fix: activation page reload issue after activating (#964)

Co-authored-by: jyong <jyong@dify.ai>

README.md

@@ -30,10 +30,10 @@ Visual data analysis, log review, and annotation for applications
 - [x] **Spark**
 - [x] **Wenxin**
 - [x] **Tongyi**
-- [x] **ChatGLM**
+
 
 We provide the following free resources for registered Dify cloud users (sign up at [dify.ai](https://dify.ai)):
-* 1000 free Claude model queries to build Claude-powered apps
+* 600,000 free Claude model tokens to build Claude-powered apps
 * 200 free OpenAI queries to build OpenAI-based apps
 
   

README_CN.md

@@ -36,7 +36,7 @@
 
 
 我们为所有注册云端版的用户免费提供以下资源（登录 [dify.ai](https://cloud.dify.ai) 即可使用）：
-* 1000 次 Claude 模型的消息调用额度，用于创建基于 Claude 模型的 AI 应用
+* 60 万 Tokens Claude 模型的消息调用额度，用于创建基于 Claude 模型的 AI 应用
 * 200 次 OpenAI 模型的消息调用额度，用于创建基于 OpenAI 模型的 AI 应用
 * 300 万 讯飞星火大模型 Token 的调用额度，用于创建基于讯飞星火大模型的 AI 应用
 * 100 万 MiniMax Token 的调用额度，用于创建基于 MiniMax 模型的 AI 应用

api/Dockerfile

@@ -16,7 +16,7 @@ EXPOSE 5001
 WORKDIR /app/api
 
 RUN apt-get update && \
-    apt-get install -y bash curl wget vim gcc g++ python3-dev libc-dev libffi-dev
+    apt-get install -y bash curl wget vim gcc g++ python3-dev libc-dev libffi-dev nodejs
 
 COPY requirements.txt /app/api/requirements.txt
 

api/commands.py

@@ -20,7 +20,7 @@ from models.model import Account
 import secrets
 import base64
 
-from models.provider import Provider, ProviderType, ProviderQuotaType
+from models.provider import Provider, ProviderType, ProviderQuotaType, ProviderModel
 
 
 @click.command('reset-password', help='Reset the account password.')
@@ -102,6 +102,7 @@ def reset_encrypt_key_pair():
     tenant.encrypt_public_key = generate_key_pair(tenant.id)
 
     db.session.query(Provider).filter(Provider.provider_type == 'custom').delete()
+    db.session.query(ProviderModel).delete()
     db.session.commit()
 
     click.echo(click.style('Congratulations! '

api/config.py

@@ -48,9 +48,7 @@ DEFAULTS = {
     'WEAVIATE_GRPC_ENABLED': 'True',
     'WEAVIATE_BATCH_SIZE': 100,
     'CELERY_BACKEND': 'database',
-    'PDF_PREVIEW': 'True',
     'LOG_LEVEL': 'INFO',
-    'DISABLE_PROVIDER_CONFIG_VALIDATION': 'False',
     'HOSTED_OPENAI_QUOTA_LIMIT': 200,
     'HOSTED_OPENAI_ENABLED': 'False',
     'HOSTED_OPENAI_PAID_ENABLED': 'False',
@@ -102,13 +100,12 @@ class Config:
         self.CONSOLE_URL = get_env('CONSOLE_URL')
         self.API_URL = get_env('API_URL')
         self.APP_URL = get_env('APP_URL')
-        self.CURRENT_VERSION = "0.3.14"
+        self.CURRENT_VERSION = "0.3.15"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
         self.TESTING = False
         self.LOG_LEVEL = get_env('LOG_LEVEL')
-        self.PDF_PREVIEW = get_bool_env('PDF_PREVIEW')
 
         # Your App secret key will be used for securely signing the session cookie
         # Make sure you are changing this key for your deployment with a strong key.
@@ -236,10 +233,6 @@ class Config:
         self.STRIPE_API_KEY = get_env('STRIPE_API_KEY')
         self.STRIPE_WEBHOOK_SECRET = get_env('STRIPE_WEBHOOK_SECRET')
 
-        # By default it is False
-        # You could disable it for compatibility with certain OpenAPI providers
-        self.DISABLE_PROVIDER_CONFIG_VALIDATION = get_bool_env('DISABLE_PROVIDER_CONFIG_VALIDATION')
-
         # notion import setting
         self.NOTION_CLIENT_ID = get_env('NOTION_CLIENT_ID')
         self.NOTION_CLIENT_SECRET = get_env('NOTION_CLIENT_SECRET')

api/controllers/console/apikey.py

@@ -1,4 +1,5 @@
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 import flask_restful
 from flask_restful import Resource, fields, marshal_with
 from werkzeug.exceptions import Forbidden

api/controllers/console/app/app.py

@@ -3,7 +3,9 @@ import json
 import logging
 from datetime import datetime
 
-from flask_login import login_required, current_user
+import flask
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, fields, marshal_with, abort, inputs
 from werkzeug.exceptions import Forbidden
 
@@ -316,7 +318,7 @@ class AppApi(Resource):
 
         if current_user.current_tenant.current_role not in ['admin', 'owner']:
             raise Forbidden()
-        
+
         app = _get_app(app_id, current_user.current_tenant_id)
 
         db.session.delete(app)

api/controllers/console/app/audio.py

@@ -2,7 +2,7 @@
 import logging
 
 from flask import request
-from flask_login import login_required
+from core.login.login import login_required
 from werkzeug.exceptions import InternalServerError, NotFound
 
 import services

api/controllers/console/app/completion.py

@@ -5,7 +5,7 @@ from typing import Generator, Union
 
 import flask_login
 from flask import Response, stream_with_context
-from flask_login import login_required
+from core.login.login import login_required
 from werkzeug.exceptions import InternalServerError, NotFound
 
 import services

api/controllers/console/app/conversation.py

@@ -1,7 +1,8 @@
 from datetime import datetime
 
 import pytz
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, fields, marshal_with
 from flask_restful.inputs import int_range
 from sqlalchemy import or_, func

api/controllers/console/app/generator.py

@@ -1,4 +1,5 @@
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse
 
 from controllers.console import api

api/controllers/console/app/message.py

@@ -3,7 +3,7 @@ import logging
 from typing import Union, Generator
 
 from flask import Response, stream_with_context
-from flask_login import current_user, login_required
+from flask_login import current_user
 from flask_restful import Resource, reqparse, marshal_with, fields
 from flask_restful.inputs import int_range
 from werkzeug.exceptions import InternalServerError, NotFound
@@ -16,6 +16,7 @@ from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.model_providers.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \
     ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError
+from core.login.login import login_required
 from libs.helper import uuid_value, TimestampField
 from libs.infinite_scroll_pagination import InfiniteScrollPagination
 from extensions.ext_database import db

api/controllers/console/app/model_config.py

@@ -3,12 +3,13 @@ import json
 
 from flask import request
 from flask_restful import Resource
-from flask_login import login_required, current_user
+from flask_login import current_user
 
 from controllers.console import api
 from controllers.console.app import _get_app
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
+from core.login.login import login_required
 from events.app_event import app_model_config_was_updated
 from extensions.ext_database import db
 from models.model import AppModelConfig

api/controllers/console/app/site.py

@@ -1,5 +1,6 @@
 # -*- coding:utf-8 -*-
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, fields, marshal_with
 from werkzeug.exceptions import NotFound, Forbidden
 

api/controllers/console/app/statistic.py

@@ -4,7 +4,8 @@ from datetime import datetime
 
 import pytz
 from flask import jsonify
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse
 
 from controllers.console import api

api/controllers/console/auth/data_source_oauth.py

@@ -5,9 +5,12 @@ from typing import Optional
 import flask_login
 import requests
 from flask import request, redirect, current_app, session
-from flask_login import current_user, login_required
+from flask_login import current_user
+
 from flask_restful import Resource
 from werkzeug.exceptions import Forbidden
+
+from core.login.login import login_required
 from libs.oauth_data_source import NotionOAuth
 from controllers.console import api
 from ..setup import setup_required

api/controllers/console/datasets/data_source.py

@@ -3,7 +3,8 @@ import json
 
 from cachetools import TTLCache
 from flask import request, current_app
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, marshal_with, fields, reqparse, marshal
 from werkzeug.exceptions import NotFound
 

api/controllers/console/datasets/datasets.py

@@ -1,6 +1,7 @@
 # -*- coding:utf-8 -*-
 from flask import request
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, fields, marshal, marshal_with
 from werkzeug.exceptions import NotFound, Forbidden
 import services

api/controllers/console/datasets/datasets_document.py

@@ -4,7 +4,8 @@ from datetime import datetime
 from typing import List
 
 from flask import request
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, fields, marshal, marshal_with, reqparse
 from sqlalchemy import desc, asc
 from werkzeug.exceptions import NotFound, Forbidden
@@ -764,11 +765,13 @@ class DocumentMetadataApi(DocumentResource):
         metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[doc_type]
 
         document.doc_metadata = {}
-
-        for key, value_type in metadata_schema.items():
-            value = doc_metadata.get(key)
-            if value is not None and isinstance(value, value_type):
-                document.doc_metadata[key] = value
+        if doc_type == 'others':
+            document.doc_metadata = doc_metadata
+        else:
+            for key, value_type in metadata_schema.items():
+                value = doc_metadata.get(key)
+                if value is not None and isinstance(value, value_type):
+                    document.doc_metadata[key] = value
 
         document.doc_type = doc_type
         document.updated_at = datetime.utcnow()

api/controllers/console/datasets/datasets_segments.py

@@ -1,9 +1,8 @@
 # -*- coding:utf-8 -*-
 import uuid
 from datetime import datetime
-
 from flask import request
-from flask_login import login_required, current_user
+from flask_login import current_user
 from flask_restful import Resource, reqparse, fields, marshal
 from werkzeug.exceptions import NotFound, Forbidden
 
@@ -15,6 +14,7 @@ from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.model_providers.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.model_providers.model_factory import ModelFactory
+from core.login.login import login_required
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from models.dataset import DocumentSegment

api/controllers/console/datasets/file.py

@@ -8,7 +8,8 @@ from pathlib import Path
 
 from cachetools import TTLCache
 from flask import request, current_app
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, marshal_with, fields
 from werkzeug.exceptions import NotFound
 

api/controllers/console/datasets/hit_testing.py

@@ -1,6 +1,7 @@
 import logging
 
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, marshal, fields
 from werkzeug.exceptions import InternalServerError, NotFound, Forbidden
 

api/controllers/console/explore/installed_app.py

@@ -1,7 +1,8 @@
 # -*- coding:utf-8 -*-
 from datetime import datetime
 
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, fields, marshal_with, inputs
 from sqlalchemy import and_
 from werkzeug.exceptions import NotFound, Forbidden, BadRequest

api/controllers/console/explore/recommended_app.py

@@ -1,5 +1,6 @@
 # -*- coding:utf-8 -*-
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, fields, marshal_with
 from sqlalchemy import and_
 

api/controllers/console/explore/wraps.py

@@ -1,4 +1,5 @@
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource
 from functools import wraps
 

api/controllers/console/universal_chat/wraps.py

@@ -1,7 +1,8 @@
 import json
 from functools import wraps
 
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required

api/controllers/console/workspace/account.py

@@ -3,7 +3,8 @@ from datetime import datetime
 
 import pytz
 from flask import current_app, request
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, fields, marshal_with
 
 from services.errors.account import CurrentPasswordIncorrectError as ServiceCurrentPasswordIncorrectError

api/controllers/console/workspace/members.py

@@ -1,6 +1,7 @@
 # -*- coding:utf-8 -*-
 from flask import current_app
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse, marshal_with, abort, fields, marshal
 
 import services

api/controllers/console/workspace/model_providers.py

@@ -1,4 +1,5 @@
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse
 from werkzeug.exceptions import Forbidden
 

api/controllers/console/workspace/models.py

@@ -1,4 +1,5 @@
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse
 
 from controllers.console import api

api/controllers/console/workspace/providers.py

@@ -1,5 +1,6 @@
 # -*- coding:utf-8 -*-
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, reqparse
 from werkzeug.exceptions import Forbidden
 

api/controllers/console/workspace/tool_providers.py

@@ -1,6 +1,7 @@
 import json
 
-from flask_login import login_required, current_user
+from flask_login import current_user
+from core.login.login import login_required
 from flask_restful import Resource, abort, reqparse
 from werkzeug.exceptions import Forbidden
 

api/controllers/console/workspace/workspace.py

@@ -2,10 +2,13 @@
 import logging
 
 from flask import request
-from flask_login import login_required, current_user
-from flask_restful import Resource, fields, marshal_with, reqparse, marshal
+from flask_login import current_user
+from core.login.login import login_required
+from flask_restful import Resource, fields, marshal_with, reqparse, marshal, inputs
+from flask_restful.inputs import int_range
 
 from controllers.console import api
+from controllers.console.admin import admin_required
 from controllers.console.setup import setup_required
 from controllers.console.error import AccountNotLinkTenantError
 from controllers.console.wraps import account_initialization_required
@@ -43,6 +46,13 @@ tenants_fields = {
     'current': fields.Boolean
 }
 
