feat: bump version 0.4.3 (#1930)

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

.github/workflows/api-model-runtime-tests.yml

@@ -31,28 +31,19 @@ jobs:
       HUGGINGFACE_EMBEDDINGS_ENDPOINT_URL: c
       MOCK_SWITCH: true
 
-
     steps:
     - name: Checkout code
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
-
-    - name: Cache pip dependencies
-      uses: actions/cache@v2
-      with:
-        path: ~/.cache/pip
-        key: ${{ runner.os }}-pip-${{ hashFiles('api/requirements.txt') }}
-        restore-keys: ${{ runner.os }}-pip-
+        cache: 'pip'
+        cache-dependency-path: ./api/requirements.txt
 
     - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install pytest
-        pip install -r api/requirements.txt
+      run: pip install -r ./api/requirements.txt
 
     - name: Run pytest
       run: pytest api/tests/integration_tests/model_runtime/anthropic api/tests/integration_tests/model_runtime/azure_openai api/tests/integration_tests/model_runtime/openai api/tests/integration_tests/model_runtime/chatglm api/tests/integration_tests/model_runtime/google api/tests/integration_tests/model_runtime/xinference api/tests/integration_tests/model_runtime/huggingface_hub/test_llm.py

.github/workflows/style.yml

@@ -0,0 +1,34 @@
+name: Style check
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - deploy/dev
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Setup NodeJS
+      uses: actions/setup-node@v4
+      with:
+        node-version: 18
+        cache: yarn
+        cache-dependency-path: ./web/package.json
+
+    - name: Web dependencies
+      run: |
+        cd ./web
+        yarn install --frozen-lockfile
+
+    - name: Web style check
+      run: |
+        cd ./web
+        yarn run lint

README.md

@@ -91,7 +91,7 @@ After running, you can access the Dify dashboard in your browser at [http://loca
 
 ### Helm Chart
 
-A big thanks to @BorisPolonsky for providing us with a [Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
+Big thanks to @BorisPolonsky for providing us with a [Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
 You can go to https://github.com/BorisPolonsky/dify-helm for deployment information.
 
 ### Configuration

api/.env.example

@@ -65,6 +65,7 @@ WEAVIATE_BATCH_SIZE=100
 # Qdrant configuration, use `http://localhost:6333` for local mode or `https://your-qdrant-cluster-url.qdrant.io` for remote mode
 QDRANT_URL=http://localhost:6333
 QDRANT_API_KEY=difyai123456
+QDRANT_CLIENT_TIMEOUT=20
 
 # Milvus configuration
 MILVUS_HOST=127.0.0.1

api/config.py

@@ -36,6 +36,7 @@ DEFAULTS = {
     'SENTRY_PROFILES_SAMPLE_RATE': 1.0,
     'WEAVIATE_GRPC_ENABLED': 'True',
     'WEAVIATE_BATCH_SIZE': 100,
+    'QDRANT_CLIENT_TIMEOUT': 20,
     'CELERY_BACKEND': 'database',
     'LOG_LEVEL': 'INFO',
     'HOSTED_OPENAI_QUOTA_LIMIT': 200,
@@ -87,7 +88,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.4.1"
+        self.CURRENT_VERSION = "0.4.3"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -197,6 +198,7 @@ class Config:
         # qdrant settings
         self.QDRANT_URL = get_env('QDRANT_URL')
         self.QDRANT_API_KEY = get_env('QDRANT_API_KEY')
+        self.QDRANT_CLIENT_TIMEOUT = get_env('QDRANT_CLIENT_TIMEOUT')
 
         # milvus / zilliz setting
         self.MILVUS_HOST = get_env('MILVUS_HOST')

api/controllers/console/app/app.py

@@ -141,15 +141,9 @@ class AppListApi(Resource):
                     model_type=ModelType.LLM
                 )
             except ProviderTokenNotInitError:
-                raise ProviderNotInitializeError(
-                    f"No Default System Reasoning Model available. Please configure "
-                    f"in the Settings -> Model Provider.")
+                model_instance = None
 
-            if not model_instance:
-                raise ProviderNotInitializeError(
-                    f"No Default System Reasoning Model available. Please configure "
-                    f"in the Settings -> Model Provider.")
-            else:
+            if model_instance:
                 model_dict = app_model_config.model_dict
                 model_dict['provider'] = model_instance.provider
                 model_dict['name'] = model_instance.model

api/controllers/console/app/audio.py

@@ -58,7 +58,7 @@ class ChatMessageAudioApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/app/completion.py

@@ -78,7 +78,7 @@ class CompletionMessageApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -153,7 +153,7 @@ class ChatMessageApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/app/generator.py

@@ -38,7 +38,7 @@ class RuleGenerateApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
 
         return rules
 

api/controllers/console/app/message.py

@@ -228,7 +228,7 @@ class MessageMoreLikeThisApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -256,7 +256,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
                 yield "data: " + json.dumps(
                     api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:
@@ -296,7 +296,7 @@ class MessageSuggestedQuestionApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/console/explore/audio.py

@@ -54,7 +54,7 @@ class ChatAudioApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/explore/completion.py

@@ -70,7 +70,7 @@ class CompletionApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -134,7 +134,7 @@ class ChatApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -175,7 +175,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/console/explore/message.py

@@ -104,7 +104,7 @@ class MessageMoreLikeThisApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception:
@@ -131,7 +131,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:
@@ -169,7 +169,7 @@ class MessageSuggestedQuestionApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/console/universal_chat/audio.py

@@ -54,7 +54,7 @@ class UniversalChatAudioApi(UniversalChatResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/universal_chat/chat.py

@@ -89,7 +89,7 @@ class UniversalChatApi(UniversalChatResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -126,7 +126,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/console/universal_chat/message.py

@@ -133,7 +133,7 @@ class UniversalChatMessageSuggestedQuestionApi(UniversalChatResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/service_api/app/audio.py

@@ -50,7 +50,7 @@ class AudioApi(AppApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/service_api/app/completion.py

@@ -67,7 +67,7 @@ class CompletionApi(AppApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -131,7 +131,7 @@ class ChatApi(AppApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -171,7 +171,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/web/audio.py

@@ -52,7 +52,7 @@ class AudioApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/web/completion.py

@@ -64,7 +64,7 @@ class CompletionApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -124,7 +124,7 @@ class ChatApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -164,7 +164,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/web/message.py

@@ -138,7 +138,7 @@ class MessageMoreLikeThisApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception:
@@ -165,7 +165,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:
@@ -202,7 +202,7 @@ class MessageSuggestedQuestionApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/core/app_runner/agent_app_runner.py

@@ -75,7 +75,7 @@ class AgentApplicationRunner(AppRunner):
         # reorganize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         #          memory(optional)
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=app_orchestration_config.model_config,
             prompt_template_entity=app_orchestration_config.prompt_template,
@@ -153,7 +153,7 @@ class AgentApplicationRunner(AppRunner):
             # reorganize all inputs and template to prompt messages
             # Include: prompt template, inputs, query(optional), files(optional)
             #          memory(optional), external data, dataset context(optional)
-            prompt_messages, stop = self.originze_prompt_messages(
+            prompt_messages, stop = self.organize_prompt_messages(
                 app_record=app_record,
                 model_config=app_orchestration_config.model_config,
                 prompt_template_entity=app_orchestration_config.prompt_template,
@@ -237,8 +237,8 @@ class AgentApplicationRunner(AppRunner):
         all_message_tokens = 0
         all_answer_tokens = 0
         for agent_thought in agent_thoughts:
-            all_message_tokens += agent_thought.message_tokens
-            all_answer_tokens += agent_thought.answer_tokens
+            all_message_tokens += agent_thought.message_token
+            all_answer_tokens += agent_thought.answer_token
 
         model_type_instance = model_config.provider_model_bundle.model_type_instance
         model_type_instance = cast(LargeLanguageModel, model_type_instance)

api/core/app_runner/app_runner.py

@@ -50,7 +50,7 @@ class AppRunner:
             max_tokens = 0
 
         # get prompt messages without memory and context
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=model_config,
             prompt_template_entity=prompt_template_entity,
@@ -107,7 +107,7 @@ class AppRunner:
                         or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
                     model_config.parameters[parameter_rule.name] = max_tokens
 
-    def originze_prompt_messages(self, app_record: App,
+    def organize_prompt_messages(self, app_record: App,
                                  model_config: ModelConfigEntity,
                                  prompt_template_entity: PromptTemplateEntity,
                                  inputs: dict[str, str],

api/core/app_runner/basic_app_runner.py

@@ -79,7 +79,7 @@ class BasicApplicationRunner(AppRunner):
         # organize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         #          memory(optional)
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=app_orchestration_config.model_config,
             prompt_template_entity=app_orchestration_config.prompt_template,
@@ -164,7 +164,7 @@ class BasicApplicationRunner(AppRunner):
         # reorganize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         #          memory(optional), external data, dataset context(optional)
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=app_orchestration_config.model_config,
             prompt_template_entity=app_orchestration_config.prompt_template,

api/core/application_manager.py

@@ -473,7 +473,7 @@ class ApplicationManager:
         more_like_this_dict = copy_app_model_config_dict.get('more_like_this')
         if more_like_this_dict:
             if 'enabled' in more_like_this_dict and more_like_this_dict['enabled']:
-                properties['more_like_this'] = copy_app_model_config_dict.get('opening_statement')
+                properties['more_like_this'] = True
 
         # speech to text
         speech_to_text_dict = copy_app_model_config_dict.get('speech_to_text')

api/core/application_queue_manager.py

@@ -173,7 +173,7 @@ class ApplicationQueueManager:
             return
 
         user_prefix = 'account' if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end-user'
-        if result != f"{user_prefix}-{user_id}":
+        if result.decode('utf-8') != f"{user_prefix}-{user_id}":
             return
 
         stopped_cache_key = cls._generate_stopped_cache_key(task_id)

api/core/data_loader/file_extractor.py

@@ -65,7 +65,8 @@ class FileExtractor:
             elif file_extension == '.pdf':
                 loader = PdfLoader(file_path, upload_file=upload_file)
             elif file_extension in ['.md', '.markdown']:
-                loader = UnstructuredMarkdownLoader(file_path, unstructured_api_url)
+                loader = UnstructuredMarkdownLoader(file_path, unstructured_api_url) if is_automatic \
+                    else MarkdownLoader(file_path, autodetect_encoding=True)
             elif file_extension in ['.htm', '.html']:
                 loader = HTMLLoader(file_path)
             elif file_extension == '.docx':
@@ -84,7 +85,8 @@ class FileExtractor:
                 loader = UnstructuredXmlLoader(file_path, unstructured_api_url)
             else:
                 # txt
-                loader = UnstructuredTextLoader(file_path, unstructured_api_url)
+                loader = UnstructuredTextLoader(file_path, unstructured_api_url) if is_automatic \
+                    else TextLoader(file_path, autodetect_encoding=True)
         else:
             if file_extension == '.xlsx':
                 loader = ExcelLoader(file_path)

api/core/entities/provider_configuration.py

@@ -1,5 +1,6 @@
 import datetime
 import json
+import logging
 import time
 from json import JSONDecodeError
 from typing import Optional, List, Dict, Tuple, Iterator
@@ -9,6 +10,7 @@ from pydantic import BaseModel
 from core.entities.model_entities import ModelWithProviderEntity, ModelStatus, SimpleModelProviderEntity
 from core.entities.provider_entities import SystemConfiguration, CustomConfiguration, SystemConfigurationStatus
 from core.helper import encrypter
+from core.helper.model_provider_cache import ProviderCredentialsCache, ProviderCredentialsCacheType
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.entities.provider_entities import ProviderEntity, CredentialFormSchema, FormType
 from core.model_runtime.model_providers import model_provider_factory
@@ -18,6 +20,8 @@ from core.model_runtime.utils import encoders
 from extensions.ext_database import db
 from models.provider import ProviderType, Provider, ProviderModel, TenantPreferredModelProvider
 
+logger = logging.getLogger(__name__)
+
 
 class ProviderConfiguration(BaseModel):
     """
@@ -168,6 +172,14 @@ class ProviderConfiguration(BaseModel):
             db.session.add(provider_record)
             db.session.commit()
 
+        provider_model_credentials_cache = ProviderCredentialsCache(
+            tenant_id=self.tenant_id,
+            identity_id=provider_record.id,
+            cache_type=ProviderCredentialsCacheType.PROVIDER
+        )
+
+        provider_model_credentials_cache.delete()
+
         self.switch_preferred_provider_type(ProviderType.CUSTOM)
 
     def delete_custom_credentials(self) -> None:
@@ -190,6 +202,14 @@ class ProviderConfiguration(BaseModel):
             db.session.delete(provider_record)
             db.session.commit()
 
+            provider_model_credentials_cache = ProviderCredentialsCache(
+                tenant_id=self.tenant_id,
+                identity_id=provider_record.id,
+                cache_type=ProviderCredentialsCacheType.PROVIDER
+            )
+
+            provider_model_credentials_cache.delete()
+
     def get_custom_model_credentials(self, model_type: ModelType, model: str, obfuscated: bool = False) \
             -> Optional[dict]:
         """
@@ -311,6 +331,14 @@ class ProviderConfiguration(BaseModel):
             db.session.add(provider_model_record)
             db.session.commit()
 
+        provider_model_credentials_cache = ProviderCredentialsCache(
+            tenant_id=self.tenant_id,
+            identity_id=provider_model_record.id,
+            cache_type=ProviderCredentialsCacheType.MODEL
+        )
+
+        provider_model_credentials_cache.delete()
+
     def delete_custom_model_credentials(self, model_type: ModelType, model: str) -> None:
         """
         Delete custom model credentials.
@@ -332,6 +360,14 @@ class ProviderConfiguration(BaseModel):
             db.session.delete(provider_model_record)
             db.session.commit()
 
+            provider_model_credentials_cache = ProviderCredentialsCache(
+                tenant_id=self.tenant_id,
+                identity_id=provider_model_record.id,
+                cache_type=ProviderCredentialsCacheType.MODEL
+            )
+
+            provider_model_credentials_cache.delete()
+
     def get_provider_instance(self) -> ModelProvider:
         """
         Get provider instance.
@@ -544,13 +580,17 @@ class ProviderConfiguration(BaseModel):
             if model_configuration.model_type not in model_types:
                 continue
 
