Merge branch 'main' into chore/bump-version-to-0.15.0

api/.env.example

@@ -23,6 +23,9 @@ FILES_ACCESS_TIMEOUT=300
 # Access token expiration time in minutes
 ACCESS_TOKEN_EXPIRE_MINUTES=60
 
+# Refresh token expiration time in days
+REFRESH_TOKEN_EXPIRE_DAYS=30
+
 # celery configuration
 CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
 

api/app.py

@@ -14,7 +14,10 @@ def is_db_command():
 
     app = create_migrations_app()
 else:
-    if os.environ.get("FLASK_DEBUG", "False") != "True":
+    # It seems that JetBrains Python debugger does not work well with gevent,
+    # so we need to disable gevent in debug mode.
+    # If you are using debugpy and set GEVENT_SUPPORT=True, you can debug with gevent.
+    if (flask_debug := os.environ.get("FLASK_DEBUG", "0")) and flask_debug.lower() in {"false", "0", "no"}:
         from gevent import monkey  # type: ignore
 
         # gevent

api/configs/feature/__init__.py

@@ -488,6 +488,11 @@ class AuthConfig(BaseSettings):
         default=60,
     )
 
+    REFRESH_TOKEN_EXPIRE_DAYS: PositiveFloat = Field(
+        description="Expiration time for refresh tokens in days",
+        default=30,
+    )
+
     LOGIN_LOCKOUT_DURATION: PositiveInt = Field(
         description="Time (in seconds) a user must wait before retrying login after exceeding the rate limit.",
         default=86400,
@@ -667,6 +672,11 @@ class IndexingConfig(BaseSettings):
         default=4000,
     )
 
+    CHILD_CHUNKS_PREVIEW_NUMBER: PositiveInt = Field(
+        description="Maximum number of child chunks to preview",
+        default=50,
+    )
+
 
 class MultiModalTransferConfig(BaseSettings):
     MULTIMODAL_SEND_FORMAT: Literal["base64", "url"] = Field(

api/controllers/console/app/app.py

@@ -57,12 +57,13 @@ def uuid_list(value):
         )
         parser.add_argument("name", type=str, location="args", required=False)
         parser.add_argument("tag_ids", type=uuid_list, location="args", required=False)
+        parser.add_argument("is_created_by_me", type=inputs.boolean, location="args", required=False)
 
         args = parser.parse_args()
 
         # get app list
         app_service = AppService()
-        app_pagination = app_service.get_paginate_apps(current_user.current_tenant_id, args)
+        app_pagination = app_service.get_paginate_apps(current_user.id, current_user.current_tenant_id, args)
         if not app_pagination:
             return {"data": [], "total": 0, "page": 1, "limit": 20, "has_more": False}
 

api/core/app/task_pipeline/based_generate_task_pipeline.py

@@ -15,7 +15,6 @@
 from core.app.entities.task_entities import (
     ErrorStreamResponse,
     PingStreamResponse,
-    TaskState,
 )
 from core.errors.error import QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
@@ -30,22 +29,12 @@ class BasedGenerateTaskPipeline:
     BasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
-    _task_state: TaskState
-    _application_generate_entity: AppGenerateEntity
-
     def __init__(
         self,
         application_generate_entity: AppGenerateEntity,
         queue_manager: AppQueueManager,
         stream: bool,
     ) -> None:
-        """
-        Initialize GenerateTaskPipeline.
-        :param application_generate_entity: application generate entity
-        :param queue_manager: queue manager
-        :param user: user
-        :param stream: stream
-        """
         self._application_generate_entity = application_generate_entity
         self._queue_manager = queue_manager
         self._start_at = time.perf_counter()

api/core/app/task_pipeline/message_cycle_manage.py

@@ -31,10 +31,19 @@
 
 
 class MessageCycleManage:
-    _application_generate_entity: Union[
-        ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity, AdvancedChatAppGenerateEntity
-    ]
-    _task_state: Union[EasyUITaskState, WorkflowTaskState]
+    def __init__(
+        self,
+        *,
+        application_generate_entity: Union[
+            ChatAppGenerateEntity,
+            CompletionAppGenerateEntity,
+            AgentChatAppGenerateEntity,
+            AdvancedChatAppGenerateEntity,
+        ],
+        task_state: Union[EasyUITaskState, WorkflowTaskState],
+    ) -> None:
+        self._application_generate_entity = application_generate_entity
+        self._task_state = task_state
 
     def _generate_conversation_name(self, *, conversation_id: str, query: str) -> Optional[Thread]:
         """

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -34,7 +34,6 @@
     ParallelBranchStartStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
-    WorkflowTaskState,
 )
 from core.file import FILE_MODEL_IDENTITY, File
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -58,13 +57,20 @@
     WorkflowRunStatus,
 )
 
