowledge_base/update_docs_by_id接口
@@ -587,32 +599,33 @@ class ApiRequest:
         return self._get_response_value(response)
 
     def upload_kb_docs(
-        self,
-        files: List[Union[str, Path, bytes]],
-        knowledge_base_name: str,
-        override: bool = False,
-        to_vector_store: bool = True,
-        chunk_size=CHUNK_SIZE,
-        chunk_overlap=OVERLAP_SIZE,
-        zh_title_enhance=ZH_TITLE_ENHANCE,
-        docs: Dict = {},
-        not_refresh_vs_cache: bool = False,
+            self,
+            files: List[Union[str, Path, bytes]],
+            knowledge_base_name: str,
+            override: bool = False,
+            to_vector_store: bool = True,
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=OVERLAP_SIZE,
+            zh_title_enhance=ZH_TITLE_ENHANCE,
+            docs: Dict = {},
+            not_refresh_vs_cache: bool = False,
     ):
         '''
         对应api.py/knowledge_base/upload_docs接口
         '''
+
         def convert_file(file, filename=None):
-            if isinstance(file, bytes): # raw bytes
+            if isinstance(file, bytes):  # raw bytes
                 file = BytesIO(file)
-            elif hasattr(file, "read"): # a file io like object
+            elif hasattr(file, "read"):  # a file io like object
                 filename = filename or file.name
-            else: # a local path
+            else:  # a local path
                 file = Path(file).absolute().open("rb")
                 filename = filename or os.path.split(file.name)[-1]
             return filename, file
 
         files = [convert_file(file) for file in files]
-        data={
+        data = {
             "knowledge_base_name": knowledge_base_name,
             "override": override,
             "to_vector_store": to_vector_store,
@@ -633,11 +646,11 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def delete_kb_docs(
-        self,
-        knowledge_base_name: str,
-        file_names: List[str],
-        delete_content: bool = False,
-        not_refresh_vs_cache: bool = False,
+            self,
+            knowledge_base_name: str,
+            file_names: List[str],
+            delete_content: bool = False,
+            not_refresh_vs_cache: bool = False,
     ):
         '''
         对应api.py/knowledge_base/delete_docs接口
@@ -655,8 +668,7 @@ class ApiRequest:
         )
         return self._get_response_value(response, as_json=True)
 
-
-    def update_kb_info(self,knowledge_base_name,kb_info):
+    def update_kb_info(self, knowledge_base_name, kb_info):
         '''
         对应api.py/knowledge_base/update_info接口
         '''
@@ -672,15 +684,15 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def update_kb_docs(
-        self,
-        knowledge_base_name: str,
-        file_names: List[str],
-        override_custom_docs: bool = False,
-        chunk_size=CHUNK_SIZE,
-        chunk_overlap=OVERLAP_SIZE,
-        zh_title_enhance=ZH_TITLE_ENHANCE,
-        docs: Dict = {},
-        not_refresh_vs_cache: bool = False,
+            self,
+            knowledge_base_name: str,
+            file_names: List[str],
+            override_custom_docs: bool = False,
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=OVERLAP_SIZE,
+            zh_title_enhance=ZH_TITLE_ENHANCE,
+            docs: Dict = {},
+            not_refresh_vs_cache: bool = False,
     ):
         '''
         对应api.py/knowledge_base/update_docs接口
@@ -706,14 +718,14 @@ class ApiRequest:
         return self._get_response_value(response, as_json=True)
 
     def recreate_vector_store(
-        self,
-        knowledge_base_name: str,
-        allow_empty_kb: bool = True,
-        vs_type: str = DEFAULT_VS_TYPE,
-        embed_model: str = EMBEDDING_MODEL,
-        chunk_size=CHUNK_SIZE,
-        chunk_overlap=OVERLAP_SIZE,
-        zh_title_enhance=ZH_TITLE_ENHANCE,
+            self,
+            knowledge_base_name: str,
+            allow_empty_kb: bool = True,
+            vs_type: str = DEFAULT_VS_TYPE,
+            embed_model: str = EMBEDDING_MODEL,
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=OVERLAP_SIZE,
+            zh_title_enhance=ZH_TITLE_ENHANCE,
     ):
         '''
         对应api.py/knowledge_base/recreate_vector_store接口
@@ -738,8 +750,8 @@ class ApiRequest:
 