+workspace_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'status': fields.String,
+    'created_at': TimestampField
+}
+
 
 class TenantListApi(Resource):
     @setup_required
@@ -57,6 +67,38 @@ class TenantListApi(Resource):
         return {'workspaces': marshal(tenants, tenants_fields)}, 200
 
 
+class WorkspaceListApi(Resource):
+    @setup_required
+    @admin_required
+    def get(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('page', type=inputs.int_range(1, 99999), required=False, default=1, location='args')
+        parser.add_argument('limit', type=inputs.int_range(1, 100), required=False, default=20, location='args')
+        args = parser.parse_args()
+
+        tenants = db.session.query(Tenant).order_by(Tenant.created_at.desc())\
+            .paginate(page=args['page'], per_page=args['limit'])
+
+        has_more = False
+        if len(tenants.items) == args['limit']:
+            current_page_first_tenant = tenants[-1]
+            rest_count = db.session.query(Tenant).filter(
+                Tenant.created_at < current_page_first_tenant.created_at,
+                Tenant.id != current_page_first_tenant.id
+            ).count()
+
+            if rest_count > 0:
+                has_more = True
+        total = db.session.query(Tenant).count()
+        return {
+            'data': marshal(tenants.items, workspace_fields),
+            'has_more': has_more,
+            'limit': args['limit'],
+            'page': args['page'],
+            'total': total
+                }, 200
+
+
 class TenantApi(Resource):
     @setup_required
     @login_required
@@ -92,6 +134,7 @@ class SwitchWorkspaceApi(Resource):
 
 
 api.add_resource(TenantListApi, '/workspaces')  # GET for getting all tenants
+api.add_resource(WorkspaceListApi, '/all-workspaces')  # GET for getting all tenants
 api.add_resource(TenantApi, '/workspaces/current', endpoint='workspaces_current')  # GET for getting current tenant info
 api.add_resource(TenantApi, '/info', endpoint='info')  # Deprecated
 api.add_resource(SwitchWorkspaceApi, '/workspaces/switch')  # POST for switching tenant

api/controllers/service_api/wraps.py

@@ -17,7 +17,7 @@ def validate_app_token(view=None):
         def decorated(*args, **kwargs):
             api_token = validate_and_get_api_token('app')
 
-            app_model = db.session.query(App).get(api_token.app_id)
+            app_model = db.session.query(App).filter(App.id == api_token.app_id).first()
             if not app_model:
                 raise NotFound()
 
@@ -44,7 +44,7 @@ def validate_dataset_token(view=None):
         def decorated(*args, **kwargs):
             api_token = validate_and_get_api_token('dataset')
 
-            dataset = db.session.query(Dataset).get(api_token.dataset_id)
+            dataset = db.session.query(Dataset).filter(Dataset.id == api_token.dataset_id).first()
             if not dataset:
                 raise NotFound()
 
@@ -64,14 +64,14 @@ def validate_and_get_api_token(scope=None):
     Validate and get API token.
     """
     auth_header = request.headers.get('Authorization')
-    if auth_header is None:
-        raise Unauthorized()
+    if auth_header is None or ' ' not in auth_header:
+        raise Unauthorized("Authorization header must be provided and start with 'Bearer'")
 
     auth_scheme, auth_token = auth_header.split(None, 1)
     auth_scheme = auth_scheme.lower()
 
     if auth_scheme != 'bearer':
-        raise Unauthorized()
+        raise Unauthorized("Authorization scheme must be 'Bearer'")
 
     api_token = db.session.query(ApiToken).filter(
         ApiToken.token == auth_token,
@@ -79,7 +79,7 @@ def validate_and_get_api_token(scope=None):
     ).first()
 
     if not api_token:
-        raise Unauthorized()
+        raise Unauthorized("Access token is invalid")
 
     api_token.last_used_at = datetime.utcnow()
     db.session.commit()

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -10,6 +10,7 @@ from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.conversation_message_task import ConversationMessageTask
+from core.model_providers.models.entity.message import PromptMessage
 from core.model_providers.models.llm.base import BaseLLM
 
 
@@ -68,6 +69,10 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.status = 'llm_end'
             if response.llm_output:
                 self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
+            else:
+                self._current_loop.prompt_tokens = self.model_instant.get_num_tokens(
+                    [PromptMessage(content=self._current_loop.prompt)]
+                )
             completion_generation = response.generations[0][0]
             if isinstance(completion_generation, ChatGeneration):
                 completion_message = completion_generation.message
@@ -81,6 +86,10 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
 
             if response.llm_output:
                 self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
+            else:
+                self._current_loop.completion_tokens = self.model_instant.get_num_tokens(
+                    [PromptMessage(content=self._current_loop.completion)]
+                )
 
     def on_llm_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any

api/core/conversation_message_task.py

@@ -119,9 +119,11 @@ class ConversationMessageTask:
             message="",
             message_tokens=0,
             message_unit_price=0,
+            message_price_unit=0,
             answer="",
             answer_tokens=0,
             answer_unit_price=0,
+            answer_price_unit=0,
             provider_response_latency=0,
             total_price=0,
             currency=self.model_instance.get_currency(),
@@ -140,17 +142,24 @@ class ConversationMessageTask:
     def save_message(self, llm_message: LLMMessage, by_stopped: bool = False):
         message_tokens = llm_message.prompt_tokens
         answer_tokens = llm_message.completion_tokens
-        message_unit_price = self.model_instance.get_token_price(1, MessageType.HUMAN)
-        answer_unit_price = self.model_instance.get_token_price(1, MessageType.ASSISTANT)
 
-        total_price = self.calc_total_price(message_tokens, message_unit_price, answer_tokens, answer_unit_price)
+        message_unit_price = self.model_instance.get_tokens_unit_price(MessageType.HUMAN)
+        message_price_unit = self.model_instance.get_price_unit(MessageType.HUMAN)
+        answer_unit_price = self.model_instance.get_tokens_unit_price(MessageType.ASSISTANT)
+        answer_price_unit = self.model_instance.get_price_unit(MessageType.ASSISTANT)
+
+        message_total_price = self.model_instance.calc_tokens_price(message_tokens, MessageType.HUMAN)
+        answer_total_price = self.model_instance.calc_tokens_price(answer_tokens, MessageType.ASSISTANT)
+        total_price = message_total_price + answer_total_price
 
         self.message.message = llm_message.prompt
         self.message.message_tokens = message_tokens
         self.message.message_unit_price = message_unit_price
+        self.message.message_price_unit = message_price_unit
         self.message.answer = PromptBuilder.process_template(llm_message.completion.strip()) if llm_message.completion else ''
         self.message.answer_tokens = answer_tokens
         self.message.answer_unit_price = answer_unit_price
+        self.message.answer_price_unit = answer_price_unit
         self.message.provider_response_latency = llm_message.latency
         self.message.total_price = total_price
 
@@ -192,7 +201,9 @@ class ConversationMessageTask:
             tool=agent_loop.tool_name,
             tool_input=agent_loop.tool_input,
             message=agent_loop.prompt,
+            message_price_unit=0,
             answer=agent_loop.completion,
+            answer_price_unit=0,
             created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
             created_by=self.user.id
         )
@@ -206,25 +217,26 @@ class ConversationMessageTask:
 
     def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instant: BaseLLM,
                      agent_loop: AgentLoop):
-        agent_message_unit_price = agent_model_instant.get_token_price(1, MessageType.HUMAN)
-        agent_answer_unit_price = agent_model_instant.get_token_price(1, MessageType.ASSISTANT)
+        agent_message_unit_price = agent_model_instant.get_tokens_unit_price(MessageType.HUMAN)
+        agent_message_price_unit = agent_model_instant.get_price_unit(MessageType.HUMAN)
+        agent_answer_unit_price = agent_model_instant.get_tokens_unit_price(MessageType.ASSISTANT)
+        agent_answer_price_unit = agent_model_instant.get_price_unit(MessageType.ASSISTANT)
 
         loop_message_tokens = agent_loop.prompt_tokens
         loop_answer_tokens = agent_loop.completion_tokens
 
-        loop_total_price = self.calc_total_price(
-            loop_message_tokens,
-            agent_message_unit_price,
-            loop_answer_tokens,
-            agent_answer_unit_price
-        )
+        loop_message_total_price = agent_model_instant.calc_tokens_price(loop_message_tokens, MessageType.HUMAN)
+        loop_answer_total_price = agent_model_instant.calc_tokens_price(loop_answer_tokens, MessageType.ASSISTANT)
+        loop_total_price = loop_message_total_price + loop_answer_total_price
 
         message_agent_thought.observation = agent_loop.tool_output
         message_agent_thought.tool_process_data = ''  # currently not support
         message_agent_thought.message_token = loop_message_tokens
         message_agent_thought.message_unit_price = agent_message_unit_price
+        message_agent_thought.message_price_unit = agent_message_price_unit
         message_agent_thought.answer_token = loop_answer_tokens
         message_agent_thought.answer_unit_price = agent_answer_unit_price
+        message_agent_thought.answer_price_unit = agent_answer_price_unit
         message_agent_thought.latency = agent_loop.latency
         message_agent_thought.tokens = agent_loop.prompt_tokens + agent_loop.completion_tokens
         message_agent_thought.total_price = loop_total_price
@@ -243,15 +255,6 @@ class ConversationMessageTask:
 
         db.session.add(dataset_query)
 
-    def calc_total_price(self, message_tokens, message_unit_price, answer_tokens, answer_unit_price):
-        message_tokens_per_1k = (decimal.Decimal(message_tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                                  rounding=decimal.ROUND_HALF_UP)
-        answer_tokens_per_1k = (decimal.Decimal(answer_tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                                rounding=decimal.ROUND_HALF_UP)
-
-        total_price = message_tokens_per_1k * message_unit_price + answer_tokens_per_1k * answer_unit_price
-        return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
-
     def end(self):
         self._pub_handler.pub_end()
 

api/core/docstore/dataset_docstore.py

@@ -103,6 +103,7 @@ class DatesetDocumentStore:
                     content=doc.page_content,
                     word_count=len(doc.page_content),
                     tokens=tokens,
+                    enabled=False,
                     created_by=self._user_id,
                 )
                 if 'answer' in doc.metadata and doc.metadata['answer']:

api/core/indexing_runner.py

@@ -278,7 +278,7 @@ class IndexingRunner:
                     "total_segments": total_segments * 20,
                     "tokens": total_segments * 2000,
                     "total_price": '{:f}'.format(
-                        text_generation_model.get_token_price(total_segments * 2000, MessageType.HUMAN)),
+                        text_generation_model.calc_tokens_price(total_segments * 2000, MessageType.HUMAN)),
                     "currency": embedding_model.get_currency(),
                     "qa_preview": document_qa_list,
                     "preview": preview_texts
@@ -286,7 +286,7 @@ class IndexingRunner:
         return {
             "total_segments": total_segments,
             "tokens": tokens,
-            "total_price": '{:f}'.format(embedding_model.get_token_price(tokens)),
+            "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)),
             "currency": embedding_model.get_currency(),
             "preview": preview_texts
         }
@@ -371,7 +371,7 @@ class IndexingRunner:
                     "total_segments": total_segments * 20,
                     "tokens": total_segments * 2000,
                     "total_price": '{:f}'.format(
-                        text_generation_model.get_token_price(total_segments * 2000, MessageType.HUMAN)),
+                        text_generation_model.calc_tokens_price(total_segments * 2000, MessageType.HUMAN)),
                     "currency": embedding_model.get_currency(),
                     "qa_preview": document_qa_list,
                     "preview": preview_texts
@@ -379,7 +379,7 @@ class IndexingRunner:
         return {
             "total_segments": total_segments,
             "tokens": tokens,
-            "total_price": '{:f}'.format(embedding_model.get_token_price(tokens)),
+            "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)),
             "currency": embedding_model.get_currency(),
             "preview": preview_texts
         }
@@ -691,6 +691,7 @@ class IndexingRunner:
                 DocumentSegment.status == "indexing"
             ).update({
                 DocumentSegment.status: "completed",
+                DocumentSegment.enabled: True,
                 DocumentSegment.completed_at: datetime.datetime.utcnow()
             })
 