-            custom_model_schema = (
-                provider_instance.get_model_instance(model_configuration.model_type)
-                .get_customizable_model_schema_from_credentials(
-                    model_configuration.model,
-                    model_configuration.credentials
+            try:
+                custom_model_schema = (
+                    provider_instance.get_model_instance(model_configuration.model_type)
+                    .get_customizable_model_schema_from_credentials(
+                        model_configuration.model,
+                        model_configuration.credentials
+                    )
                 )
-            )
+            except Exception as ex:
+                logger.warning(f'get custom model schema failed, {ex}')
+                continue
 
             if not custom_model_schema:
                 continue

api/core/extension/extensible.py

@@ -61,7 +61,7 @@ class Extensible:
 
                     builtin_file_path = os.path.join(subdir_path, '__builtin__')
                     if os.path.exists(builtin_file_path):
-                        with open(builtin_file_path, 'r') as f:
+                        with open(builtin_file_path, 'r', encoding='utf-8') as f:
                             position = int(f.read().strip())
 
                 if (extension_name + '.py') not in file_names:
@@ -93,7 +93,7 @@ class Extensible:
                     json_path = os.path.join(subdir_path, 'schema.json')
                     json_data = {}
                     if os.path.exists(json_path):
-                        with open(json_path, 'r') as f:
+                        with open(json_path, 'r', encoding='utf-8') as f:
                             json_data = json.load(f)
 
                 extensions[extension_name] = ModuleExtension(

api/core/external_data_tool/weather_search/schema.json

@@ -1,35 +0,0 @@
-{
-    "label": {
-        "en-US": "Weather Search",
-        "zh-Hans": "天气查询"
-    },
-    "form_schema": [
-        {
-            "type": "select",
-            "label": {
-                "en-US": "Temperature Unit",
-                "zh-Hans": "温度单位"
-            },
-            "variable": "temperature_unit",
-            "required": true,
-            "options": [
-                {
-                    "label": {
-                        "en-US": "Fahrenheit",
-                        "zh-Hans": "华氏度"
-                    },
-                    "value": "fahrenheit"
-                },
-                {
-                    "label": {
-                        "en-US": "Centigrade",
-                        "zh-Hans": "摄氏度"
-                    },
-                    "value": "centigrade"
-                }
-            ],
-            "default": "centigrade",
-            "placeholder": "Please select temperature unit"
-        }
-    ]
-}

api/core/external_data_tool/weather_search/weather_search.py

@@ -1,45 +0,0 @@
-from typing import Optional
-
-from core.external_data_tool.base import ExternalDataTool
-
-
-class WeatherSearch(ExternalDataTool):
-    """
-    The name of custom type must be unique, keep the same with directory and file name.
-    """
-    name: str = "weather_search"
-
-    @classmethod
-    def validate_config(cls, tenant_id: str, config: dict) -> None:
-        """
-        schema.json validation. It will be called when user save the config.
-
-        Example:
-            .. code-block:: python
-                config = {
-                    "temperature_unit": "centigrade"
-                }
-
-        :param tenant_id: the id of workspace
-        :param config: the variables of form config
-        :return:
-        """
-
-        if not config.get('temperature_unit'):
-            raise ValueError('temperature unit is required')
-
-    def query(self, inputs: dict, query: Optional[str] = None) -> str:
-        """
-        Query the external data tool.
-
-        :param inputs: user inputs
-        :param query: the query of chat app
-        :return: the tool query result
-        """
-        city = inputs.get('city')
-        temperature_unit = self.config.get('temperature_unit')
-
-        if temperature_unit == 'fahrenheit':
-            return f'Weather in {city} is 32°F'
-        else:
-            return f'Weather in {city} is 0°C'

api/core/helper/model_provider_cache.py

@@ -0,0 +1,51 @@
+import json
+from enum import Enum
+from json import JSONDecodeError
+from typing import Optional
+
+from extensions.ext_redis import redis_client
+
+
+class ProviderCredentialsCacheType(Enum):
+    PROVIDER = "provider"
+    MODEL = "provider_model"
+
+
+class ProviderCredentialsCache:
+    def __init__(self, tenant_id: str, identity_id: str, cache_type: ProviderCredentialsCacheType):
+        self.cache_key = f"{cache_type.value}_credentials:tenant_id:{tenant_id}:id:{identity_id}"
+
+    def get(self) -> Optional[dict]:
+        """
+        Get cached model provider credentials.
+
+        :return:
+        """
+        cached_provider_credentials = redis_client.get(self.cache_key)
+        if cached_provider_credentials:
+            try:
+                cached_provider_credentials = cached_provider_credentials.decode('utf-8')
+                cached_provider_credentials = json.loads(cached_provider_credentials)
+            except JSONDecodeError:
+                return None
+
+            return cached_provider_credentials
+        else:
+            return None
+
+    def set(self, credentials: dict) -> None:
+        """
+        Cache model provider credentials.
+
+        :param credentials: provider credentials
+        :return:
+        """
+        redis_client.setex(self.cache_key, 3600, json.dumps(credentials))
+
+    def delete(self) -> None:
+        """
+        Delete cached model provider credentials.
+
+        :return:
+        """
+        redis_client.delete(self.cache_key)

api/core/index/vector_index/qdrant_vector_index.py

@@ -18,6 +18,7 @@ from models.dataset import Dataset, DatasetCollectionBinding
 class QdrantConfig(BaseModel):
     endpoint: str
     api_key: Optional[str]
+    timeout: float = 20
     root_path: Optional[str]
 
     def to_qdrant_params(self):
@@ -33,6 +34,7 @@ class QdrantConfig(BaseModel):
             return {
                 'url': self.endpoint,
                 'api_key': self.api_key,
+                'timeout': self.timeout
             }
 
 

api/core/index/vector_index/vector_index.py

@@ -49,7 +49,8 @@ class VectorIndex:
                 config=QdrantConfig(
                     endpoint=config.get('QDRANT_URL'),
                     api_key=config.get('QDRANT_API_KEY'),
-                    root_path=current_app.root_path
+                    root_path=current_app.root_path,
+                    timeout=config.get('QDRANT_CLIENT_TIMEOUT')
                 ),
                 embeddings=embeddings
             )

api/core/indexing_runner.py

@@ -5,12 +5,12 @@ import re
 import threading
 import time
 import uuid
-from typing import Optional, List, cast
+from typing import Optional, List, cast, Type, Union, Literal, AbstractSet, Collection, Any
 
 from flask import current_app, Flask
 from flask_login import current_user
 from langchain.schema import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
+from langchain.text_splitter import TextSplitter, TS, TokenTextSplitter
 from sqlalchemy.orm.exc import ObjectDeletedError
 
 from core.data_loader.file_extractor import FileExtractor
@@ -23,7 +23,8 @@ from core.errors.error import ProviderTokenNotInitError
 from core.model_runtime.entities.model_entities import ModelType, PriceType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter
+from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
+from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter, EnhanceRecursiveCharacterTextSplitter
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
@@ -58,7 +59,7 @@ class IndexingRunner:
                     first()
 
                 # load file
-                text_docs = self._load_data(dataset_document)
+                text_docs = self._load_data(dataset_document, processing_rule.mode == 'automatic')
 
                 # get splitter
                 splitter = self._get_splitter(processing_rule)
@@ -112,15 +113,14 @@ class IndexingRunner:
             for document_segment in document_segments:
                 db.session.delete(document_segment)
             db.session.commit()
-
-            # load file
-            text_docs = self._load_data(dataset_document)
-
             # get the process rule
             processing_rule = db.session.query(DatasetProcessRule). \
                 filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
                 first()
 
+            # load file
+            text_docs = self._load_data(dataset_document, processing_rule.mode == 'automatic')
+
             # get splitter
             splitter = self._get_splitter(processing_rule)
 
@@ -237,14 +237,15 @@ class IndexingRunner:
         preview_texts = []
         total_segments = 0
         for file_detail in file_details:
-            # load data from file
-            text_docs = FileExtractor.load(file_detail)
 
             processing_rule = DatasetProcessRule(
                 mode=tmp_processing_rule["mode"],
                 rules=json.dumps(tmp_processing_rule["rules"])
             )
 
+            # load data from file
+            text_docs = FileExtractor.load(file_detail, is_automatic=processing_rule.mode == 'automatic')
+
             # get splitter
             splitter = self._get_splitter(processing_rule)
 
@@ -381,13 +382,15 @@ class IndexingRunner:
                 )
                 total_segments += len(documents)
 
-                embedding_model_type_instance = embedding_model_instance.model_type_instance
-                embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
+                embedding_model_type_instance = None
+                if embedding_model_instance:
+                    embedding_model_type_instance = embedding_model_instance.model_type_instance
+                    embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
 
                 for document in documents:
                     if len(preview_texts) < 5:
                         preview_texts.append(document.page_content)
-                    if indexing_technique == 'high_quality' or embedding_model_instance:
+                    if indexing_technique == 'high_quality' and embedding_model_type_instance:
                         tokens += embedding_model_type_instance.get_num_tokens(
                             model=embedding_model_instance.model,
                             credentials=embedding_model_instance.credentials,
@@ -456,7 +459,7 @@ class IndexingRunner:
                 one_or_none()
 
             if file_detail:
-                text_docs = FileExtractor.load(file_detail, is_automatic=True)
+                text_docs = FileExtractor.load(file_detail, is_automatic=automatic)
         elif dataset_document.data_source_type == 'notion_import':
             loader = NotionLoader.from_document(dataset_document)
             text_docs = loader.load()
@@ -502,7 +505,8 @@ class IndexingRunner:
             if separator:
                 separator = separator.replace('\\n', '\n')
 
-            character_splitter = FixedRecursiveCharacterTextSplitter.from_tiktoken_encoder(
+
+            character_splitter = FixedRecursiveCharacterTextSplitter.from_gpt2_encoder(
                 chunk_size=segmentation["max_tokens"],
                 chunk_overlap=0,
                 fixed_separator=separator,
@@ -510,7 +514,7 @@ class IndexingRunner:
             )
         else:
             # Automatic segmentation
-            character_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+            character_splitter = EnhanceRecursiveCharacterTextSplitter.from_gpt2_encoder(
                 chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
                 chunk_overlap=0,
                 separators=["\n\n", "。", ".", " ", ""]

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -147,13 +147,13 @@ class AIModel(ABC):
         # read _position.yaml file
         position_map = {}
         if os.path.exists(position_file_path):
-            with open(position_file_path, 'r') as f:
+            with open(position_file_path, 'r', encoding='utf-8') as f:
                 position_map = yaml.safe_load(f)
 
         # traverse all model_schema_yaml_paths
         for model_schema_yaml_path in model_schema_yaml_paths:
             # read yaml data from yaml file
-            with open(model_schema_yaml_path, 'r') as f:
+            with open(model_schema_yaml_path, 'r', encoding='utf-8') as f:
                 yaml_data = yaml.safe_load(f)
 
             new_parameter_rules = []
@@ -236,16 +236,6 @@ class AIModel(ABC):
         :param credentials: model credentials
         :return: model schema
         """
-        if 'schema' in credentials:
-            schema_dict = json.loads(credentials['schema'])
-
-            try:
-                model_instance = AIModelEntity.parse_obj(schema_dict)
-                return model_instance
-            except ValidationError as e:
-                logging.exception(f"Invalid model schema for {model}")
-                return self._get_customizable_model_schema(model, credentials)
-
         return self._get_customizable_model_schema(model, credentials)
     
     def _get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -132,8 +132,8 @@ class LargeLanguageModel(AIModel):
         system_fingerprint = None
         real_model = model
 
-        for chunk in result:
-            try:
+        try:
+            for chunk in result:
                 yield chunk
 
                 self._trigger_new_chunk_callbacks(
@@ -156,8 +156,8 @@ class LargeLanguageModel(AIModel):
 
                 if chunk.system_fingerprint:
                     system_fingerprint = chunk.system_fingerprint
-            except Exception as e:
-                raise self._transform_invoke_error(e)
+        except Exception as e:
+            raise self._transform_invoke_error(e)
 
         self._trigger_after_invoke_callbacks(
             model=model,

api/core/model_runtime/model_providers/__base/model_provider.py

@@ -47,7 +47,7 @@ class ModelProvider(ABC):
         yaml_path = os.path.join(current_path, f'{provider_name}.yaml')
         yaml_data = {}
         if os.path.exists(yaml_path):
-            with open(yaml_path, 'r') as f:
+            with open(yaml_path, 'r', encoding='utf-8') as f:
                 yaml_data = yaml.safe_load(f)
 
         try:

api/core/model_runtime/model_providers/anthropic/llm/llm.py

@@ -252,6 +252,9 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :param messages: List of PromptMessage to combine.
         :return: Combined string with necessary human_prompt and ai_prompt tags.
         """
+        if not messages:
+            return ''
+
         messages = messages.copy()  # don't mutate the original list
         if not isinstance(messages[-1], AssistantPromptMessage):
             messages.append(AssistantPromptMessage(content=""))

api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py

@@ -54,7 +54,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
         _iter = range(0, len(tokens), max_chunks)
 
         for i in _iter:
-            embeddings, embedding_used_tokens = self._embedding_invoke(
+            embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                 model=model,
                 client=client,
                 texts=tokens[i: i + max_chunks],
@@ -62,7 +62,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             )
 
             used_tokens += embedding_used_tokens
-            batched_embeddings += [data for data in embeddings]
+            batched_embeddings += embeddings_batch
 
         results: list[list[list[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: list[list[int]] = [[] for _ in range(len(texts))]
@@ -73,7 +73,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
         for i in range(len(texts)):
             _result = results[i]
             if len(_result) == 0:
-                embeddings, embedding_used_tokens = self._embedding_invoke(
+                embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                     model=model,
                     client=client,
                     texts=[""],
@@ -81,7 +81,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
                 )
 
                 used_tokens += embedding_used_tokens
-                average = embeddings[0]
+                average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
             embeddings[i] = (average / np.linalg.norm(average)).tolist()

api/core/model_runtime/model_providers/google/llm/llm.py

@@ -3,6 +3,7 @@ from typing import Optional, Generator, Union, List
 import google.generativeai as genai
 import google.api_core.exceptions as exceptions
 import google.generativeai.client as client
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
 
 from google.generativeai.types import GenerateContentResponse, ContentType
 from google.generativeai.types.content_types import to_part
@@ -124,7 +125,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             last_msg = prompt_messages[-1]
             content = self._format_message_to_glm_content(last_msg)
             history.append(content)
-        else:    
+        else:
             for msg in prompt_messages:     # makes message roles strictly alternating
                 content = self._format_message_to_glm_content(msg)
                 if history and history[-1]["role"] == content["role"]:
@@ -139,13 +140,21 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         new_custom_client = new_client_manager.make_client("generative")
 
         google_model._client = new_custom_client
+
+        safety_settings={
+            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        }
         
         response = google_model.generate_content(
             contents=history,
             generation_config=genai.types.GenerationConfig(
                 **config_kwargs
             ),
-            stream=stream
+            stream=stream,
+            safety_settings=safety_settings
         )
 
         if stream:
@@ -169,7 +178,6 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             content=response.text
         )
 
-
         # calculate num tokens
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
         completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
@@ -202,11 +210,11 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         for chunk in response:
             content = chunk.text
             index += 1
-
+           
             assistant_prompt_message = AssistantPromptMessage(
                 content=content if content else '',
             )
-
+  
             if not response._done:
                 
                 # transform assistant message to prompt message

api/core/model_runtime/model_providers/huggingface_hub/llm/llm.py

@@ -154,20 +154,31 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
                 content=chunk.token.text
             )
 
-            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+            if chunk.details:
+                prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+                completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
-            yield LLMResultChunk(
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=assistant_prompt_message,
-                    usage=usage,
-                ),
-            )
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message,
+                        usage=usage,
+                        finish_reason=chunk.details.finish_reason,
+                    ),
+                )
+            else:
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message,
+                    ),
+                )
 
     def _handle_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], response: any) -> LLMResult:
         if isinstance(response, str):

api/core/model_runtime/model_providers/localai/llm/llm.py

@@ -1,7 +1,7 @@
 from typing import Generator, List, Optional, Union, cast
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMResultChunk, LLMResultChunkDelta, LLMMode
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, AssistantPromptMessage, UserPromptMessage, SystemPromptMessage
-from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule, ParameterType, FetchFrom, ModelType
+from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule, ParameterType, FetchFrom, ModelType, ModelPropertyKey
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.errors.invoke import InvokeConnectionError, InvokeServerUnavailableError, InvokeRateLimitError, \
@@ -156,9 +156,9 @@ class LocalAILarguageModel(LargeLanguageModel):
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         completion_model = None
         if credentials['completion_type'] == 'chat_completion':
-            completion_model = LLMMode.CHAT
+            completion_model = LLMMode.CHAT.value
         elif credentials['completion_type'] == 'completion':
-            completion_model = LLMMode.COMPLETION
+            completion_model = LLMMode.COMPLETION.value
         else:
             raise ValueError(f"Unknown completion type {credentials['completion_type']}")
             
@@ -202,7 +202,7 @@ class LocalAILarguageModel(LargeLanguageModel):
             ),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
-            model_properties={ 'mode': completion_model } if completion_model else {},
+            model_properties={ ModelPropertyKey.MODE: completion_model } if completion_model else {},
             parameter_rules=rules
         )
 

api/core/model_runtime/model_providers/model_provider_factory.py

@@ -30,6 +30,10 @@ class ModelProviderExtension(BaseModel):
 class ModelProviderFactory:
     model_provider_extensions: dict[str, ModelProviderExtension] = None
 
