Merge remote-tracking branch 'origin/feat/plugins' into dev/plugin-deploy

api/.env.example

@@ -42,6 +42,11 @@ REDIS_SENTINEL_USERNAME=
 REDIS_SENTINEL_PASSWORD=
 REDIS_SENTINEL_SOCKET_TIMEOUT=0.1
 
+# redis Cluster configuration.
+REDIS_USE_CLUSTERS=false
+REDIS_CLUSTERS=
+REDIS_CLUSTERS_PASSWORD=
+
 # PostgreSQL database configuration
 DB_USERNAME=postgres
 DB_PASSWORD=difyai123456
@@ -234,6 +239,10 @@ ANALYTICDB_ACCOUNT=testaccount
 ANALYTICDB_PASSWORD=testpassword
 ANALYTICDB_NAMESPACE=dify
 ANALYTICDB_NAMESPACE_PASSWORD=difypassword
+ANALYTICDB_HOST=gp-test.aliyuncs.com
+ANALYTICDB_PORT=5432
+ANALYTICDB_MIN_CONNECTION=1
+ANALYTICDB_MAX_CONNECTION=5
 
 # OpenSearch configuration
 OPENSEARCH_HOST=127.0.0.1

api/commands.py

@@ -590,7 +590,7 @@ def upgrade_db():
             click.echo(click.style("Database migration successful!", fg="green"))
 
         except Exception as e:
-            logging.exception(f"Database migration failed: {e}")
+            logging.exception("Failed to execute database migration")
         finally:
             lock.release()
     else:
@@ -634,7 +634,7 @@ where sites.id is null limit 1000"""
                 except Exception as e:
                     failed_app_ids.append(app_id)
                     click.echo(click.style("Failed to fix missing site for app {}".format(app_id), fg="red"))
-                    logging.exception(f"Fix app related site missing issue failed, error: {e}")
+                    logging.exception(f"Failed to fix app related site missing issue, app_id: {app_id}")
                     continue
 
             if not processed_count:

api/configs/feature/__init__.py

@@ -674,6 +674,11 @@ class DataSetConfig(BaseSettings):
         default=False,
     )
 
+    PLAN_SANDBOX_CLEAN_MESSAGE_DAY_SETTING: PositiveInt = Field(
+        description="Interval in days for message cleanup operations - plan: sandbox",
+        default=30,
+    )
+
 
 class WorkspaceConfig(BaseSettings):
     """

api/configs/middleware/cache/redis_config.py

@@ -68,3 +68,18 @@ class RedisConfig(BaseSettings):
         description="Socket timeout in seconds for Redis Sentinel connections",
         default=0.1,
     )
+
+    REDIS_USE_CLUSTERS: bool = Field(
+        description="Enable Redis Clusters mode for high availability",
+        default=False,
+    )
+
+    REDIS_CLUSTERS: Optional[str] = Field(
+        description="Comma-separated list of Redis Clusters nodes (host:port)",
+        default=None,
+    )
+
+    REDIS_CLUSTERS_PASSWORD: Optional[str] = Field(
+        description="Password for Redis Clusters authentication (if required)",
+        default=None,
+    )

api/configs/middleware/vdb/analyticdb_config.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, PositiveInt
 
 
 class AnalyticdbConfig(BaseModel):
@@ -40,3 +40,11 @@ class AnalyticdbConfig(BaseModel):
         description="The password for accessing the specified namespace within the AnalyticDB instance"
         " (if namespace feature is enabled).",
     )
+    ANALYTICDB_HOST: Optional[str] = Field(
+        default=None, description="The host of the AnalyticDB instance you want to connect to."
+    )
+    ANALYTICDB_PORT: PositiveInt = Field(
+        default=5432, description="The port of the AnalyticDB instance you want to connect to."
+    )
+    ANALYTICDB_MIN_CONNECTION: PositiveInt = Field(default=1, description="Min connection of the AnalyticDB database.")
+    ANALYTICDB_MAX_CONNECTION: PositiveInt = Field(default=5, description="Max connection of the AnalyticDB database.")

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.11.1",
+        default="0.11.2",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/app/app.py

@@ -9,6 +9,7 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
+    enterprise_license_required,
     setup_required,
 )
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -29,6 +30,7 @@ class AppListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         """Get app list"""
 
@@ -188,6 +190,7 @@ class AppApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     @get_app_model
     @marshal_with(app_detail_fields_with_site)
     def get(self, app_model):

api/controllers/console/app/audio.py

@@ -70,7 +70,7 @@ class ChatMessageAudioApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle post request to ChatMessageAudioApi")
             raise InternalServerError()
 
 
@@ -128,7 +128,7 @@ class ChatMessageTextApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle post request to ChatMessageTextApi")
             raise InternalServerError()
 
 
@@ -170,7 +170,7 @@ class TextModesApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle get request to TextModesApi")
             raise InternalServerError()
 
 

api/controllers/console/auth/forgot_password.py

@@ -14,7 +14,7 @@ from controllers.console.auth.error import (
     InvalidTokenError,
     PasswordMismatchError,
 )
-from controllers.console.error import EmailSendIpLimitError, NotAllowedRegister
+from controllers.console.error import AccountNotFound, EmailSendIpLimitError
 from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
 from extensions.ext_database import db
@@ -51,7 +51,7 @@ class ForgotPasswordSendEmailApi(Resource):
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
                 return {"result": "fail", "data": token, "code": "account_not_found"}
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         else:
             token = AccountService.send_reset_password_email(account=account, email=args["email"], language=language)
 

api/controllers/console/auth/login.py

@@ -16,9 +16,9 @@ from controllers.console.auth.error import (
 )
 from controllers.console.error import (
     AccountBannedError,
+    AccountNotFound,
     EmailSendIpLimitError,
     NotAllowedCreateWorkspace,
-    NotAllowedRegister,
 )
 from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
@@ -76,7 +76,7 @@ class LoginApi(Resource):
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
                 return {"result": "fail", "data": token, "code": "account_not_found"}
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         # SELF_HOSTED only have one workspace
         tenants = TenantService.get_join_tenants(account)
         if len(tenants) == 0:
@@ -119,7 +119,7 @@ class ResetPasswordSendEmailApi(Resource):
             if FeatureService.get_system_features().is_allow_register:
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         else:
             token = AccountService.send_reset_password_email(account=account, language=language)
 
@@ -148,7 +148,7 @@ class EmailCodeLoginSendEmailApi(Resource):
             if FeatureService.get_system_features().is_allow_register:
                 token = AccountService.send_email_code_login_email(email=args["email"], language=language)
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         else:
             token = AccountService.send_email_code_login_email(account=account, language=language)
 

api/controllers/console/datasets/datasets.py

@@ -10,7 +10,7 @@ from controllers.console import api
 from controllers.console.apikey import api_key_fields, api_key_list
 from controllers.console.app.error import ProviderNotInitializeError
 from controllers.console.datasets.error import DatasetInUseError, DatasetNameDuplicateError, IndexingEstimateError
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.indexing_runner import IndexingRunner
 from core.model_runtime.entities.model_entities import ModelType
@@ -44,6 +44,7 @@ class DatasetListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         page = request.args.get("page", default=1, type=int)
         limit = request.args.get("limit", default=20, type=int)

api/controllers/console/datasets/datasets_document.py

@@ -959,7 +959,7 @@ class DocumentRetryApi(DocumentResource):
                     raise DocumentAlreadyFinishedError()
                 retry_documents.append(document)
             except Exception as e:
-                logging.exception(f"Document {document_id} retry failed: {str(e)}")
+                logging.exception(f"Failed to retry document, document id: {document_id}")
                 continue
         # retry document
         DocumentService.retry_document(dataset_id, retry_documents)

api/controllers/console/error.py

@@ -52,8 +52,8 @@ class AccountBannedError(BaseHTTPException):
     code = 400
 
 
-class NotAllowedRegister(BaseHTTPException):
-    error_code = "unauthorized"
+class AccountNotFound(BaseHTTPException):
+    error_code = "account_not_found"
     description = "Account not found."
     code = 400
 
@@ -86,3 +86,9 @@ class NoFileUploadedError(BaseHTTPException):
     error_code = "no_file_uploaded"
     description = "Please upload your file."
     code = 400
+
+
+class UnauthorizedAndForceLogout(BaseHTTPException):
+    error_code = "unauthorized_and_force_logout"
+    description = "Unauthorized and force logout."
+    code = 401

api/controllers/console/remote_files.py

@@ -45,7 +45,7 @@ class RemoteFileUploadApi(Resource):
 
         resp = ssrf_proxy.head(url=url)
         if resp.status_code != httpx.codes.OK:
-            resp = ssrf_proxy.get(url=url, timeout=3)
+            resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
         resp.raise_for_status()
 
         file_info = helpers.guess_file_info_from_response(resp)

api/controllers/console/workspace/account.py

@@ -14,7 +14,7 @@ from controllers.console.workspace.error import (
     InvalidInvitationCodeError,
     RepeatPasswordNotMatchError,
 )
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from extensions.ext_database import db
 from fields.member_fields import account_fields
 from libs.helper import TimestampField, timezone
@@ -79,6 +79,7 @@ class AccountProfileApi(Resource):
     @login_required
     @account_initialization_required
     @marshal_with(account_fields)
+    @enterprise_license_required
     def get(self):
         return current_user
 

api/controllers/console/workspace/members.py

@@ -1,3 +1,5 @@
+from urllib import parse
+
 from flask_login import current_user
 from flask_restful import Resource, abort, marshal_with, reqparse
 