api/core/login/login.py

@@ -0,0 +1,108 @@
+import os
+from functools import wraps
+
+import flask_login
+from flask import current_app
+from flask import g
+from flask import has_request_context
+from flask import request
+from flask_login import user_logged_in
+from flask_login.config import EXEMPT_METHODS
+from werkzeug.exceptions import Unauthorized
+from werkzeug.local import LocalProxy
+
+from extensions.ext_database import db
+from models.account import Account, Tenant, TenantAccountJoin
+
+#: A proxy for the current user. If no user is logged in, this will be an
+#: anonymous user
+current_user = LocalProxy(lambda: _get_user())
+
+
+def login_required(func):
+    """
+    If you decorate a view with this, it will ensure that the current user is
+    logged in and authenticated before calling the actual view. (If they are
+    not, it calls the :attr:`LoginManager.unauthorized` callback.) For
+    example::
+
+        @app.route('/post')
+        @login_required
+        def post():
+            pass
+
+    If there are only certain times you need to require that your user is
+    logged in, you can do so with::
+
+        if not current_user.is_authenticated:
+            return current_app.login_manager.unauthorized()
+
+    ...which is essentially the code that this function adds to your views.
+
+    It can be convenient to globally turn off authentication when unit testing.
+    To enable this, if the application configuration variable `LOGIN_DISABLED`
+    is set to `True`, this decorator will be ignored.
+
+    .. Note ::
+
+        Per `W3 guidelines for CORS preflight requests
+        <http://www.w3.org/TR/cors/#cross-origin-request-with-preflight-0>`_,
+        HTTP ``OPTIONS`` requests are exempt from login checks.
+
+    :param func: The view function to decorate.
+    :type func: function
+    """
+
+    @wraps(func)
+    def decorated_view(*args, **kwargs):
+        auth_header = request.headers.get('Authorization')
+        admin_api_key_enable = os.getenv('ADMIN_API_KEY_ENABLE', default='False')
+        if admin_api_key_enable:
+            if auth_header:
+                if ' ' not in auth_header:
+                    raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
+                auth_scheme, auth_token = auth_header.split(None, 1)
+                auth_scheme = auth_scheme.lower()
+                if auth_scheme != 'bearer':
+                    raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
+                admin_api_key = os.getenv('ADMIN_API_KEY')
+
+                if admin_api_key:
+                    if os.getenv('ADMIN_API_KEY') == auth_token:
+                        workspace_id = request.headers.get('X-WORKSPACE-ID')
+                        if workspace_id:
+                            tenant_account_join = db.session.query(Tenant, TenantAccountJoin) \
+                                .filter(Tenant.id == workspace_id) \
+                                .filter(TenantAccountJoin.tenant_id == Tenant.id) \
+                                .filter(TenantAccountJoin.role == 'owner') \
+                                .one_or_none()
+                            if tenant_account_join:
+                                tenant, ta = tenant_account_join
+                                account = Account.query.filter_by(id=ta.account_id).first()
+                                # Login admin
+                                if account:
+                                    account.current_tenant = tenant
+                                    current_app.login_manager._update_request_context_with_user(account)
+                                    user_logged_in.send(current_app._get_current_object(), user=_get_user())
+        if request.method in EXEMPT_METHODS or current_app.config.get("LOGIN_DISABLED"):
+            pass
+        elif not current_user.is_authenticated:
+            return current_app.login_manager.unauthorized()
+
+        # flask 1.x compatibility
+        # current_app.ensure_sync is only available in Flask >= 2.0
+        if callable(getattr(current_app, "ensure_sync", None)):
+            return current_app.ensure_sync(func)(*args, **kwargs)
+        return func(*args, **kwargs)
+
+    return decorated_view
+
+
+def _get_user():
+    if has_request_context():
+        if "_login_user" not in g:
+            current_app.login_manager._load_user()
+
+        return g._login_user
+
+    return None

api/core/model_providers/model_provider_factory.py

@@ -57,6 +57,12 @@ class ModelProviderFactory:
         elif provider_name == 'huggingface_hub':
             from core.model_providers.providers.huggingface_hub_provider import HuggingfaceHubProvider
             return HuggingfaceHubProvider
+        elif provider_name == 'xinference':
+            from core.model_providers.providers.xinference_provider import XinferenceProvider
+            return XinferenceProvider
+        elif provider_name == 'openllm':
+            from core.model_providers.providers.openllm_provider import OpenLLMProvider
+            return OpenLLMProvider
         else:
             raise NotImplementedError
 

api/core/model_providers/models/embedding/azure_openai_embedding.py

@@ -26,10 +26,20 @@ class AzureOpenAIEmbedding(BaseEmbedding):
             openai_api_version=AZURE_OPENAI_API_VERSION,
             chunk_size=16,
             max_retries=1,
-            **self.credentials
+            openai_api_key=self.credentials.get('openai_api_key'),
+            openai_api_base=self.credentials.get('openai_api_base')
         )
 
         super().__init__(model_provider, client, name)
+    
+    @property
+    def base_model_name(self) -> str:
+        """
+        get base model name (not deployment)
+        
+        :return: str
+        """
+        return self.credentials.get("base_model_name")
 
     def get_num_tokens(self, text: str) -> int:
         """
@@ -48,16 +58,6 @@ class AzureOpenAIEmbedding(BaseEmbedding):
         # calculate the number of tokens in the encoded text
         return len(tokenized_text)
 
-    def get_token_price(self, tokens: int):
-        tokens_per_1k = (decimal.Decimal(tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                  rounding=decimal.ROUND_HALF_UP)
-
-        total_price = tokens_per_1k * decimal.Decimal('0.0001')
-        return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
-
-    def get_currency(self):
-        return 'USD'
-
     def handle_exceptions(self, ex: Exception) -> Exception:
         if isinstance(ex, openai.error.InvalidRequestError):
             logging.warning("Invalid request to Azure OpenAI API.")
@@ -71,7 +71,7 @@ class AzureOpenAIEmbedding(BaseEmbedding):
         elif isinstance(ex, openai.error.RateLimitError):
             return LLMRateLimitError('Azure ' + str(ex))
         elif isinstance(ex, openai.error.AuthenticationError):
-            raise LLMAuthorizationError('Azure ' + str(ex))
+            return LLMAuthorizationError('Azure ' + str(ex))
         elif isinstance(ex, openai.error.OpenAIError):
             return LLMBadRequestError('Azure ' + ex.__class__.__name__ + ":" + str(ex))
         else:

api/core/model_providers/models/embedding/base.py

@@ -1,5 +1,6 @@
 from abc import abstractmethod
 from typing import Any
+import decimal
 
 import tiktoken
 from langchain.schema.language_model import _get_token_ids_default_method
@@ -7,7 +8,8 @@ from langchain.schema.language_model import _get_token_ids_default_method
 from core.model_providers.models.base import BaseProviderModel
 from core.model_providers.models.entity.model_params import ModelType
 from core.model_providers.providers.base import BaseModelProvider
-
+import logging
+logger = logging.getLogger(__name__)
 
 class BaseEmbedding(BaseProviderModel):
     name: str
@@ -17,6 +19,63 @@ class BaseEmbedding(BaseProviderModel):
         super().__init__(model_provider, client)
         self.name = name
 
+    @property
+    def base_model_name(self) -> str:
+        """
+        get base model name
+        
+        :return: str
+        """
+        return self.name
+
+    @property
+    def price_config(self) -> dict:
+        def get_or_default():
+            default_price_config = {
+                    'completion': decimal.Decimal('0'),
+                    'unit': decimal.Decimal('0'),
+                    'currency': 'USD'
+                }
+            rules = self.model_provider.get_rules()
+            price_config = rules['price_config'][self.base_model_name] if 'price_config' in rules else default_price_config
+            price_config = {
+                'completion': decimal.Decimal(price_config['completion']),
+                'unit': decimal.Decimal(price_config['unit']),
+                'currency': price_config['currency']
+            }
+            return price_config
+        
+        self._price_config = self._price_config if hasattr(self, '_price_config') else get_or_default()
+
+        logger.debug(f"model: {self.name} price_config: {self._price_config}")
+        return self._price_config
+
+    def calc_tokens_price(self, tokens: int) -> decimal.Decimal:
+        """
+        calc tokens total price.
+
+        :param tokens:
+        :return: decimal.Decimal('0.0000001')
+        """
+        unit_price = self.price_config['completion']
+        unit = self.price_config['unit']
+        total_price = tokens * unit_price * unit
+        total_price = total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
+        logging.debug(f"tokens={tokens}, unit_price={unit_price}, unit={unit}, total_price:{total_price}")
+        return total_price
+
+    def get_tokens_unit_price(self) -> decimal.Decimal:
+        """
+        get token price.
+
+        :return: decimal.Decimal('0.0001')
+        
+        """
+        unit_price = self.price_config['completion']
+        unit_price = unit_price.quantize(decimal.Decimal('0.0001'), rounding=decimal.ROUND_HALF_UP)
+        logger.debug(f'unit_price:{unit_price}')
+        return unit_price
+
     def get_num_tokens(self, text: str) -> int:
         """
         get num tokens of text.
@@ -29,11 +88,14 @@ class BaseEmbedding(BaseProviderModel):
 
         return len(_get_token_ids_default_method(text))
 
-    def get_token_price(self, tokens: int):
-        return 0
-
     def get_currency(self):
-        return 'USD'
+        """
+        get token currency.
+
+        :return: get from price config, default 'USD'
+        """
+        currency = self.price_config['currency']
+        return currency
 
     @abstractmethod
     def handle_exceptions(self, ex: Exception) -> Exception:

api/core/model_providers/models/embedding/minimax_embedding.py

@@ -1,6 +1,3 @@
-import decimal
-import logging
-
 from langchain.embeddings import MiniMaxEmbeddings
 
 from core.model_providers.error import LLMBadRequestError
@@ -22,12 +19,6 @@ class MinimaxEmbedding(BaseEmbedding):
 
         super().__init__(model_provider, client, name)
 
-    def get_token_price(self, tokens: int):
-        return decimal.Decimal('0')
-
-    def get_currency(self):
-        return 'RMB'
-
     def handle_exceptions(self, ex: Exception) -> Exception:
         if isinstance(ex, ValueError):
             return LLMBadRequestError(f"Minimax: {str(ex)}")

api/core/model_providers/models/embedding/openai_embedding.py

@@ -42,16 +42,6 @@ class OpenAIEmbedding(BaseEmbedding):
         # calculate the number of tokens in the encoded text
         return len(tokenized_text)
 
-    def get_token_price(self, tokens: int):
-        tokens_per_1k = (decimal.Decimal(tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                  rounding=decimal.ROUND_HALF_UP)
-
-        total_price = tokens_per_1k * decimal.Decimal('0.0001')
-        return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
-
-    def get_currency(self):
-        return 'USD'
-
     def handle_exceptions(self, ex: Exception) -> Exception:
         if isinstance(ex, openai.error.InvalidRequestError):
             logging.warning("Invalid request to OpenAI API.")
@@ -65,7 +55,7 @@ class OpenAIEmbedding(BaseEmbedding):
         elif isinstance(ex, openai.error.RateLimitError):
             return LLMRateLimitError(str(ex))
         elif isinstance(ex, openai.error.AuthenticationError):
-            raise LLMAuthorizationError(str(ex))
+            return LLMAuthorizationError(str(ex))
         elif isinstance(ex, openai.error.OpenAIError):
             return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
         else:

api/core/model_providers/models/embedding/replicate_embedding.py

@@ -22,13 +22,6 @@ class ReplicateEmbedding(BaseEmbedding):
 
         super().__init__(model_provider, client, name)
 
-    def get_token_price(self, tokens: int):
-        # replicate only pay for prediction seconds
-        return decimal.Decimal('0')
-
-    def get_currency(self):
-        return 'USD'
-
     def handle_exceptions(self, ex: Exception) -> Exception:
         if isinstance(ex, (ModelError, ReplicateError)):
             return LLMBadRequestError(f"Replicate: {str(ex)}")

api/core/model_providers/models/embedding/xinference_embedding.py

@@ -0,0 +1,27 @@
+from core.third_party.langchain.embeddings.xinference_embedding import XinferenceEmbedding as XinferenceEmbeddings
+from replicate.exceptions import ModelError, ReplicateError
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.providers.base import BaseModelProvider
+from core.model_providers.models.embedding.base import BaseEmbedding
+
+
+class XinferenceEmbedding(BaseEmbedding):
+    def __init__(self, model_provider: BaseModelProvider, name: str):
+        credentials = model_provider.get_model_credentials(
+            model_name=name,
+            model_type=self.type
+        )
+
+        client = XinferenceEmbeddings(
+            server_url=credentials['server_url'],
+            model_uid=credentials['model_uid'],
+        )
+
+        super().__init__(model_provider, client, name)
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        if isinstance(ex, (ModelError, ReplicateError)):
+            return LLMBadRequestError(f"Xinference embedding: {str(ex)}")
+        else:
+            return ex

api/core/model_providers/models/llm/anthropic_model.py

@@ -54,32 +54,6 @@ class AnthropicModel(BaseLLM):
         prompts = self._get_prompt_from_messages(messages)
         return max(self._client.get_num_tokens_from_messages(prompts) - len(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        model_unit_prices = {
-            'claude-instant-1': {
-                'prompt': decimal.Decimal('1.63'),
-                'completion': decimal.Decimal('5.51'),
-            },
-            'claude-2': {
-                'prompt': decimal.Decimal('11.02'),
-                'completion': decimal.Decimal('32.68'),
-            },
-        }
-
-        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
-            unit_price = model_unit_prices[self.name]['prompt']
-        else:
-            unit_price = model_unit_prices[self.name]['completion']
-
-        tokens_per_1m = (decimal.Decimal(tokens) / 1000000).quantize(decimal.Decimal('0.000001'),
-                                                                     rounding=decimal.ROUND_HALF_UP)
-
-        total_price = tokens_per_1m * unit_price
-        return total_price.quantize(decimal.Decimal('0.00000001'), rounding=decimal.ROUND_HALF_UP)
-
-    def get_currency(self):
-        return 'USD'
-
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
         for k, v in provider_model_kwargs.items():