+    def __init__(self) -> None:
+        # for cache in memory
+        self.get_providers()
+
     def get_providers(self) -> list[ProviderEntity]:
         """
         Get all providers
@@ -212,7 +216,7 @@ class ModelProviderFactory:
         # read _position.yaml file
         position_map = {}
         if os.path.exists(position_file_path):
-            with open(position_file_path, 'r') as f:
+            with open(position_file_path, 'r', encoding='utf-8') as f:
                 position_map = yaml.safe_load(f)
 
         # traverse all model_provider_dir_paths

api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py

@@ -68,7 +68,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
 
         for i in _iter:
             # call embedding model
-            embeddings, embedding_used_tokens = self._embedding_invoke(
+            embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                 model=model,
                 client=client,
                 texts=tokens[i: i + max_chunks],
@@ -76,7 +76,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             )
 
             used_tokens += embedding_used_tokens
-            batched_embeddings += [data for data in embeddings]
+            batched_embeddings += embeddings_batch
 
         results: list[list[list[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: list[list[int]] = [[] for _ in range(len(texts))]
@@ -87,7 +87,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
         for i in range(len(texts)):
             _result = results[i]
             if len(_result) == 0:
-                embeddings, embedding_used_tokens = self._embedding_invoke(
+                embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                     model=model,
                     client=client,
                     texts=[""],
@@ -95,7 +95,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
                 )
 
                 used_tokens += embedding_used_tokens
-                average = embeddings[0]
+                average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
             embeddings[i] = (average / np.linalg.norm(average)).tolist()

api/core/model_runtime/model_providers/openai_api_compatible/_common.py

@@ -117,9 +117,9 @@ class _CommonOAI_API_Compat:
 
         if model_type == ModelType.LLM:
             if credentials['mode'] == 'chat':
-                entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT
+                entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT.value
             elif credentials['mode'] == 'completion':
-                entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION
+                entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION.value
             else:
                 raise ValueError(f"Unknown completion type {credentials['completion_type']}")
         

api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py

@@ -1,19 +1,21 @@
 import logging
 from decimal import Decimal
+from urllib.parse import urljoin
 
 import requests
 import json
 
 from typing import Optional, Generator, Union, List, cast
 
-from sympy import comp
-
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.utils import helper
 
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessage, AssistantPromptMessage, PromptMessageContent, \
-    PromptMessageContentType, PromptMessageFunction, PromptMessageTool, UserPromptMessage, SystemPromptMessage, ToolPromptMessage
-from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType, PriceConfig, ParameterRule, DefaultParameterName, \
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessage, \
+    AssistantPromptMessage, PromptMessageContent, \
+    PromptMessageContentType, PromptMessageFunction, PromptMessageTool, UserPromptMessage, SystemPromptMessage, \
+    ToolPromptMessage
+from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType, PriceConfig, ParameterRule, \
+    DefaultParameterName, \
     ParameterType, ModelPropertyKey, FetchFrom, AIModelEntity
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.errors.invoke import InvokeError
@@ -72,7 +74,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         :return:
         """
         return self._num_tokens_from_messages(model, prompt_messages, tools)
-        
+
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
         Validate model credentials using requests to ensure compatibility with all providers following OpenAI's API standard.
@@ -91,6 +93,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                 headers["Authorization"] = f"Bearer {api_key}"
 
             endpoint_url = credentials['endpoint_url']
+            if not endpoint_url.endswith('/'):
+                endpoint_url += '/'
 
             # prepare the payload for a simple ping to the model
             data = {
@@ -107,11 +111,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                         "content": "ping"
                     },
                 ]
+                endpoint_url = urljoin(endpoint_url, 'chat/completions')
             elif completion_type is LLMMode.COMPLETION:
                 data['prompt'] = 'ping'
+                endpoint_url = urljoin(endpoint_url, 'completions')
             else:
                 raise ValueError("Unsupported completion type for model configuration.")
-        
+
             # send a post request to validate the credentials
             response = requests.post(
                 endpoint_url,
@@ -121,8 +127,24 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             )
 
             if response.status_code != 200:
-                raise CredentialsValidateFailedError(f'Credentials validation failed with status code {response.status_code}: {response.text}')
+                raise CredentialsValidateFailedError(
+                    f'Credentials validation failed with status code {response.status_code}')
 
+            try:
+                json_result = response.json()
+            except json.JSONDecodeError as e:
+                raise CredentialsValidateFailedError(f'Credentials validation failed: JSON decode error')
+
+            if (completion_type is LLMMode.CHAT
+                    and ('object' not in json_result or json_result['object'] != 'chat.completion')):
+                raise CredentialsValidateFailedError(
+                    f'Credentials validation failed: invalid response object, must be \'chat.completion\'')
+            elif (completion_type is LLMMode.COMPLETION
+                  and ('object' not in json_result or json_result['object'] != 'text_completion')):
+                raise CredentialsValidateFailedError(
+                    f'Credentials validation failed: invalid response object, must be \'text_completion\'')
+        except CredentialsValidateFailedError:
+            raise
         except Exception as ex:
             raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {str(ex)}')
 
@@ -136,8 +158,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             model_type=ModelType.LLM,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: credentials.get('context_size'),
-                ModelPropertyKey.MODE: 'chat'
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')),
+                ModelPropertyKey.MODE: credentials.get('mode'),
             },
             parameter_rules=[
                 ParameterRule(
@@ -199,11 +221,11 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         return entity
 
-
     # validate_credentials method has been rewritten to use the requests library for compatibility with all providers following OpenAI's API standard.
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict, 
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, stream: bool = True, \
-                        user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
+                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                  stream: bool = True, \
+                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
         """
         Invoke llm completion model
 
@@ -225,7 +247,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             headers["Authorization"] = f"Bearer {api_key}"
 
         endpoint_url = credentials["endpoint_url"]
-    
+        if not endpoint_url.endswith('/'):
+            endpoint_url += '/'
+
         data = {
             "model": model,
             "stream": stream,
@@ -235,8 +259,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         completion_type = LLMMode.value_of(credentials['mode'])
 
         if completion_type is LLMMode.CHAT:
+            endpoint_url = urljoin(endpoint_url, 'chat/completions')
             data['messages'] = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
         elif completion_type == LLMMode.COMPLETION:
+            endpoint_url = urljoin(endpoint_url, 'completions')
             data['prompt'] = prompt_messages[0].content
         else:
             raise ValueError("Unsupported completion type for model configuration.")
@@ -247,8 +273,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             data["tool_choice"] = "auto"
 
             for tool in tools:
-                formatted_tools.append( helper.dump_model(PromptMessageFunction(function=tool)))
-            
+                formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
+
             data["tools"] = formatted_tools
 
         if stop:
@@ -256,7 +282,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         if user:
             data["user"] = user
-    
+
         response = requests.post(
             endpoint_url,
             headers=headers,
@@ -277,8 +303,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: requests.Response, 
-                                        prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(self, model: str, credentials: dict, response: requests.Response,
+                                         prompt_messages: list[PromptMessage]) -> Generator:
         """
         Handle llm stream response
 
@@ -315,51 +341,64 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             if chunk:
                 decoded_chunk = chunk.decode('utf-8').strip().lstrip('data: ').lstrip()
 
+                chunk_json = None
                 try:
                     chunk_json = json.loads(decoded_chunk)
                 # stream ended
                 except json.JSONDecodeError as e:
                     yield create_final_llm_result_chunk(
-                        index=chunk_index + 1, 
+                        index=chunk_index + 1,
                         message=AssistantPromptMessage(content=""),
                         finish_reason="Non-JSON encountered."
                     )
 
-                if len(chunk_json['choices']) == 0:
+                if not chunk_json or len(chunk_json['choices']) == 0:
                     continue
 
-                delta = chunk_json['choices'][0]['delta']
-                chunk_index = chunk_json['choices'][0]['index']
+                choice = chunk_json['choices'][0]
+                chunk_index = choice['index'] if 'index' in choice else chunk_index
 
-                if delta.get('finish_reason') is None and (delta.get('content') is None or delta.get('content') == ''):
+                if 'delta' in choice:
+                    delta = choice['delta']
+                    if delta.get('content') is None or delta.get('content') == '':
+                        continue
+
+                    assistant_message_tool_calls = delta.get('tool_calls', None)
+                    # assistant_message_function_call = delta.delta.function_call
+
+                    # extract tool calls from response
+                    if assistant_message_tool_calls:
+                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
+                    # function_call = self._extract_response_function_call(assistant_message_function_call)
+                    # tool_calls = [function_call] if function_call else []
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(
+                        content=delta.get('content', ''),
+                        tool_calls=tool_calls if assistant_message_tool_calls else []
+                    )
+
+                    full_assistant_content += delta.get('content', '')
+                elif 'text' in choice:
+                    if choice.get('text') is None or choice.get('text') == '':
+                        continue
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(
+                        content=choice.get('text', '')
+                    )
+
+                    full_assistant_content += choice.get('text', '')
+                else:
                     continue
-                
-                assistant_message_tool_calls = delta.get('tool_calls', None)
-                # assistant_message_function_call = delta.delta.function_call
-
-                # extract tool calls from response
-                if assistant_message_tool_calls:
-                    tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
-                # function_call = self._extract_response_function_call(assistant_message_function_call)
-                # tool_calls = [function_call] if function_call else []
-
-                # transform assistant message to prompt message
-                assistant_prompt_message = AssistantPromptMessage(
-                    content=delta.get('content', ''),
-                    tool_calls=tool_calls if assistant_message_tool_calls else []
-                )
-
-                full_assistant_content += delta.get('content', '')
 
                 # check payload indicator for completion
                 if chunk_json['choices'][0].get('finish_reason') is not None:
-                   
                     yield create_final_llm_result_chunk(
                         index=chunk_index,
                         message=assistant_prompt_message,
                         finish_reason=chunk_json['choices'][0]['finish_reason']
                     )
-
                 else:
                     yield LLMResultChunk(
                         model=model,
@@ -375,10 +414,12 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     message=AssistantPromptMessage(content=""),
                     finish_reason="End of stream."
                 )
-            
-    def _handle_generate_response(self, model: str, credentials: dict, response: requests.Response, 
-                                        prompt_messages: list[PromptMessage]) -> LLMResult:
-        
+
+            chunk_index += 1
+
+    def _handle_generate_response(self, model: str, credentials: dict, response: requests.Response,
+                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+
         response_json = response.json()
 
         completion_type = LLMMode.value_of(credentials['mode'])
@@ -457,7 +498,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}
             if message.tool_calls:
-                message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call in
+                message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
+                                              in
                                               message.tool_calls]
                 # function_call = message.tool_calls[0]
                 # message_dict["function_call"] = {
@@ -486,7 +528,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             message_dict["name"] = message.name
 
         return message_dict
-    
+
     def _num_tokens_from_string(self, model: str, text: str,
                                 tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
@@ -509,10 +551,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         """
         Approximate num tokens with GPT2 tokenizer.
         """
-       
+
         tokens_per_message = 3
         tokens_per_name = 1
-       
+
         num_tokens = 0
         messages_dict = [self._convert_prompt_message_to_dict(m) for m in messages]
         for message in messages_dict:
@@ -601,7 +643,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     num_tokens += self._get_num_tokens_by_gpt2(required_field)
 
         return num_tokens
-    
+
     def _extract_response_tool_calls(self,
                                      response_tool_calls: list[dict]) \
             -> list[AssistantPromptMessage.ToolCall]:

api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.yaml

@@ -33,8 +33,8 @@ model_credential_schema:
     type: text-input
     required: true
     placeholder:
-      zh_Hans: 在此输入您的 API endpoint URL
-      en_US: Enter your API endpoint URL  
+      zh_Hans: Base URL, eg. https://api.openai.com/v1
+      en_US: Base URL, eg. https://api.openai.com/v1
   - variable: mode
     show_on:
       - variable: __model_type

api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py

@@ -1,6 +1,7 @@
 import time
 from decimal import Decimal
 from typing import Optional
+from urllib.parse import urljoin
 import requests
 import json
 
@@ -42,8 +43,11 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         if api_key:
             headers["Authorization"] = f"Bearer {api_key}"
 
+        endpoint_url = credentials.get('endpoint_url')
+        if not endpoint_url.endswith('/'):
+            endpoint_url += '/'
 
-        endpoint_url = credentials['endpoint_url']
+        endpoint_url = urljoin(endpoint_url, 'embeddings')
 
         extra_model_kwargs = {}
         if user:
@@ -144,8 +148,11 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             if api_key:
                 headers["Authorization"] = f"Bearer {api_key}"
 
+            endpoint_url = credentials.get('endpoint_url')
+            if not endpoint_url.endswith('/'):
+                endpoint_url += '/'
 
-            endpoint_url = credentials['endpoint_url']
+            endpoint_url = urljoin(endpoint_url, 'embeddings')
 
             payload = {
                 'input': 'ping',
@@ -160,8 +167,19 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             )
 
             if response.status_code != 200:
-                raise CredentialsValidateFailedError(f"Invalid response status: {response.status_code}")
+                raise CredentialsValidateFailedError(
+                    f'Credentials validation failed with status code {response.status_code}')
 
+            try:
+                json_result = response.json()
+            except json.JSONDecodeError as e:
+                raise CredentialsValidateFailedError(f'Credentials validation failed: JSON decode error')
+
+            if 'model' not in json_result:
+                raise CredentialsValidateFailedError(
+                    f'Credentials validation failed: invalid response')
+        except CredentialsValidateFailedError:
+            raise
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
@@ -175,7 +193,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: credentials.get('context_size'),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

api/core/model_runtime/model_providers/openllm/llm/llm.py

@@ -6,7 +6,7 @@ from core.model_runtime.model_providers.openllm.llm.openllm_generate import Open
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMResultChunk, LLMResultChunkDelta, LLMMode
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, AssistantPromptMessage, UserPromptMessage, SystemPromptMessage
-from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule, ParameterType, FetchFrom, ModelType
+from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule, ParameterType, FetchFrom, ModelType, ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.errors.invoke import InvokeConnectionError, InvokeServerUnavailableError, InvokeRateLimitError, \
     InvokeAuthorizationError, InvokeBadRequestError, InvokeError
@@ -198,7 +198,7 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties={ 
-                'mode':  LLMMode.COMPLETION,
+                ModelPropertyKey.MODE: LLMMode.COMPLETION.value,
             },
             parameter_rules=rules
         )

api/core/model_runtime/model_providers/replicate/llm/llm.py

@@ -8,7 +8,7 @@ from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.llm_entities import LLMResult, LLMMode, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, AssistantPromptMessage, \
     PromptMessageRole, UserPromptMessage, SystemPromptMessage
-from core.model_runtime.entities.model_entities import ParameterRule, AIModelEntity, FetchFrom, ModelType
+from core.model_runtime.entities.model_entities import ParameterRule, AIModelEntity, FetchFrom, ModelType, ModelPropertyKey
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.replicate._common import _CommonReplicate
@@ -91,7 +91,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties={
-                'mode': model_type.value
+                ModelPropertyKey.MODE: model_type.value
             },
             parameter_rules=self._get_customizable_model_parameter_rules(model, credentials)
         )
@@ -116,7 +116,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
         )
 
         for key, value in input_properties:
-            if key not in ['system_prompt', 'prompt']:
+            if key not in ['system_prompt', 'prompt'] and 'stop' not in key:
                 value_type = value.get('type')
 
                 if not value_type:
@@ -151,9 +151,17 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
         index = -1
         current_completion: str = ""
         stop_condition_reached = False
+
+        prediction_output_length = 10000
+        is_prediction_output_finished = False
+
         for output in prediction.output_iterator():
             current_completion += output
 
+            if not is_prediction_output_finished and prediction.status == 'succeeded':
+                prediction_output_length = len(prediction.output) - 1
+                is_prediction_output_finished = True
+
             if stop:
                 for s in stop:
                     if s in current_completion:
@@ -172,20 +180,30 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
                 content=output if output else ''
             )
 
