Add api for sessions and add max_tokens for tenant_llm (#3472)

### What problem does this PR solve?

Add api for sessions and add max_tokens for tenant_llm

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: liuhua <10215101452@stu.ecun.edu.cn>

api/apps/llm_app.py

             mdl = ChatModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
-                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], 
-                                 {"temperature": 0.9,'max_tokens':50})
-                if m.find("**ERROR**") >=0:
+                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}],
+                                 {"temperature": 0.9, 'max_tokens': 50})
+                if m.find("**ERROR**") >= 0:
                     raise Exception(m)
                 chat_passed = True
             except Exception as e:
             llm_config[n] = req[n]
 
     for llm in LLMService.query(fid=factory):
+        llm_config["max_tokens"]=llm.max_tokens
         if not TenantLLMService.filter_update(
                 [TenantLLM.tenant_id == current_user.id,
                  TenantLLM.llm_factory == factory,
                 llm_name=llm.llm_name,
                 model_type=llm.model_type,
                 api_key=llm_config["api_key"],
-                api_base=llm_config["api_base"]
+                api_base=llm_config["api_base"],
+                max_tokens=llm_config["max_tokens"]
             )
 
     return get_json_result(data=True)
         api_key = apikey_json(["bedrock_ak", "bedrock_sk", "bedrock_region"])
 
     elif factory == "LocalAI":
-        llm_name = req["llm_name"]+"___LocalAI"
+        llm_name = req["llm_name"] + "___LocalAI"
         api_key = "xxxxxxxxxxxxxxx"
-        
+
     elif factory == "HuggingFace":
-        llm_name = req["llm_name"]+"___HuggingFace"
+        llm_name = req["llm_name"] + "___HuggingFace"
         api_key = "xxxxxxxxxxxxxxx"
 
     elif factory == "OpenAI-API-Compatible":
-        llm_name = req["llm_name"]+"___OpenAI-API"
-        api_key = req.get("api_key","xxxxxxxxxxxxxxx")
+        llm_name = req["llm_name"] + "___OpenAI-API"
+        api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
 
-    elif factory =="XunFei Spark":
+    elif factory == "XunFei Spark":
         llm_name = req["llm_name"]
         if req["model_type"] == "chat":
             api_key = req.get("spark_api_password", "xxxxxxxxxxxxxxx")
         elif req["model_type"] == "tts":
-            api_key = apikey_json(["spark_app_id", "spark_api_secret","spark_api_key"])
+            api_key = apikey_json(["spark_app_id", "spark_api_secret", "spark_api_key"])
 
     elif factory == "BaiduYiyan":
         llm_name = req["llm_name"]
         "model_type": req["model_type"],
         "llm_name": llm_name,
         "api_base": req.get("api_base", ""),
-        "api_key": api_key
+        "api_key": api_key,
+        "max_tokens": req.get("max_tokens")
     }
 
     msg = ""
     if llm["model_type"] == LLMType.EMBEDDING.value:
         mdl = EmbeddingModel[factory](
             key=llm['api_key'],
-            model_name=llm["llm_name"], 
+            model_name=llm["llm_name"],
             base_url=llm["api_base"])
         try:
             arr, tc = mdl.encode(["Test if the api key is available"])
         )
         try:
             m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
-                             "temperature": 0.9})
+                "temperature": 0.9})
             if not tc:
                 raise Exception(m)
         except Exception as e:
                 e)
     elif llm["model_type"] == LLMType.RERANK:
         mdl = RerankModel[factory](
-            key=llm["api_key"], 
-            model_name=llm["llm_name"], 
+            key=llm["api_key"],
+            model_name=llm["llm_name"],
             base_url=llm["api_base"]
         )
         try:
                 e)
     elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
         mdl = CvModel[factory](
-            key=llm["api_key"], 
-            model_name=llm["llm_name"], 
+            key=llm["api_key"],
+            model_name=llm["llm_name"],
             base_url=llm["api_base"]
         )
         try:
         return get_data_error_result(message=msg)
 
     if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, TenantLLM.llm_name == llm["llm_name"]], llm):
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory,
+             TenantLLM.llm_name == llm["llm_name"]], llm):
         TenantLLMService.save(**llm)
 
     return get_json_result(data=True)
 def delete_llm():
     req = request.json
     TenantLLMService.filter_delete(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], TenantLLM.llm_name == req["llm_name"]])
+        [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"],
+         TenantLLM.llm_name == req["llm_name"]])
     return get_json_result(data=True)
 
 
 def delete_factory():
     req = request.json
     TenantLLMService.filter_delete(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"]])
+        [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"]])
     return get_json_result(data=True)
 