api/core/model_providers/models/llm/azure_openai_model.py

@@ -29,7 +29,6 @@ class AzureOpenAIModel(BaseLLM):
             self.model_mode = ModelMode.COMPLETION
         else:
             self.model_mode = ModelMode.CHAT
-
         super().__init__(model_provider, name, model_kwargs, streaming, callbacks)
 
     def _init_client(self) -> Any:
@@ -83,6 +82,15 @@ class AzureOpenAIModel(BaseLLM):
         """
         prompts = self._get_prompt_from_messages(messages)
         return self._client.generate([prompts], stop, callbacks)
+    
+    @property
+    def base_model_name(self) -> str:
+        """
+        get base model name (not deployment)
+        
+        :return: str
+        """
+        return self.credentials.get("base_model_name")
 
     def get_num_tokens(self, messages: List[PromptMessage]) -> int:
         """
@@ -97,45 +105,6 @@ class AzureOpenAIModel(BaseLLM):
         else:
             return max(self._client.get_num_tokens_from_messages(prompts) - len(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        model_unit_prices = {
-            'gpt-4': {
-                'prompt': decimal.Decimal('0.03'),
-                'completion': decimal.Decimal('0.06'),
-            },
-            'gpt-4-32k': {
-                'prompt': decimal.Decimal('0.06'),
-                'completion': decimal.Decimal('0.12')
-            },
-            'gpt-35-turbo': {
-                'prompt': decimal.Decimal('0.0015'),
-                'completion': decimal.Decimal('0.002')
-            },
-            'gpt-35-turbo-16k': {
-                'prompt': decimal.Decimal('0.003'),
-                'completion': decimal.Decimal('0.004')
-            },
-            'text-davinci-003': {
-                'prompt': decimal.Decimal('0.02'),
-                'completion': decimal.Decimal('0.02')
-            },
-        }
-
-        base_model_name = self.credentials.get("base_model_name")
-        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
-            unit_price = model_unit_prices[base_model_name]['prompt']
-        else:
-            unit_price = model_unit_prices[base_model_name]['completion']
-
-        tokens_per_1k = (decimal.Decimal(tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                  rounding=decimal.ROUND_HALF_UP)
-
-        total_price = tokens_per_1k * unit_price
-        return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
-
-    def get_currency(self):
-        return 'USD'
-
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
         if self.name == 'text-davinci-003':
@@ -166,7 +135,7 @@ class AzureOpenAIModel(BaseLLM):
         elif isinstance(ex, openai.error.RateLimitError):
             return LLMRateLimitError('Azure ' + str(ex))
         elif isinstance(ex, openai.error.AuthenticationError):
-            raise LLMAuthorizationError('Azure ' + str(ex))
+            return LLMAuthorizationError('Azure ' + str(ex))
         elif isinstance(ex, openai.error.OpenAIError):
             return LLMBadRequestError('Azure ' + ex.__class__.__name__ + ":" + str(ex))
         else:

api/core/model_providers/models/llm/base.py

@@ -1,5 +1,6 @@
 from abc import abstractmethod
 from typing import List, Optional, Any, Union
+import decimal
 
 from langchain.callbacks.manager import Callbacks
 from langchain.schema import LLMResult, SystemMessage, AIMessage, HumanMessage, BaseMessage, ChatGeneration
@@ -10,6 +11,8 @@ from core.model_providers.models.entity.message import PromptMessage, MessageTyp
 from core.model_providers.models.entity.model_params import ModelType, ModelKwargs, ModelMode, ModelKwargsRules
 from core.model_providers.providers.base import BaseModelProvider
 from core.third_party.langchain.llms.fake import FakeLLM
+import logging
+logger = logging.getLogger(__name__)
 
 
 class BaseLLM(BaseProviderModel):
@@ -60,6 +63,39 @@ class BaseLLM(BaseProviderModel):
     def _init_client(self) -> Any:
         raise NotImplementedError
 
+    @property
+    def base_model_name(self) -> str:
+        """
+        get llm base model name
+
+        :return: str
+        """
+        return self.name
+
+    @property
+    def price_config(self) -> dict:
+        def get_or_default():
+            default_price_config = {
+                    'prompt': decimal.Decimal('0'),
+                    'completion': decimal.Decimal('0'),
+                    'unit': decimal.Decimal('0'),
+                    'currency': 'USD'
+                }
+            rules = self.model_provider.get_rules()
+            price_config = rules['price_config'][self.base_model_name] if 'price_config' in rules else default_price_config
+            price_config = {
+                'prompt': decimal.Decimal(price_config['prompt']),
+                'completion': decimal.Decimal(price_config['completion']),
+                'unit': decimal.Decimal(price_config['unit']),
+                'currency': price_config['currency']
+            }
+            return price_config
+        
+        self._price_config = self._price_config if hasattr(self, '_price_config') else get_or_default()
+
+        logger.debug(f"model: {self.name} price_config: {self._price_config}")
+        return self._price_config
+
     def run(self, messages: List[PromptMessage],
             stop: Optional[List[str]] = None,
             callbacks: Callbacks = None,
@@ -161,25 +197,64 @@ class BaseLLM(BaseProviderModel):
         """
         raise NotImplementedError
 
-    @abstractmethod
-    def get_token_price(self, tokens: int, message_type: MessageType):
+    def calc_tokens_price(self, tokens: int, message_type: MessageType) -> decimal.Decimal:
         """
-        get token price.
+        calc tokens total price.
 
         :param tokens:
         :param message_type:
         :return:
         """
-        raise NotImplementedError
+        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
+            unit_price = self.price_config['prompt']
+        else:
+            unit_price = self.price_config['completion']
+        unit = self.get_price_unit(message_type)
 
-    @abstractmethod
-    def get_currency(self):
+        total_price = tokens * unit_price * unit
+        total_price = total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
+        logging.debug(f"tokens={tokens}, unit_price={unit_price}, unit={unit}, total_price:{total_price}")
+        return total_price
+
+    def get_tokens_unit_price(self, message_type: MessageType) -> decimal.Decimal:
+        """
+        get token price.
+
+        :param message_type:
+        :return: decimal.Decimal('0.0001')
+        """
+        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
+            unit_price = self.price_config['prompt']
+        else:
+            unit_price = self.price_config['completion']
+        unit_price = unit_price.quantize(decimal.Decimal('0.0001'), rounding=decimal.ROUND_HALF_UP)
+        logging.debug(f"unit_price={unit_price}")
+        return unit_price
+
+    def get_price_unit(self, message_type: MessageType) -> decimal.Decimal:
+        """
+        get price unit.
+
+        :param message_type:
+        :return: decimal.Decimal('0.000001')
+        """
+        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
+            price_unit = self.price_config['unit']
+        else:
+            price_unit = self.price_config['unit']
+
+        price_unit = price_unit.quantize(decimal.Decimal('0.000001'), rounding=decimal.ROUND_HALF_UP)
+        logging.debug(f"price_unit={price_unit}")
+        return price_unit
+
+    def get_currency(self) -> str:
         """
         get token currency.
 
-        :return:
+        :return: get from price config, default 'USD'
         """
-        raise NotImplementedError
+        currency = self.price_config['currency']
+        return currency
 
     def get_model_kwargs(self):
         return self.model_kwargs

api/core/model_providers/models/llm/chatglm_model.py

@@ -47,9 +47,6 @@ class ChatGLMModel(BaseLLM):
         prompts = self._get_prompt_from_messages(messages)
         return max(self._client.get_num_tokens(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        return decimal.Decimal('0')
-
     def get_currency(self):
         return 'RMB'
 

api/core/model_providers/models/llm/huggingface_hub_model.py

@@ -62,13 +62,6 @@ class HuggingfaceHubModel(BaseLLM):
         prompts = self._get_prompt_from_messages(messages)
         return self._client.get_num_tokens(prompts)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        # not support calc price
-        return decimal.Decimal('0')
-
-    def get_currency(self):
-        return 'USD'
-
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
         self.client.model_kwargs = provider_model_kwargs

api/core/model_providers/models/llm/minimax_model.py

@@ -51,9 +51,6 @@ class MinimaxModel(BaseLLM):
         prompts = self._get_prompt_from_messages(messages)
         return max(self._client.get_num_tokens(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        return decimal.Decimal('0')
-
     def get_currency(self):
         return 'RMB'
 

api/core/model_providers/models/llm/openai_model.py

@@ -46,7 +46,8 @@ class OpenAIModel(BaseLLM):
             self.model_mode = ModelMode.COMPLETION
         else:
             self.model_mode = ModelMode.CHAT
-
+        
+        # TODO load price config from configs(db)
         super().__init__(model_provider, name, model_kwargs, streaming, callbacks)
 
     def _init_client(self) -> Any:
@@ -117,44 +118,6 @@ class OpenAIModel(BaseLLM):
         else:
             return max(self._client.get_num_tokens_from_messages(prompts) - len(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        model_unit_prices = {
-            'gpt-4': {
-                'prompt': decimal.Decimal('0.03'),
-                'completion': decimal.Decimal('0.06'),
-            },
-            'gpt-4-32k': {
-                'prompt': decimal.Decimal('0.06'),
-                'completion': decimal.Decimal('0.12')
-            },
-            'gpt-3.5-turbo': {
-                'prompt': decimal.Decimal('0.0015'),
-                'completion': decimal.Decimal('0.002')
-            },
-            'gpt-3.5-turbo-16k': {
-                'prompt': decimal.Decimal('0.003'),
-                'completion': decimal.Decimal('0.004')
-            },
-            'text-davinci-003': {
-                'prompt': decimal.Decimal('0.02'),
-                'completion': decimal.Decimal('0.02')
-            },
-        }
-
-        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
-            unit_price = model_unit_prices[self.name]['prompt']
-        else:
-            unit_price = model_unit_prices[self.name]['completion']
-
-        tokens_per_1k = (decimal.Decimal(tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                  rounding=decimal.ROUND_HALF_UP)
-
-        total_price = tokens_per_1k * unit_price
-        return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
-
-    def get_currency(self):
-        return 'USD'
-
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
         if self.name in COMPLETION_MODELS:
@@ -185,7 +148,7 @@ class OpenAIModel(BaseLLM):
         elif isinstance(ex, openai.error.RateLimitError):
             return LLMRateLimitError(str(ex))
         elif isinstance(ex, openai.error.AuthenticationError):
-            raise LLMAuthorizationError(str(ex))
+            return LLMAuthorizationError(str(ex))
         elif isinstance(ex, openai.error.OpenAIError):
             return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
         else:

api/core/model_providers/models/llm/openllm_model.py

@@ -0,0 +1,60 @@
+from typing import List, Optional, Any
+
+from langchain.callbacks.manager import Callbacks
+from langchain.schema import LLMResult
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.entity.message import PromptMessage
+from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+from core.third_party.langchain.llms.openllm import OpenLLM
+
+
+class OpenLLMModel(BaseLLM):
+    model_mode: ModelMode = ModelMode.COMPLETION
+
+    def _init_client(self) -> Any:
+        self.provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
+
+        client = OpenLLM(
+            server_url=self.credentials.get('server_url'),
+            callbacks=self.callbacks,
+            llm_kwargs=self.provider_model_kwargs
+        )
+
+        return client
+
+    def _run(self, messages: List[PromptMessage],
+             stop: Optional[List[str]] = None,
+             callbacks: Callbacks = None,
+             **kwargs) -> LLMResult:
+        """
+        run predict by prompt messages and stop words.
+
+        :param messages:
+        :param stop:
+        :param callbacks:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return self._client.generate([prompts], stop, callbacks)
+
+    def get_num_tokens(self, messages: List[PromptMessage]) -> int:
+        """
+        get num tokens of prompt messages.
+
+        :param messages:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return max(self._client.get_num_tokens(prompts), 0)
+
+    def _set_model_kwargs(self, model_kwargs: ModelKwargs):
+        pass
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"OpenLLM: {str(ex)}")
+
+    @classmethod
+    def support_streaming(cls):
+        return False

api/core/model_providers/models/llm/replicate_model.py

@@ -81,13 +81,6 @@ class ReplicateModel(BaseLLM):
 
         return self._client.get_num_tokens(prompts)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        # replicate only pay for prediction seconds
-        return decimal.Decimal('0')
-
-    def get_currency(self):
-        return 'USD'
-
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
         self.client.input = provider_model_kwargs

api/core/model_providers/models/llm/spark_model.py

@@ -50,9 +50,6 @@ class SparkModel(BaseLLM):
         contents = [message.content for message in messages]
         return max(self._client.get_num_tokens("".join(contents)), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        return decimal.Decimal('0')
-
     def get_currency(self):
         return 'RMB'
 

api/core/model_providers/models/llm/tongyi_model.py

@@ -53,9 +53,6 @@ class TongyiModel(BaseLLM):
         prompts = self._get_prompt_from_messages(messages)
         return max(self._client.get_num_tokens(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        return decimal.Decimal('0')
-
     def get_currency(self):
         return 'RMB'
 

api/core/model_providers/models/llm/wenxin_model.py

@@ -16,6 +16,7 @@ class WenxinModel(BaseLLM):
 
     def _init_client(self) -> Any:
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
+        # TODO load price_config from configs(db)
         return Wenxin(
             streaming=self.streaming,
             callbacks=self.callbacks,
@@ -48,36 +49,6 @@ class WenxinModel(BaseLLM):
         prompts = self._get_prompt_from_messages(messages)
         return max(self._client.get_num_tokens(prompts), 0)
 