@@ -57,11 +59,12 @@ class MemberInviteEmailApi(Resource):
                 token = RegisterService.invite_new_member(
                     inviter.current_tenant, invitee_email, interface_language, role=invitee_role, inviter=inviter
                 )
+                encoded_invitee_email = parse.quote(invitee_email)
                 invitation_results.append(
                     {
                         "status": "success",
                         "email": invitee_email,
-                        "url": f"{console_web_url}/activate?email={invitee_email}&token={token}",
+                        "url": f"{console_web_url}/activate?email={encoded_invitee_email}&token={token}",
                     }
                 )
             except AccountAlreadyInTenantError:

api/controllers/console/workspace/models.py

@@ -72,7 +72,10 @@ class DefaultModelApi(Resource):
                     model=model_setting["model"],
                 )
             except Exception as ex:
-                logging.exception(f"{model_setting['model_type']} save error: {ex}")
+                logging.exception(
+                    f"Failed to update default model, model type: {model_setting['model_type']},"
+                    f" model:{model_setting.get('model')}"
+                )
                 raise ex
 
         return {"result": "success"}
@@ -156,7 +159,10 @@ class ModelProviderModelApi(Resource):
                         credentials=args["credentials"],
                     )
                 except CredentialsValidateFailedError as ex:
-                    logging.exception(f"save model credentials error: {ex}")
+                    logging.exception(
+                        f"Failed to save model credentials, tenant_id: {tenant_id},"
+                        f" model: {args.get('model')}, model_type: {args.get('model_type')}"
+                    )
                     raise ValueError(str(ex))
 
         return {"result": "success"}, 200

api/controllers/console/workspace/tool_providers.py

@@ -7,7 +7,7 @@ from werkzeug.exceptions import Forbidden
 
 from configs import dify_config
 from controllers.console import api
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.model_runtime.utils.encoders import jsonable_encoder
 from libs.helper import alphanumeric, uuid_value
 from libs.login import login_required
@@ -608,6 +608,7 @@ class ToolLabelsApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         return jsonable_encoder(ToolLabelsService.list_tool_labels())
 

api/controllers/console/wraps.py

@@ -9,10 +9,10 @@ from configs import dify_config
 from controllers.console.workspace.error import AccountNotInitializedError
 from extensions.ext_database import db
 from models.model import DifySetup
-from services.feature_service import FeatureService
+from services.feature_service import FeatureService, LicenseStatus
 from services.operation_service import OperationService
 
-from .error import NotInitValidateError, NotSetupError
+from .error import NotInitValidateError, NotSetupError, UnauthorizedAndForceLogout
 
 
 def account_initialization_required(view):
@@ -147,3 +147,15 @@ def setup_required(view):
         return view(*args, **kwargs)
 
     return decorated
+
+
+def enterprise_license_required(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        settings = FeatureService.get_system_features()
+        if settings.license.status in [LicenseStatus.INACTIVE, LicenseStatus.EXPIRED, LicenseStatus.LOST]:
+            raise UnauthorizedAndForceLogout("Your license is invalid. Please contact your administrator.")
+
+        return view(*args, **kwargs)
+
+    return decorated

api/controllers/web/audio.py

@@ -59,7 +59,7 @@ class AudioApi(WebApiResource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error: {str(e)}")
+            logging.exception("Failed to handle post request to AudioApi")
             raise InternalServerError()
 
 
@@ -117,7 +117,7 @@ class TextApi(WebApiResource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error: {str(e)}")
+            logging.exception("Failed to handle post request to TextApi")
             raise InternalServerError()
 
 

api/core/app/app_config/features/file_upload/manager.py

@@ -16,9 +16,7 @@ class FileUploadConfigManager:
         file_upload_dict = config.get("file_upload")
         if file_upload_dict:
             if file_upload_dict.get("enabled"):
-                transform_methods = file_upload_dict.get("allowed_file_upload_methods") or file_upload_dict.get(
-                    "allowed_upload_methods", []
-                )
+                transform_methods = file_upload_dict.get("allowed_file_upload_methods", [])
                 data = {
                     "image_config": {
                         "number_limits": file_upload_dict["number_limits"],

api/core/app/apps/advanced_chat/app_generator.py

@@ -373,5 +373,5 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(f"Failed to process generate task pipeline, conversation_id: {conversation.id}")
                 raise e

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -242,7 +242,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     start_listener_time = time.time()
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
             except Exception as e:
-                logger.exception(e)
+                logger.exception(f"Failed to listen audio message, task_id: {task_id}")
                 break
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)

api/core/app/apps/base_app_generator.py

@@ -34,8 +34,8 @@ class BaseAppGenerator:
                 tenant_id=app_config.tenant_id,
                 config=FileUploadConfig(
                     allowed_file_types=entity_dictionary[k].allowed_file_types,
-                    allowed_extensions=entity_dictionary[k].allowed_file_extensions,
-                    allowed_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
+                    allowed_file_extensions=entity_dictionary[k].allowed_file_extensions,
+                    allowed_file_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
                 ),
             )
             for k, v in user_inputs.items()
@@ -48,8 +48,8 @@ class BaseAppGenerator:
                 tenant_id=app_config.tenant_id,
                 config=FileUploadConfig(
                     allowed_file_types=entity_dictionary[k].allowed_file_types,
-                    allowed_extensions=entity_dictionary[k].allowed_file_extensions,
-                    allowed_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
+                    allowed_file_extensions=entity_dictionary[k].allowed_file_extensions,
+                    allowed_file_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
                 ),
             )
             for k, v in user_inputs.items()
@@ -92,6 +92,9 @@ class BaseAppGenerator:
             )
 
         if variable_entity.type == VariableEntityType.NUMBER and isinstance(value, str):
+            # handle empty string case
+            if not value.strip():
+                return None
             # may raise ValueError if user_input_value is not a valid number
             try:
                 if "." in value:

api/core/app/apps/message_based_app_generator.py

@@ -80,7 +80,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(f"Failed to handle response, conversation_id: {conversation.id}")
                 raise e
 
     def _get_conversation_by_user(

api/core/app/apps/workflow/app_generator.py

@@ -310,5 +310,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(
+                    f"Fails to process generate task pipeline, task_id: {application_generate_entity.task_id}"
+                )
                 raise e

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -216,7 +216,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 else:
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
             except Exception as e:
-                logger.exception(e)
+                logger.exception(f"Fails to get audio trunk, task_id: {task_id}")
                 break
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)

api/core/app/task_pipeline/message_cycle_manage.py

@@ -86,7 +86,7 @@ class MessageCycleManage:
                     conversation.name = name
                 except Exception as e:
                     if dify_config.DEBUG:
-                        logging.exception(f"generate conversation name failed: {e}")
+                        logging.exception(f"generate conversation name failed, conversation_id: {conversation_id}")
                     pass
 
                 db.session.merge(conversation)

api/core/file/models.py

@@ -28,8 +28,8 @@ class FileUploadConfig(BaseModel):
 
     image_config: Optional[ImageConfig] = None
     allowed_file_types: Sequence[FileType] = Field(default_factory=list)
-    allowed_extensions: Sequence[str] = Field(default_factory=list)
-    allowed_upload_methods: Sequence[FileTransferMethod] = Field(default_factory=list)
+    allowed_file_extensions: Sequence[str] = Field(default_factory=list)
+    allowed_file_upload_methods: Sequence[FileTransferMethod] = Field(default_factory=list)
     number_limits: int = 0
 
 

api/core/helper/moderation.py

@@ -55,7 +55,7 @@ def check_moderation(tenant_id: str, model_config: ModelConfigWithCredentialsEnt
                 if moderation_result is True:
                     return True
             except Exception as ex:
-                logger.exception(ex)
+                logger.exception(f"Fails to check moderation, provider_name: {provider_name}")
                 raise InvokeBadRequestError("Rate limit exceeded, please try again later.")
 
     return False

api/core/helper/module_import_helper.py

@@ -29,7 +29,7 @@ def import_module_from_source(*, module_name: str, py_file_path: AnyStr, use_laz
         spec.loader.exec_module(module)
         return module
     except Exception as e:
-        logging.exception(f"Failed to load module {module_name} from {py_file_path}: {str(e)}")
+        logging.exception(f"Failed to load module {module_name} from script file '{py_file_path}'")
         raise e
 