-from .exc import WorkflowNodeExecutionNotFoundError, WorkflowRunNotFoundError
+from .exc import WorkflowRunNotFoundError
 
 
 class WorkflowCycleManage:
-    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
-    _task_state: WorkflowTaskState
-    _workflow_system_variables: dict[SystemVariableKey, Any]
+    def __init__(
+        self,
+        *,
+        application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity],
+        workflow_system_variables: dict[SystemVariableKey, Any],
+    ) -> None:
+        self._workflow_run: WorkflowRun | None = None
+        self._workflow_node_executions: dict[str, WorkflowNodeExecution] = {}
+        self._application_generate_entity = application_generate_entity
+        self._workflow_system_variables = workflow_system_variables
 
     def _handle_workflow_run_start(
         self,
@@ -240,24 +246,26 @@ def _handle_workflow_run_failed(
         workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
         workflow_run.exceptions_count = exceptions_count
 
-        stmt = select(WorkflowNodeExecution).where(
+        stmt = select(WorkflowNodeExecution.node_execution_id).where(
             WorkflowNodeExecution.tenant_id == workflow_run.tenant_id,
             WorkflowNodeExecution.app_id == workflow_run.app_id,
             WorkflowNodeExecution.workflow_id == workflow_run.workflow_id,
             WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
             WorkflowNodeExecution.workflow_run_id == workflow_run.id,
             WorkflowNodeExecution.status == WorkflowNodeExecutionStatus.RUNNING.value,
         )
-
-        running_workflow_node_executions = session.scalars(stmt).all()
+        ids = session.scalars(stmt).all()
+        # Use self._get_workflow_node_execution here to make sure the cache is updated
+        running_workflow_node_executions = [
+            self._get_workflow_node_execution(session=session, node_execution_id=id) for id in ids if id
+        ]
 
         for workflow_node_execution in running_workflow_node_executions:
+            now = datetime.now(UTC).replace(tzinfo=None)
             workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
             workflow_node_execution.error = error
-            workflow_node_execution.finished_at = datetime.now(UTC).replace(tzinfo=None)
-            workflow_node_execution.elapsed_time = (
-                workflow_node_execution.finished_at - workflow_node_execution.created_at
-            ).total_seconds()
+            workflow_node_execution.finished_at = now
+            workflow_node_execution.elapsed_time = (now - workflow_node_execution.created_at).total_seconds()
 
         if trace_manager:
             trace_manager.add_trace_task(
@@ -299,6 +307,8 @@ def _handle_node_execution_start(
         workflow_node_execution.created_at = datetime.now(UTC).replace(tzinfo=None)
 
         session.add(workflow_node_execution)
+
+        self._workflow_node_executions[event.node_execution_id] = workflow_node_execution
         return workflow_node_execution
 
     def _handle_workflow_node_execution_success(
@@ -326,6 +336,7 @@ def _handle_workflow_node_execution_success(
         workflow_node_execution.finished_at = finished_at
         workflow_node_execution.elapsed_time = elapsed_time
 
+        workflow_node_execution = session.merge(workflow_node_execution)
         return workflow_node_execution
 
     def _handle_workflow_node_execution_failed(
@@ -365,6 +376,7 @@ def _handle_workflow_node_execution_failed(
         workflow_node_execution.elapsed_time = elapsed_time
         workflow_node_execution.execution_metadata = execution_metadata
 
+        workflow_node_execution = session.merge(workflow_node_execution)
         return workflow_node_execution
 
     def _handle_workflow_node_execution_retried(
@@ -416,6 +428,8 @@ def _handle_workflow_node_execution_retried(
         workflow_node_execution.index = event.node_run_index
 
         session.add(workflow_node_execution)
+
+        self._workflow_node_executions[event.node_execution_id] = workflow_node_execution
         return workflow_node_execution
 
     #################################################
@@ -812,22 +826,20 @@ def _get_file_var_from_value(self, value: Union[dict, list]) -> Mapping[str, Any
         return None
 
     def _get_workflow_run(self, *, session: Session, workflow_run_id: str) -> WorkflowRun:
-        """
-        Refetch workflow run
-        :param workflow_run_id: workflow run id
-        :return:
-        """
+        if self._workflow_run and self._workflow_run.id == workflow_run_id:
+            cached_workflow_run = self._workflow_run
+            cached_workflow_run = session.merge(cached_workflow_run)
+            return cached_workflow_run
         stmt = select(WorkflowRun).where(WorkflowRun.id == workflow_run_id)
         workflow_run = session.scalar(stmt)
         if not workflow_run:
             raise WorkflowRunNotFoundError(workflow_run_id)
+        self._workflow_run = workflow_run
 
