Merge branch 'main' into feat/attachments

.github/workflows/api-tests.yml

@@ -76,7 +76,7 @@ jobs:
       - name: Run Workflow
         run: poetry run -C api bash dev/pytest/pytest_workflow.sh
 
-      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale)
+      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale, ElasticSearch)
         uses: hoverkraft-tech/compose-action@v2.0.0
         with:
           compose-file: |
@@ -90,5 +90,6 @@ jobs:
             pgvecto-rs
             pgvector
             chroma
+            elasticsearch
       - name: Test Vector Stores
         run: poetry run -C api bash dev/pytest/pytest_vdb.sh

.github/workflows/expose_service_ports.sh

@@ -6,5 +6,6 @@ yq eval '.services.chroma.ports += ["8000:8000"]' -i docker/docker-compose.yaml
 yq eval '.services["milvus-standalone"].ports += ["19530:19530"]' -i docker/docker-compose.yaml
 yq eval '.services.pgvector.ports += ["5433:5432"]' -i docker/docker-compose.yaml
 yq eval '.services["pgvecto-rs"].ports += ["5431:5432"]' -i docker/docker-compose.yaml
+yq eval '.services["elasticsearch"].ports += ["9200:9200"]' -i docker/docker-compose.yaml
 
-echo "Ports exposed for sandbox, weaviate, qdrant, chroma, milvus, pgvector, pgvecto-rs."
+echo "Ports exposed for sandbox, weaviate, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch"

api/.env.example

@@ -130,6 +130,12 @@ TENCENT_VECTOR_DB_DATABASE=dify
 TENCENT_VECTOR_DB_SHARD=1
 TENCENT_VECTOR_DB_REPLICAS=2
 
+# ElasticSearch configuration
+ELASTICSEARCH_HOST=127.0.0.1
+ELASTICSEARCH_PORT=9200
+ELASTICSEARCH_USERNAME=elastic
+ELASTICSEARCH_PASSWORD=elastic
+
 # PGVECTO_RS configuration
 PGVECTO_RS_HOST=localhost
 PGVECTO_RS_PORT=5431

api/commands.py

@@ -344,6 +344,14 @@ def migrate_knowledge_vector_database():
                         "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
+                elif vector_type == VectorType.ELASTICSEARCH:
+                    dataset_id = dataset.id
+                    index_name = Dataset.gen_collection_name_by_id(dataset_id)
+                    index_struct_dict = {
+                        "type": 'elasticsearch',
+                        "vector_store": {"class_prefix": index_name}
+                    }
+                    dataset.index_struct = json.dumps(index_struct_dict)
                 else:
                     raise ValueError(f"Vector store {vector_type} is not supported.")
 

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description='Dify version',
-        default='0.6.16',
+        default='0.7.0',
     )
 
     COMMIT_SHA: str = Field(

api/contexts/__init__.py

@@ -1,3 +1,7 @@
 from contextvars import ContextVar
 
+from core.workflow.entities.variable_pool import VariablePool
+
 tenant_id: ContextVar[str] = ContextVar('tenant_id')
+
+workflow_variable_pool: ContextVar[VariablePool] = ContextVar('workflow_variable_pool')

api/controllers/console/datasets/datasets.py

@@ -555,7 +555,7 @@ class DatasetRetrievalSettingApi(Resource):
                         RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
-            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE:
+            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE | VectorType.ELASTICSEARCH:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value,
@@ -579,7 +579,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                         RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
-            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE:
+            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE | VectorType.ELASTICSEARCH:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value,

api/controllers/console/datasets/datasets_document.py

@@ -178,11 +178,20 @@ class DatasetDocumentListApi(Resource):
                 .subquery()
 