-    def get_token_price(self, tokens: int, message_type: MessageType):
-        model_unit_prices = {
-            'ernie-bot': {
-                'prompt': decimal.Decimal('0.012'),
-                'completion': decimal.Decimal('0.012'),
-            },
-            'ernie-bot-turbo': {
-                'prompt': decimal.Decimal('0.008'),
-                'completion': decimal.Decimal('0.008')
-            },
-            'bloomz-7b': {
-                'prompt': decimal.Decimal('0.006'),
-                'completion': decimal.Decimal('0.006')
-            }
-        }
-
-        if message_type == MessageType.HUMAN or message_type == MessageType.SYSTEM:
-            unit_price = model_unit_prices[self.name]['prompt']
-        else:
-            unit_price = model_unit_prices[self.name]['completion']
-
-        tokens_per_1k = (decimal.Decimal(tokens) / 1000).quantize(decimal.Decimal('0.001'),
-                                                                  rounding=decimal.ROUND_HALF_UP)
-
-        total_price = tokens_per_1k * unit_price
-        return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
-
-    def get_currency(self):
-        return 'RMB'
-
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
         for k, v in provider_model_kwargs.items():

api/core/model_providers/models/llm/xinference_model.py

@@ -0,0 +1,70 @@
+from typing import List, Optional, Any
+
+from langchain.callbacks.manager import Callbacks
+from langchain.schema import LLMResult
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.entity.message import PromptMessage
+from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+from core.third_party.langchain.llms.xinference_llm import XinferenceLLM
+
+
+class XinferenceModel(BaseLLM):
+    model_mode: ModelMode = ModelMode.COMPLETION
+
+    def _init_client(self) -> Any:
+        self.provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
+
+        client = XinferenceLLM(
+            server_url=self.credentials['server_url'],
+            model_uid=self.credentials['model_uid'],
+        )
+
+        client.callbacks = self.callbacks
+
+        return client
+
+    def _run(self, messages: List[PromptMessage],
+             stop: Optional[List[str]] = None,
+             callbacks: Callbacks = None,
+             **kwargs) -> LLMResult:
+        """
+        run predict by prompt messages and stop words.
+
+        :param messages:
+        :param stop:
+        :param callbacks:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return self._client.generate(
+            [prompts],
+            stop,
+            callbacks,
+            generate_config={
+                "stop": stop,
+                "stream": self.streaming,
+                **self.provider_model_kwargs,
+            }
+        )
+
+    def get_num_tokens(self, messages: List[PromptMessage]) -> int:
+        """
+        get num tokens of prompt messages.
+
+        :param messages:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return max(self._client.get_num_tokens(prompts), 0)
+
+    def _set_model_kwargs(self, model_kwargs: ModelKwargs):
+        pass
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"Xinference: {str(ex)}")
+
+    @classmethod
+    def support_streaming(cls):
+        return True

api/core/model_providers/models/moderation/openai_moderation.py

@@ -41,7 +41,7 @@ class OpenAIModeration(BaseProviderModel):
         elif isinstance(ex, openai.error.RateLimitError):
             return LLMRateLimitError(str(ex))
         elif isinstance(ex, openai.error.AuthenticationError):
-            raise LLMAuthorizationError(str(ex))
+            return LLMAuthorizationError(str(ex))
         elif isinstance(ex, openai.error.OpenAIError):
             return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
         else:

api/core/model_providers/models/speech2text/openai_whisper.py

@@ -40,7 +40,7 @@ class OpenAIWhisper(BaseSpeech2Text):
         elif isinstance(ex, openai.error.RateLimitError):
             return LLMRateLimitError(str(ex))
         elif isinstance(ex, openai.error.AuthenticationError):
-            raise LLMAuthorizationError(str(ex))
+            return LLMAuthorizationError(str(ex))
         elif isinstance(ex, openai.error.OpenAIError):
             return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
         else:

api/core/model_providers/providers/openllm_provider.py

@@ -0,0 +1,138 @@
+import json
+from typing import Type
+
+from core.helper import encrypter
+from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType
+from core.model_providers.models.llm.openllm_model import OpenLLMModel
+from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
+
+from core.model_providers.models.base import BaseProviderModel
+from core.third_party.langchain.llms.openllm import OpenLLM
+from models.provider import ProviderType
+
+
+class OpenLLMProvider(BaseModelProvider):
+    @property
+    def provider_name(self):
+        """
+        Returns the name of a provider.
+        """
+        return 'openllm'
+
+    def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]:
+        return []
+
+    def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]:
+        """
+        Returns the model class.
+
+        :param model_type:
+        :return:
+        """
+        if model_type == ModelType.TEXT_GENERATION:
+            model_class = OpenLLMModel
+        else:
+            raise NotImplementedError
+
+        return model_class
+
+    def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules:
+        """
+        get model parameter rules.
+
+        :param model_name:
+        :param model_type:
+        :return:
+        """
+        return ModelKwargsRules(
+            temperature=KwargRule[float](min=0.01, max=2, default=1),
+            top_p=KwargRule[float](min=0, max=1, default=0.7),
+            presence_penalty=KwargRule[float](min=-2, max=2, default=0),
+            frequency_penalty=KwargRule[float](min=-2, max=2, default=0),
+            max_tokens=KwargRule[int](alias='max_new_tokens', min=10, max=4000, default=128),
+        )
+
+    @classmethod
+    def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict):
+        """
+        check model credentials valid.
+
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        """
+        if 'server_url' not in credentials:
+            raise CredentialsValidateFailedError('OpenLLM Server URL must be provided.')
+
+        try:
+            credential_kwargs = {
+                'server_url': credentials['server_url']
+            }
+
+            llm = OpenLLM(
+                llm_kwargs={
+                    'max_new_tokens': 10
+                },
+                **credential_kwargs
+            )
+
+            llm("ping")
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @classmethod
+    def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType,
+                                  credentials: dict) -> dict:
+        """
+        encrypt model credentials for save.
+
+        :param tenant_id:
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        :return:
+        """
+        credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url'])
+        return credentials
+
+    def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict:
+        """
+        get credentials for llm use.
+
+        :param model_name:
+        :param model_type:
+        :param obfuscated:
+        :return:
+        """
+        if self.provider.provider_type != ProviderType.CUSTOM.value:
+            raise NotImplementedError
+
+        provider_model = self._get_provider_model(model_name, model_type)
+
+        if not provider_model.encrypted_config:
+            return {
+                'server_url': None
+            }
+
+        credentials = json.loads(provider_model.encrypted_config)
+        if credentials['server_url']:
+            credentials['server_url'] = encrypter.decrypt_token(
+                self.provider.tenant_id,
+                credentials['server_url']
+            )
+
+            if obfuscated:
+                credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url'])
+
+        return credentials
+
+    @classmethod
+    def is_provider_credentials_valid_or_raise(cls, credentials: dict):
+        return
+
+    @classmethod
+    def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict:
+        return {}
+
+    def get_provider_credentials(self, obfuscated: bool = False) -> dict:
+        return {}

api/core/model_providers/providers/replicate_provider.py

@@ -116,7 +116,8 @@ class ReplicateProvider(BaseModelProvider):
                     and 'Embedding' not in rst.openapi_schema['components']['schemas']:
                 raise CredentialsValidateFailedError(f"Model {model_name}:{version} is not a Embedding model.")
             elif model_type == ModelType.TEXT_GENERATION \
-                    and ('type' not in rst.openapi_schema['components']['schemas']['Output']['items']
+                    and ('items' not in rst.openapi_schema['components']['schemas']['Output']
+                         or 'type' not in rst.openapi_schema['components']['schemas']['Output']['items']
                          or rst.openapi_schema['components']['schemas']['Output']['items']['type'] != 'string'):
                 raise CredentialsValidateFailedError(f"Model {model_name}:{version} is not a Text Generation model.")
         except ReplicateError as e:

api/core/model_providers/providers/xinference_provider.py

@@ -0,0 +1,193 @@
+import json
+from typing import Type
+
+import requests
+from xinference.client import RESTfulGenerateModelHandle, RESTfulChatModelHandle, RESTfulChatglmCppChatModelHandle
+
+from core.helper import encrypter
+from core.model_providers.models.embedding.xinference_embedding import XinferenceEmbedding
+from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType
+from core.model_providers.models.llm.xinference_model import XinferenceModel
+from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
+
+from core.model_providers.models.base import BaseProviderModel
+from core.third_party.langchain.llms.xinference_llm import XinferenceLLM
+from models.provider import ProviderType
+
+
+class XinferenceProvider(BaseModelProvider):
+    @property
+    def provider_name(self):
+        """
+        Returns the name of a provider.
+        """
+        return 'xinference'
+
+    def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]:
+        return []
+
+    def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]:
+        """
+        Returns the model class.
+
+        :param model_type:
+        :return:
+        """
+        if model_type == ModelType.TEXT_GENERATION:
+            model_class = XinferenceModel
+        elif model_type == ModelType.EMBEDDINGS:
+            model_class = XinferenceEmbedding
+        else:
+            raise NotImplementedError
+
+        return model_class
+
+    def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules:
+        """
+        get model parameter rules.
+
+        :param model_name:
+        :param model_type:
+        :return:
+        """
+        credentials = self.get_model_credentials(model_name, model_type)
+        if credentials['model_format'] == "ggmlv3" and credentials["model_handle_type"] == "chatglm":
+            return ModelKwargsRules(
+                temperature=KwargRule[float](min=0.01, max=2, default=1),
+                top_p=KwargRule[float](min=0, max=1, default=0.7),
+                presence_penalty=KwargRule[float](enabled=False),
+                frequency_penalty=KwargRule[float](enabled=False),
+                max_tokens=KwargRule[int](min=10, max=4000, default=256),
+            )
+        elif credentials['model_format'] == "ggmlv3":
+            return ModelKwargsRules(
+                temperature=KwargRule[float](min=0.01, max=2, default=1),
+                top_p=KwargRule[float](min=0, max=1, default=0.7),
+                presence_penalty=KwargRule[float](min=-2, max=2, default=0),
+                frequency_penalty=KwargRule[float](min=-2, max=2, default=0),
+                max_tokens=KwargRule[int](min=10, max=4000, default=256),
+            )
+        else:
+            return ModelKwargsRules(
+                temperature=KwargRule[float](min=0.01, max=2, default=1),
+                top_p=KwargRule[float](min=0, max=1, default=0.7),
+                presence_penalty=KwargRule[float](enabled=False),
+                frequency_penalty=KwargRule[float](enabled=False),
+                max_tokens=KwargRule[int](alias='max_new_tokens', min=10, max=4000, default=256),
+            )
+
+
+    @classmethod
+    def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict):
+        """
+        check model credentials valid.
+
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        """
+        if 'server_url' not in credentials:
+            raise CredentialsValidateFailedError('Xinference Server URL must be provided.')
+
+        if 'model_uid' not in credentials:
+            raise CredentialsValidateFailedError('Xinference Model UID must be provided.')
+
+        try:
+            credential_kwargs = {
+                'server_url': credentials['server_url'],
+                'model_uid': credentials['model_uid'],
+            }
+
+            llm = XinferenceLLM(
+                **credential_kwargs
+            )
+
+            llm("ping")
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @classmethod
+    def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType,
+                                  credentials: dict) -> dict:
+        """
+        encrypt model credentials for save.
+
+        :param tenant_id:
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        :return:
+        """
+        extra_credentials = cls._get_extra_credentials(credentials)
+        credentials.update(extra_credentials)
+
+        credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url'])
+
+        return credentials
+
+    def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict:
+        """
+        get credentials for llm use.
+
+        :param model_name:
+        :param model_type:
+        :param obfuscated:
+        :return:
+        """
+        if self.provider.provider_type != ProviderType.CUSTOM.value:
+            raise NotImplementedError
+
+        provider_model = self._get_provider_model(model_name, model_type)
+
+        if not provider_model.encrypted_config:
+            return {
+                'server_url': None,
+                'model_uid': None,
+            }
+
+        credentials = json.loads(provider_model.encrypted_config)
+        if credentials['server_url']:
+            credentials['server_url'] = encrypter.decrypt_token(
+                self.provider.tenant_id,
+                credentials['server_url']
+            )
+
+            if obfuscated:
+                credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url'])
+
+        return credentials
+
+    @classmethod
+    def _get_extra_credentials(self, credentials: dict) -> dict:
+        url = f"{credentials['server_url']}/v1/models/{credentials['model_uid']}"
+        response = requests.get(url)
+        if response.status_code != 200:
+            raise RuntimeError(
+                f"Failed to get the model description, detail: {response.json()['detail']}"
+            )
+        desc = response.json()
+
+        extra_credentials = {
+            'model_format': desc['model_format'],
+        }
+        if desc["model_format"] == "ggmlv3" and "chatglm" in desc["model_name"]:
+            extra_credentials['model_handle_type'] = 'chatglm'
+        elif "generate" in desc["model_ability"]:
+            extra_credentials['model_handle_type'] = 'generate'
+        elif "chat" in desc["model_ability"]:
+            extra_credentials['model_handle_type'] = 'chat'
+        else:
+            raise NotImplementedError(f"Model handle type not supported.")
+
+        return extra_credentials
+
+    @classmethod
+    def is_provider_credentials_valid_or_raise(cls, credentials: dict):
+        return
+
+    @classmethod
+    def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict:
+        return {}
+
+    def get_provider_credentials(self, obfuscated: bool = False) -> dict:
+        return {}