 

api/core/helper/ssrf_proxy.py

@@ -39,6 +39,7 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
         )
 
     retries = 0
+    stream = kwargs.pop("stream", False)
     while retries <= max_retries:
         try:
             if dify_config.SSRF_PROXY_ALL_URL:
@@ -52,6 +53,8 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
                     response = client.request(method=method, url=url, **kwargs)
 
             if response.status_code not in STATUS_FORCELIST:
+                if stream:
+                    return response.iter_bytes()
                 return response
             else:
                 logging.warning(

api/core/indexing_runner.py

@@ -29,6 +29,7 @@ from core.rag.splitter.fixed_text_splitter import (
     FixedRecursiveCharacterTextSplitter,
 )
 from core.rag.splitter.text_splitter import TextSplitter
+from core.tools.utils.text_processing_utils import remove_leading_symbols
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
@@ -500,11 +501,7 @@ class IndexingRunner:
                     document_node.metadata["doc_hash"] = hash
                     # delete Splitter character
                     page_content = document_node.page_content
-                    if page_content.startswith(".") or page_content.startswith("。"):
-                        page_content = page_content[1:]
-                    else:
-                        page_content = page_content
-                    document_node.page_content = page_content
+                    document_node.page_content = remove_leading_symbols(page_content)
 
                     if document_node.page_content:
                         split_documents.append(document_node)
@@ -554,7 +551,7 @@ class IndexingRunner:
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
-                logging.exception(e)
+                logging.exception("Failed to format qa document")
 
             all_qa_documents.extend(format_documents)
 

api/core/llm_generator/llm_generator.py

@@ -102,7 +102,7 @@ class LLMGenerator:
         except InvokeError:
             questions = []
         except Exception as e:
-            logging.exception(e)
+            logging.exception("Failed to generate suggested questions after answer")
             questions = []
 
         return questions
@@ -148,7 +148,7 @@ class LLMGenerator:
                 error = str(e)
                 error_step = "generate rule config"
             except Exception as e:
-                logging.exception(e)
+                logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
                 rule_config["error"] = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -234,7 +234,7 @@ class LLMGenerator:
                 error_step = "generate conversation opener"
 
         except Exception as e:
-            logging.exception(e)
+            logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
             rule_config["error"] = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -286,7 +286,9 @@ class LLMGenerator:
             error = str(e)
             return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
-            logging.exception(e)
+            logging.exception(
+                f"Failed to invoke LLM model, model: {model_config.get('name')}, language: {code_language}"
+            )
             return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
 
     @classmethod

api/core/model_runtime/model_providers/gitee_ai/_assets/Gitee-AI-Logo.svg

@@ -1,3 +0,0 @@
-<svg width="40" height="40" viewBox="0 0 40 40" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path fill-rule="evenodd" clip-rule="evenodd" d="M25.132 24.3947C25.497 25.7527 25.8984 27.1413 26.3334 28.5834C26.7302 29.8992 25.5459 30.4167 25.0752 29.1758C24.571 27.8466 24.0885 26.523 23.6347 25.1729C21.065 26.4654 18.5025 27.5424 15.5961 28.7541C16.7581 33.0256 17.8309 36.5984 19.4952 39.9935C19.4953 39.9936 19.4953 39.9937 19.4954 39.9938C19.6631 39.9979 19.8313 40 20 40C31.0457 40 40 31.0457 40 20C40 16.0335 38.8453 12.3366 36.8537 9.22729C31.6585 9.69534 27.0513 10.4562 22.8185 11.406C22.8882 12.252 22.9677 13.0739 23.0555 13.855C23.3824 16.7604 23.9112 19.5281 24.6137 22.3836C27.0581 21.2848 29.084 20.3225 30.6816 19.522C32.2154 18.7535 33.6943 18.7062 31.2018 20.6594C29.0388 22.1602 27.0644 23.3566 25.132 24.3947ZM36.1559 8.20846C33.0001 3.89184 28.1561 0.887462 22.5955 0.166882C22.4257 2.86234 22.4785 6.26344 22.681 9.50447C26.7473 8.88859 31.1721 8.46032 36.1559 8.20846ZM19.9369 9.73661e-05C19.7594 2.92694 19.8384 6.65663 20.19 9.91293C17.3748 10.4109 14.7225 11.0064 12.1592 11.7038C12.0486 10.4257 11.9927 9.25764 11.9927 8.24178C11.9927 7.5054 11.3957 6.90844 10.6593 6.90844C9.92296 6.90844 9.32601 7.5054 9.32601 8.24178C9.32601 9.47868 9.42873 10.898 9.61402 12.438C8.33567 12.8278 7.07397 13.2443 5.81918 13.688C5.12493 13.9336 4.76118 14.6954 5.0067 15.3896C5.25223 16.0839 6.01406 16.4476 6.7083 16.2021C7.7931 15.8185 8.88482 15.4388 9.98927 15.0659C10.5222 18.3344 11.3344 21.9428 12.2703 25.4156C12.4336 26.0218 12.6062 26.6262 12.7863 27.2263C9.34168 28.4135 5.82612 29.3782 2.61128 29.8879C0.949407 26.9716 0 23.5967 0 20C0 8.97534 8.92023 0.0341108 19.9369 9.73661e-05ZM4.19152 32.2527C7.45069 36.4516 12.3458 39.3173 17.9204 39.8932C16.5916 37.455 14.9338 33.717 13.5405 29.5901C10.4404 30.7762 7.25883 31.6027 4.19152 32.2527ZM22.9735 23.1135C22.1479 20.41 21.4462 17.5441 20.9225 14.277C20.746 13.5841 20.5918 12.8035 20.4593 11.9636C17.6508 12.6606 14.9992 13.4372 12.4356 14.2598C12.8479 17.4766 13.5448 21.1334 14.5118 24.7218C14.662 25.2792 14.8081 25.8248 14.9514 26.3594L14.9516 26.3603L14.9524 26.3634L14.9526 26.3639L14.973 26.4401C16.1833 25.9872 17.3746 25.5123 18.53 25.0259C20.1235 24.3552 21.6051 23.7165 22.9735 23.1135Z" fill="#141519"/>
-</svg>

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

@@ -1,47 +0,0 @@
-from dashscope.common.error import (
-    AuthenticationError,
-    InvalidParameter,
-    RequestFailure,
-    ServiceUnavailableError,
-    UnsupportedHTTPMethod,
-    UnsupportedModel,
-)
-
-from core.model_runtime.errors.invoke import (
-    InvokeAuthorizationError,
-    InvokeBadRequestError,
-    InvokeConnectionError,
-    InvokeError,
-    InvokeRateLimitError,
-    InvokeServerUnavailableError,
-)
-
-
-class _CommonGiteeAI:
-    @property
-    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        """
-        Map model invoke error to unified error
-        The key is the error type thrown to the caller
-        The value is the error type thrown by the model,
-        which needs to be converted into a unified error type for the caller.
-
-        :return: Invoke error mapping
-        """
-        return {
-            InvokeConnectionError: [
-                RequestFailure,
-            ],
-            InvokeServerUnavailableError: [
-                ServiceUnavailableError,
-            ],
-            InvokeRateLimitError: [],
-            InvokeAuthorizationError: [
-                AuthenticationError,
-            ],
-            InvokeBadRequestError: [
-                InvalidParameter,
-                UnsupportedModel,
-                UnsupportedHTTPMethod,
-            ],
-        }

api/core/model_runtime/model_providers/gitee_ai/gitee_ai.py

@@ -1,36 +0,0 @@
-import logging
-
-import requests
-
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.model_provider import ModelProvider
-
-logger = logging.getLogger(__name__)
-
-
-class GiteeAIProvider(ModelProvider):
-    def validate_provider_credentials(self, credentials: dict) -> None:
-        """
-        Validate provider credentials
-        if validate failed, raise exception
-
-        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
-        """
-        try:
-            api_key = credentials.get("api_key")
-            if not api_key:
-                raise CredentialsValidateFailedError("Credentials validation failed: api_key not given")
-
-            # send a get request to validate the credentials
-            headers = {"Authorization": f"Bearer {api_key}"}
-            response = requests.get("https://ai.gitee.com/api/base/account/me", headers=headers, timeout=(10, 300))
-
-            if response.status_code != 200:
-                raise CredentialsValidateFailedError(
-                    f"Credentials validation failed with status code {response.status_code}"
-                )
-        except CredentialsValidateFailedError as ex:
-            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/gitee_ai/gitee_ai.yaml