-            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+            if index < prediction_output_length:
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message
+                    )
+                )
+            else:
+                prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+                completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
-            yield LLMResultChunk(
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=assistant_prompt_message,
-                    usage=usage,
-                ),
-            )
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message,
+                        usage=usage
+                    )
+                )
 
     def _handle_generate_response(self, model: str, credentials: dict, prediction: Prediction, stop: list[str],
                                   prompt_messages: list[PromptMessage]) -> LLMResult:

api/core/model_runtime/model_providers/spark/spark.py

@@ -19,13 +19,23 @@ class SparkProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            # Use `claude-instant-1` model for validate,
             model_instance.validate_credentials(
                 model='spark-1.5',
                 credentials=credentials
             )
         except CredentialsValidateFailedError as ex:
-            raise ex
+            try:
+                model_instance = self.get_model_instance(ModelType.LLM)
+
+                model_instance.validate_credentials(
+                    model='spark-3',
+                    credentials=credentials
+                )
+            except CredentialsValidateFailedError as ex:
+                raise ex
+            except Exception as ex:
+                logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+                raise ex
         except Exception as ex:
             logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
             raise ex

api/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -52,9 +52,13 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         :param tools: tools for tool calling
         :return:
         """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+
         response = dashscope.Tokenization.call(
             model=model,
             prompt=self._convert_messages_to_prompt(prompt_messages),
+            **credentials_kwargs
         )
         
         if response.status_code == HTTPStatus.OK:
@@ -108,10 +112,6 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         # transform credentials to kwargs for model instance
         credentials_kwargs = self._to_credential_kwargs(credentials)
 
-        dashscope.api_key = credentials_kwargs['api_key']
-
-        print(credentials_kwargs, 'credentials_kwargs')
-
         client = EnhanceTongyi(
             model_name=model,
             streaming=stream,
@@ -121,7 +121,8 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         params = {
             'model': model,
             'prompt': self._convert_messages_to_prompt(prompt_messages),
-            **model_parameters
+            **model_parameters,
+            **credentials_kwargs
         }
         if stream:
             responses = stream_generate_with_retry(
@@ -222,7 +223,6 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         :param credentials:
         :return:
         """
-        print(credentials, 'credentials')
         credentials_kwargs = {
             "api_key": credentials['dashscope_api_key'],
         }

api/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -18,7 +18,7 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, UserPromptMessage, SystemPromptMessage, AssistantPromptMessage
 from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import FetchFrom, ModelType, ParameterRule, ParameterType
+from core.model_runtime.entities.model_entities import FetchFrom, ModelType, ParameterRule, ParameterType, ModelPropertyKey
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.xinference.llm.xinference_helper import XinferenceHelper, XinferenceModelExtraParameter
 from core.model_runtime.errors.invoke import InvokeConnectionError, InvokeServerUnavailableError, InvokeRateLimitError, \
@@ -56,10 +56,18 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             }
         """
         try:
-            XinferenceHelper.get_xinference_extra_parameter(
+            extra_param = XinferenceHelper.get_xinference_extra_parameter(
                 server_url=credentials['server_url'],
                 model_uid=credentials['model_uid']
             )
+            if 'completion_type' not in credentials:
+                if 'chat' in extra_param.model_ability:
+                    credentials['completion_type'] = 'chat'
+                elif 'generate' in extra_param.model_ability:
+                    credentials['completion_type'] = 'completion'
+                else:
+                    raise ValueError(f'xinference model ability {extra_param.model_ability} is not supported')
+
         except RuntimeError as e:
             raise CredentialsValidateFailedError(f'Xinference credentials validate failed: {e}')
         except KeyError as e:
@@ -256,17 +264,26 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         ]
 
         completion_type = None
-        extra_args = XinferenceHelper.get_xinference_extra_parameter(
-            server_url=credentials['server_url'],
-            model_uid=credentials['model_uid']
-        )
 
-        if 'chat' in extra_args.model_ability:
-            completion_type = LLMMode.CHAT
-        elif 'generate' in extra_args.model_ability:
-            completion_type = LLMMode.COMPLETION
+        if 'completion_type' in credentials:
+            if credentials['completion_type'] == 'chat':
+                completion_type = LLMMode.CHAT.value
+            elif credentials['completion_type'] == 'completion':
+                completion_type = LLMMode.COMPLETION.value
+            else:
+                raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
         else:
-            raise NotImplementedError(f'xinference model ability {extra_args.model_ability} is not supported')
+            extra_args = XinferenceHelper.get_xinference_extra_parameter(
+                server_url=credentials['server_url'],
+                model_uid=credentials['model_uid']
+            )
+
+            if 'chat' in extra_args.model_ability:
+                completion_type = LLMMode.CHAT.value
+            elif 'generate' in extra_args.model_ability:
+                completion_type = LLMMode.COMPLETION.value
+            else:
+                raise ValueError(f'xinference model ability {extra_args.model_ability} is not supported')
 
         entity = AIModelEntity(
             model=model,
@@ -276,7 +293,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties={ 
-                'mode':  completion_type,
+                ModelPropertyKey.MODE: completion_type,
             },
             parameter_rules=rules
         )

api/core/model_runtime/model_providers/xinference/llm/xinference_helper.py

@@ -33,10 +33,13 @@ class XinferenceHelper:
 
     @staticmethod
     def _clean_cache() -> None:
-        with cache_lock:
-            for model_uid, model in cache.items():
-                if model['expires'] < time():
+        try:
+            with cache_lock:
+                expired_keys = [model_uid for model_uid, model in cache.items() if model['expires'] < time()]
+                for model_uid in expired_keys:
                     del cache[model_uid]
+        except RuntimeError as e:
+            pass
 
     @staticmethod
     def _get_xinference_extra_parameter(server_url: str, model_uid: str) -> XinferenceModelExtraParameter:

api/core/model_runtime/model_providers/zhipuai/llm/llm.py

@@ -8,8 +8,9 @@ from typing import (
     Union
 )
 
-from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, UserPromptMessage, AssistantPromptMessage, \
-    SystemPromptMessage
+from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, UserPromptMessage, \
+    AssistantPromptMessage, \
+    SystemPromptMessage, PromptMessageRole
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, \
     LLMResultChunkDelta
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -111,13 +112,39 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         if len(prompt_messages) == 0:
             raise ValueError('At least one message is required')
         
-        if prompt_messages[0].role.value == 'system':
+        if prompt_messages[0].role == PromptMessageRole.SYSTEM:
             if not prompt_messages[0].content:
                 prompt_messages = prompt_messages[1:]
 
+        # resolve zhipuai model not support system message and user message, assistant message must be in sequence
+        new_prompt_messages = []
+        for prompt_message in prompt_messages:
+            copy_prompt_message = prompt_message.copy()
+            if copy_prompt_message.role in [PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL]:
+                if not isinstance(copy_prompt_message.content, str):
+                    # not support image message
+                    continue
+
+                if new_prompt_messages and new_prompt_messages[-1].role == PromptMessageRole.USER:
+                    new_prompt_messages[-1].content += "\n\n" + copy_prompt_message.content
+                else:
+                    if copy_prompt_message.role == PromptMessageRole.USER:
+                        new_prompt_messages.append(copy_prompt_message)
+                    else:
+                        new_prompt_message = UserPromptMessage(content=copy_prompt_message.content)
+                        new_prompt_messages.append(new_prompt_message)
+            else:
+                if new_prompt_messages and new_prompt_messages[-1].role == PromptMessageRole.ASSISTANT:
+                    new_prompt_messages[-1].content += "\n\n" + copy_prompt_message.content
+                else:
+                    new_prompt_messages.append(copy_prompt_message)
+
         params = {
             'model': model,
-            'prompt': [{ 'role': prompt_message.role.value, 'content': prompt_message.content } for prompt_message in prompt_messages],
+            'prompt': [{
+                'role': prompt_message.role.value,
+                'content': prompt_message.content
+            } for prompt_message in new_prompt_messages],
             **model_parameters
         }
 

api/core/model_runtime/model_providers/zhipuai/zhipuai.yaml

@@ -24,7 +24,7 @@ provider_credential_schema:
   - variable: api_key
     label:
       en_US: APIKey
-    type: text-input
+    type: secret-input
     required: true
     placeholder:
       zh_Hans: 在此输入您的 APIKey

api/core/moderation/cloud_service/cloud_service.py

@@ -1,93 +0,0 @@
-from core.moderation.base import Moderation, ModerationAction, ModerationInputsResult, ModerationOutputsResult
-
-
-class CloudServiceModeration(Moderation):
-    """
-    The name of custom type must be unique, keep the same with directory and file name.
-    """
-    name: str = "cloud_service"
-
-    @classmethod
-    def validate_config(cls, tenant_id: str, config: dict) -> None:
-        """
-        schema.json validation. It will be called when user save the config.
-
-        Example:
-            .. code-block:: python
-                config = {
-                    "cloud_provider": "GoogleCloud",
-                    "api_endpoint": "https://api.example.com",
-                    "api_keys": "123456",
-                    "inputs_config": {
-                        "enabled": True,
-                        "preset_response": "Your content violates our usage policy. Please revise and try again."
-                    },
-                    "outputs_config": {
-                        "enabled": True,
-                        "preset_response": "Your content violates our usage policy. Please revise and try again."
-                    }
-                }
-
-        :param tenant_id: the id of workspace
-        :param config: the variables of form config
-        :return:
-        """
-
-        cls._validate_inputs_and_outputs_config(config, True)
-
-        if not config.get("cloud_provider"):
-            raise ValueError("cloud_provider is required")
-
-        if not config.get("api_endpoint"):
-            raise ValueError("api_endpoint is required")
-
-        if not config.get("api_keys"):
-            raise ValueError("api_keys is required")
-
-    def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
-        """
-        Moderation for inputs.
-
-        :param inputs: user inputs
-        :param query: the query of chat app, there is empty if is completion app
-        :return: the moderation result
-        """
-        flagged = False
-        preset_response = ""
-
-        if self.config['inputs_config']['enabled']:
-            preset_response = self.config['inputs_config']['preset_response']
-
-            if query:
-                inputs['query__'] = query
-            flagged = self._is_violated(inputs)
-
-        # return ModerationInputsResult(flagged=flagged, action=ModerationAction.OVERRIDED, inputs=inputs, query=query)
-        return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
-
-    def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
-        """
-        Moderation for outputs.
-
-        :param text: the text of LLM response
-        :return: the moderation result
-        """
-        flagged = False
-        preset_response = ""
-
-        if self.config['outputs_config']['enabled']:
-            preset_response = self.config['outputs_config']['preset_response']
-
-            flagged = self._is_violated({'text': text})
-
-        # return ModerationOutputsResult(flagged=flagged, action=ModerationAction.OVERRIDED, text=text)
-        return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
-
-    def _is_violated(self, inputs: dict):
-        """
-        The main logic of moderation.
-
-        :param inputs:
-        :return: the moderation result
-        """
-        return False

api/core/moderation/cloud_service/schema.json

@@ -1,65 +0,0 @@
-{
-    "label": {
-        "en-US": "Cloud Service",
-        "zh-Hans": "云服务"
-    },
-    "form_schema": [
-        {
-            "type": "select",
-            "label": {
-                "en-US": "Cloud Provider",
-                "zh-Hans": "云厂商"
-            },
-            "variable": "cloud_provider",
-            "required": true,
-            "options": [
-                {
-                    "label": {
-                        "en-US": "AWS",
-                        "zh-Hans": "亚马逊"
-                    },
-                    "value": "AWS"
-                },
-                {
-                    "label": {
-                        "en-US": "Google Cloud",
-                        "zh-Hans": "谷歌云"
-                    },
-                    "value": "GoogleCloud"
-                },
-                {
-                    "label": {
-                        "en-US": "Azure Cloud",
-                        "zh-Hans": "微软云"
-                    },
-                    "value": "Azure"
-                }
-            ],
-            "default": "GoogleCloud",
-            "placeholder": ""
-        },
-        {
-            "type": "text-input",
-            "label": {
-                "en-US": "API Endpoint",
-                "zh-Hans": "API Endpoint"
-            },
-            "variable": "api_endpoint",
-            "required": true,
-            "max_length": 100,
-            "default": "",
-            "placeholder": "https://api.example.com"
-        },
-        {
-            "type": "paragraph",
-            "label": {
-                "en-US": "API Key",
-                "zh-Hans": "API Key"
-            },
-            "variable": "api_keys",
-            "required": true,
-            "default": "",
-            "placeholder": "Paste your API key here"
-        }
-    ]
-}

api/core/prompt/prompt_transform.py

@@ -207,7 +207,7 @@ class PromptTransform:
 
         json_file_path = os.path.join(prompt_path, f'{prompt_name}.json')
         # Open the JSON file and read its content
-        with open(json_file_path, 'r') as json_file:
+        with open(json_file_path, 'r', encoding='utf-8') as json_file:
             return json.load(json_file)
 
     def _get_simple_chat_app_chat_model_prompt_messages(self, prompt_rules: dict,

api/core/provider_manager.py

@@ -10,6 +10,7 @@ from core.entities.provider_configuration import ProviderConfigurations, Provide
 from core.entities.provider_entities import CustomConfiguration, CustomProviderConfiguration, CustomModelConfiguration, \
     SystemConfiguration, QuotaConfiguration
 from core.helper import encrypter
+from core.helper.model_provider_cache import ProviderCredentialsCache, ProviderCredentialsCacheType
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.entities.provider_entities import ProviderEntity, CredentialFormSchema, FormType
 from core.model_runtime.model_providers import model_provider_factory
@@ -79,9 +80,6 @@ class ProviderManager:
         # Get All preferred provider types of the workspace
         provider_name_to_preferred_model_provider_records_dict = self._get_all_preferred_model_providers(tenant_id)
 
-        # Get decoding rsa key and cipher for decrypting credentials
-        decoding_rsa_key, decoding_cipher_rsa = encrypter.get_decrypt_decoding(tenant_id)
-
         provider_configurations = ProviderConfigurations(
             tenant_id=tenant_id
         )
@@ -100,19 +98,17 @@ class ProviderManager:
 
             # Convert to custom configuration
             custom_configuration = self._to_custom_configuration(
+                tenant_id,
                 provider_entity,
                 provider_records,
-                provider_model_records,
-                decoding_rsa_key,
-                decoding_cipher_rsa
+                provider_model_records
             )
 
             # Convert to system configuration
             system_configuration = self._to_system_configuration(
+                tenant_id,
                 provider_entity,
-                provider_records,
-                decoding_rsa_key,
-                decoding_cipher_rsa
+                provider_records
             )
 
             # Get preferred provider type
@@ -401,28 +397,29 @@ class ProviderManager:
                                 Provider.tenant_id == tenant_id,
                                 Provider.provider_name == provider_name,
                                 Provider.provider_type == ProviderType.SYSTEM.value,
-                                Provider.quota_type == ProviderQuotaType.TRIAL.value,
-                                Provider.is_valid == True
+                                Provider.quota_type == ProviderQuotaType.TRIAL.value
                             ).first()
 
+                            if provider_record and not provider_record.is_valid:
+                                provider_record.is_valid = True
+                                db.session.commit()
+
                         provider_name_to_provider_records_dict[provider_name].append(provider_record)
 
         return provider_name_to_provider_records_dict
 
     def _to_custom_configuration(self,
+                                 tenant_id: str,
                                  provider_entity: ProviderEntity,
                                  provider_records: list[Provider],
-                                 provider_model_records: list[ProviderModel],
-                                 decoding_rsa_key,
-                                 decoding_cipher_rsa) -> CustomConfiguration:
+                                 provider_model_records: list[ProviderModel]) -> CustomConfiguration:
         """
         Convert to custom configuration.
 