api/core/model_providers/rules/_providers.json

@@ -8,5 +8,7 @@
   "wenxin",
   "chatglm",
   "replicate",
-  "huggingface_hub"
+  "huggingface_hub",
+  "xinference",
+  "openllm"
 ]

api/core/model_providers/rules/anthropic.json

@@ -11,5 +11,19 @@
         "quota_unit": "tokens",
         "quota_limit": 600000
     },
-    "model_flexibility": "fixed"
+    "model_flexibility": "fixed",
+    "price_config": {
+        "claude-instant-1": {
+            "prompt": "1.63",
+            "completion": "5.51",
+            "unit": "0.000001",
+            "currency": "USD"
+        },
+        "claude-2": {
+            "prompt": "11.02",
+            "completion": "32.68",
+            "unit": "0.000001",
+            "currency": "USD"
+        }
+    }
 }

api/core/model_providers/rules/azure_openai.json

@@ -3,5 +3,48 @@
         "custom"
     ],
     "system_config": null,
-    "model_flexibility": "configurable"
+    "model_flexibility": "configurable",
+    "price_config":{
+        "gpt-4": {
+            "prompt": "0.03",
+            "completion": "0.06",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "gpt-4-32k": {
+            "prompt": "0.06",
+            "completion": "0.12",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "gpt-35-turbo": {
+            "prompt": "0.002",
+            "completion": "0.0015",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "gpt-35-turbo-16k": {
+            "prompt": "0.003",
+            "completion": "0.004",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "text-davinci-002": {
+            "prompt": "0.02",
+            "completion": "0.02",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "text-davinci-003": {
+            "prompt": "0.02",
+            "completion": "0.02",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "text-embedding-ada-002":{
+            "completion": "0.0001",
+            "unit": "0.001",
+            "currency": "USD"
+        }
+    }
 }

api/core/model_providers/rules/minimax.json

@@ -9,5 +9,24 @@
         ],
         "quota_unit": "tokens"
     },
-    "model_flexibility": "fixed"
+    "model_flexibility": "fixed",
+    "price_config": {
+        "abab5.5-chat": {
+            "prompt": "0.015",
+            "completion": "0.015",
+            "unit": "0.001",
+            "currency": "RMB"
+        },
+        "abab5-chat": {
+            "prompt": "0.015",
+            "completion": "0.015",
+            "unit": "0.001",
+            "currency": "RMB"
+        },
+        "embo-01": {
+            "completion": "0",
+            "unit": "0.0001",
+            "currency": "RMB"
+        }
+    }
 }

api/core/model_providers/rules/openai.json

@@ -10,5 +10,42 @@
         "quota_unit": "times",
         "quota_limit": 200
     },
-    "model_flexibility": "fixed"
+    "model_flexibility": "fixed",
+    "price_config": {
+        "gpt-4": {
+            "prompt": "0.03",
+            "completion": "0.06",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "gpt-4-32k": {
+            "prompt": "0.06",
+            "completion": "0.12",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "gpt-3.5-turbo": {
+            "prompt": "0.0015",
+            "completion": "0.002",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "gpt-3.5-turbo-16k": {
+            "prompt": "0.003",
+            "completion": "0.004",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "text-davinci-003": {
+            "prompt": "0.02",
+            "completion": "0.02",
+            "unit": "0.001",
+            "currency": "USD"
+        },
+        "text-embedding-ada-002":{
+            "completion": "0.0001",
+            "unit": "0.001",
+            "currency": "USD"
+        }
+    }
 }

api/core/model_providers/rules/openllm.json

@@ -0,0 +1,7 @@
+{
+    "support_provider_types": [
+        "custom"
+    ],
+    "system_config": null,
+    "model_flexibility": "configurable"
+}

api/core/model_providers/rules/spark.json

@@ -9,5 +9,19 @@
         ],
         "quota_unit": "tokens"
     },
-    "model_flexibility": "fixed"
+    "model_flexibility": "fixed",
+    "price_config": {
+        "spark": {
+            "prompt": "0.18",
+            "completion": "0.18",
+            "unit": "0.0001",
+            "currency": "RMB"
+        },
+        "spark-v2": {
+            "prompt": "0.36",
+            "completion": "0.36",
+            "unit": "0.0001",
+            "currency": "RMB"
+        }
+    }
 }

api/core/model_providers/rules/wenxin.json

@@ -3,5 +3,25 @@
         "custom"
     ],
     "system_config": null,
-    "model_flexibility": "fixed"
+    "model_flexibility": "fixed",
+    "price_config": {
+        "ernie-bot": {
+            "prompt": "0.012",
+            "completion": "0.012",
+            "unit": "0.001",
+            "currency": "RMB"
+        },
+        "ernie-bot-turbo": {
+            "prompt": "0.008",
+            "completion": "0.008",
+            "unit": "0.001",
+            "currency": "RMB"
+        },
+        "bloomz-7b": {
+            "prompt": "0.006",
+            "completion": "0.006",
+            "unit": "0.001",
+            "currency": "RMB"
+        }
+    }
 }

api/core/model_providers/rules/xinference.json

@@ -0,0 +1,7 @@
+{
+    "support_provider_types": [
+        "custom"
+    ],
+    "system_config": null,
+    "model_flexibility": "configurable"
+}

api/core/third_party/langchain/embeddings/xinference_embedding.py

@@ -0,0 +1,21 @@
+from typing import List
+
+import numpy as np
+from langchain.embeddings import XinferenceEmbeddings
+
+
+class XinferenceEmbedding(XinferenceEmbeddings):
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        vectors = super().embed_documents(texts)
+
+        normalized_vectors = [(vector / np.linalg.norm(vector)).tolist() for vector in vectors]
+
+        return normalized_vectors
+
+    def embed_query(self, text: str) -> List[float]:
+        vector = super().embed_query(text)
+
+        normalized_vector = (vector / np.linalg.norm(vector)).tolist()
+
+        return normalized_vector

api/core/third_party/langchain/llms/openllm.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import logging
+from typing import (
+    Any,
+    Dict,
+    List,
+    Optional,
+)
+
+import requests
+from langchain.llms.utils import enforce_stop_tokens
+from pydantic import Field
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain.llms.base import LLM
+
+logger = logging.getLogger(__name__)
+
+
+class OpenLLM(LLM):
+    """OpenLLM, supporting both in-process model
+    instance and remote OpenLLM servers.
+
+    If you have a OpenLLM server running, you can also use it remotely:
+        .. code-block:: python
+
+            from langchain.llms import OpenLLM
+            llm = OpenLLM(server_url='http://localhost:3000')
+            llm("What is the difference between a duck and a goose?")
+    """
+
+    server_url: Optional[str] = None
+    """Optional server URL that currently runs a LLMServer with 'openllm start'."""
+    llm_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Key word arguments to be passed to openllm.LLM"""
+
+    @property
+    def _llm_type(self) -> str:
+        return "openllm"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> str:
+        params = {
+            "prompt": prompt,
+            "llm_config": self.llm_kwargs
+        }
+
+        headers = {"Content-Type": "application/json"}
+        response = requests.post(
+            f'{self.server_url}/v1/generate',
+            headers=headers,
+            json=params
+        )
+
+        if not response.ok:
+            raise ValueError(f"OpenLLM HTTP {response.status_code} error: {response.text}")
+
+        json_response = response.json()
+        completion = json_response["responses"][0]
+
+        if stop is not None:
+            completion = enforce_stop_tokens(completion, stop)
+
+        return completion
+
+    async def _acall(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        raise NotImplementedError(
+            "Async call is not supported for OpenLLM at the moment."
+        )

api/core/third_party/langchain/llms/xinference_llm.py

@@ -0,0 +1,132 @@
+from typing import Optional, List, Any, Union, Generator
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms import Xinference
+from langchain.llms.utils import enforce_stop_tokens
+from xinference.client import RESTfulChatglmCppChatModelHandle, \
+    RESTfulChatModelHandle, RESTfulGenerateModelHandle
+
+
+class XinferenceLLM(Xinference):
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call the xinference model and return the output.
+
+        Args:
+            prompt: The prompt to use for generation.
+            stop: Optional list of stop words to use when generating.
+            generate_config: Optional dictionary for the configuration used for
+                generation.
+
+        Returns:
+            The generated string by the model.
+        """
+        model = self.client.get_model(self.model_uid)
+
+        if isinstance(model, RESTfulChatModelHandle):
+            generate_config: "LlamaCppGenerateConfig" = kwargs.get("generate_config", {})
+
+            if stop:
+                generate_config["stop"] = stop
+
+            if generate_config and generate_config.get("stream"):
+                combined_text_output = ""
+                for token in self._stream_generate(
+                        model=model,
+                        prompt=prompt,
+                        run_manager=run_manager,
+                        generate_config=generate_config,
+                ):
+                    combined_text_output += token
+                return combined_text_output
+            else:
+                completion = model.chat(prompt=prompt, generate_config=generate_config)
+                return completion["choices"][0]["message"]["content"]
+        elif isinstance(model, RESTfulGenerateModelHandle):
+            generate_config: "LlamaCppGenerateConfig" = kwargs.get("generate_config", {})
+
+            if stop:
+                generate_config["stop"] = stop
+
+            if generate_config and generate_config.get("stream"):
+                combined_text_output = ""
+                for token in self._stream_generate(
+                    model=model,
+                    prompt=prompt,
+                    run_manager=run_manager,
+                    generate_config=generate_config,
+                ):
+                    combined_text_output += token
+                return combined_text_output
+
+            else:
+                completion = model.generate(prompt=prompt, generate_config=generate_config)
+                return completion["choices"][0]["text"]
+        elif isinstance(model, RESTfulChatglmCppChatModelHandle):
+            generate_config: "ChatglmCppGenerateConfig" = kwargs.get("generate_config", {})
+
+            if generate_config and generate_config.get("stream"):
+                combined_text_output = ""
+                for token in self._stream_generate(
+                    model=model,
+                    prompt=prompt,
+                    run_manager=run_manager,
+                    generate_config=generate_config,
+                ):
+                    combined_text_output += token
+                completion = combined_text_output
+            else:
+                completion = model.chat(prompt=prompt, generate_config=generate_config)
+                completion = completion["choices"][0]["message"]["content"]
+
+            if stop is not None:
+                completion = enforce_stop_tokens(completion, stop)
+
+            return completion
+
+
+    def _stream_generate(
+        self,
+        model: Union["RESTfulGenerateModelHandle", "RESTfulChatModelHandle", "RESTfulChatglmCppChatModelHandle"],
+        prompt: str,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        generate_config: Optional[Union["LlamaCppGenerateConfig", "PytorchGenerateConfig", "ChatglmCppGenerateConfig"]] = None,
+    ) -> Generator[str, None, None]:
+        """
+        Args:
+            prompt: The prompt to use for generation.
+            model: The model used for generation.
+            stop: Optional list of stop words to use when generating.
+            generate_config: Optional dictionary for the configuration used for
+                generation.
+
+        Yields:
+            A string token.
+        """
+        if isinstance(model, RESTfulGenerateModelHandle):
+            streaming_response = model.generate(
+                prompt=prompt, generate_config=generate_config
+            )
+        else:
+            streaming_response = model.chat(
+                prompt=prompt, generate_config=generate_config
+            )
+
+        for chunk in streaming_response:
+            if isinstance(chunk, dict):
+                choices = chunk.get("choices", [])
+                if choices:
+                    choice = choices[0]
+                    if isinstance(choice, dict):
+                        token = choice.get("text", "")
+                        log_probs = choice.get("logprobs")
+                        if run_manager:
+                            run_manager.on_llm_new_token(
+                                token=token, verbose=self.verbose, log_probs=log_probs
+                            )
+                        yield token

api/core/tool/web_reader_tool.py

@@ -88,6 +88,11 @@ class WebReaderTool(BaseTool):
             texts = character_splitter.split_text(page_contents)
             docs = [Document(page_content=t) for t in texts]
 