@@ -1,35 +0,0 @@
-provider: gitee_ai
-label:
-  en_US: Gitee AI
-  zh_Hans: Gitee AI
-description:
-  en_US: 快速体验大模型，领先探索 AI 开源世界
-  zh_Hans: 快速体验大模型，领先探索 AI 开源世界
-icon_small:
-  en_US: Gitee-AI-Logo.svg
-icon_large:
-  en_US: Gitee-AI-Logo-full.svg
-help:
-  title:
-    en_US: Get your token from Gitee AI
-    zh_Hans: 从 Gitee AI 获取 token
-  url:
-    en_US: https://ai.gitee.com/dashboard/settings/tokens
-supported_model_types:
-  - llm
-  - text-embedding
-  - rerank
-  - speech2text
-  - tts
-configurate_methods:
-  - predefined-model
-provider_credential_schema:
-  credential_form_schemas:
-    - variable: api_key
-      label:
-        en_US: API Key
-      type: secret-input
-      required: true
-      placeholder:
-        zh_Hans: 在此输入您的 API Key
-        en_US: Enter your API Key

api/core/model_runtime/model_providers/gitee_ai/llm/Qwen2-72B-Instruct.yaml

@@ -1,105 +0,0 @@
-model: Qwen2-72B-Instruct
-label:
-  zh_Hans: Qwen2-72B-Instruct
-  en_US: Qwen2-72B-Instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 6400
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/Qwen2-7B-Instruct.yaml

@@ -1,105 +0,0 @@
-model: Qwen2-7B-Instruct
-label:
-  zh_Hans: Qwen2-7B-Instruct
-  en_US: Qwen2-7B-Instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 32768
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/Qwen2.5-72B-Instruct.yaml

@@ -1,95 +0,0 @@
-model: Qwen2.5-72B-Instruct
-label:
-  zh_Hans: Qwen2.5-72B-Instruct
-  en_US: Qwen2.5-72B-Instruct
-model_type: llm
-features:
-  - agent-thought
-  - tool-call
-  - stream-tool-call
-model_properties:
-  mode: chat
-  context_size: 32768
-parameter_rules:
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/Yi-1.5-34B-Chat.yaml

@@ -1,105 +0,0 @@
-model: Yi-1.5-34B-Chat
-label:
-  zh_Hans: Yi-1.5-34B-Chat
-  en_US: Yi-1.5-34B-Chat
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 4096
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/_position.yaml

@@ -1,8 +0,0 @@
-- Qwen2.5-72B-Instruct
-- Qwen2-7B-Instruct
-- Qwen2-72B-Instruct
-- Yi-1.5-34B-Chat
-- glm-4-9b-chat
-- deepseek-coder-33B-instruct-chat
-- deepseek-coder-33B-instruct-completions
-- codegeex4-all-9b

api/core/model_runtime/model_providers/gitee_ai/llm/codegeex4-all-9b.yaml

@@ -1,105 +0,0 @@
-model: codegeex4-all-9b
-label:
-  zh_Hans: codegeex4-all-9b
-  en_US: codegeex4-all-9b
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 40960
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/deepseek-coder-33B-instruct-chat.yaml

@@ -1,105 +0,0 @@
-model: deepseek-coder-33B-instruct-chat
-label:
-  zh_Hans: deepseek-coder-33B-instruct-chat
-  en_US: deepseek-coder-33B-instruct-chat
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 9000
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/deepseek-coder-33B-instruct-completions.yaml

@@ -1,91 +0,0 @@
-model: deepseek-coder-33B-instruct-completions
-label:
-  zh_Hans: deepseek-coder-33B-instruct-completions
-  en_US: deepseek-coder-33B-instruct-completions
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: completion
-  context_size: 9000
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/glm-4-9b-chat.yaml

@@ -1,105 +0,0 @@
-model: glm-4-9b-chat
-label:
-  zh_Hans: glm-4-9b-chat
-  en_US: glm-4-9b-chat
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 32768
-parameter_rules:
-  - name: stream
-    use_template: boolean
-    label:
-      en_US: "Stream"
-      zh_Hans: "流式"
-    type: boolean
-    default: true
-    required: true
-    help:
-      en_US: "Whether to return the results in batches through streaming. If set to true, the generated text will be pushed to the user in real time during the generation process."
-      zh_Hans: "是否通过流式分批返回结果。如果设置为 true，生成过程中实时地向用户推送每一部分生成的文本。"
-
-  - name: max_tokens
-    use_template: max_tokens
-    label:
-      en_US: "Max Tokens"
-      zh_Hans: "最大Token数"
-    type: int
-    default: 512
-    min: 1
-    required: true
-    help:
-      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
-      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
-
-  - name: temperature
-    use_template: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    use_template: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_k
-    use_template: top_k
-    label:
-      en_US: "Top K"
-      zh_Hans: "Top K"
-    type: int
-    default: 50
-    min: 0
-    max: 100
-    required: true
-    help:
-      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
-      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: -1.0
-    max: 1.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
-      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

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

@@ -1,51 +0,0 @@
-from collections.abc import Generator
-from typing import Optional, Union
-
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
-from core.model_runtime.entities.message_entities import (
-    PromptMessage,
-    PromptMessageTool,
-)
-from core.model_runtime.entities.model_entities import ModelFeature
-from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
-
-
-class GiteeAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    MODEL_TO_IDENTITY: dict[str, str] = {
-        "Yi-1.5-34B-Chat": "Yi-34B-Chat",
-        "deepseek-coder-33B-instruct-completions": "deepseek-coder-33B-instruct",
-        "deepseek-coder-33B-instruct-chat": "deepseek-coder-33B-instruct",
-    }
-
-    def _invoke(
-        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]:
-        self._add_custom_parameters(credentials, model, model_parameters)
-        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials, model, None)
-        super().validate_credentials(model, credentials)
-
-    def _add_custom_parameters(self, credentials: dict, model: str, model_parameters: dict) -> None:
-        if model is None:
-            model = "bge-large-zh-v1.5"
-
-        model_identity = GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model)
-        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model_identity}/"
-        if model.endswith("completions"):
-            credentials["mode"] = LLMMode.COMPLETION.value
-        else:
-            credentials["mode"] = LLMMode.CHAT.value
-
-        schema = self.get_model_schema(model, credentials)
-        if ModelFeature.TOOL_CALL in schema.features or ModelFeature.MULTI_TOOL_CALL in schema.features:
-            credentials["function_calling_type"] = "tool_call"

api/core/model_runtime/model_providers/gitee_ai/rerank/_position.yaml

@@ -1 +0,0 @@
-- bge-reranker-v2-m3

api/core/model_runtime/model_providers/gitee_ai/rerank/bge-reranker-v2-m3.yaml

@@ -1,4 +0,0 @@
-model: bge-reranker-v2-m3
-model_type: rerank
-model_properties:
-  context_size: 1024

api/core/model_runtime/model_providers/gitee_ai/rerank/rerank.py

@@ -1,128 +0,0 @@
-from typing import Optional
-
-import httpx
-
-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType
-from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
-from core.model_runtime.errors.invoke import (
-    InvokeAuthorizationError,
-    InvokeBadRequestError,
-    InvokeConnectionError,
-    InvokeError,
-    InvokeRateLimitError,
-    InvokeServerUnavailableError,
-)
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.rerank_model import RerankModel
-
-
-class GiteeAIRerankModel(RerankModel):
-    """
-    Model class for rerank model.
-    """
-
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        query: str,
-        docs: list[str],
-        score_threshold: Optional[float] = None,
-        top_n: Optional[int] = None,
-        user: Optional[str] = None,
-    ) -> RerankResult:
-        """
-        Invoke rerank model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param query: search query
-        :param docs: docs for reranking
-        :param score_threshold: score threshold
-        :param top_n: top n documents to return
-        :param user: unique user id
-        :return: rerank result
-        """
-        if len(docs) == 0:
-            return RerankResult(model=model, docs=[])
-
-        base_url = credentials.get("base_url", "https://ai.gitee.com/api/serverless")
-        base_url = base_url.removesuffix("/")
-
-        try:
-            body = {"model": model, "query": query, "documents": docs}
-            if top_n is not None:
-                body["top_n"] = top_n
-            response = httpx.post(
-                f"{base_url}/{model}/rerank",
-                json=body,
-                headers={"Authorization": f"Bearer {credentials.get('api_key')}"},
-            )
-
-            response.raise_for_status()
-            results = response.json()
-
-            rerank_documents = []
-            for result in results["results"]:
-                rerank_document = RerankDocument(
-                    index=result["index"],
-                    text=result["document"]["text"],
-                    score=result["relevance_score"],
-                )
-                if score_threshold is None or result["relevance_score"] >= score_threshold:
-                    rerank_documents.append(rerank_document)
-            return RerankResult(model=model, docs=rerank_documents)
-        except httpx.HTTPStatusError as e:
-            raise InvokeServerUnavailableError(str(e))
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            self._invoke(
-                model=model,
-                credentials=credentials,
-                query="What is the capital of the United States?",
-                docs=[
-                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
-                    "Census, Carson City had a population of 55,274.",
-                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
-                    "are a political division controlled by the United States. Its capital is Saipan.",
-                ],
-                score_threshold=0.01,
-            )
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))
-
-    @property
-    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        """
-        Map model invoke error to unified error
-        """
-        return {
-            InvokeConnectionError: [httpx.ConnectError],
-            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
-            InvokeRateLimitError: [],
-            InvokeAuthorizationError: [httpx.HTTPStatusError],
-            InvokeBadRequestError: [httpx.RequestError],
-        }
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
-        """
-        generate custom model entities from credentials
-        """
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            model_type=ModelType.RERANK,
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
-        )
-
-        return entity