+        :param tenant_id: workspace id
         :param provider_entity: provider entity
         :param provider_records: provider records
         :param provider_model_records: provider model records
-        :param decoding_rsa_key: decoding rsa key
-        :param decoding_cipher_rsa: decoding cipher rsa
         :return:
         """
         # Get provider credential secret variables
@@ -445,18 +442,48 @@ class ProviderManager:
         # Get custom provider credentials
         custom_provider_configuration = None
         if custom_provider_record:
-            try:
-                provider_credentials = json.loads(custom_provider_record.encrypted_config)
-            except JSONDecodeError:
-                provider_credentials = {}
+            provider_credentials_cache = ProviderCredentialsCache(
+                tenant_id=tenant_id,
+                identity_id=custom_provider_record.id,
+                cache_type=ProviderCredentialsCacheType.PROVIDER
+            )
 
-            for variable in provider_credential_secret_variables:
-                if variable in provider_credentials:
-                    provider_credentials[variable] = encrypter.decrypt_token_with_decoding(
-                        provider_credentials.get(variable),
-                        decoding_rsa_key,
-                        decoding_cipher_rsa
-                    )
+            # Get cached provider credentials
+            cached_provider_credentials = provider_credentials_cache.get()
+
+            if not cached_provider_credentials:
+                try:
+                    # fix origin data
+                    if (custom_provider_record.encrypted_config
+                            and not custom_provider_record.encrypted_config.startswith("{")):
+                        provider_credentials = {
+                            "openai_api_key": custom_provider_record.encrypted_config
+                        }
+                    else:
+                        provider_credentials = json.loads(custom_provider_record.encrypted_config)
+                except JSONDecodeError:
+                    provider_credentials = {}
+
+                # Get decoding rsa key and cipher for decrypting credentials
+                decoding_rsa_key, decoding_cipher_rsa = encrypter.get_decrypt_decoding(tenant_id)
+
+                for variable in provider_credential_secret_variables:
+                    if variable in provider_credentials:
+                        try:
+                            provider_credentials[variable] = encrypter.decrypt_token_with_decoding(
+                                provider_credentials.get(variable),
+                                decoding_rsa_key,
+                                decoding_cipher_rsa
+                            )
+                        except ValueError:
+                            pass
+
+                # cache provider credentials
+                provider_credentials_cache.set(
+                    credentials=provider_credentials
+                )
+            else:
+                provider_credentials = cached_provider_credentials
 
             custom_provider_configuration = CustomProviderConfiguration(
                 credentials=provider_credentials
@@ -474,18 +501,41 @@ class ProviderManager:
             if not provider_model_record.encrypted_config:
                 continue
 
-            try:
-                provider_model_credentials = json.loads(provider_model_record.encrypted_config)
-            except JSONDecodeError:
-                continue
+            provider_model_credentials_cache = ProviderCredentialsCache(
+                tenant_id=tenant_id,
+                identity_id=provider_model_record.id,
+                cache_type=ProviderCredentialsCacheType.MODEL
+            )
 
-            for variable in model_credential_secret_variables:
-                if variable in provider_model_credentials:
-                    provider_model_credentials[variable] = encrypter.decrypt_token_with_decoding(
-                        provider_model_credentials.get(variable),
-                        decoding_rsa_key,
-                        decoding_cipher_rsa
-                    )
+            # Get cached provider model credentials
+            cached_provider_model_credentials = provider_model_credentials_cache.get()
+
+            if not cached_provider_model_credentials:
+                try:
+                    provider_model_credentials = json.loads(provider_model_record.encrypted_config)
+                except JSONDecodeError:
+                    continue
+
+                # Get decoding rsa key and cipher for decrypting credentials
+                decoding_rsa_key, decoding_cipher_rsa = encrypter.get_decrypt_decoding(tenant_id)
+
+                for variable in model_credential_secret_variables:
+                    if variable in provider_model_credentials:
+                        try:
+                            provider_model_credentials[variable] = encrypter.decrypt_token_with_decoding(
+                                provider_model_credentials.get(variable),
+                                decoding_rsa_key,
+                                decoding_cipher_rsa
+                            )
+                        except ValueError:
+                            pass
+
+                # cache provider model credentials
+                provider_model_credentials_cache.set(
+                    credentials=provider_model_credentials
+                )
+            else:
+                provider_model_credentials = cached_provider_model_credentials
 
             custom_model_configurations.append(
                 CustomModelConfiguration(
@@ -501,17 +551,15 @@ class ProviderManager:
         )
 
     def _to_system_configuration(self,
+                                 tenant_id: str,
                                  provider_entity: ProviderEntity,
-                                 provider_records: list[Provider],
-                                 decoding_rsa_key,
-                                 decoding_cipher_rsa) -> SystemConfiguration:
+                                 provider_records: list[Provider]) -> SystemConfiguration:
         """
         Convert to system configuration.
 
+        :param tenant_id: workspace id
         :param provider_entity: provider entity
         :param provider_records: provider records
-        :param decoding_rsa_key: decoding rsa key
-        :param decoding_cipher_rsa: decoding cipher rsa
         :return:
         """
         # Get hosting configuration
@@ -564,26 +612,49 @@ class ProviderManager:
             provider_record = quota_type_to_provider_records_dict.get(current_quota_type)
 
             if provider_record:
-                try:
-                    provider_credentials = json.loads(provider_record.encrypted_config)
-                except JSONDecodeError:
-                    provider_credentials = {}
-
-                # Get provider credential secret variables
-                provider_credential_secret_variables = self._extract_secret_variables(
-                    provider_entity.provider_credential_schema.credential_form_schemas
-                    if provider_entity.provider_credential_schema else []
+                provider_credentials_cache = ProviderCredentialsCache(
+                    tenant_id=tenant_id,
+                    identity_id=provider_record.id,
+                    cache_type=ProviderCredentialsCacheType.PROVIDER
                 )
 
-                for variable in provider_credential_secret_variables:
-                    if variable in provider_credentials:
-                        provider_credentials[variable] = encrypter.decrypt_token_with_decoding(
-                            provider_credentials.get(variable),
-                            decoding_rsa_key,
-                            decoding_cipher_rsa
-                        )
+                # Get cached provider credentials
+                cached_provider_credentials = provider_credentials_cache.get()
 
-                current_using_credentials = provider_credentials
+                if not cached_provider_credentials:
+                    try:
+                        provider_credentials = json.loads(provider_record.encrypted_config)
+                    except JSONDecodeError:
+                        provider_credentials = {}
+
+                    # Get provider credential secret variables
+                    provider_credential_secret_variables = self._extract_secret_variables(
+                        provider_entity.provider_credential_schema.credential_form_schemas
+                        if provider_entity.provider_credential_schema else []
+                    )
+
+                    # Get decoding rsa key and cipher for decrypting credentials
+                    decoding_rsa_key, decoding_cipher_rsa = encrypter.get_decrypt_decoding(tenant_id)
+
+                    for variable in provider_credential_secret_variables:
+                        if variable in provider_credentials:
+                            try:
+                                provider_credentials[variable] = encrypter.decrypt_token_with_decoding(
+                                    provider_credentials.get(variable),
+                                    decoding_rsa_key,
+                                    decoding_cipher_rsa
+                                )
+                            except ValueError:
+                                pass
+
+                    current_using_credentials = provider_credentials
+
+                    # cache provider credentials
+                    provider_credentials_cache.set(
+                        credentials=current_using_credentials
+                    )
+                else:
+                    current_using_credentials = cached_provider_credentials
             else:
                 current_using_credentials = {}
 

api/core/spiltter/fixed_text_splitter.py

@@ -7,10 +7,38 @@ from typing import (
     Optional,
 )
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter, TS, Type, Union, AbstractSet, Literal, Collection
 
+from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
 
-class FixedRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
+class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
+    """
+        This class is used to implement from_gpt2_encoder, to prevent using of tiktoken
+    """
+    @classmethod
+    def from_gpt2_encoder(
+        cls: Type[TS],
+        encoding_name: str = "gpt2",
+        model_name: Optional[str] = None,
+        allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
+        disallowed_special: Union[Literal["all"], Collection[str]] = "all",
+        **kwargs: Any,
+    ):
+        def _token_encoder(text: str) -> int:
+            return GPT2Tokenizer.get_num_tokens(text)
+
+        if issubclass(cls, TokenTextSplitter):
+            extra_kwargs = {
+                "encoding_name": encoding_name,
+                "model_name": model_name,
+                "allowed_special": allowed_special,
+                "disallowed_special": disallowed_special,
+            }
+            kwargs = {**kwargs, **extra_kwargs}
+
+        return cls(length_function=_token_encoder, **kwargs)
+
+class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter):
     def __init__(self, fixed_separator: str = "\n\n", separators: Optional[List[str]] = None, **kwargs: Any):
         """Create a new TextSplitter."""
         super().__init__(**kwargs)
@@ -65,4 +93,4 @@ class FixedRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
         if _good_splits:
             merged_text = self._merge_splits(_good_splits, separator)
             final_chunks.extend(merged_text)
-        return final_chunks
+        return final_chunks

api/extensions/ext_celery.py

@@ -46,11 +46,11 @@ def init_app(app: Flask) -> Celery:
     beat_schedule = {
         'clean_embedding_cache_task': {
             'task': 'schedule.clean_embedding_cache_task.clean_embedding_cache_task',
-            'schedule': timedelta(minutes=1),
+            'schedule': timedelta(days=7),
         },
         'clean_unused_datasets_task': {
             'task': 'schedule.clean_unused_datasets_task.clean_unused_datasets_task',
-            'schedule': timedelta(minutes=10),
+            'schedule': timedelta(days=7),
         }
     }
     celery_app.conf.update(

api/services/model_provider_service.py

@@ -6,7 +6,7 @@ from typing import Optional, cast, Tuple
 import requests
 from flask import current_app
 
-from core.entities.model_entities import ModelWithProviderEntity, ModelStatus, DefaultModelEntity
+from core.entities.model_entities import ModelStatus
 from core.model_runtime.entities.model_entities import ModelType, ParameterRule
 from core.model_runtime.model_providers import model_provider_factory
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel

api/templates/invite_member_mail_template_en-US.html

@@ -60,7 +60,7 @@
             <p>Dear {{ to }},</p>
             <p>{{ inviter_name }} is pleased to invite you to join our workspace on Dify, a platform specifically designed for LLM application development. On Dify, you can explore, create, and collaborate to build and operate AI applications.</p>
             <p>You can now log in to Dify using the GitHub or Google account associated with this email.</p>
-            <p style="text-align: center;"><a class="button" href="{{ url }}">Login Here</a></p>
+            <p style="text-align: center;"><a style="color: #fff; text-decoration: none" class="button" href="{{ url }}">Login Here</a></p>
         </div>
         <div class="footer">
             <p>Best regards,</p>

api/templates/invite_member_mail_template_zh-CN.html

@@ -60,7 +60,7 @@
             <p>尊敬的 {{ to }}，</p>
             <p>{{ inviter_name }} 现邀请您加入我们在 Dify 的工作区，这是一个专为 LLM 应用开发而设计的平台。在 Dify 上，您可以探索、创造和合作，构建和运营 AI 应用。</p>
             <p>您现在可以使用与此邮件相对应的 GitHub 或 Google 账号登录 Dify。</p>
-            <p style="text-align: center;"><a class="button" href="{{ url }}">在此登录</a></p>
+            <p style="text-align: center;"><a style="color: #fff; text-decoration: none" class="button" href="{{ url }}">在此登录</a></p>
         </div>
         <div class="footer">
             <p>此致，</p>

docker/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3.1'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.4.1
+    image: langgenius/dify-api:0.4.3
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -92,6 +92,8 @@ services:
       QDRANT_URL: http://qdrant:6333
       # The Qdrant API key.
       QDRANT_API_KEY: difyai123456
+      # The Qdrant clinet timeout setting.
+      QDRANT_CLIENT_TIMEOUT: 20
       # Milvus configuration Only available when VECTOR_STORE is `milvus`.
       # The milvus host.
       MILVUS_HOST: 127.0.0.1
@@ -128,7 +130,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.4.1
+    image: langgenius/dify-api:0.4.3
     restart: always
     environment:
       # Startup mode, 'worker' starts the Celery worker for processing the queue.
@@ -170,6 +172,8 @@ services:
       QDRANT_URL: http://qdrant:6333
       # The Qdrant API key.
       QDRANT_API_KEY: difyai123456
+      # The Qdrant clinet timeout setting.
+      QDRANT_CLIENT_TIMEOUT: 20
       # Milvus configuration Only available when VECTOR_STORE is `milvus`.
       # The milvus host.
       MILVUS_HOST: 127.0.0.1
@@ -196,7 +200,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.4.1
+    image: langgenius/dify-web:0.4.3
     restart: always
     environment:
       EDITION: SELF_HOSTED

web/.eslintrc.json

@@ -23,6 +23,7 @@
         ]
       }
     ],
-    "react-hooks/exhaustive-deps": "warn"
+    "react-hooks/exhaustive-deps": "warn",
+    "react/display-name": "warn"
   }
-}
+}

web/README.md

@@ -10,7 +10,7 @@ First, install the dependencies:
 ```bash
 npm install
 # or
-yarn
+yarn install --frozen-lockfile
 ```
 
 Then, configure the environment variables. Create a file named `.env.local` in the current directory and copy the contents from `.env.example`. Modify the values of these environment variables according to your requirements:

web/app/(commonLayout)/datasets/DatasetFooter.tsx

@@ -1,6 +1,6 @@
 'use client'
 
-import { useTranslation } from "react-i18next"
+import { useTranslation } from 'react-i18next'
 
 const DatasetFooter = () => {
   const { t } = useTranslation()

web/app/(shareLayout)/completion/[token]/page.tsx

@@ -10,4 +10,4 @@ const TextGeneration: FC<IMainProps> = () => {
   )
 }
 
-export default React.memo(TextGeneration)
+export default React.memo(TextGeneration)

web/app/components/app/chat/loading-anim/index.tsx

@@ -1,13 +1,14 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import s from './style.module.css'
 
-export interface ILoaidingAnimProps {
+export type ILoaidingAnimProps = {
   type: 'text' | 'avatar'
 }
 
 const LoaidingAnim: FC<ILoaidingAnimProps> = ({
-  type
+  type,
 }) => {
   return (
     <div className={`${s['dot-flashing']} ${s[type]}`}></div>

web/app/components/app/chat/mermaid/index.tsx

@@ -23,7 +23,6 @@ const style = {
   overflow: 'auto',
 }
 
-// eslint-disable-next-line react/display-name
 const Flowchart = React.forwardRef((props: {
   PrimitiveCode: string
 }, ref) => {

web/app/components/app/configuration/base/group-name/index.tsx

@@ -1,12 +1,13 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 
-export interface IGroupNameProps {
+export type IGroupNameProps = {
   name: string
 }
 
 const GroupName: FC<IGroupNameProps> = ({
-  name
+  name,
 }) => {
   return (
     <div className='flex items-center mb-1'>

web/app/components/app/configuration/base/icons/more-like-this-icon.tsx

@@ -1,7 +1,8 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 
-const MoreLikeThisIcon: FC = ({ }) => {
+const MoreLikeThisIcon: FC = () => {
   return (
     <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
       <path fillRule="evenodd" clipRule="evenodd" d="M5.83914 0.666748H10.1609C10.6975 0.666741 11.1404 0.666734 11.5012 0.696212C11.8759 0.726829 12.2204 0.792538 12.544 0.957399C13.0457 1.21306 13.4537 1.62101 13.7093 2.12277C13.8742 2.44633 13.9399 2.7908 13.9705 3.16553C14 3.52633 14 3.96923 14 4.50587V7.41171C14 7.62908 14 7.73776 13.9652 7.80784C13.9303 7.87806 13.8939 7.91566 13.8249 7.95288C13.756 7.99003 13.6262 7.99438 13.3665 8.00307C12.8879 8.01909 12.4204 8.14633 11.997 8.36429C10.9478 7.82388 9.62021 7.82912 8.53296 8.73228C7.15064 9.88056 6.92784 11.8645 8.0466 13.2641C8.36602 13.6637 8.91519 14.1949 9.40533 14.6492C9.49781 14.7349 9.54405 14.7777 9.5632 14.8041C9.70784 15.003 9.5994 15.2795 9.35808 15.3271C9.32614 15.3334 9.26453 15.3334 9.14129 15.3334H5.83912C5.30248 15.3334 4.85958 15.3334 4.49878 15.304C4.12405 15.2733 3.77958 15.2076 3.45603 15.0428C2.95426 14.7871 2.54631 14.3792 2.29065 13.8774C2.12579 13.5538 2.06008 13.2094 2.02946 12.8346C1.99999 12.4738 1.99999 12.0309 2 11.4943V4.50587C1.99999 3.96924 1.99999 3.52632 2.02946 3.16553C2.06008 2.7908 2.12579 2.44633 2.29065 2.12277C2.54631 1.62101 2.95426 1.21306 3.45603 0.957399C3.77958 0.792538 4.12405 0.726829 4.49878 0.696212C4.85957 0.666734 5.3025 0.666741 5.83914 0.666748ZM4.66667 5.33342C4.29848 5.33342 4 5.63189 4 6.00008C4 6.36827 4.29848 6.66675 4.66667 6.66675H8.66667C9.03486 6.66675 9.33333 6.36827 9.33333 6.00008C9.33333 5.63189 9.03486 5.33342 8.66667 5.33342H4.66667ZM4 8.66675C4 8.29856 4.29848 8.00008 4.66667 8.00008H6C6.36819 8.00008 6.66667 8.29856 6.66667 8.66675C6.66667 9.03494 6.36819 9.33342 6 9.33342H4.66667C4.29848 9.33342 4 9.03494 4 8.66675ZM4.66667 2.66675C4.29848 2.66675 4 2.96523 4 3.33342C4 3.7016 4.29848 4.00008 4.66667 4.00008H10.6667C11.0349 4.00008 11.3333 3.7016 11.3333 3.33342C11.3333 2.96523 11.0349 2.66675 10.6667 2.66675H4.66667Z" fill="#DD2590" />

web/app/components/app/configuration/base/operation-btn/index.tsx

@@ -1,9 +1,10 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
 import { PlusIcon } from '@heroicons/react/20/solid'
 