     # LLM模型相关操作
     def list_running_models(
-        self,
-        controller_address: str = None,
+            self,
+            controller_address: str = None,
     ):
         '''
         获取Fastchat中正运行的模型列表
@@ -755,8 +767,7 @@ class ApiRequest:
             "/llm_model/list_running_models",
             json=data,
         )
-        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", []))
-
+        return self._get_response_value(response, as_json=True, value_func=lambda r: r.get("data", []))
 
     def get_default_llm_model(self, local_first: bool = True) -> Tuple[str, bool]:
         '''
@@ -764,6 +775,7 @@ class ApiRequest:
         当 local_first=True 时，优先返回运行中的本地模型，否则优先按LLM_MODELS配置顺序返回。
         返回类型为（model_name, is_local_model）
         '''
+
         def ret_sync():
             running_models = self.list_running_models()
             if not running_models:
@@ -780,7 +792,7 @@ class ApiRequest:
                     model = m
                     break
 
-            if not model: # LLM_MODELS中配置的模型都不在running_models里
+            if not model:  # LLM_MODELS中配置的模型都不在running_models里
                 model = list(running_models)[0]
             is_local = not running_models[model].get("online_api")
             return model, is_local
@@ -801,7 +813,7 @@ class ApiRequest:
                     model = m
                     break
 
-            if not model: # LLM_MODELS中配置的模型都不在running_models里
+            if not model:  # LLM_MODELS中配置的模型都不在running_models里
                 model = list(running_models)[0]
             is_local = not running_models[model].get("online_api")
             return model, is_local
@@ -812,8 +824,8 @@ class ApiRequest:
             return ret_sync()
 
     def list_config_models(
-        self,
-        types: List[str] = ["local", "online"],
+            self,
+            types: List[str] = ["local", "online"],
     ) -> Dict[str, Dict]:
         '''
         获取服务器configs中配置的模型列表，返回形式为{"type": {model_name: config}, ...}。
@@ -825,23 +837,23 @@ class ApiRequest:
             "/llm_model/list_config_models",
             json=data,
         )
-        return self._get_response_value(response, as_json=True, value_func=lambda r:r.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 = None,
+            self,
+            model_name: str = None,
     ) -> Dict:
         '''
         获取服务器上模型配置
         '''
-        data={
+        data = {
             "model_name": model_name,
         }
         response = self.post(
             "/llm_model/get_model_config",
             json=data,
         )
-        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
+        return self._get_response_value(response, as_json=True, value_func=lambda r: r.get("data", {}))
 
     def list_search_engines(self) -> List[str]:
         '''
@@ -850,12 +862,12 @@ class ApiRequest:
         response = self.post(
             "/server/list_search_engines",
         )
-        return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
+        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,
+            self,
+            model_name: str,
+            controller_address: str = None,
     ):
         '''
         停止某个LLM模型。
@@ -873,10 +885,10 @@ class ApiRequest:
         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,
+            self,
+            model_name: str,
+            new_model_name: str,
+            controller_address: str = None,
     ):
         '''
         向fastchat controller请求切换LLM模型。
@@ -959,10 +971,10 @@ class ApiRequest:
             return ret_sync()
 
     def embed_texts(
-        self,
-        texts: List[str],
-        embed_model: str = EMBEDDING_MODEL,
-        to_query: bool = False,
+            self,
+            texts: List[str],
+            embed_model: str = EMBEDDING_MODEL,
+            to_query: bool = False,
     ) -> List[List[float]]:
         '''
         对文本进行向量化，可选模型包括本地 embed_models 和支持 embeddings 的在线模型
@@ -979,10 +991,10 @@ class ApiRequest:
         return self._get_response_value(resp, as_json=True, value_func=lambda r: r.get("data"))
 
     def chat_feedback(
-        self,
-        message_id: str,
-        score: int,
-        reason: str = "",
+            self,
+            message_id: str,
+            score: int,
+            reason: str = "",
     ) -> int:
         '''
         反馈对话评价
@@ -1019,9 +1031,9 @@ 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):
+            and key in data
+            and "code" in data
+            and data["code"] == 200):
         return data[key]
     return ""