api/core/model_runtime/model_providers/gitee_ai/speech2text/_position.yaml

@@ -1,2 +0,0 @@
-- whisper-base
-- whisper-large

api/core/model_runtime/model_providers/gitee_ai/speech2text/speech2text.py

@@ -1,53 +0,0 @@
-import os
-from typing import IO, Optional
-
-import requests
-
-from core.model_runtime.errors.invoke import InvokeBadRequestError
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
-from core.model_runtime.model_providers.gitee_ai._common import _CommonGiteeAI
-
-
-class GiteeAISpeech2TextModel(_CommonGiteeAI, Speech2TextModel):
-    """
-    Model class for OpenAI Compatible Speech to text model.
-    """
-
-    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
-        """
-        Invoke speech2text model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param file: audio file
-        :param user: unique user id
-        :return: text for given audio file
-        """
-        # doc: https://ai.gitee.com/docs/openapi/serverless#tag/serverless/POST/{service}/speech-to-text
-
-        endpoint_url = f"https://ai.gitee.com/api/serverless/{model}/speech-to-text"
-        files = [("file", file)]
-        _, file_ext = os.path.splitext(file.name)
-        headers = {"Content-Type": f"audio/{file_ext}", "Authorization": f"Bearer {credentials.get('api_key')}"}
-        response = requests.post(endpoint_url, headers=headers, files=files)
-        if response.status_code != 200:
-            raise InvokeBadRequestError(response.text)
-        response_data = response.json()
-        return response_data["text"]
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            audio_file_path = self._get_demo_file_path()
-
-            with open(audio_file_path, "rb") as audio_file:
-                self._invoke(model, credentials, audio_file)
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))

api/core/model_runtime/model_providers/gitee_ai/speech2text/whisper-base.yaml

@@ -1,5 +0,0 @@
-model: whisper-base
-model_type: speech2text
-model_properties:
-  file_upload_limit: 1
-  supported_file_extensions: flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm

api/core/model_runtime/model_providers/gitee_ai/speech2text/whisper-large.yaml

@@ -1,5 +0,0 @@
-model: whisper-large
-model_type: speech2text
-model_properties:
-  file_upload_limit: 1
-  supported_file_extensions: flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm

api/core/model_runtime/model_providers/gitee_ai/text_embedding/_position.yaml

@@ -1,3 +0,0 @@
-- bge-large-zh-v1.5
-- bge-small-zh-v1.5
-- bge-m3

api/core/model_runtime/model_providers/gitee_ai/text_embedding/bge-large-zh-v1.5.yaml

@@ -1,8 +0,0 @@
-model: bge-large-zh-v1.5
-label:
-  zh_Hans: bge-large-zh-v1.5
-  en_US: bge-large-zh-v1.5
-model_type: text-embedding
-model_properties:
-  context_size: 200000
-  max_chunks: 20

api/core/model_runtime/model_providers/gitee_ai/text_embedding/bge-m3.yaml

@@ -1,8 +0,0 @@
-model: bge-m3
-label:
-  zh_Hans: bge-m3
-  en_US: bge-m3
-model_type: text-embedding
-model_properties:
-  context_size: 200000
-  max_chunks: 20

api/core/model_runtime/model_providers/gitee_ai/text_embedding/bge-small-zh-v1.5.yaml

@@ -1,8 +0,0 @@
-model: bge-small-zh-v1.5
-label:
-  zh_Hans: bge-small-zh-v1.5
-  en_US: bge-small-zh-v1.5
-model_type: text-embedding
-model_properties:
-  context_size: 200000
-  max_chunks: 20

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

@@ -1,31 +0,0 @@
-from typing import Optional
-
-from core.entities.embedding_type import EmbeddingInputType
-from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
-from core.model_runtime.model_providers.openai_api_compatible.text_embedding.text_embedding import (
-    OAICompatEmbeddingModel,
-)
-
-
-class GiteeAIEmbeddingModel(OAICompatEmbeddingModel):
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        texts: list[str],
-        user: Optional[str] = None,
-        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
-    ) -> TextEmbeddingResult:
-        self._add_custom_parameters(credentials, model)
-        return super()._invoke(model, credentials, texts, user, input_type)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials, None)
-        super().validate_credentials(model, credentials)
-
-    @staticmethod
-    def _add_custom_parameters(credentials: dict, model: str) -> None:
-        if model is None:
-            model = "bge-m3"
-
-        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model}/v1/"

api/core/model_runtime/model_providers/gitee_ai/tts/ChatTTS.yaml

@@ -1,11 +0,0 @@
-model: ChatTTS
-model_type: tts
-model_properties:
-  default_voice: 'default'
-  voices:
-    - mode: 'default'
-      name: 'Default'
-      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
-  word_limit: 3500
-  audio_type: 'mp3'
-  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/FunAudioLLM-CosyVoice-300M.yaml

@@ -1,11 +0,0 @@
-model: FunAudioLLM-CosyVoice-300M
-model_type: tts
-model_properties:
-  default_voice: 'default'
-  voices:
-    - mode: 'default'
-      name: 'Default'
-      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
-  word_limit: 3500
-  audio_type: 'mp3'
-  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/_position.yaml

@@ -1,4 +0,0 @@
-- speecht5_tts
-- ChatTTS
-- fish-speech-1.2-sft
-- FunAudioLLM-CosyVoice-300M

api/core/model_runtime/model_providers/gitee_ai/tts/fish-speech-1.2-sft.yaml

@@ -1,11 +0,0 @@
-model: fish-speech-1.2-sft
-model_type: tts
-model_properties:
-  default_voice: 'default'
-  voices:
-    - mode: 'default'
-      name: 'Default'
-      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
-  word_limit: 3500
-  audio_type: 'mp3'
-  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/speecht5_tts.yaml

@@ -1,11 +0,0 @@
-model: speecht5_tts
-model_type: tts
-model_properties:
-  default_voice: 'default'
-  voices:
-    - mode: 'default'
-      name: 'Default'
-      language: [ 'zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID' ]
-  word_limit: 3500
-  audio_type: 'mp3'
-  max_workers: 5

api/core/model_runtime/model_providers/gitee_ai/tts/tts.py

@@ -1,79 +0,0 @@
-from typing import Optional
-
-import requests
-
-from core.model_runtime.errors.invoke import InvokeBadRequestError
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.tts_model import TTSModel
-from core.model_runtime.model_providers.gitee_ai._common import _CommonGiteeAI
-
-
-class GiteeAIText2SpeechModel(_CommonGiteeAI, TTSModel):
-    """
-    Model class for OpenAI Speech to text model.
-    """
-
-    def _invoke(
-        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
-    ) -> any:
-        """
-        _invoke text2speech model
-
-        :param model: model name
-        :param tenant_id: user tenant id
-        :param credentials: model credentials
-        :param content_text: text content to be translated
-        :param voice: model timbre
-        :param user: unique user id
-        :return: text translated to audio file
-        """
-        return self._tts_invoke_streaming(model=model, credentials=credentials, content_text=content_text, voice=voice)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        validate credentials text2speech model
-
-        :param model: model name
-        :param credentials: model credentials
-        :return: text translated to audio file
-        """
-        try:
-            self._tts_invoke_streaming(
-                model=model,
-                credentials=credentials,
-                content_text="Hello Dify!",
-                voice=self._get_model_default_voice(model, credentials),
-            )
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))
-
-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
-        """
-        _tts_invoke_streaming text2speech model
-        :param model: model name
-        :param credentials: model credentials
-        :param content_text: text content to be translated
-        :param voice: model timbre
-        :return: text translated to audio file
-        """
-        try:
-            # doc: https://ai.gitee.com/docs/openapi/serverless#tag/serverless/POST/{service}/text-to-speech
-            endpoint_url = "https://ai.gitee.com/api/serverless/" + model + "/text-to-speech"
-
-            headers = {"Content-Type": "application/json"}
-            api_key = credentials.get("api_key")
-            if api_key:
-                headers["Authorization"] = f"Bearer {api_key}"
-
-            payload = {"inputs": content_text}
-            response = requests.post(endpoint_url, headers=headers, json=payload)
-
-            if response.status_code != 200:
-                raise InvokeBadRequestError(response.text)
-
-            data = response.content
-
-            for i in range(0, len(data), 1024):
-                yield data[i : i + 1024]
-        except Exception as ex:
-            raise InvokeBadRequestError(str(ex))