-export interface IOperationBtnProps {
+export type IOperationBtnProps = {
   type: 'add' | 'edit'
   actionName?: string
   onClick: () => void
@@ -14,13 +15,13 @@ const iconMap = {
   edit: (<svg width="14" height="14" viewBox="0 0 14 14" fill="none" xmlns="http://www.w3.org/2000/svg">
     <path d="M6.99998 11.6666H12.25M1.75 11.6666H2.72682C3.01217 11.6666 3.15485 11.6666 3.28912 11.6344C3.40816 11.6058 3.52196 11.5587 3.62635 11.4947C3.74408 11.4226 3.84497 11.3217 4.04675 11.1199L11.375 3.79164C11.8583 3.30839 11.8583 2.52488 11.375 2.04164C10.8918 1.55839 10.1083 1.55839 9.62501 2.04164L2.29674 9.3699C2.09496 9.57168 1.99407 9.67257 1.92192 9.7903C1.85795 9.89469 1.81081 10.0085 1.78224 10.1275C1.75 10.2618 1.75 10.4045 1.75 10.6898V11.6666Z" stroke="#344054" strokeWidth="1.25" strokeLinecap="round" strokeLinejoin="round" />
   </svg>
-  )
+  ),
 }
 
 const OperationBtn: FC<IOperationBtnProps> = ({
   type,
   actionName,
-  onClick
+  onClick,
 }) => {
   const { t } = useTranslation()
   return (

web/app/components/app/configuration/base/var-highlight/index.tsx

@@ -1,9 +1,10 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 
 import s from './style.module.css'
 
-export interface IVarHighlightProps {
+export type IVarHighlightProps = {
   name: string
 }
 
@@ -31,6 +32,4 @@ export const varHighlightHTML = ({ name }: IVarHighlightProps) => {
   return html
 }
 
-
-
 export default React.memo(VarHighlight)

web/app/components/app/configuration/base/warning-mask/has-not-set-api.tsx

@@ -1,10 +1,11 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
 import WarningMask from '.'
 import Button from '@/app/components/base/button'
 
-export interface IHasNotSetAPIProps {
+export type IHasNotSetAPIProps = {
   isTrailFinished: boolean
   onSetting: () => void
 }
@@ -18,7 +19,7 @@ const icon = (
 
 const HasNotSetAPI: FC<IHasNotSetAPIProps> = ({
   isTrailFinished,
-  onSetting
+  onSetting,
 }) => {
   const { t } = useTranslation()
 

web/app/components/app/configuration/base/warning-mask/index.tsx

@@ -1,9 +1,10 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 
 import s from './style.module.css'
 
-export interface IWarningMaskProps {
+export type IWarningMaskProps = {
   title: string
   description: string
   footer: React.ReactNode

web/app/components/app/configuration/config-model/index.tsx

@@ -1,423 +0,0 @@
-'use client'
-import type { FC } from 'react'
-import React, { useEffect, useState } from 'react'
-import cn from 'classnames'
-import { useTranslation } from 'react-i18next'
-import { useBoolean, useClickAway, useGetState } from 'ahooks'
-import { InformationCircleIcon } from '@heroicons/react/24/outline'
-import produce from 'immer'
-import ParamItem from './param-item'
-import { SlidersH } from '@/app/components/base/icons/src/vender/line/mediaAndDevices'
-import Radio from '@/app/components/base/radio'
-import Panel from '@/app/components/base/panel'
-import type { CompletionParams } from '@/models/debug'
-import { TONE_LIST } from '@/config'
-import Toast from '@/app/components/base/toast'
-import { AlertTriangle } from '@/app/components/base/icons/src/vender/solid/alertsAndFeedback'
-import { formatNumber } from '@/utils/format'
-import { Brush01 } from '@/app/components/base/icons/src/vender/solid/editor'
-import { Scales02 } from '@/app/components/base/icons/src/vender/solid/FinanceAndECommerce'
-import { Target04 } from '@/app/components/base/icons/src/vender/solid/general'
-import { Sliders02 } from '@/app/components/base/icons/src/vender/solid/mediaAndDevices'
-import { fetchModelParams } from '@/service/debug'
-import Loading from '@/app/components/base/loading'
-import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints'
-import type { ModelModeType } from '@/types/app'
-import ModelIcon from '@/app/components/header/account-setting/model-provider-page/model-icon'
-import ModelName from '@/app/components/header/account-setting/model-provider-page/model-name'
-import ModelSelector from '@/app/components/header/account-setting/model-provider-page/model-selector'
-import { useTextGenerationCurrentProviderAndModelAndModelList } from '@/app/components/header/account-setting/model-provider-page/hooks'
-
-export type IConfigModelProps = {
-  isAdvancedMode: boolean
-  mode: string
-  modelId: string
-  provider: string
-  setModel: (model: { id: string; provider: string; mode: ModelModeType; features: string[] }) => void
-  completionParams: CompletionParams
-  onCompletionParamsChange: (newParams: CompletionParams) => void
-  disabled: boolean
-}
-
-const ConfigModel: FC<IConfigModelProps> = ({
-  isAdvancedMode,
-  modelId,
-  provider,
-  setModel,
-  completionParams,
-  onCompletionParamsChange,
-  disabled,
-}) => {
-  const { t } = useTranslation()
-  const [isShowConfig, { setFalse: hideConfig, toggle: toogleShowConfig }] = useBoolean(false)
-  const [maxTokenSettingTipVisible, setMaxTokenSettingTipVisible] = useState(false)
-  const configContentRef = React.useRef(null)
-  const {
-    currentProvider,
-    currentModel: currModel,
-    textGenerationModelList,
-  } = useTextGenerationCurrentProviderAndModelAndModelList(
-    { provider, model: modelId },
-  )
-
-  const media = useBreakpoints()
-  const isMobile = media === MediaType.mobile
-
-  // Cache loaded model param
-  const [allParams, setAllParams, getAllParams] = useGetState<Record<string, Record<string, any>>>({})
-  const currParams = allParams[provider]?.[modelId]
-  const hasEnableParams = currParams && Object.keys(currParams).some(key => currParams[key].enabled)
-  const allSupportParams = ['temperature', 'top_p', 'presence_penalty', 'frequency_penalty', 'max_tokens']
-  const currSupportParams = currParams ? allSupportParams.filter(key => currParams[key].enabled) : allSupportParams
-  if (isAdvancedMode)
-    currSupportParams.push('stop')
-
-  useEffect(() => {
-    (async () => {
-      if (!allParams[provider]?.[modelId]) {
-        const res = await fetchModelParams(provider, modelId)
-        const newAllParams = produce(allParams, (draft) => {
-          if (!draft[provider])
-            draft[provider] = {}
-
-          draft[provider][modelId] = res
-        })
-        setAllParams(newAllParams)
-      }
-    })()
-  }, [provider, modelId, allParams, setAllParams])
-
-  useClickAway(() => {
-    hideConfig()
-  }, configContentRef)
-
-  const selectedModel = { name: modelId } // options.find(option => option.id === modelId)
-
-  const ensureModelParamLoaded = (provider: string, modelId: string) => {
-    return new Promise<void>((resolve) => {
-      if (getAllParams()[provider]?.[modelId]) {
-        resolve()
-        return
-      }
-      const runId = setInterval(() => {
-        if (getAllParams()[provider]?.[modelId]) {
-          resolve()
-          clearInterval(runId)
-        }
-      }, 500)
-    })
-  }
-
-  const transformValue = (value: number, fromRange: [number, number], toRange: [number, number]): number => {
-    const [fromStart = 0, fromEnd] = fromRange
-    const [toStart = 0, toEnd] = toRange
-
-    // The following three if is to avoid precision loss
-    if (fromStart === toStart && fromEnd === toEnd)
-      return value
-
-    if (value <= fromStart)
-      return toStart
-
-    if (value >= fromEnd)
-      return toEnd
-
-    const fromLength = fromEnd - fromStart
-    const toLength = toEnd - toStart
-
-    let adjustedValue = (value - fromStart) * (toLength / fromLength) + toStart
-    adjustedValue = parseFloat(adjustedValue.toFixed(2))
-    return adjustedValue
-  }
-
-  const handleSelectModel = ({ id, provider: nextProvider, mode, features }: { id: string; provider: string; mode: ModelModeType; features: string[] }) => {
-    return async () => {
-      const prevParamsRule = getAllParams()[provider]?.[modelId]
-
-      setModel({
-        id,
-        provider: nextProvider || 'openai',
-        mode,
-        features,
-      })
-
-      await ensureModelParamLoaded(nextProvider, id)
-
-      const nextParamsRule = getAllParams()[nextProvider]?.[id]
-      // debugger
-      const nextSelectModelMaxToken = nextParamsRule.max_tokens.max
-      const newConCompletionParams = produce(completionParams, (draft: any) => {
-        if (nextParamsRule.max_tokens.enabled) {
-          if (completionParams.max_tokens > nextSelectModelMaxToken) {
-            Toast.notify({
-              type: 'warning',
-              message: t('common.model.params.setToCurrentModelMaxTokenTip', { maxToken: formatNumber(nextSelectModelMaxToken) }),
-            })
-            draft.max_tokens = parseFloat((nextSelectModelMaxToken * 0.8).toFixed(2))
-          }
-          // prev don't have max token
-          if (!completionParams.max_tokens)
-            draft.max_tokens = nextParamsRule.max_tokens.default
-        }
-        else {
-          delete draft.max_tokens
-        }
-
-        allSupportParams.forEach((key) => {
-          if (key === 'max_tokens')
-            return
-
-          if (!nextParamsRule[key].enabled) {
-            delete draft[key]
-            return
-          }
-
-          if (draft[key] === undefined) {
-            draft[key] = nextParamsRule[key].default || 0
-            return
-          }
-
-          if (!prevParamsRule[key].enabled) {
-            draft[key] = nextParamsRule[key].default || 0
-            return
-          }
-
-          draft[key] = transformValue(
-            draft[key],
-            [prevParamsRule[key].min, prevParamsRule[key].max],
-            [nextParamsRule[key].min, nextParamsRule[key].max],
-          )
-        })
-      })
-      onCompletionParamsChange(newConCompletionParams)
-    }
-  }
-
-  // only openai support this
-  function matchToneId(completionParams: CompletionParams): number {
-    const remvoedCustomeTone = TONE_LIST.slice(0, -1)
-    const CUSTOM_TONE_ID = 4
-    const tone = remvoedCustomeTone.find((tone) => {
-      return tone.config?.temperature === completionParams.temperature
-        && tone.config?.top_p === completionParams.top_p
-        && tone.config?.presence_penalty === completionParams.presence_penalty
-        && tone.config?.frequency_penalty === completionParams.frequency_penalty
-    })
-    return tone ? tone.id : CUSTOM_TONE_ID
-  }
-
-  // tone is a preset of completionParams.
-  const [toneId, setToneId] = React.useState(matchToneId(completionParams)) // default is Balanced
-  const toneTabBgClassName = ({
-    1: 'bg-[#F5F8FF]',
-    2: 'bg-[#F4F3FF]',
-    3: 'bg-[#F6FEFC]',
-  })[toneId] || ''
-  // set completionParams by toneId
-  const handleToneChange = (id: number) => {
-    if (id === 4)
-      return // custom tone
-    const tone = TONE_LIST.find(tone => tone.id === id)
-    if (tone) {
-      setToneId(id)
-      onCompletionParamsChange({
-        ...tone.config,
-        max_tokens: completionParams.max_tokens,
-      } as CompletionParams)
-    }
-  }
-
-  useEffect(() => {
-    setToneId(matchToneId(completionParams))
-  }, [completionParams])
-
-  const handleParamChange = (key: string, value: number | string[]) => {
-    if (value === undefined)
-      return
-    if ((completionParams as any)[key] === value)
-      return
-
-    if (key === 'stop') {
-      onCompletionParamsChange({
-        ...completionParams,
-        [key]: value as string[],
-      })
-    }
-    else {
-      const currParamsRule = getAllParams()[provider]?.[modelId]
-      let notOutRangeValue = parseFloat((value as number).toFixed(2))
-      notOutRangeValue = Math.max(currParamsRule[key].min, notOutRangeValue)
-      notOutRangeValue = Math.min(currParamsRule[key].max, notOutRangeValue)
-      onCompletionParamsChange({
-        ...completionParams,
-        [key]: notOutRangeValue,
-      })
-    }
-  }
-  const ableStyle = 'bg-indigo-25 border-[#2A87F5] cursor-pointer'
-  const diabledStyle = 'bg-[#FFFCF5] border-[#F79009]'
-
-  const getToneIcon = (toneId: number) => {
-    const className = 'w-[14px] h-[14px]'
-    const res = ({
-      1: <Brush01 className={className} />,
-      2: <Scales02 className={className} />,
-      3: <Target04 className={className} />,
-      4: <Sliders02 className={className} />,
-    })[toneId]
-    return res
-  }
-  useEffect(() => {
-    if (!currParams)
-      return
-
-    const max = currParams.max_tokens.max
-    const isSupportMaxToken = currParams.max_tokens.enabled
-    if (isSupportMaxToken && currentProvider?.provider !== 'anthropic' && completionParams.max_tokens > max * 2 / 3)
-      setMaxTokenSettingTipVisible(true)
-    else
-      setMaxTokenSettingTipVisible(false)
-  }, [currParams, completionParams.max_tokens, setMaxTokenSettingTipVisible, currentProvider])
-  return (
-    <div className='relative' ref={configContentRef}>
-      <div
-        className={cn('flex items-center border h-8 px-2 space-x-2 rounded-lg', disabled ? diabledStyle : ableStyle)}
-        onClick={() => !disabled && toogleShowConfig()}
-      >
-        {
-          currentProvider && (
-            <ModelIcon
-              className='!w-5 !h-5'
-              provider={currentProvider}
-            />
-          )
-        }
-        {
-          currModel && (
-            <ModelName
-              className='text-gray-900'
-              modelItem={currModel}
-              showMode={isAdvancedMode}
-            />
-          )
-        }
-        {disabled ? <InformationCircleIcon className='w-4 h-4 text-[#F79009]' /> : <SlidersH className='w-4 h-4 text-indigo-600' />}
-      </div>
-      {isShowConfig && (
-        <Panel
-          className='absolute z-20 top-8 left-0 sm:left-[unset] sm:right-0 !w-fit sm:!w-[496px] bg-white !overflow-visible shadow-md'
-          keepUnFold
-          headerIcon={
-            <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
-              <path d="M8.26865 0.790031C8.09143 0.753584 7.90866 0.753584 7.73144 0.790031C7.52659 0.832162 7.3435 0.934713 7.19794 1.01624L7.15826 1.03841L6.17628 1.58395C5.85443 1.76276 5.73846 2.16863 5.91727 2.49049C6.09608 2.81234 6.50195 2.9283 6.82381 2.74949L7.80579 2.20395C7.90681 2.14782 7.95839 2.11946 7.99686 2.10091L8.00004 2.09938L8.00323 2.10091C8.0417 2.11946 8.09327 2.14782 8.1943 2.20395L9.17628 2.74949C9.49814 2.9283 9.90401 2.81234 10.0828 2.49048C10.2616 2.16863 10.1457 1.76276 9.82381 1.58395L8.84183 1.03841L8.80215 1.01624C8.65659 0.934713 8.4735 0.832162 8.26865 0.790031Z" fill="#1C64F2" />
-              <path d="M12.8238 3.25062C12.5019 3.07181 12.0961 3.18777 11.9173 3.50963C11.7385 3.83148 11.8544 4.23735 12.1763 4.41616L12.6272 4.66668L12.1763 4.91719C11.8545 5.096 11.7385 5.50186 11.9173 5.82372C12.0961 6.14558 12.502 6.26154 12.8238 6.08273L13.3334 5.79966V6.33339C13.3334 6.70158 13.6319 7.00006 14 7.00006C14.3682 7.00006 14.6667 6.70158 14.6667 6.33339V5.29435L14.6668 5.24627C14.6673 5.12441 14.6678 4.98084 14.6452 4.83482C14.6869 4.67472 14.6696 4.49892 14.5829 4.34286C14.4904 4.1764 14.3371 4.06501 14.1662 4.02099C14.0496 3.93038 13.9239 3.86116 13.8173 3.8024L13.7752 3.77915L12.8238 3.25062Z" fill="#1C64F2" />
-              <path d="M3.8238 4.41616C4.14566 4.23735 4.26162 3.83148 4.08281 3.50963C3.90401 3.18777 3.49814 3.07181 3.17628 3.25062L2.22493 3.77915L2.18284 3.8024C2.07615 3.86116 1.95045 3.9304 1.83382 4.02102C1.66295 4.06506 1.50977 4.17643 1.41731 4.34286C1.33065 4.49886 1.31323 4.67459 1.35493 4.83464C1.33229 4.98072 1.33281 5.12436 1.33326 5.24627L1.33338 5.29435V6.33339C1.33338 6.70158 1.63185 7.00006 2.00004 7.00006C2.36823 7.00006 2.66671 6.70158 2.66671 6.33339V5.79961L3.17632 6.08273C3.49817 6.26154 3.90404 6.14558 4.08285 5.82372C4.26166 5.50186 4.1457 5.096 3.82384 4.91719L3.3729 4.66666L3.8238 4.41616Z" fill="#1C64F2" />
-              <path d="M2.66671 9.66672C2.66671 9.29853 2.36823 9.00006 2.00004 9.00006C1.63185 9.00006 1.33338 9.29853 1.33338 9.66672V10.7058L1.33326 10.7538C1.33262 10.9298 1.33181 11.1509 1.40069 11.3594C1.46024 11.5397 1.55759 11.7051 1.68622 11.8447C1.835 12.0061 2.02873 12.1128 2.18281 12.1977L2.22493 12.221L3.17628 12.7495C3.49814 12.9283 3.90401 12.8123 4.08281 12.4905C4.26162 12.1686 4.14566 11.7628 3.8238 11.584L2.87245 11.0554C2.76582 10.9962 2.71137 10.9656 2.67318 10.9413L2.66995 10.9392L2.66971 10.9354C2.66699 10.8902 2.66671 10.8277 2.66671 10.7058V9.66672Z" fill="#1C64F2" />
-              <path d="M14.6667 9.66672C14.6667 9.29853 14.3682 9.00006 14 9.00006C13.6319 9.00006 13.3334 9.29853 13.3334 9.66672V10.7058C13.3334 10.8277 13.3331 10.8902 13.3304 10.9354L13.3301 10.9392L13.3269 10.9413C13.2887 10.9656 13.2343 10.9962 13.1276 11.0554L12.1763 11.584C11.8544 11.7628 11.7385 12.1686 11.9173 12.4905C12.0961 12.8123 12.5019 12.9283 12.8238 12.7495L13.7752 12.221L13.8172 12.1977C13.9713 12.1128 14.1651 12.0061 14.3139 11.8447C14.4425 11.7051 14.5398 11.5397 14.5994 11.3594C14.6683 11.1509 14.6675 10.9298 14.6668 10.7538L14.6667 10.7058V9.66672Z" fill="#1C64F2" />
-              <path d="M6.82381 13.2506C6.50195 13.0718 6.09608 13.1878 5.91727 13.5096C5.73846 13.8315 5.85443 14.2374 6.17628 14.4162L7.15826 14.9617L7.19793 14.9839C7.29819 15.04 7.41625 15.1061 7.54696 15.1556C7.66589 15.2659 7.82512 15.3333 8.00008 15.3333C8.17507 15.3333 8.33431 15.2659 8.45324 15.1556C8.58391 15.1061 8.70193 15.04 8.80215 14.9839L8.84183 14.9617L9.82381 14.4162C10.1457 14.2374 10.2616 13.8315 10.0828 13.5096C9.90401 13.1878 9.49814 13.0718 9.17628 13.2506L8.66675 13.5337V13C8.66675 12.6318 8.36827 12.3333 8.00008 12.3333C7.63189 12.3333 7.33341 12.6318 7.33341 13V13.5337L6.82381 13.2506Z" fill="#1C64F2" />
-              <path d="M6.82384 6.58385C6.50199 6.40505 6.09612 6.52101 5.91731 6.84286C5.7385 7.16472 5.85446 7.57059 6.17632 7.7494L7.33341 8.39223V9.66663C7.33341 10.0348 7.63189 10.3333 8.00008 10.3333C8.36827 10.3333 8.66675 10.0348 8.66675 9.66663V8.39223L9.82384 7.7494C10.1457 7.57059 10.2617 7.16472 10.0829 6.84286C9.90404 6.52101 9.49817 6.40505 9.17632 6.58385L8.00008 7.23732L6.82384 6.58385Z" fill="#1C64F2" />
-            </svg>
-          }
-          title={t('appDebug.modelConfig.title')}
-        >
-          <div className='py-3 pl-10 pr-6 text-sm'>
-            <div className="flex items-center justify-between my-5 h-9">
-              <div>{t('appDebug.modelConfig.model')}</div>
-              <ModelSelector
-                defaultModel={{ model: modelId, provider }}
-                modelList={textGenerationModelList}
-                onSelect={({ provider, model }) => {
-                  const targetProvider = textGenerationModelList.find(modelItem => modelItem.provider === provider)
-                  const targetModelItem = targetProvider?.models.find(modelItem => modelItem.model === model)
-                  handleSelectModel({
-                    id: model,
-                    provider,
-                    mode: targetModelItem?.model_properties.mode as ModelModeType,
-                    features: targetModelItem?.features || [],
-                  })()
-                }}
-              />
-            </div>
-            {hasEnableParams && (
-              <div className="border-b border-gray-100"></div>
-            )}
-
-            {/* Tone type */}
-            {['openai', 'azure_openai'].includes(provider) && (
-              <div className="mt-5 mb-4">
-                <div className="mb-3 text-sm text-gray-900">{t('appDebug.modelConfig.setTone')}</div>
-                <Radio.Group className={cn('!rounded-lg', toneTabBgClassName)} value={toneId} onChange={handleToneChange}>
-                  <>
-                    {TONE_LIST.slice(0, 3).map(tone => (
-                      <div className='grow flex items-center' key={tone.id}>
-                        <Radio
-                          value={tone.id}
-                          className={cn(tone.id === toneId && 'rounded-md border border-gray-200 shadow-md', '!mr-0 grow !px-1 sm:!px-2 !justify-center text-[13px] font-medium')}
-                          labelClassName={cn(tone.id === toneId
-                            ? ({
-                              1: 'text-[#6938EF]',
-                              2: 'text-[#444CE7]',
-                              3: 'text-[#107569]',
-                            })[toneId]
-                            : 'text-[#667085]', 'flex items-center space-x-2')}
-                        >
-                          <>
-                            {getToneIcon(tone.id)}
-                            {!isMobile && <div>{t(`common.model.tone.${tone.name}`) as string}</div>}
-                            <div className=""></div>
-                          </>
-                        </Radio>
-                        {tone.id !== toneId && tone.id + 1 !== toneId && (<div className='h-5 border-r border-gray-200'></div>)}
-                      </div>
-                    ))}
-                  </>
-                  <Radio
-                    value={TONE_LIST[3].id}
-                    className={cn(toneId === 4 && 'rounded-md border border-gray-200 shadow-md', '!mr-0 grow !px-1 sm:!px-2 !justify-center text-[13px] font-medium')}
-                    labelClassName={cn('flex items-center space-x-2 ', toneId === 4 ? 'text-[#155EEF]' : 'text-[#667085]')}
-                  >
-                    <>
-                      {getToneIcon(TONE_LIST[3].id)}
-                      {!isMobile && <div>{t(`common.model.tone.${TONE_LIST[3].name}`) as string}</div>}
-                    </>
-                  </Radio>
-                </Radio.Group>
-              </div>
-            )}
-
-            {/* Params */}
-            <div className={cn(hasEnableParams && 'mt-4', 'space-y-4', !allParams[provider]?.[modelId] && 'flex items-center min-h-[200px]')}>
-              {(allParams[provider]?.[modelId])
-                ? (
-                  currSupportParams.map(key => (<ParamItem
-                    key={key}
-                    id={key}
-                    name={t(`common.model.params.${key === 'stop' ? 'stop_sequences' : key}`)}
-                    tip={t(`common.model.params.${key === 'stop' ? 'stop_sequences' : key}Tip`)}
-                    {...currParams[key] as any}
-                    value={(completionParams as any)[key] as any}
-                    onChange={handleParamChange}
-                    inputType={key === 'stop' ? 'inputTag' : 'slider'}
-                  />))
-                )
-                : (
-                  <Loading type='area' />
-                )}
-            </div>
-          </div>
-          {
-            maxTokenSettingTipVisible && (
-              <div className='flex py-2 pr-4 pl-5 rounded-bl-xl rounded-br-xl bg-[#FFFAEB] border-t border-[#FEF0C7]'>
-                <AlertTriangle className='shrink-0 mr-2 mt-[3px] w-3 h-3 text-[#F79009]' />
-                <div className='mr-2 text-xs font-medium text-gray-700'>{t('common.model.params.maxTokenSettingTip')}</div>
-              </div>
-            )
-          }
-        </Panel>
-      )}
-    </div>
-
-  )
-}
-
-export default React.memo(ConfigModel)