 
 @manager.route('/list', methods=['GET'])
 @login_required
 def list_app():
-    self_deploied = ["Youdao","FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio"]
-    weighted = ["Youdao","FastEmbed", "BAAI"] if settings.LIGHTEN != 0 else []
+    self_deploied = ["Youdao", "FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio"]
+    weighted = ["Youdao", "FastEmbed", "BAAI"] if settings.LIGHTEN != 0 else []
     model_type = request.args.get("model_type")
     try:
         objs = TenantLLMService.query(tenant_id=current_user.id)
         for m in llms:
             m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deploied
 
-        llm_set = set([m["llm_name"]+"@"+m["fid"] for m in llms])
+        llm_set = set([m["llm_name"] + "@" + m["fid"] for m in llms])
         for o in objs:
-            if not o.api_key:continue
-            if o.llm_name+"@"+o.llm_factory in llm_set:continue
+            if not o.api_key: continue
+            if o.llm_name + "@" + o.llm_factory in llm_set: continue
             llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
 
         res = {}
         for m in llms:
-            if model_type and m["model_type"].find(model_type)<0:
+            if model_type and m["model_type"].find(model_type) < 0:
                 continue
             if m["fid"] not in res:
                 res[m["fid"]] = []
 
         return get_json_result(data=res)
     except Exception as e:
-        return server_error_response(e)
+        return server_error_response(e)

api/apps/sdk/session.py

 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import re
 import json
 from functools import partial
 from uuid import uuid4
-
+from api.db import LLMType
 from flask import request, Response
-
+from api.db.services.dialog_service import ask
 from agent.canvas import Canvas
 from api.db import StatusEnum
 from api.db.db_models import API4Conversation
 from api.db.services.api_service import API4ConversationService
 from api.db.services.canvas_service import UserCanvasService
 from api.db.services.dialog_service import DialogService, ConversationService, chat
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.utils import get_uuid
 from api.utils.api_utils import get_error_data_result
 from api.utils.api_utils import get_result, token_required
+from api.db.services.llm_service import LLMBundle
 
 
 @manager.route('/chats/<chat_id>/sessions', methods=['POST'])
                 yield "data:" + json.dumps({"code": 500, "message": str(e),
                                             "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
                                            ensure_ascii=False) + "\n\n"
-            yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"code": 0, "data": True}, ensure_ascii=False) + "\n\n"
 
         resp = Response(sse(), mimetype="text/event-stream")
         resp.headers.add_header("Cache-control", "no-cache")
 