+            if len(docs) == 0:
+                return "No content found."
+
+            docs = docs[1:]
+
             # only use first 5 docs
             if len(docs) > 5:
                 docs = docs[:5]

api/events/event_handlers/generate_conversation_name_when_first_message_created.py

@@ -20,6 +20,10 @@ def handle(sender, **kwargs):
             # generate conversation name
             try:
                 name = LLMGenerator.generate_conversation_name(app_model.tenant_id, message.query, message.answer)
+
+                if len(name) > 75:
+                    name = name[:75] + '...'
+
                 conversation.name = name
             except:
                 conversation.name = 'New Chat'

api/migrations/versions/853f9b9cd3b6_add_message_price_unit.py

@@ -0,0 +1,43 @@
+"""add message price unit
+
+Revision ID: 853f9b9cd3b6
+Revises: e8883b0148c9
+Create Date: 2023-08-19 17:01:57.471562
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = '853f9b9cd3b6'
+down_revision = 'e8883b0148c9'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+
+    with op.batch_alter_table('message_agent_thoughts', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('message_price_unit', sa.Numeric(precision=10, scale=7), server_default=sa.text('0.001'), nullable=False))
+        batch_op.add_column(sa.Column('answer_price_unit', sa.Numeric(precision=10, scale=7), server_default=sa.text('0.001'), nullable=False))
+
+    with op.batch_alter_table('messages', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('message_price_unit', sa.Numeric(precision=10, scale=7), server_default=sa.text('0.001'), nullable=False))
+        batch_op.add_column(sa.Column('answer_price_unit', sa.Numeric(precision=10, scale=7), server_default=sa.text('0.001'), nullable=False))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('messages', schema=None) as batch_op:
+        batch_op.drop_column('answer_price_unit')
+        batch_op.drop_column('message_price_unit')
+
+    with op.batch_alter_table('message_agent_thoughts', schema=None) as batch_op:
+        batch_op.drop_column('answer_price_unit')
+        batch_op.drop_column('message_price_unit')
+
+    # ### end Alembic commands ###

api/models/model.py

@@ -421,9 +421,11 @@ class Message(db.Model):
     message = db.Column(db.JSON, nullable=False)
     message_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
     message_unit_price = db.Column(db.Numeric(10, 4), nullable=False)
+    message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
     answer = db.Column(db.Text, nullable=False)
     answer_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
     answer_unit_price = db.Column(db.Numeric(10, 4), nullable=False)
+    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
     provider_response_latency = db.Column(db.Float, nullable=False, server_default=db.text('0'))
     total_price = db.Column(db.Numeric(10, 7))
     currency = db.Column(db.String(255), nullable=False)
@@ -705,9 +707,11 @@ class MessageAgentThought(db.Model):
     message = db.Column(db.Text, nullable=True)
     message_token = db.Column(db.Integer, nullable=True)
     message_unit_price = db.Column(db.Numeric, nullable=True)
+    message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
     answer = db.Column(db.Text, nullable=True)
     answer_token = db.Column(db.Integer, nullable=True)
     answer_unit_price = db.Column(db.Numeric, nullable=True)
+    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
     tokens = db.Column(db.Integer, nullable=True)
     total_price = db.Column(db.Numeric, nullable=True)
     currency = db.Column(db.String, nullable=True)

api/requirements.txt

@@ -48,4 +48,5 @@ dashscope~=1.5.0
 huggingface_hub~=0.16.4
 transformers~=4.31.0
 stripe~=5.5.0
-pandas==1.5.3
+pandas==1.5.3
+xinference==0.2.0

api/services/dataset_service.py

@@ -284,8 +284,9 @@ class DocumentService:
             "github_link": str,
             "open_source_license": str,
             "commit_date": str,
-            "commit_author": str
-        }
+            "commit_author": str,
+        },
+        "others": dict
     }
 
     @staticmethod
@@ -370,8 +371,8 @@ class DocumentService:
             raise DocumentIndexingError()
         # update document to be recover
         document.is_paused = False
-        document.paused_by = current_user.id
-        document.paused_at = time.time()
+        document.paused_by = None
+        document.paused_at = None
 
         db.session.add(document)
         db.session.commit()
@@ -972,7 +973,7 @@ class SegmentService:
                 db.session.add(segment)
                 db.session.commit()
                 # update segment vector index
-                VectorService.create_segment_vector(args['keywords'], segment, dataset)
+                VectorService.update_segment_vector(args['keywords'], segment, dataset)
         except Exception as e:
             logging.exception("update segment index failed")
             segment.enabled = False

api/tests/integration_tests/.env.example

@@ -32,4 +32,11 @@ WENXIN_API_KEY=
 WENXIN_SECRET_KEY=
 
 # ChatGLM Credentials
-CHATGLM_API_BASE=
+CHATGLM_API_BASE=
+
+# Xinference Credentials
+XINFERENCE_SERVER_URL=
+XINFERENCE_MODEL_UID=
+
+# OpenLLM Credentials
+OPENLLM_SERVER_URL=

api/tests/integration_tests/models/embedding/test_xinference_embedding.py

@@ -0,0 +1,65 @@
+import json
+import os
+from unittest.mock import patch, MagicMock
+
+from core.model_providers.models.embedding.xinference_embedding import XinferenceEmbedding
+from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.providers.xinference_provider import XinferenceProvider
+from models.provider import Provider, ProviderType, ProviderModel
+
+
+def get_mock_provider():
+    return Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name='xinference',
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config='',
+        is_valid=True,
+    )
+
+
+def get_mock_embedding_model(mocker):
+    model_name = 'vicuna-v1.3'
+    server_url = os.environ['XINFERENCE_SERVER_URL']
+    model_uid = os.environ['XINFERENCE_MODEL_UID']
+    model_provider = XinferenceProvider(provider=get_mock_provider())
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        provider_name='xinference',
+        model_name=model_name,
+        model_type=ModelType.EMBEDDINGS.value,
+        encrypted_config=json.dumps({
+            'server_url': server_url,
+            'model_uid': model_uid
+        }),
+        is_valid=True,
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    return XinferenceEmbedding(
+        model_provider=model_provider,
+        name=model_name
+    )
+
+
+def decrypt_side_effect(tenant_id, encrypted_api_key):
+    return encrypted_api_key
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_embed_documents(mock_decrypt, mocker):
+    embedding_model = get_mock_embedding_model(mocker)
+    rst = embedding_model.client.embed_documents(['test', 'test1'])
+    assert isinstance(rst, list)
+    assert len(rst) == 2
+    assert len(rst[0]) == 4096
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_embed_query(mock_decrypt, mocker):
+    embedding_model = get_mock_embedding_model(mocker)
+    rst = embedding_model.client.embed_query('test')
+    assert isinstance(rst, list)
+    assert len(rst) == 4096

api/tests/integration_tests/models/llm/test_anthropic_model.py

@@ -50,7 +50,9 @@ def test_get_num_tokens(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_run(mock_decrypt):
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model('claude-2')
     messages = [PromptMessage(content='Human: 1 + 1=? \nAssistant: ')]
     rst = model.run(
@@ -58,4 +60,3 @@ def test_run(mock_decrypt):
         stop=['\nHuman:'],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == '2'

api/tests/integration_tests/models/llm/test_azure_openai_model.py

@@ -76,6 +76,8 @@ def test_chat_get_num_tokens(mock_decrypt, mocker):
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
 def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     openai_model = get_mock_azure_openai_model('gpt-35-turbo', mocker)
     messages = [PromptMessage(content='Human: Are you Human? you MUST only answer `y` or `n`? \nAssistant: ')]
     rst = openai_model.run(
@@ -83,4 +85,3 @@ def test_run(mock_decrypt, mocker):
         stop=['\nHuman:'],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == 'n'

api/tests/integration_tests/models/llm/test_huggingface_hub_model.py

@@ -95,6 +95,8 @@ def test_inference_endpoints_get_num_tokens(mock_decrypt, mock_model_info, mocke
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
 def test_hosted_inference_api_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model(
         'google/flan-t5-base',
         'hosted_inference_api',
@@ -111,6 +113,8 @@ def test_hosted_inference_api_run(mock_decrypt, mocker):
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
 def test_inference_endpoints_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model(
         '',
         'inference_endpoints',
@@ -121,4 +125,3 @@ def test_inference_endpoints_run(mock_decrypt, mocker):
         [PromptMessage(content='Answer the following yes/no question. Can you write a whole Haiku in a single tweet?')],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == 'no'

api/tests/integration_tests/models/llm/test_minimax_model.py

@@ -54,11 +54,12 @@ def test_get_num_tokens(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_run(mock_decrypt):
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model('abab5.5-chat')
     rst = model.run(
         [PromptMessage(content='Human: Are you a real Human? you MUST only answer `y` or `n`? \nAssistant: ')],
         stop=['\nHuman:'],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == 'n'

api/tests/integration_tests/models/llm/test_openai_model.py

@@ -58,7 +58,9 @@ def test_chat_get_num_tokens(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_run(mock_decrypt):
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     openai_model = get_mock_openai_model('text-davinci-003')
     rst = openai_model.run(
         [PromptMessage(content='Human: Are you Human? you MUST only answer `y` or `n`? \nAssistant: ')],
@@ -69,7 +71,9 @@ def test_run(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_chat_run(mock_decrypt):
+def test_chat_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     openai_model = get_mock_openai_model('gpt-3.5-turbo')
     messages = [PromptMessage(content='Human: Are you Human? you MUST only answer `y` or `n`? \nAssistant: ')]
     rst = openai_model.run(
@@ -77,4 +81,3 @@ def test_chat_run(mock_decrypt):
         stop=['\nHuman:'],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == 'n'

api/tests/integration_tests/models/llm/test_openllm_model.py

@@ -0,0 +1,72 @@
+import json
+import os
+from unittest.mock import patch, MagicMock
+
+from core.model_providers.models.entity.message import PromptMessage, MessageType
+from core.model_providers.models.entity.model_params import ModelKwargs, ModelType
+from core.model_providers.models.llm.openllm_model import OpenLLMModel
+from core.model_providers.providers.openllm_provider import OpenLLMProvider
+from models.provider import Provider, ProviderType, ProviderModel
+
+
+def get_mock_provider():
+    return Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name='openllm',
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config='',
+        is_valid=True,
+    )
+
+
+def get_mock_model(model_name, mocker):
+    model_kwargs = ModelKwargs(
+        max_tokens=10,
+        temperature=0.01
+    )
+    server_url = os.environ['OPENLLM_SERVER_URL']
+    model_provider = OpenLLMProvider(provider=get_mock_provider())
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        provider_name='openllm',
+        model_name=model_name,
+        model_type=ModelType.TEXT_GENERATION.value,
+        encrypted_config=json.dumps({
+            'server_url': server_url
+        }),
+        is_valid=True,
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    return OpenLLMModel(
+        model_provider=model_provider,
+        name=model_name,
+        model_kwargs=model_kwargs
+    )
+
+
+def decrypt_side_effect(tenant_id, encrypted_api_key):
+    return encrypted_api_key
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_get_num_tokens(mock_decrypt, mocker):
+    model = get_mock_model('facebook/opt-125m', mocker)
+    rst = model.get_num_tokens([
+        PromptMessage(type=MessageType.HUMAN, content='Who is your manufacturer?')
+    ])
+    assert rst == 5
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
+    model = get_mock_model('facebook/opt-125m', mocker)
+    messages = [PromptMessage(content='Human: who are you? \nAnswer: ')]
+    rst = model.run(
+        messages
+    )
+    assert len(rst.content) > 0

api/tests/integration_tests/models/llm/test_replicate_model.py

@@ -65,6 +65,8 @@ def test_get_num_tokens(mock_decrypt, mocker):
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
 def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model('a16z-infra/llama-2-13b-chat', '2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52', mocker)
     messages = [PromptMessage(content='Human: 1+1=? \nAnswer: ')]
     rst = model.run(

api/tests/integration_tests/models/llm/test_spark_model.py

@@ -58,7 +58,9 @@ def test_get_num_tokens(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_run(mock_decrypt):
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model('spark')
     messages = [PromptMessage(content='Human: 1 + 1=? \nAssistant: Integer answer is:')]
     rst = model.run(
@@ -66,4 +68,3 @@ def test_run(mock_decrypt):
         stop=['\nHuman:'],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == '2'

api/tests/integration_tests/models/llm/test_tongyi_model.py

@@ -52,7 +52,9 @@ def test_get_num_tokens(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_run(mock_decrypt):
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model('qwen-v1')
     rst = model.run(
         [PromptMessage(content='Human: Are you Human? you MUST only answer `y` or `n`? \nAssistant: ')],

api/tests/integration_tests/models/llm/test_wenxin_model.py

@@ -52,7 +52,9 @@ def test_get_num_tokens(mock_decrypt):
 