         return workflow_run
 
     def _get_workflow_node_execution(self, session: Session, node_execution_id: str) -> WorkflowNodeExecution:
-        stmt = select(WorkflowNodeExecution).where(WorkflowNodeExecution.node_execution_id == node_execution_id)
-        workflow_node_execution = session.scalar(stmt)
-        if not workflow_node_execution:
-            raise WorkflowNodeExecutionNotFoundError(node_execution_id)
-
-        return workflow_node_execution
+        if node_execution_id not in self._workflow_node_executions:
+            raise ValueError(f"Workflow node execution not found: {node_execution_id}")
+        cached_workflow_node_execution = self._workflow_node_executions[node_execution_id]
+        return cached_workflow_node_execution

api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py

@@ -1,13 +1,13 @@
+from concurrent.futures import ProcessPoolExecutor
 from os.path import abspath, dirname, join
 from threading import Lock
 from typing import Any, cast
 
-import gevent.threadpool  # type: ignore
 from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
 
 _tokenizer: Any = None
 _lock = Lock()
-_pool = gevent.threadpool.ThreadPool(1)
+_executor = ProcessPoolExecutor(max_workers=1)
 
 
 class GPT2Tokenizer:
@@ -22,8 +22,8 @@ def _get_num_tokens_by_gpt2(text: str) -> int:
 
     @staticmethod
     def get_num_tokens(text: str) -> int:
-        future = _pool.spawn(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
-        result = future.get(block=True)
+        future = _executor.submit(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
+        result = future.result()
         return cast(int, result)
 
     @staticmethod

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

@@ -24,8 +24,5 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
         super().validate_credentials(model, credentials)
 
     @staticmethod
-    def _add_custom_parameters(credentials: dict, model: Optional[str]) -> None:
-        if model is None:
-            model = "bge-m3"
-
-        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model}/v1/"
+    def _add_custom_parameters(credentials: dict, model: str) -> None:
+        credentials["endpoint_url"] = "https://ai.gitee.com/v1"

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

@@ -9,6 +9,8 @@ supported_model_types:
   - llm
   - text-embedding
   - rerank
+  - speech2text
+  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -118,3 +120,19 @@ model_credential_schema:
           label:
             en_US: Not Support
             zh_Hans: 不支持
+    - variable: voices
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: Available Voices (comma-separated)
+        zh_Hans: 可用声音（用英文逗号分隔）
+      type: text-input
+      required: false
+      default: "Chinese Female"
+      placeholder:
+        en_US: "Chinese Female, Chinese Male, Japanese Male, Cantonese Female, English Female, English Male, Korean Female"
+        zh_Hans: "Chinese Female, Chinese Male, Japanese Male, Cantonese Female, English Female, English Male, Korean Female"
+      help:
+        en_US: "List voice names separated by commas. First voice will be used as default."
+        zh_Hans: "用英文逗号分隔的声音列表。第一个声音将作为默认值。"

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

@@ -1,7 +1,5 @@
 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,
@@ -24,9 +22,10 @@ def _invoke(
         stream: bool = True,
         user: str | None = None,
     ) -> LLMResult | Generator:
+        compatible_credentials = self._get_compatible_credentials(credentials)
         return super()._invoke(
             model,
-            credentials,
+            compatible_credentials,
             prompt_messages,
             model_parameters,
             tools,
@@ -36,10 +35,15 @@ def _invoke(
         )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        super().validate_credentials(model, credentials)
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        super().validate_credentials(model, compatible_credentials)
+
+    def _get_compatible_credentials(self, credentials: dict) -> dict:
+        credentials = credentials.copy()
+        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
+        credentials["endpoint_url"] = f"{base_url}/v1-openai"
+        return 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/speech2text/speech2text.py

@@ -0,0 +1,43 @@
+from typing import IO, Optional
+
+from core.model_runtime.model_providers.openai_api_compatible.speech2text.speech2text import OAICompatSpeech2TextModel
+
+
+class GPUStackSpeech2TextModel(OAICompatSpeech2TextModel):
+    """
+    Model class for GPUStack 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
+        """
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        return super()._invoke(model, compatible_credentials, file)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        """
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        super().validate_credentials(model, compatible_credentials)
+
+    def _get_compatible_credentials(self, credentials: dict) -> dict:
+        """
+        Get compatible credentials
+
+        :param credentials: model credentials
+        :return: compatible credentials
+        """
+        compatible_credentials = credentials.copy()
+        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
+        compatible_credentials["endpoint_url"] = f"{base_url}/v1-openai"
+        return compatible_credentials