 @manager.route('/chats/<chat_id>/sessions', methods=['GET'])
 @token_required
-def list(chat_id,tenant_id):
+def list_session(chat_id,tenant_id):
     if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
         return get_error_data_result(message=f"You don't own the assistant {chat_id}.")
     id = request.args.get("id")
         if not conv:
             return get_error_data_result(message="The chat doesn't own the session")
         ConversationService.delete_by_id(id)
-    return get_result()
+    return get_result()
+
+@manager.route('/sessions/ask', methods=['POST'])
+@token_required
+def ask_about(tenant_id):
+    req = request.json
+    if not req.get("question"):
+        return get_error_data_result("`question` is required.")
+    if not req.get("dataset_ids"):
+        return get_error_data_result("`dataset_ids` is required.")
+    if not isinstance(req.get("dataset_ids"),list):
+        return get_error_data_result("`dataset_ids` should be a list.")
+    req["kb_ids"]=req.pop("dataset_ids")
+    for kb_id in req["kb_ids"]:
+        if not KnowledgebaseService.accessible(kb_id,tenant_id):
+            return get_error_data_result(f"You don't own the dataset {kb_id}.")
+        kbs = KnowledgebaseService.query(id=kb_id)
+        kb = kbs[0]
+        if kb.chunk_num == 0:
+            return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file")
+    uid = tenant_id
+    def stream():
+        nonlocal req, uid
+        try:
+            for ans in ask(req["question"], req["kb_ids"], uid):
+                yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
+        except Exception as e:
+            yield "data:" + json.dumps({"code": 500, "message": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    resp = Response(stream(), mimetype="text/event-stream")
+    resp.headers.add_header("Cache-control", "no-cache")
+    resp.headers.add_header("Connection", "keep-alive")
+    resp.headers.add_header("X-Accel-Buffering", "no")
+    resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+    return resp
+
+
+@manager.route('/sessions/related_questions', methods=['POST'])
+@token_required
+def related_questions(tenant_id):
+    req = request.json
+    if not req.get("question"):
+        return get_error_data_result("`question` is required.")
+    question = req["question"]
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+    prompt = """
+Objective: To generate search terms related to the user's search keywords, helping users find more valuable information.
+Instructions:
+ - Based on the keywords provided by the user, generate 5-10 related search terms.
+ - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information.
+ - Use common, general terms as much as possible, avoiding obscure words or technical jargon.
+ - Keep the term length between 2-4 words, concise and clear.
+ - DO NOT translate, use the language of the original keywords.
+
+### Example:
+Keywords: Chinese football
+Related search terms:
+1. Current status of Chinese football
+2. Reform of Chinese football
+3. Youth training of Chinese football
+4. Chinese football in the Asian Cup
+5. Chinese football in the World Cup
+
+Reason:
+ - When searching, users often only use one or two keywords, making it difficult to fully express their information needs.
+ - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. 
+ - At the same time, related terms can also help search engines better understand user needs and return more accurate search results.
+
+"""
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": f"""
+Keywords: {question}
+Related search terms:
+    """}], {"temperature": 0.9})
+    return get_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])

api/db/db_models.py

 import inspect
 import os
 import sys
+import typing
 import operator
 from enum import Enum
 from functools import wraps
     Field, Model, Metadata
 )
 from playhouse.pool import PooledMySQLDatabase, PooledPostgresqlDatabase
+
+
 from api.db import SerializedType, ParserType
 from api import settings
 from api import utils
 
+
 def singleton(cls, *args, **kw):
     instances = {}
 
                 f"the serialized type {self._serialized_type} is not supported")
 
 
-def is_continuous_field(cls: type) -> bool:
+def is_continuous_field(cls: typing.Type) -> bool:
     if cls in CONTINUOUS_FIELD_TYPE:
         return True
     for p in cls.__bases__:
         if p in CONTINUOUS_FIELD_TYPE:
             return True
-        elif p is not Field and p is not object:
+        elif p != Field and p != object:
             if is_continuous_field(p):
                 return True
     else:
     def to_dict(self):
         return self.__dict__['__data__']
 
-    def to_human_model_dict(self, only_primary_with: list | None = None):
+    def to_human_model_dict(self, only_primary_with: list = None):
         model_dict = self.__dict__['__data__']
 
         if not only_primary_with:
         super(JsonSerializedField, self).__init__(serialized_type=SerializedType.JSON, object_hook=object_hook,
                                                   object_pairs_hook=object_pairs_hook, **kwargs)
 
+
 class PooledDatabase(Enum):
     MYSQL = PooledMySQLDatabase
     POSTGRES = PooledPostgresqlDatabase
         self.database_connection = PooledDatabase[settings.DATABASE_TYPE.upper()].value(db_name, **database_config)
         logging.info('init database on cluster mode successfully')
 
+
 class PostgresDatabaseLock:
     def __init__(self, lock_name, timeout=10, db=None):
         self.lock_name = lock_name
 
         return magic
 
+
 class MysqlDatabaseLock:
     def __init__(self, lock_name, timeout=10, db=None):
         self.lock_name = lock_name
         index=True)
     api_key = CharField(max_length=1024, null=True, help_text="API KEY", index=True)
     api_base = CharField(max_length=255, null=True, help_text="API Base")
-
+    max_tokens = IntegerField(default=8192, index=True)
     used_tokens = IntegerField(default=0, index=True)
 
     def __str__(self):
         default="simple",
         help_text="simple|advanced",
         index=True)
-    prompt_config = JSONField(null=False, default={"system": "", "prologue": "Hi! I'm your assistant, what can I do for you?",
-                                                   "parameters": [], "empty_response": "Sorry! No relevant content was found in the knowledge base!"})
+    prompt_config = JSONField(null=False,
+                              default={"system": "", "prologue": "Hi! I'm your assistant, what can I do for you?",
+                                       "parameters": [],
+                                       "empty_response": "Sorry! No relevant content was found in the knowledge base!"})
 