 
 @patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
-def test_run(mock_decrypt):
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
     model = get_mock_model('ernie-bot')
     messages = [PromptMessage(content='Human: 1 + 1=? \nAssistant: Integer answer is:')]
     rst = model.run(
@@ -60,4 +62,3 @@ def test_run(mock_decrypt):
         stop=['\nHuman:'],
     )
     assert len(rst.content) > 0
-    assert rst.content.strip() == '2'

api/tests/integration_tests/models/llm/test_xinference_model.py

@@ -0,0 +1,74 @@
+import json
+import os
+from unittest.mock import patch, MagicMock
+
+from core.model_providers.models.entity.message import PromptMessage, MessageType
+from core.model_providers.models.entity.model_params import ModelKwargs, ModelType
+from core.model_providers.models.llm.xinference_model import XinferenceModel
+from core.model_providers.providers.xinference_provider import XinferenceProvider
+from models.provider import Provider, ProviderType, ProviderModel
+
+
+def get_mock_provider():
+    return Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name='xinference',
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config='',
+        is_valid=True,
+    )
+
+
+def get_mock_model(model_name, mocker):
+    model_kwargs = ModelKwargs(
+        max_tokens=10,
+        temperature=0.01
+    )
+    server_url = os.environ['XINFERENCE_SERVER_URL']
+    model_uid = os.environ['XINFERENCE_MODEL_UID']
+    model_provider = XinferenceProvider(provider=get_mock_provider())
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        provider_name='xinference',
+        model_name=model_name,
+        model_type=ModelType.TEXT_GENERATION.value,
+        encrypted_config=json.dumps({
+            'server_url': server_url,
+            'model_uid': model_uid
+        }),
+        is_valid=True,
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    return XinferenceModel(
+        model_provider=model_provider,
+        name=model_name,
+        model_kwargs=model_kwargs
+    )
+
+
+def decrypt_side_effect(tenant_id, encrypted_api_key):
+    return encrypted_api_key
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_get_num_tokens(mock_decrypt, mocker):
+    model = get_mock_model('llama-2-chat', mocker)
+    rst = model.get_num_tokens([
+        PromptMessage(type=MessageType.HUMAN, content='Who is your manufacturer?')
+    ])
+    assert rst == 5
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_run(mock_decrypt, mocker):
+    mocker.patch('core.model_providers.providers.base.BaseModelProvider.update_last_used', return_value=None)
+
+    model = get_mock_model('llama-2-chat', mocker)
+    messages = [PromptMessage(content='Human: 1+1=? \nAnswer: ')]
+    rst = model.run(
+        messages
+    )
+    assert len(rst.content) > 0

api/tests/unit_tests/model_providers/test_openllm_provider.py

@@ -0,0 +1,122 @@
+import pytest
+from unittest.mock import patch, MagicMock
+import json
+
+from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.providers.base import CredentialsValidateFailedError
+from core.model_providers.providers.openllm_provider import OpenLLMProvider
+from models.provider import ProviderType, Provider, ProviderModel
+
+PROVIDER_NAME = 'openllm'
+MODEL_PROVIDER_CLASS = OpenLLMProvider
+VALIDATE_CREDENTIAL = {
+    'server_url': 'http://127.0.0.1:3333/'
+}
+
+
+def encrypt_side_effect(tenant_id, encrypt_key):
+    return f'encrypted_{encrypt_key}'
+
+
+def decrypt_side_effect(tenant_id, encrypted_key):
+    return encrypted_key.replace('encrypted_', '')
+
+
+def test_is_credentials_valid_or_raise_valid(mocker):
+    mocker.patch('core.third_party.langchain.llms.openllm.OpenLLM._call',
+                 return_value="abc")
+
+    MODEL_PROVIDER_CLASS.is_model_credentials_valid_or_raise(
+        model_name='username/test_model_name',
+        model_type=ModelType.TEXT_GENERATION,
+        credentials=VALIDATE_CREDENTIAL.copy()
+    )
+
+
+def test_is_credentials_valid_or_raise_invalid(mocker):
+    # raise CredentialsValidateFailedError if credential is not in credentials
+    with pytest.raises(CredentialsValidateFailedError):
+        MODEL_PROVIDER_CLASS.is_model_credentials_valid_or_raise(
+            model_name='test_model_name',
+            model_type=ModelType.TEXT_GENERATION,
+            credentials={}
+        )
+
+    # raise CredentialsValidateFailedError if credential is invalid
+    with pytest.raises(CredentialsValidateFailedError):
+        MODEL_PROVIDER_CLASS.is_model_credentials_valid_or_raise(
+            model_name='test_model_name',
+            model_type=ModelType.TEXT_GENERATION,
+            credentials={'server_url': 'invalid'})
+
+
+@patch('core.helper.encrypter.encrypt_token', side_effect=encrypt_side_effect)
+def test_encrypt_model_credentials(mock_encrypt):
+    api_key = 'http://127.0.0.1:3333/'
+    result = MODEL_PROVIDER_CLASS.encrypt_model_credentials(
+        tenant_id='tenant_id',
+        model_name='test_model_name',
+        model_type=ModelType.TEXT_GENERATION,
+        credentials=VALIDATE_CREDENTIAL.copy()
+    )
+    mock_encrypt.assert_called_with('tenant_id', api_key)
+    assert result['server_url'] == f'encrypted_{api_key}'
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_get_model_credentials_custom(mock_decrypt, mocker):
+    provider = Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name=PROVIDER_NAME,
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config=None,
+        is_valid=True,
+    )
+
+    encrypted_credential = VALIDATE_CREDENTIAL.copy()
+    encrypted_credential['server_url'] = 'encrypted_' + encrypted_credential['server_url']
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        encrypted_config=json.dumps(encrypted_credential)
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    model_provider = MODEL_PROVIDER_CLASS(provider=provider)
+    result = model_provider.get_model_credentials(
+        model_name='test_model_name',
+        model_type=ModelType.TEXT_GENERATION
+    )
+    assert result['server_url'] == 'http://127.0.0.1:3333/'
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_get_model_credentials_obfuscated(mock_decrypt, mocker):
+    provider = Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name=PROVIDER_NAME,
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config=None,
+        is_valid=True,
+    )
+
+    encrypted_credential = VALIDATE_CREDENTIAL.copy()
+    encrypted_credential['server_url'] = 'encrypted_' + encrypted_credential['server_url']
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        encrypted_config=json.dumps(encrypted_credential)
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    model_provider = MODEL_PROVIDER_CLASS(provider=provider)
+    result = model_provider.get_model_credentials(
+        model_name='test_model_name',
+        model_type=ModelType.TEXT_GENERATION,
+        obfuscated=True
+    )
+    middle_token = result['server_url'][6:-2]
+    assert len(middle_token) == max(len(VALIDATE_CREDENTIAL['server_url']) - 8, 0)
+    assert all(char == '*' for char in middle_token)

api/tests/unit_tests/model_providers/test_xinference_provider.py

@@ -0,0 +1,130 @@
+import pytest
+from unittest.mock import patch, MagicMock
+import json
+
+from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.providers.base import CredentialsValidateFailedError
+from core.model_providers.providers.xinference_provider import XinferenceProvider
+from models.provider import ProviderType, Provider, ProviderModel
+
+PROVIDER_NAME = 'xinference'
+MODEL_PROVIDER_CLASS = XinferenceProvider
+VALIDATE_CREDENTIAL = {
+    'model_uid': 'fake-model-uid',
+    'server_url': 'http://127.0.0.1:9997/'
+}
+
+
+def encrypt_side_effect(tenant_id, encrypt_key):
+    return f'encrypted_{encrypt_key}'
+
+
+def decrypt_side_effect(tenant_id, encrypted_key):
+    return encrypted_key.replace('encrypted_', '')
+
+
+def test_is_credentials_valid_or_raise_valid(mocker):
+    mocker.patch('core.third_party.langchain.llms.xinference_llm.XinferenceLLM._call',
+                 return_value="abc")
+
+    MODEL_PROVIDER_CLASS.is_model_credentials_valid_or_raise(
+        model_name='username/test_model_name',
+        model_type=ModelType.TEXT_GENERATION,
+        credentials=VALIDATE_CREDENTIAL.copy()
+    )
+
+
+def test_is_credentials_valid_or_raise_invalid():
+    # raise CredentialsValidateFailedError if replicate_api_token is not in credentials
+    with pytest.raises(CredentialsValidateFailedError):
+        MODEL_PROVIDER_CLASS.is_model_credentials_valid_or_raise(
+            model_name='test_model_name',
+            model_type=ModelType.TEXT_GENERATION,
+            credentials={}
+        )
+
+    # raise CredentialsValidateFailedError if replicate_api_token is invalid
+    with pytest.raises(CredentialsValidateFailedError):
+        MODEL_PROVIDER_CLASS.is_model_credentials_valid_or_raise(
+            model_name='test_model_name',
+            model_type=ModelType.TEXT_GENERATION,
+            credentials={'server_url': 'invalid'})
+
+
+@patch('core.helper.encrypter.encrypt_token', side_effect=encrypt_side_effect)
+def test_encrypt_model_credentials(mock_encrypt, mocker):
+    api_key = 'http://127.0.0.1:9997/'
+
+    mocker.patch('core.model_providers.providers.xinference_provider.XinferenceProvider._get_extra_credentials',
+                 return_value={
+                     'model_handle_type': 'generate',
+                     'model_format': 'ggmlv3'
+                 })
+
+    result = MODEL_PROVIDER_CLASS.encrypt_model_credentials(
+        tenant_id='tenant_id',
+        model_name='test_model_name',
+        model_type=ModelType.TEXT_GENERATION,
+        credentials=VALIDATE_CREDENTIAL.copy()
+    )
+    mock_encrypt.assert_called_with('tenant_id', api_key)
+    assert result['server_url'] == f'encrypted_{api_key}'
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_get_model_credentials_custom(mock_decrypt, mocker):
+    provider = Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name=PROVIDER_NAME,
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config=None,
+        is_valid=True,
+    )
+
+    encrypted_credential = VALIDATE_CREDENTIAL.copy()
+    encrypted_credential['server_url'] = 'encrypted_' + encrypted_credential['server_url']
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        encrypted_config=json.dumps(encrypted_credential)
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    model_provider = MODEL_PROVIDER_CLASS(provider=provider)
+    result = model_provider.get_model_credentials(
+        model_name='test_model_name',
+        model_type=ModelType.TEXT_GENERATION
+    )
+    assert result['server_url'] == 'http://127.0.0.1:9997/'
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_get_model_credentials_obfuscated(mock_decrypt, mocker):
+    provider = Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name=PROVIDER_NAME,
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config=None,
+        is_valid=True,
+    )
+
+    encrypted_credential = VALIDATE_CREDENTIAL.copy()
+    encrypted_credential['server_url'] = 'encrypted_' + encrypted_credential['server_url']
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        encrypted_config=json.dumps(encrypted_credential)
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    model_provider = MODEL_PROVIDER_CLASS(provider=provider)
+    result = model_provider.get_model_credentials(
+        model_name='test_model_name',
+        model_type=ModelType.TEXT_GENERATION,
+        obfuscated=True
+    )
+    middle_token = result['server_url'][6:-2]
+    assert len(middle_token) == max(len(VALIDATE_CREDENTIAL['server_url']) - 8, 0)
+    assert all(char == '*' for char in middle_token)

docker/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3.1'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.3.14
+    image: langgenius/dify-api:0.3.15
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -124,7 +124,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.3.14
+    image: langgenius/dify-api:0.3.15
     restart: always
     environment:
       # Startup mode, 'worker' starts the Celery worker for processing the queue.
@@ -176,7 +176,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.3.14
+    image: langgenius/dify-web:0.3.15
     restart: always
     environment:
       EDITION: SELF_HOSTED

web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/cardView.tsx

@@ -17,6 +17,7 @@ import type { App } from '@/types/app'
 import type { UpdateAppSiteCodeResponse } from '@/models/app'
 import { asyncRunSafe } from '@/utils'
 import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
+import type { IAppCardProps } from '@/app/components/app/overview/appCard'
 
 export type ICardViewProps = {
   appId: string
@@ -68,7 +69,7 @@ const CardView: FC<ICardViewProps> = ({ appId }) => {
     handleError(err)
   }
 
-  const onSaveSiteConfig = async (params: any) => {
+  const onSaveSiteConfig: IAppCardProps['onSaveSiteConfig'] = async (params) => {
     const [err] = await asyncRunSafe<App>(
       updateAppSiteConfig({
         url: `/apps/${appId}/site`,

web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/page.tsx

@@ -16,7 +16,6 @@ const Overview = async ({
   const { t } = await useTranslation(locale, 'app-overview')
   return (
     <div className="h-full px-16 py-6 overflow-scroll">
-      {/* <WelcomeBanner /> */}
       <ApikeyInfoPanel />
       <div className='flex flex-row items-center justify-between mb-4 text-xl text-gray-900'>
         {t('overview.title')}