api/core/model_runtime/model_providers/gpustack/_assets/icon_l_en.svg

@@ -1,15 +0,0 @@
-<svg width="68" height="24" viewBox="0 0 68 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g id="Gemini">
-<path id="Union" fill-rule="evenodd" clip-rule="evenodd" d="M50.6875 4.37014C48.3498 4.59292 46.5349 6.41319 46.3337 8.72764C46.1446 6.44662 44.2677 4.56074 41.9805 4.3737C44.2762 4.1997 46.152 2.28299 46.3373 0C46.4882 2.28911 48.405 4.20047 50.6875 4.37014ZM15.4567 9.41141L13.9579 10.9076C9.92941 6.64892 2.69298 9.97287 3.17317 15.8112C3.22394 23.108 14.5012 24.4317 15.3628 16.8809H9.52096L9.50061 14.9149H17.3595C18.8163 23.1364 8.44367 27.0292 3.19453 21.238C0.847044 18.7556 0.363651 14.7682 1.83717 11.7212C4.1129 6.62089 11.6505 5.29845 15.4567 9.41141ZM45.5915 23.5989H47.6945C47.6944 22.9155 47.6945 22.2307 47.6946 21.5452V21.5325C47.6948 19.8907 47.695 18.2453 47.6924 16.6072C47.6914 15.9407 47.6161 15.2823 47.4024 14.647C46.4188 11.2828 41.4255 11.4067 39.8332 14.214C38.5637 11.4171 34.4009 11.5236 32.8538 14.0084L32.8082 13.9976V12.4806L32.4233 12.4804H32.4224C31.8687 12.4801 31.3324 12.4798 30.7949 12.4811V23.5848L32.8977 23.5672C32.8981 22.9411 32.8979 22.3122 32.8977 21.6822V21.6812V21.6802V21.6791V21.6781V21.6771V21.676V21.676V21.6759V21.6758V21.6757V21.6757V21.6756C32.8973 20.204 32.8969 18.7261 32.904 17.2614C32.8889 15.3646 34.5674 13.5687 36.5358 14.124C37.7794 14.3298 38.1851 15.6148 38.1761 16.7257C38.1821 17.7019 38.18 18.6824 38.178 19.6633V19.6638C38.1752 20.9756 38.1724 22.2881 38.1891 23.5919L40.2846 23.5731C40.2929 22.7511 40.2881 21.9245 40.2832 21.0966C40.2753 19.7402 40.2674 18.3805 40.317 17.0328C40.4418 15.2122 42.0141 13.6186 43.9064 14.1168C45.2685 14.3231 45.6136 15.7748 45.5882 16.9545C45.5938 18.4959 45.5929 20.0492 45.5921 21.5968V21.5991V21.6014V21.6037V21.606V21.6083V21.6106C45.5917 22.2749 45.5913 22.9382 45.5915 23.5989ZM20.6167 18.4408C20.5625 21.9486 25.2121 23.6996 27.2993 20.0558L29.1566 20.9592C27.8157 23.7067 24.2337 24.7424 21.5381 23.4213C18.0052 21.7253 17.41 16.5007 20.0334 13.7517C21.4609 12.1752 23.7291 11.7901 25.7206 12.3653C28.3408 13.1257 29.4974 15.8937 29.326 18.4399C27.5547 18.4415 25.7971 18.4412 24.0364 18.4409C22.8993 18.4407 21.7609 18.4405 20.6167 18.4408ZM27.1041 16.6957C26.7048 13.1033 21.2867 13.2256 20.7494 16.6957H27.1041ZM53.543 23.5999H55.6206L55.6206 22.4361C55.6205 20.7877 55.6205 19.1443 55.6207 17.4939C55.6208 16.8853 55.7234 16.297 56.0063 15.7531C56.6115 14.3862 58.1745 13.7002 59.5927 14.1774C60.7512 14.4455 61.2852 15.6069 61.2762 16.7154C61.2774 18.3497 61.2771 19.9826 61.2769 21.6162V21.6166V21.617V21.6174V21.6179L61.2766 23.6007H63.3698C63.3913 22.0924 63.3869 20.584 63.3826 19.0755V19.0754V19.0753V19.0753V19.0752C63.3799 18.1682 63.3773 17.2612 63.3803 16.3541C63.3796 15.8622 63.3103 15.3765 63.1698 14.9052C62.3248 11.5142 57.3558 11.2385 55.5828 14.0038L55.5336 13.9905V12.4917H53.539C53.4898 12.7313 53.4934 23.4113 53.543 23.5999ZM49.6211 12.4944H51.7065V23.5994H49.6211V12.4944ZM65.1035 23.5991H67.1831C67.2367 23.2198 67.2133 12.6566 67.1634 12.4983H65.1035V23.5991ZM52.1504 8.67829C52.1709 10.4847 49.2418 10.7058 49.1816 8.65714C49.2189 6.5948 52.2437 6.81331 52.1504 8.67829ZM66.1387 10.1324C64.2712 10.1609 64.1316 7.19881 66.1559 7.17114C68.1709 7.19817 68.0215 10.2087 66.1387 10.1324Z" fill="url(#paint0_linear_14286_118464)"/>
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-<defs>
-<linearGradient id="paint0_linear_14286_118464" x1="-2" y1="0.999998" x2="67.9999" y2="27.5002" gradientUnits="userSpaceOnUse">
-<stop stop-color="#7798E0"/>
-<stop offset="0.210002" stop-color="#086FFF"/>
-<stop offset="0.345945" stop-color="#086FFF"/>
-<stop offset="0.591777" stop-color="#479AFF"/>
-<stop offset="0.895892" stop-color="#B7C4FA"/>
-<stop offset="1" stop-color="#B5C5F9"/>
-</linearGradient>
-</defs>
-</svg>

api/core/model_runtime/model_providers/gpustack/_assets/icon_s_en.svg

@@ -1,11 +0,0 @@
-<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-<rect width="24" height="24" rx="6" fill="url(#paint0_linear_7301_16076)"/>
-<path d="M20 12.0116C15.7043 12.42 12.3692 15.757 11.9995 20C11.652 15.8183 8.20301 12.361 4 12.0181C8.21855 11.6991 11.6656 8.1853 12.006 4C12.2833 8.19653 15.8057 11.7005 20 12.0116Z" fill="white" fill-opacity="0.88"/>
-<defs>
-<linearGradient id="paint0_linear_7301_16076" x1="-9" y1="29.5" x2="19.4387" y2="1.43791" gradientUnits="userSpaceOnUse">
-<stop offset="0.192878" stop-color="#1C7DFF"/>
-<stop offset="0.520213" stop-color="#1C69FF"/>
-<stop offset="1" stop-color="#F0DCD6"/>
-</linearGradient>
-</defs>
-</svg>

api/core/model_runtime/model_providers/gpustack/gpustack.py

@@ -1,10 +0,0 @@
-import logging
-
-from core.model_runtime.model_providers.__base.model_provider import ModelProvider
-
-logger = logging.getLogger(__name__)
-
-
-class GPUStackProvider(ModelProvider):
-    def validate_provider_credentials(self, credentials: dict) -> None:
-        pass

api/core/model_runtime/model_providers/gpustack/gpustack.yaml

@@ -1,120 +0,0 @@
-provider: gpustack
-label:
-  en_US: GPUStack
-icon_small:
-  en_US: icon_s_en.png
-icon_large:
-  en_US: icon_l_en.png
-supported_model_types:
-  - llm
-  - text-embedding
-  - rerank
-configurate_methods:
-  - customizable-model
-model_credential_schema:
-  model:
-    label:
-      en_US: Model Name
-      zh_Hans: 模型名称
-    placeholder:
-      en_US: Enter your model name
-      zh_Hans: 输入模型名称
-  credential_form_schemas:
-    - variable: endpoint_url
-      label:
-        zh_Hans: 服务器地址
-        en_US: Server URL
-      type: text-input
-      required: true
-      placeholder:
-        zh_Hans: 输入 GPUStack 的服务器地址，如 http://192.168.1.100
-        en_US: Enter the GPUStack server URL, e.g. http://192.168.1.100
-    - variable: api_key
-      label:
-        en_US: API Key
-      type: secret-input
-      required: true
-      placeholder:
-        zh_Hans: 输入您的 API Key
-        en_US: Enter your API Key
-    - variable: mode
-      show_on:
-        - variable: __model_type
-          value: llm
-      label:
-        en_US: Completion mode
-      type: select
-      required: false
-      default: chat
-      placeholder:
-        zh_Hans: 选择补全类型
-        en_US: Select completion type
-      options:
-        - value: completion
-          label:
-            en_US: Completion
-            zh_Hans: 补全
-        - value: chat
-          label:
-            en_US: Chat
-            zh_Hans: 对话
-    - variable: context_size
-      label:
-        zh_Hans: 模型上下文长度
-        en_US: Model context size
-      required: true
-      type: text-input
-      default: "8192"
-      placeholder:
-        zh_Hans: 输入您的模型上下文长度
-        en_US: Enter your Model context size
-    - variable: max_tokens_to_sample
-      label:
-        zh_Hans: 最大 token 上限
-        en_US: Upper bound for max tokens
-      show_on:
-        - variable: __model_type
-          value: llm
-      default: "8192"
-      type: text-input
-    - variable: function_calling_type
-      show_on:
-        - variable: __model_type
-          value: llm
-      label:
-        en_US: Function calling
-      type: select
-      required: false
-      default: no_call
-      options:
-        - value: function_call
-          label:
-            en_US: Function Call
-            zh_Hans: Function Call
-        - value: tool_call
-          label:
-            en_US: Tool Call
-            zh_Hans: Tool Call
-        - value: no_call
-          label:
-            en_US: Not Support
-            zh_Hans: 不支持
-    - variable: vision_support
-      show_on:
-        - variable: __model_type
-          value: llm
-      label:
-        zh_Hans: Vision 支持
-        en_US: Vision Support
-      type: select
-      required: false
-      default: no_support
-      options:
-        - value: support
-          label:
-            en_US: Support
-            zh_Hans: 支持
-        - value: no_support
-          label:
-            en_US: Not Support
-            zh_Hans: 不支持