     similarity_threshold = FloatField(default=0.2)
     vector_similarity_weight = FloatField(default=0.3)
         null=False,
         default="1",
         help_text="it needs to insert reference index into answer or not")
-    
+
     rerank_id = CharField(
         max_length=128,
         null=False,
             pass
         try:
             migrate(
-                migrator.add_column("tenant","tts_id",
-                    CharField(max_length=256,null=True,help_text="default tts model ID",index=True))
+                migrator.add_column("tenant", "tts_id",
+                                    CharField(max_length=256, null=True, help_text="default tts model ID", index=True))
             )
         except Exception:
             pass
             )
         except Exception:
             pass
-
+        try:
+            migrate(
+                migrator.add_column("tenant_llm","max_tokens",IntegerField(default=8192,index=True))
+            )
+        except Exception:
+            pass

rag/llm/embedding_model.py

     def __init__(self, key, model_name, base_url="https://api.together.xyz/v1"):
         if not base_url:
             base_url = "https://api.together.xyz/v1"
-        super().__init__(key, model_name, base_url)
+        super().__init__(key, model_name, base_url=base_url)
 
 
 class PerfXCloudEmbed(OpenAIEmbed):

web/src/interfaces/request/llm.ts

   model_type: string;
   api_base?: string; // chat|embedding|speech2text|image2text
   api_key: string;
+  max_tokens: number;
 }
 
 export interface IDeleteLlmRequestBody {

web/src/locales/en.ts

       maxTokensMessage: 'Max Tokens is required',
       maxTokensTip:
         'This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words).',
+      maxTokensInvalidMessage: 'Please enter a valid number for Max Tokens.',
+      maxTokensMinMessage: 'Max Tokens cannot be less than 0.',
       quote: 'Show Quote',
       quoteTip: 'Should the source of the original text be displayed?',
       selfRag: 'Self-RAG',
     setting: {
       profile: 'Profile',
       profileDescription: 'Update your photo and personal details here.',
+      maxTokens: 'Max Tokens',
+      maxTokensMessage: 'Max Tokens is required',
+      maxTokensTip:
+        'This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words).',
+      maxTokensInvalidMessage: 'Please enter a valid number for Max Tokens.',
+      maxTokensMinMessage: 'Max Tokens cannot be less than 0.',
       password: 'Password',
       passwordDescription:
         'Please enter your current password to change your password.',

web/src/locales/es.ts

       maxTokensMessage: 'El máximo de tokens es obligatorio',
       maxTokensTip:
         'Esto establece la longitud máxima de la salida del modelo, medida en el número de tokens (palabras o piezas de palabras).',
+      maxTokensInvalidMessage: 'Por favor, ingresa un número válido para Max Tokens.',
+      maxTokensMinMessage: 'Max Tokens no puede ser menor que 0.',
       quote: 'Mostrar cita',
       quoteTip: '¿Debe mostrarse la fuente del texto original?',
       selfRag: 'Self-RAG',
     setting: {
       profile: 'Perfil',
       profileDescription: 'Actualiza tu foto y tus datos personales aquí.',
+      maxTokens: 'Máximo de tokens',
+      maxTokensMessage: 'El máximo de tokens es obligatorio',
+      maxTokensTip:
+        'Esto establece la longitud máxima de la salida del modelo, medida en el número de tokens (palabras o piezas de palabras).',
+      maxTokensInvalidMessage: 'Por favor, ingresa un número válido para Max Tokens.',
+      maxTokensMinMessage: 'Max Tokens no puede ser menor que 0.',
       password: 'Contraseña',
       passwordDescription:
         'Por favor ingresa tu contraseña actual para cambiarla.',