web/app/components/app/configuration/config-model/model-mode-type-label.tsx

@@ -1,29 +0,0 @@
-'use client'
-import type { FC } from 'react'
-import React from 'react'
-import { useTranslation } from 'react-i18next'
-import cn from 'classnames'
-import type { ModelModeType } from '@/types/app'
-
-type Props = {
-  className?: string
-  type: ModelModeType
-  isHighlight?: boolean
-}
-
-const ModelModeTypeLabel: FC<Props> = ({
-  className,
-  type,
-  isHighlight,
-}) => {
-  const { t } = useTranslation()
-
-  return (
-    <div
-      className={cn(className, isHighlight ? 'border-indigo-300 text-indigo-600' : 'border-gray-300 text-gray-500', 'flex items-center h-4 px-1 border rounded text-xs font-semibold uppercase text-ellipsis overflow-hidden whitespace-nowrap')}
-    >
-      {t(`appDebug.modelConfig.modeType.${type}`)}
-    </div>
-  )
-}
-export default React.memo(ModelModeTypeLabel)

web/app/components/app/configuration/config-model/model-name.tsx

@@ -1,26 +0,0 @@
-'use client'
-import type { FC } from 'react'
-import React from 'react'
-
-export type IModelNameProps = {
-  modelId: string
-  modelDisplayName?: string
-}
-
-export const supportI18nModelName = [
-  'gpt-3.5-turbo', 'gpt-3.5-turbo-16k',
-  'gpt-4', 'gpt-4-32k',
-  'text-davinci-003', 'text-embedding-ada-002', 'whisper-1',
-  'claude-instant-1', 'claude-2',
-]
-
-const ModelName: FC<IModelNameProps> = ({
-  modelDisplayName,
-}) => {
-  return (
-    <span className='text-ellipsis overflow-hidden whitespace-nowrap' title={modelDisplayName}>
-      {modelDisplayName}
-    </span>
-  )
-}
-export default React.memo(ModelName)

web/app/components/app/configuration/config-model/param-item.tsx

@@ -1,95 +0,0 @@
-'use client'
-import type { FC } from 'react'
-import React, { useEffect } from 'react'
-import { useTranslation } from 'react-i18next'
-import Tooltip from '@/app/components/base/tooltip'
-import Slider from '@/app/components/base/slider'
-import TagInput from '@/app/components/base/tag-input'
-
-export const getFitPrecisionValue = (num: number, precision: number | null) => {
-  if (!precision || !(`${num}`).includes('.'))
-    return num
-
-  const currNumPrecision = (`${num}`).split('.')[1].length
-  if (currNumPrecision > precision)
-    return parseFloat(num.toFixed(precision))
-
-  return num
-}
-
-export type IParamIteProps = {
-  id: string
-  name: string
-  tip: string
-  value: number | string[]
-  step?: number
-  min?: number
-  max: number
-  precision: number | null
-  onChange: (key: string, value: number | string[]) => void
-  inputType?: 'inputTag' | 'slider'
-}
-
-const TIMES_TEMPLATE = '1000000000000'
-const ParamItem: FC<IParamIteProps> = ({ id, name, tip, step = 0.1, min = 0, max, precision, value, inputType, onChange }) => {
-  const { t } = useTranslation()
-
-  const getToIntTimes = (num: number) => {
-    if (precision)
-      return parseInt(TIMES_TEMPLATE.slice(0, precision + 1), 10)
-    if (num < 5)
-      return 10
-    return 1
-  }
-
-  const times = getToIntTimes(max)
-
-  useEffect(() => {
-    if (precision)
-      onChange(id, getFitPrecisionValue(value, precision))
-  }, [value, precision])
-  return (
-    <div className="flex items-center justify-between flex-wrap gap-y-2">
-      <div className="flex flex-col flex-shrink-0">
-        <div className="flex items-center">
-          <span className="mr-[6px] text-gray-500 text-[13px] font-medium">{name}</span>
-          {/* Give tooltip different tip to avoiding hide bug */}
-          <Tooltip htmlContent={<div className="w-[200px] whitespace-pre-wrap">{tip}</div>} position='top' selector={`param-name-tooltip-${id}`}>
-            <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
-              <path d="M8.66667 10.6667H8V8H7.33333M8 5.33333H8.00667M14 8C14 8.78793 13.8448 9.56815 13.5433 10.2961C13.2417 11.0241 12.7998 11.6855 12.2426 12.2426C11.6855 12.7998 11.0241 13.2417 10.2961 13.5433C9.56815 13.8448 8.78793 14 8 14C7.21207 14 6.43185 13.8448 5.7039 13.5433C4.97595 13.2417 4.31451 12.7998 3.75736 12.2426C3.20021 11.6855 2.75825 11.0241 2.45672 10.2961C2.15519 9.56815 2 8.78793 2 8C2 6.4087 2.63214 4.88258 3.75736 3.75736C4.88258 2.63214 6.4087 2 8 2C9.5913 2 11.1174 2.63214 12.2426 3.75736C13.3679 4.88258 14 6.4087 14 8Z" stroke="#9CA3AF" strokeWidth="1.5" strokeLinecap="round" strokeLinejoin="round" />
-            </svg>
-          </Tooltip>
-        </div>
-        {inputType === 'inputTag' && <div className="text-gray-400 text-xs font-normal">{t('common.model.params.stop_sequencesPlaceholder')}</div>}
-      </div>
-      <div className="flex items-center">
-        {inputType === 'inputTag'
-          ? <TagInput
-            items={(value ?? []) as string[]}
-            onChange={newSequences => onChange(id, newSequences)}
-            customizedConfirmKey='Tab'
-          />
-          : (
-            <>
-              <div className="mr-4 w-[120px]">
-                <Slider value={value * times} min={min * times} max={max * times} onChange={(value) => {
-                  onChange(id, value / times)
-                }} />
-              </div>
-              <input type="number" min={min} max={max} step={step} className="block w-[64px] h-9 leading-9 rounded-lg border-0 pl-1 pl py-1.5 bg-gray-50 text-gray-900  placeholder:text-gray-400 focus:ring-1 focus:ring-inset focus:ring-primary-600" value={value} onChange={(e) => {
-                let value = getFitPrecisionValue(isNaN(parseFloat(e.target.value)) ? min : parseFloat(e.target.value), precision)
-                if (value < min)
-                  value = min
-
-                if (value > max)
-                  value = max
-                onChange(id, value)
-              }} />
-            </>
-          )
-        }
-      </div>
-    </div>
-  )
-}
-export default React.memo(ParamItem)

web/app/components/app/configuration/config-model/provider-name.tsx

@@ -1,24 +0,0 @@
-'use client'
-import type { FC } from 'react'
-import React from 'react'
-import { useContext } from 'use-context-selector'
-import I18n from '@/context/i18n'
-import type { ProviderEnum } from '@/app/components/header/account-setting/model-page/declarations'
-import ProviderConfig from '@/app/components/header/account-setting/model-page/configs'
-
-export type IProviderNameProps = {
-  provideName: ProviderEnum
-}
-
-const ProviderName: FC<IProviderNameProps> = ({
-  provideName,
-}) => {
-  const { locale } = useContext(I18n)
-
-  return (
-    <span>
-      {ProviderConfig[provideName]?.selector?.name[locale]}
-    </span>
-  )
-}
-export default React.memo(ProviderName)

web/app/components/app/configuration/config-var/modal-foot.tsx

@@ -1,16 +1,17 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
 import Button from '@/app/components/base/button'
 