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

@@ -1,45 +0,0 @@
-from collections.abc import Generator
-
-from yarl import URL
-
-from core.model_runtime.entities.llm_entities import LLMResult
-from core.model_runtime.entities.message_entities import (
-    PromptMessage,
-    PromptMessageTool,
-)
-from core.model_runtime.model_providers.openai_api_compatible.llm.llm import (
-    OAIAPICompatLargeLanguageModel,
-)
-
-
-class GPUStackLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        prompt_messages: list[PromptMessage],
-        model_parameters: dict,
-        tools: list[PromptMessageTool] | None = None,
-        stop: list[str] | None = None,
-        stream: bool = True,
-        user: str | None = None,
-    ) -> LLMResult | Generator:
-        return super()._invoke(
-            model,
-            credentials,
-            prompt_messages,
-            model_parameters,
-            tools,
-            stop,
-            stream,
-            user,
-        )
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        super().validate_credentials(model, credentials)
-
-    @staticmethod
-    def _add_custom_parameters(credentials: dict) -> None:
-        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1-openai")
-        credentials["mode"] = "chat"

api/core/model_runtime/model_providers/gpustack/rerank/rerank.py

@@ -1,146 +0,0 @@
-from json import dumps
-from typing import Optional
-
-import httpx
-from requests import post
-from yarl import URL
-
-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import (
-    AIModelEntity,
-    FetchFrom,
-    ModelPropertyKey,
-    ModelType,
-)
-from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
-from core.model_runtime.errors.invoke import (
-    InvokeAuthorizationError,
-    InvokeBadRequestError,
-    InvokeConnectionError,
-    InvokeError,
-    InvokeRateLimitError,
-    InvokeServerUnavailableError,
-)
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.rerank_model import RerankModel
-
-
-class GPUStackRerankModel(RerankModel):
-    """
-    Model class for GPUStack rerank model.
-    """
-
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        query: str,
-        docs: list[str],
-        score_threshold: Optional[float] = None,
-        top_n: Optional[int] = None,
-        user: Optional[str] = None,
-    ) -> RerankResult:
-        """
-        Invoke rerank model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param query: search query
-        :param docs: docs for reranking
-        :param score_threshold: score threshold
-        :param top_n: top n documents to return
-        :param user: unique user id
-        :return: rerank result
-        """
-        if len(docs) == 0:
-            return RerankResult(model=model, docs=[])
-
-        endpoint_url = credentials["endpoint_url"]
-        headers = {
-            "Authorization": f"Bearer {credentials.get('api_key')}",
-            "Content-Type": "application/json",
-        }
-
-        data = {"model": model, "query": query, "documents": docs, "top_n": top_n}
-
-        try:
-            response = post(
-                str(URL(endpoint_url) / "v1" / "rerank"),
-                headers=headers,
-                data=dumps(data),
-                timeout=10,
-            )
-            response.raise_for_status()
-            results = response.json()
-
-            rerank_documents = []
-            for result in results["results"]:
-                index = result["index"]
-                if "document" in result:
-                    text = result["document"]["text"]
-                else:
-                    text = docs[index]
-
-                rerank_document = RerankDocument(
-                    index=index,
-                    text=text,
-                    score=result["relevance_score"],
-                )
-
-                if score_threshold is None or result["relevance_score"] >= score_threshold:
-                    rerank_documents.append(rerank_document)
-
-            return RerankResult(model=model, docs=rerank_documents)
-        except httpx.HTTPStatusError as e:
-            raise InvokeServerUnavailableError(str(e))
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            self._invoke(
-                model=model,
-                credentials=credentials,
-                query="What is the capital of the United States?",
-                docs=[
-                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
-                    "Census, Carson City had a population of 55,274.",
-                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
-                    "are a political division controlled by the United States. Its capital is Saipan.",
-                ],
-                score_threshold=0.8,
-            )
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))
-
-    @property
-    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        """
-        Map model invoke error to unified error
-        """
-        return {
-            InvokeConnectionError: [httpx.ConnectError],
-            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
-            InvokeRateLimitError: [],
-            InvokeAuthorizationError: [httpx.HTTPStatusError],
-            InvokeBadRequestError: [httpx.RequestError],
-        }
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
-        """
-        generate custom model entities from credentials
-        """
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            model_type=ModelType.RERANK,
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
-        )
-
-        return entity

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

@@ -1,35 +0,0 @@
-from typing import Optional
-
-from yarl import URL
-
-from core.entities.embedding_type import EmbeddingInputType
-from core.model_runtime.entities.text_embedding_entities import (
-    TextEmbeddingResult,
-)
-from core.model_runtime.model_providers.openai_api_compatible.text_embedding.text_embedding import (
-    OAICompatEmbeddingModel,
-)
-
-
-class GPUStackTextEmbeddingModel(OAICompatEmbeddingModel):
-    """
-    Model class for GPUStack text embedding model.
-    """
-
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        texts: list[str],
-        user: Optional[str] = None,
-        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
-    ) -> TextEmbeddingResult:
-        return super()._invoke(model, credentials, texts, user, input_type)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        super().validate_credentials(model, credentials)
-
-    @staticmethod
-    def _add_custom_parameters(credentials: dict) -> None:
-        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1-openai")

api/core/model_runtime/model_providers/vertex_ai/llm/anthropic.claude-3.5-sonnet-v2.yaml

@@ -1,55 +0,0 @@
-model: claude-3-5-sonnet-v2@20241022
-label:
-  en_US: Claude 3.5 Sonnet v2
-model_type: llm
-features:
-  - agent-thought
-  - vision
-model_properties:
-  mode: chat
-  context_size: 200000
-parameter_rules:
-  - name: max_tokens
-    use_template: max_tokens
-    required: true
-    type: int
-    default: 8192
-    min: 1
-    max: 8192
-    help:
-      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
-      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
-  - name: temperature
-    use_template: temperature
-    required: false
-    type: float
-    default: 1
-    min: 0.0
-    max: 1.0
-    help:
-      zh_Hans: 生成内容的随机性。
-      en_US: The amount of randomness injected into the response.
-  - name: top_p
-    required: false
-    type: float
-    default: 0.999
-    min: 0.000
-    max: 1.000
-    help:
-      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
-      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
-  - name: top_k
-    required: false
-    type: int
-    default: 0
-    min: 0
-    # tip docs from aws has error, max value is 500
-    max: 500
-    help:
-      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
-      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
-pricing:
-  input: '0.003'
-  output: '0.015'
-  unit: '0.001'
-  currency: USD

api/core/model_runtime/model_providers/vessl_ai/_assets/icon_s_en.svg

@@ -1,3 +0,0 @@
-<svg width="1200" height="925" viewBox="0 0 1200 925" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M780.152 250.999L907.882 462.174C907.882 462.174 880.925 510.854 867.43 535.21C834.845 594.039 764.171 612.49 710.442 508.333L420.376 0H0L459.926 803.307C552.303 964.663 787.366 964.663 879.743 803.307C989.874 610.952 1089.87 441.97 1200 249.646L1052.28 0H639.519L780.152 250.999Z" fill="#3366FF"/>
-</svg>