web/src/locales/id.ts

         maxTokensMessage: 'Token Maksimum diperlukan',
         maxTokensTip:
           'Ini menetapkan panjang maksimum keluaran model, diukur dalam jumlah token (kata atau potongan kata).',
+        maxTokensInvalidMessage: 'Silakan masukkan angka yang valid untuk Max Tokens.',
+        maxTokensMinMessage: 'Max Tokens tidak boleh kurang dari 0.',
         quote: 'Tampilkan Kutipan',
         quoteTip: 'Haruskah sumber teks asli ditampilkan?',
         selfRag: 'Self-RAG',
       setting: {
         profile: 'Profil',
         profileDescription: 'Perbarui foto dan detail pribadi Anda di sini.',
+        maxTokens: 'Token Maksimum',
+        maxTokensMessage: 'Token Maksimum diperlukan',
+        maxTokensTip:
+          'Ini menetapkan panjang maksimum keluaran model, diukur dalam jumlah token (kata atau potongan kata).',
+        maxTokensInvalidMessage: 'Silakan masukkan angka yang valid untuk Max Tokens.',
+        maxTokensMinMessage: 'Max Tokens tidak boleh kurang dari 0.',
         password: 'Kata Sandi',
         passwordDescription:
           'Silakan masukkan kata sandi Anda saat ini untuk mengubah kata sandi Anda.',

web/src/locales/zh-traditional.ts

       maxTokensMessage: '最大token數是必填項',
       maxTokensTip:
         '這設置了模型輸出的最大長度，以標記（單詞或單詞片段）的數量來衡量。',
+      maxTokensInvalidMessage: '請輸入有效的最大標記數。',
+      maxTokensMinMessage: '最大標記數不能小於 0。',
       quote: '顯示引文',
       quoteTip: '是否應該顯示原文出處？',
       selfRag: 'Self-RAG',
     setting: {
       profile: '概述',
       profileDescription: '在此更新您的照片和個人詳細信息。',
+      maxTokens: '最大token數',
+      maxTokensMessage: '最大token數是必填項',
+      maxTokensTip:
+        '這設置了模型輸出的最大長度，以標記（單詞或單詞片段）的數量來衡量。',
+      maxTokensInvalidMessage: '請輸入有效的最大標記數。',
+      maxTokensMinMessage: '最大標記數不能小於 0。',
       password: '密碼',
       passwordDescription: '請輸入您當前的密碼以更改您的密碼。',
       model: '模型提供商',

web/src/locales/zh.ts

       maxTokensMessage: '最大token数是必填项',
       maxTokensTip:
         '这设置了模型输出的最大长度，以标记（单词或单词片段）的数量来衡量。',
+      maxTokensInvalidMessage: '请输入有效的最大令牌数。',
+      maxTokensMinMessage: '最大令牌数不能小于 0。',
       quote: '显示引文',
       quoteTip: '是否应该显示原文出处？',
       selfRag: 'Self-RAG',
     setting: {
       profile: '概要',
       profileDescription: '在此更新您的照片和个人详细信息。',
+      maxTokens: '最大token数',
+      maxTokensMessage: '最大token数是必填项',
+      maxTokensTip:
+        '这设置了模型输出的最大长度，以标记（单词或单词片段）的数量来衡量。',
+      maxTokensInvalidMessage: '请输入有效的最大令牌数。',
+      maxTokensMinMessage: '最大令牌数不能小于 0。',
       password: '密码',
       passwordDescription: '请输入您当前的密码以更改您的密码。',
       model: '模型提供商',

web/src/pages/user-setting/setting-model/Tencent-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Flex, Form, Input, Modal, Select, Space } from 'antd';
+import { Flex, Form, Input, Modal, Select, Space, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {
       ...omit(values),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:16000,
     };
     console.info(data);
 

web/src/pages/user-setting/setting-model/azure-openai-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Form, Input, Modal, Select, Switch } from 'antd';
+import { Form, Input, Modal, Select, Switch, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {
       ...omit(values, ['vision']),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
     console.info(data);
 
         >
           <Input placeholder={t('apiVersionMessage')} />
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
+
         <Form.Item noStyle dependencies={['model_type']}>
           {({ getFieldValue }) =>
             getFieldValue('model_type') === 'chat' && (

web/src/pages/user-setting/setting-model/bedrock-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Flex, Form, Input, Modal, Select, Space } from 'antd';
+import { Flex, Form, Input, Modal, Select, Space, InputNumber } from 'antd';
 import { useMemo } from 'react';
 import { BedrockRegionList } from '../constant';
 
     const data = {
       ...values,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
 
     onOk?.(data);
             allowClear
           ></Select>
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
+
       </Form>
     </Modal>
   );

web/src/pages/user-setting/setting-model/fish-audio-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Flex, Form, Input, Modal, Select, Space } from 'antd';
+import { Flex, Form, Input, Modal, Select, Space, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {
       ...omit(values),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
     console.info(data);
 