-export interface IModalFootProps {
+export type IModalFootProps = {
   onConfirm: () => void
   onCancel: () => void
 }
 
 const ModalFoot: FC<IModalFootProps> = ({
   onConfirm,
-  onCancel
+  onCancel,
 }) => {
   const { t } = useTranslation()
   return (

web/app/components/app/configuration/config/feature/feature-group/index.tsx

@@ -1,8 +1,9 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import GroupName from '@/app/components/app/configuration/base/group-name'
 
-export interface IFeatureGroupProps {
+export type IFeatureGroupProps = {
   title: string
   description?: string
   children: React.ReactNode
@@ -11,7 +12,7 @@ export interface IFeatureGroupProps {
 const FeatureGroup: FC<IFeatureGroupProps> = ({
   title,
   description,
-  children
+  children,
 }) => {
   return (
     <div className='mb-6'>

web/app/components/app/configuration/dataset-config/type-icon/index.tsx

@@ -1,14 +1,15 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 
-export interface ITypeIconProps {
+export type ITypeIconProps = {
   type: 'upload_file'
   size?: 'md' | 'lg'
 }
 
 // data_source_type: current only support upload_file
-const Icon = ({ type, size = "lg" }: ITypeIconProps) => {
-  const len = size === "lg" ? 32 : 24
+const Icon = ({ type, size = 'lg' }: ITypeIconProps) => {
+  const len = size === 'lg' ? 32 : 24
   const iconMap = {
     upload_file: (
       <svg width={len} height={len} viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
@@ -16,10 +17,9 @@ const Icon = ({ type, size = "lg" }: ITypeIconProps) => {
         <path fillRule="evenodd" clipRule="evenodd" d="M8.66669 12.1078C8.66668 11.7564 8.66667 11.4532 8.68707 11.2035C8.7086 10.9399 8.75615 10.6778 8.88468 10.4255C9.07642 10.0492 9.38238 9.74322 9.75871 9.55147C10.011 9.42294 10.2731 9.3754 10.5367 9.35387C10.7864 9.33346 11.0896 9.33347 11.441 9.33349L14.0978 9.33341C14.4935 9.33289 14.8415 9.33243 15.1615 9.4428C15.4417 9.53946 15.697 9.69722 15.9087 9.90465C16.1506 10.1415 16.3058 10.4529 16.4823 10.8071L17.0786 12H19.4942C20.0309 12 20.4738 12 20.8346 12.0295C21.2093 12.0601 21.5538 12.1258 21.8773 12.2907C22.3791 12.5463 22.787 12.9543 23.0427 13.456C23.2076 13.7796 23.2733 14.1241 23.3039 14.4988C23.3334 14.8596 23.3334 15.3025 23.3334 15.8391V18.8276C23.3334 19.3642 23.3334 19.8071 23.3039 20.1679C23.2733 20.5426 23.2076 20.8871 23.0427 21.2107C22.787 21.7124 22.3791 22.1204 21.8773 22.376C21.5538 22.5409 21.2093 22.6066 20.8346 22.6372C20.4738 22.6667 20.0309 22.6667 19.4942 22.6667H12.5058C11.9692 22.6667 11.5263 22.6667 11.1655 22.6372C10.7907 22.6066 10.4463 22.5409 10.1227 22.376C9.62095 22.1204 9.213 21.7124 8.95734 21.2107C8.79248 20.8871 8.72677 20.5426 8.69615 20.1679C8.66667 19.8071 8.66668 19.3642 8.66669 18.8276V12.1078ZM14.0149 10.6668C14.5418 10.6668 14.6463 10.6755 14.7267 10.7033C14.8201 10.7355 14.9052 10.7881 14.9758 10.8572C15.0366 10.9167 15.0911 11.0063 15.3267 11.4776L15.5879 12L10.0001 12C10.0004 11.69 10.0024 11.4781 10.016 11.312C10.0308 11.1309 10.0559 11.0638 10.0727 11.0308C10.1366 10.9054 10.2386 10.8034 10.364 10.7395C10.397 10.7227 10.4641 10.6976 10.6452 10.6828C10.8341 10.6673 11.0823 10.6668 11.4667 10.6668H14.0149Z" fill="#444CE7" />
         <rect x="0.25" y="0.25" width="31.5" height="31.5" rx="7.75" stroke="#E0EAFF" strokeWidth="0.5" />
       </svg>
-    )
+    ),
   }
   return iconMap[type]
-
 }
 
 const TypeIcon: FC<ITypeIconProps> = ({

web/app/components/app/configuration/features/experience-enchance-group/index.tsx

@@ -1,11 +1,12 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
 import GroupName from '../../base/group-name'
 import MoreLikeThis from './more-like-this'
 
 /*
-* Include 
+* Include
 * 1. More like this
 */
 const ExperienceEnchanceGroup: FC = () => {

web/app/components/app/configuration/features/experience-enchance-group/more-like-this/index.tsx

@@ -1,10 +1,11 @@
 'use client'
-import React, { FC } from 'react'
-import Panel from '@/app/components/app/configuration/base/feature-panel'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
-import MoreLikeThisIcon from '../../../base/icons/more-like-this-icon'
 import { XMarkIcon } from '@heroicons/react/24/outline'
 import { useLocalStorageState } from 'ahooks'
+import MoreLikeThisIcon from '../../../base/icons/more-like-this-icon'
+import Panel from '@/app/components/app/configuration/base/feature-panel'
 
 const GENERATE_NUM = 1
 

web/app/components/app/configuration/index.tsx

@@ -28,7 +28,6 @@ import type { ExternalDataTool } from '@/models/common'
 import type { DataSet } from '@/models/datasets'
 import type { ModelConfig as BackendModelConfig, VisionSettings } from '@/types/app'
 import ConfigContext from '@/context/debug-configuration'
-// import ConfigModel from '@/app/components/app/configuration/config-model'
 import Config from '@/app/components/app/configuration/config'
 import Debug from '@/app/components/app/configuration/debug'
 import Confirm from '@/app/components/base/confirm'

web/app/components/app/text-generate/index.tsx

@@ -1,7 +1,7 @@
 'use client'
 import type { FC } from 'react'
-import { format } from '@/service/base'
 import React from 'react'
+import { format } from '@/service/base'
 
 export type ITextGenerationProps = {
   value: string
@@ -16,7 +16,7 @@ const TextGeneration: FC<ITextGenerationProps> = ({
     <div
       className={className}
       dangerouslySetInnerHTML={{
-        __html: format(value)
+        __html: format(value),
       }}
     >
     </div>

web/app/components/base/app-unavailable.tsx

@@ -1,8 +1,9 @@
 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
 
-interface IAppUnavailableProps {
+type IAppUnavailableProps = {
   code?: number
   isUnknwonReason?: boolean
   unknownReason?: string

web/app/components/base/panel/index.tsx

@@ -1,11 +1,11 @@
 'use client'
-import React, { FC, useEffect } from 'react'
+import type { FC } from 'react'
+import React, { useEffect } from 'react'
 import cn from 'classnames'
 import { useBoolean } from 'ahooks'
 import { ChevronRightIcon } from '@heroicons/react/24/outline'
 
-
-export interface IPanelProps {
+export type IPanelProps = {
   className?: string
   headerIcon: React.ReactNode
   title: React.ReactNode
@@ -30,23 +30,21 @@ const Panel: FC<IPanelProps> = ({
   foldDisabled = false,
   onFoldChange,
   controlUnFold,
-  controlFold
+  controlFold,
 }) => {
-  const [fold, { setTrue: setFold, setFalse: setUnFold, toggle: toggleFold }] = useBoolean(keepUnFold ? false : true)
+  const [fold, { setTrue: setFold, setFalse: setUnFold, toggle: toggleFold }] = useBoolean(!keepUnFold)
   useEffect(() => {
     onFoldChange?.(fold)
   }, [fold])
 
   useEffect(() => {
-    if (controlUnFold) {
+    if (controlUnFold)
       setUnFold()
-    }
   }, [controlUnFold])
 
   useEffect(() => {
-    if (controlFold) {
+    if (controlFold)
       setFold()
-    }
   }, [controlFold])
 
   // overflow-hidden

web/app/components/datasets/create/empty-dataset-creation-modal/index.tsx

@@ -3,6 +3,8 @@ import React, { useState } from 'react'
 import { useRouter } from 'next/navigation'
 import { useTranslation } from 'react-i18next'
 import { useContext } from 'use-context-selector'
+import cn from 'classnames'
+import s from './index.module.css'
 import Modal from '@/app/components/base/modal'
 import Input from '@/app/components/base/input'
 import Button from '@/app/components/base/button'
@@ -10,12 +12,9 @@ import Button from '@/app/components/base/button'
 import { ToastContext } from '@/app/components/base/toast'
 import { createEmptyDataset } from '@/service/datasets'
 
-import cn from 'classnames'
-import s from './index.module.css'
-
 type IProps = {
-  show: boolean,
-  onHide: () => void,
+  show: boolean
+  onHide: () => void
 }
 
 const EmptyDatasetCreationModal = ({
@@ -27,7 +26,7 @@ const EmptyDatasetCreationModal = ({
   const { notify } = useContext(ToastContext)
   const router = useRouter()
 
-  const submit =  async () => {
+  const submit = async () => {
     if (!inputValue) {
       notify({ type: 'error', message: t('datasetCreation.stepOne.modal.nameNotEmpty') })
       return
@@ -43,7 +42,6 @@ const EmptyDatasetCreationModal = ({
     }
     catch (err) {
       notify({ type: 'error', message: t('datasetCreation.stepOne.modal.failed') })
-      return
     }
   }
 

web/app/components/datasets/create/stop-embedding-modal/index.tsx

@@ -1,16 +1,15 @@
 'use client'
 import React from 'react'
 import { useTranslation } from 'react-i18next'
+import cn from 'classnames'
+import s from './index.module.css'
 import Modal from '@/app/components/base/modal'
 import Button from '@/app/components/base/button'
 
-import cn from 'classnames'
-import s from './index.module.css'
-
 type IProps = {
-  show: boolean,
-  onConfirm: () => void,
-  onHide: () => void,
+  show: boolean
+  onConfirm: () => void
+  onHide: () => void
 }
 
 const StopEmbeddingModal = ({
@@ -34,7 +33,7 @@ const StopEmbeddingModal = ({
       <div className={s.icon}/>
       <span className={s.close} onClick={onHide}/>
       <div className={s.title}>{t('datasetCreation.stepThree.modelTitle')}</div>
-      <div className={s.content}>{t('datasetCreation.stepThree.modelContent')}</div>      
+      <div className={s.content}>{t('datasetCreation.stepThree.modelContent')}</div>
       <div className='flex flex-row-reverse'>
         <Button className='w-24 ml-2' type='primary' onClick={submit}>{t('datasetCreation.stepThree.modelButtonConfirm')}</Button>
         <Button className='w-24' onClick={onHide}>{t('datasetCreation.stepThree.modelButtonCancel')}</Button>

web/app/components/datasets/documents/detail/metadata/index.tsx

@@ -287,7 +287,6 @@ const Metadata: FC<IMetadataProps> = ({ docDetail, loading, onUpdate }) => {
   }
 
   const onSave = async () => {
-    console.log('metadataParams:', metadataParams)
     setSaveLoading(true)
     const [e] = await asyncRunSafe<CommonResponse>(modifyDocMetadata({
       datasetId,

web/app/components/develop/code.tsx

@@ -257,7 +257,7 @@ const CodeGroupContext = createContext(false)
 
 export function CodeGroup({ children, title, inputs, targetCode, ...props }: IChildrenProps) {
   const languages = Children.map(children, child =>
-    getPanelTitle(child.props.children.props)
+    getPanelTitle(child.props.children.props),
   )
   const tabGroupProps = useTabGroupProps(languages)
   const hasTabs = Children.count(children) > 1

web/app/components/develop/doc.tsx

@@ -1,9 +1,9 @@
 'use client'
+import { useContext } from 'use-context-selector'
 import TemplateEn from './template/template.en.mdx'
 import TemplateZh from './template/template.zh.mdx'
 import TemplateChatEn from './template/template_chat.en.mdx'
 import TemplateChatZh from './template/template_chat.zh.mdx'
-import { useContext } from 'use-context-selector'
 import I18n from '@/context/i18n'
 
 type IDocProps = {
@@ -14,20 +14,21 @@ const Doc = ({ appDetail }: IDocProps) => {
   const { locale } = useContext(I18n)
   const variables = appDetail?.model_config?.configs?.prompt_variables || []
   const inputs = variables.reduce((res: any, variable: any) => {
-    res[variable.key] = variable.name || '';
+    res[variable.key] = variable.name || ''
     return res
   }, {})
 
   return (
     <article className="prose prose-xl" >
-      {appDetail?.mode === 'completion' ? (
-        locale === 'en' ? <TemplateEn appDetail={appDetail} variables={variables} inputs={inputs} /> : <TemplateZh appDetail={appDetail} variables={variables} inputs={inputs} />
-      ) : (
-        locale === 'en' ? <TemplateChatEn appDetail={appDetail} variables={variables} inputs={inputs} /> : <TemplateChatZh appDetail={appDetail} variables={variables} inputs={inputs} />
-      )}
+      {appDetail?.mode === 'completion'
+        ? (
+          locale === 'en' ? <TemplateEn appDetail={appDetail} variables={variables} inputs={inputs} /> : <TemplateZh appDetail={appDetail} variables={variables} inputs={inputs} />
+        )
+        : (
+          locale === 'en' ? <TemplateChatEn appDetail={appDetail} variables={variables} inputs={inputs} /> : <TemplateChatZh appDetail={appDetail} variables={variables} inputs={inputs} />
+        )}
     </article>
   )
-
 }
 
 export default Doc