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

@@ -1,83 +0,0 @@
-from decimal import Decimal
-
-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.llm_entities import LLMMode
-from core.model_runtime.entities.model_entities import (
-    AIModelEntity,
-    DefaultParameterName,
-    FetchFrom,
-    ModelPropertyKey,
-    ModelType,
-    ParameterRule,
-    ParameterType,
-    PriceConfig,
-)
-from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
-
-
-class VesslAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
-        features = []
-
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            model_type=ModelType.LLM,
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            features=features,
-            model_properties={
-                ModelPropertyKey.MODE: credentials.get("mode"),
-            },
-            parameter_rules=[
-                ParameterRule(
-                    name=DefaultParameterName.TEMPERATURE.value,
-                    label=I18nObject(en_US="Temperature"),
-                    type=ParameterType.FLOAT,
-                    default=float(credentials.get("temperature", 0.7)),
-                    min=0,
-                    max=2,
-                    precision=2,
-                ),
-                ParameterRule(
-                    name=DefaultParameterName.TOP_P.value,
-                    label=I18nObject(en_US="Top P"),
-                    type=ParameterType.FLOAT,
-                    default=float(credentials.get("top_p", 1)),
-                    min=0,
-                    max=1,
-                    precision=2,
-                ),
-                ParameterRule(
-                    name=DefaultParameterName.TOP_K.value,
-                    label=I18nObject(en_US="Top K"),
-                    type=ParameterType.INT,
-                    default=int(credentials.get("top_k", 50)),
-                    min=-2147483647,
-                    max=2147483647,
-                    precision=0,
-                ),
-                ParameterRule(
-                    name=DefaultParameterName.MAX_TOKENS.value,
-                    label=I18nObject(en_US="Max Tokens"),
-                    type=ParameterType.INT,
-                    default=512,
-                    min=1,
-                    max=int(credentials.get("max_tokens_to_sample", 4096)),
-                ),
-            ],
-            pricing=PriceConfig(
-                input=Decimal(credentials.get("input_price", 0)),
-                output=Decimal(credentials.get("output_price", 0)),
-                unit=Decimal(credentials.get("unit", 0)),
-                currency=credentials.get("currency", "USD"),
-            ),
-        )
-
-        if credentials["mode"] == "chat":
-            entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT.value
-        elif credentials["mode"] == "completion":
-            entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION.value
-        else:
-            raise ValueError(f"Unknown completion type {credentials['completion_type']}")
-
-        return entity

api/core/model_runtime/model_providers/vessl_ai/vessl_ai.py

@@ -1,10 +0,0 @@
-import logging
-
-from core.model_runtime.model_providers.__base.model_provider import ModelProvider
-
-logger = logging.getLogger(__name__)
-
-
-class VesslAIProvider(ModelProvider):
-    def validate_provider_credentials(self, credentials: dict) -> None:
-        pass

api/core/model_runtime/model_providers/vessl_ai/vessl_ai.yaml

@@ -1,56 +0,0 @@
-provider: vessl_ai
-label:
-  en_US: VESSL AI
-icon_small:
-  en_US: icon_s_en.svg
-icon_large:
-  en_US: icon_l_en.png
-background: "#F1EFED"
-help:
-  title:
-    en_US: How to deploy VESSL AI LLM Model Endpoint
-  url:
-    en_US: https://docs.vessl.ai/guides/get-started/llama3-deployment
-supported_model_types:
-  - llm
-configurate_methods:
-  - customizable-model
-model_credential_schema:
-  model:
-    label:
-      en_US: Model Name
-    placeholder:
-      en_US: Enter model name
-  credential_form_schemas:
-    - variable: endpoint_url
-      label:
-        en_US: Endpoint Url
-      type: text-input
-      required: true
-      placeholder:
-        en_US: Enter VESSL AI service endpoint url
-    - variable: api_key
-      required: true
-      label:
-        en_US: API Key
-      type: secret-input
-      placeholder:
-        en_US: Enter VESSL AI secret key
-    - variable: mode
-      show_on:
-        - variable: __model_type
-          value: llm
-      label:
-        en_US: Completion Mode
-      type: select
-      required: false
-      default: chat
-      placeholder:
-        en_US: Select completion mode
-      options:
-        - value: completion
-          label:
-            en_US: Completion
-        - value: chat
-          label:
-            en_US: Chat

api/core/model_runtime/model_providers/x/_assets/x-ai-logo.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" fill="currentColor" viewBox="0 0 24 24" aria-hidden="true" class="" focusable="false" style="fill:currentColor;height:28px;width:28px"><path d="m3.005 8.858 8.783 12.544h3.904L6.908 8.858zM6.905 15.825 3 21.402h3.907l1.951-2.788zM16.585 2l-6.75 9.64 1.953 2.79L20.492 2zM17.292 7.965v13.437h3.2V3.395z"></path></svg>

api/core/model_runtime/model_providers/x/llm/grok-beta.yaml

@@ -1,63 +0,0 @@
-model: grok-beta
-label:
-  en_US: Grok beta
-model_type: llm
-features:
-  - multi-tool-call
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    label:
-      en_US: "Temperature"
-      zh_Hans: "采样温度"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 2.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: top_p
-    label:
-      en_US: "Top P"
-      zh_Hans: "Top P"
-    type: float
-    default: 0.7
-    min: 0.0
-    max: 1.0
-    precision: 1
-    required: true
-    help:
-      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
-      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
-
-  - name: frequency_penalty
-    use_template: frequency_penalty
-    label:
-      en_US: "Frequency Penalty"
-      zh_Hans: "频率惩罚"
-    type: float
-    default: 0
-    min: 0
-    max: 2.0
-    precision: 1
-    required: false
-    help:
-      en_US: "Number between 0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
-      zh_Hans: "介于0和2.0之间的数字。正值会根据新标记在文本中迄今为止的现有频率来惩罚它们，从而降低模型一字不差地重复同一句话的可能性。"
-
-  - name: user
-    use_template: text
-    label:
-      en_US: "User"
-      zh_Hans: "用户"
-    type: string
-    required: false
-    help:
-      en_US: "Used to track and differentiate conversation requests from different users."
-      zh_Hans: "用于追踪和区分不同用户的对话请求。"

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

@@ -1,37 +0,0 @@
-from collections.abc import Generator
-from typing import Optional, Union
-
-from yarl import URL
-
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
-from core.model_runtime.entities.message_entities import (
-    PromptMessage,
-    PromptMessageTool,
-)
-from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
-
-
-class XAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(
-        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]:
-        self._add_custom_parameters(credentials)
-        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        super().validate_credentials(model, credentials)
-
-    @staticmethod
-    def _add_custom_parameters(credentials) -> None:
-        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"])) or "https://api.x.ai/v1"
-        credentials["mode"] = LLMMode.CHAT.value
-        credentials["function_calling_type"] = "tool_call"

api/core/model_runtime/model_providers/x/x.py

@@ -1,25 +0,0 @@
-import logging
-
-from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.model_provider import ModelProvider
-
-logger = logging.getLogger(__name__)
-
-
-class XAIProvider(ModelProvider):
-    def validate_provider_credentials(self, credentials: dict) -> None:
-        """
-        Validate provider credentials
-        if validate failed, raise exception
-
-        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
-        """
-        try:
-            model_instance = self.get_model_instance(ModelType.LLM)
-            model_instance.validate_credentials(model="grok-beta", 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

api/core/model_runtime/model_providers/x/x.yaml

@@ -1,38 +0,0 @@
-provider: x
-label:
-  en_US: xAI
-description:
-  en_US: xAI is a company working on building artificial intelligence to accelerate human scientific discovery. We are guided by our mission to advance our collective understanding of the universe.
-icon_small:
-  en_US: x-ai-logo.svg
-icon_large:
-  en_US: x-ai-logo.svg
-help:
-  title:
-    en_US: Get your token from xAI
-    zh_Hans: 从 xAI 获取 token
-  url:
-    en_US: https://x.ai/api
-supported_model_types:
-  - llm
-configurate_methods:
-  - predefined-model
-provider_credential_schema:
-  credential_form_schemas:
-    - variable: api_key
-      label:
-        en_US: API Key
-      type: secret-input
-      required: true
-      placeholder:
-        zh_Hans: 在此输入您的 API Key
-        en_US: Enter your API Key
-    - variable: endpoint_url
-      label:
-        en_US: API Base
-      type: text-input
-      required: false
-      default: https://api.x.ai/v1
-      placeholder:
-        zh_Hans: 在此输入您的 API Base
-        en_US: Enter your API Base

api/core/moderation/keywords/keywords.py

@@ -1,3 +1,6 @@
+from collections.abc import Sequence
+from typing import Any
+
 from core.moderation.base import Moderation, ModerationAction, ModerationInputsResult, ModerationOutputsResult
 
 
@@ -62,5 +65,5 @@ class KeywordsModeration(Moderation):
     def _is_violated(self, inputs: dict, keywords_list: list) -> bool:
         return any(self._check_keywords_in_value(keywords_list, value) for value in inputs.values())
 
-    def _check_keywords_in_value(self, keywords_list, value) -> bool:
-        return any(keyword.lower() in value.lower() for keyword in keywords_list)
+    def _check_keywords_in_value(self, keywords_list: Sequence[str], value: Any) -> bool:
+        return any(keyword.lower() in str(value).lower() for keyword in keywords_list)

api/core/moderation/output_moderation.py

@@ -126,6 +126,6 @@ class OutputModeration(BaseModel):
             result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer)
             return result
         except Exception as e:
-            logger.exception("Moderation Output error: %s", e)
+            logger.exception(f"Moderation Output error, app_id: {app_id}")
 
         return None

api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py

@@ -49,6 +49,7 @@ class LangSmithRunModel(LangSmithTokenUsage, LangSmithMultiModel):
     reference_example_id: Optional[str] = Field(None, description="Reference example ID associated with the run")
     input_attachments: Optional[dict[str, Any]] = Field(None, description="Input attachments of the run")
     output_attachments: Optional[dict[str, Any]] = Field(None, description="Output attachments of the run")
+    dotted_order: Optional[str] = Field(None, description="Dotted order of the run")
 
     @field_validator("inputs", "outputs")
     @classmethod