         >
           <Input placeholder={t('FishAudioRefIDMessage')} />
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
+
       </Form>
     </Modal>
   );

web/src/pages/user-setting/setting-model/google-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Form, Input, Modal, Select } from 'antd';
+import { Form, Input, Modal, Select, InputNumber } from 'antd';
 
 type FieldType = IAddLlmRequestBody & {
   google_project_id: string;
     const data = {
       ...values,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
 
     onOk?.(data);
         >
           <Input placeholder={t('GoogleServiceAccountKeyMessage')} />
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
+
       </Form>
     </Modal>
   );

web/src/pages/user-setting/setting-model/hunyuan-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Form, Input, Modal, Select } from 'antd';
+import { Form, Input, Modal, Select} from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {

web/src/pages/user-setting/setting-model/index.tsx

         hideModal={hideTencentCloudAddingModal}
         onOk={onTencentCloudAddingOk}
         loading={TencentCloudAddingLoading}
-        llmFactory={'Tencent TencentCloud'}
+        llmFactory={'Tencent Cloud'}
       ></TencentCloudModal>
       <SparkModal
         visible={SparkAddingVisible}

web/src/pages/user-setting/setting-model/ollama-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Flex, Form, Input, Modal, Select, Space, Switch } from 'antd';
+import { Flex, Form, Input, Modal, Select, Space, Switch, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & { vision: boolean };
       ...omit(values, ['vision']),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
     console.info(data);
 
         >
           <Input placeholder={t('apiKeyMessage')} />
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
+
         <Form.Item noStyle dependencies={['model_type']}>
           {({ getFieldValue }) =>
             getFieldValue('model_type') === 'chat' && (

web/src/pages/user-setting/setting-model/spark-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Form, Input, Modal, Select } from 'antd';
+import { Form, Input, Modal, Select, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {
       ...omit(values, ['vision']),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
     console.info(data);
 
             )
           }
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
+
       </Form>
     </Modal>
   );

web/src/pages/user-setting/setting-model/volcengine-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Flex, Form, Input, Modal, Select, Space, Switch } from 'antd';
+import { Flex, Form, Input, Modal, Select, Space, Switch, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {
       ...omit(values, ['vision']),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
     console.info(data);
 
         >
           <Input placeholder={t('ArkApiKeyMessage')} />
         </Form.Item>
-        <Form.Item noStyle dependencies={['model_type']}>
-          {({ getFieldValue }) =>
-            getFieldValue('model_type') === 'chat' && (
-              <Form.Item
-                label={t('vision')}
-                valuePropName="checked"
-                name={'vision'}
-              >
-                <Switch />
-              </Form.Item>
-            )
-          }
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
         </Form.Item>
+
       </Form>
     </Modal>
   );

web/src/pages/user-setting/setting-model/yiyan-modal/index.tsx

 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
 import { IAddLlmRequestBody } from '@/interfaces/request/llm';
-import { Form, Input, Modal, Select } from 'antd';
+import { Form, Input, Modal, Select, InputNumber } from 'antd';
 import omit from 'lodash/omit';
 
 type FieldType = IAddLlmRequestBody & {
       ...omit(values, ['vision']),
       model_type: modelType,
       llm_factory: llmFactory,
+      max_tokens:values.max_tokens,
     };
     console.info(data);
 
         >
           <Input placeholder={t('yiyanSKMessage')} />
         </Form.Item>
+        <Form.Item<FieldType>
+          label={t('maxTokens')}
+          name="max_tokens"
+          rules={[
+            { required: true, message: t('maxTokensMessage') },
+            {
+              type: 'number',
+              message: t('maxTokensInvalidMessage'),
+            },
+            ({ getFieldValue }) => ({
+              validator(_, value) {
+                if (value < 0) {
+                  return Promise.reject(new Error(t('maxTokensMinMessage')));
+                }
+                return Promise.resolve();
+              },
+            }),
+          ]}
+        >
+          <InputNumber
+            placeholder={t('maxTokensTip')}
+            style={{ width: '100%' }}
+          />
+        </Form.Item>
       </Form>
     </Modal>
   );