             query = query.outerjoin(sub_query, sub_query.c.document_id == Document.id) \
-                .order_by(sort_logic(db.func.coalesce(sub_query.c.total_hit_count, 0)))
+                .order_by(
+                    sort_logic(db.func.coalesce(sub_query.c.total_hit_count, 0)),
+                    sort_logic(Document.position),
+                )
         elif sort == 'created_at':
-            query = query.order_by(sort_logic(Document.created_at))
+            query = query.order_by(
+                sort_logic(Document.created_at),
+                sort_logic(Document.position),
+            )
         else:
-            query = query.order_by(desc(Document.created_at))
+            query = query.order_by(
+                desc(Document.created_at),
+                desc(Document.position),
+            )
 
         paginated_documents = query.paginate(
             page=page, per_page=limit, max_per_page=100, error_out=False)

api/core/app/app_config/easy_ui_based_app/dataset/manager.py

@@ -93,6 +93,7 @@ class DatasetConfigManager:
                     reranking_model=dataset_configs.get('reranking_model'),
                     weights=dataset_configs.get('weights'),
                     reranking_enabled=dataset_configs.get('reranking_enabled', True),
+                    rerank_mode=dataset_configs["reranking_mode"],
                 )
             )
 

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

@@ -8,6 +8,8 @@ from typing import Union
 
 from flask import Flask, current_app
 from pydantic import ValidationError
+from sqlalchemy import select
+from sqlalchemy.orm import Session
 
 import contexts
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
@@ -18,15 +20,20 @@ from core.app.apps.advanced_chat.generate_task_pipeline import AdvancedChatAppGe
 from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
-from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
+from core.app.entities.app_invoke_entities import (
+    AdvancedChatAppGenerateEntity,
+    InvokeFrom,
+)
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
 from core.file.message_file_parser import MessageFileParser
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from extensions.ext_database import db
 from models.account import Account
 from models.model import App, Conversation, EndUser, Message
-from models.workflow import Workflow
+from models.workflow import ConversationVariable, Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -209,7 +216,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             # update conversation features
             conversation.override_model_configs = workflow.features
             db.session.commit()
-            db.session.refresh(conversation)
+            # db.session.refresh(conversation)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -221,15 +228,69 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             message_id=message.id
         )
 
+        # Init conversation variables
+        stmt = select(ConversationVariable).where(
+            ConversationVariable.app_id == conversation.app_id, ConversationVariable.conversation_id == conversation.id
+        )
+        with Session(db.engine) as session:
+            conversation_variables = session.scalars(stmt).all()
+            if not conversation_variables:
+                # Create conversation variables if they don't exist.
+                conversation_variables = [
+                    ConversationVariable.from_variable(
+                        app_id=conversation.app_id, conversation_id=conversation.id, variable=variable
+                    )
+                    for variable in workflow.conversation_variables
+                ]
+                session.add_all(conversation_variables)
+            # Convert database entities to variables.
+            conversation_variables = [item.to_variable() for item in conversation_variables]
+
+            session.commit()
+
+            # Increment dialogue count.
+            conversation.dialogue_count += 1
+
+            conversation_id = conversation.id
+            conversation_dialogue_count = conversation.dialogue_count
+            db.session.commit()
+            db.session.refresh(conversation)
+
+        inputs = application_generate_entity.inputs
+        query = application_generate_entity.query
+        files = application_generate_entity.files
+
+        user_id = None
+        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
+            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
+            if end_user:
+                user_id = end_user.session_id
+        else:
+            user_id = application_generate_entity.user_id
+
+        # Create a variable pool.
+        system_inputs = {
+            SystemVariable.QUERY: query,
+            SystemVariable.FILES: files,
+            SystemVariable.CONVERSATION_ID: conversation_id,
+            SystemVariable.USER_ID: user_id,
+            SystemVariable.DIALOGUE_COUNT: conversation_dialogue_count,
+        }
+        variable_pool = VariablePool(
+            system_variables=system_inputs,
+            user_inputs=inputs,
+            environment_variables=workflow.environment_variables,
+            conversation_variables=conversation_variables,
+        )
+        contexts.workflow_variable_pool.set(variable_pool)
+
         # new thread
         worker_thread = threading.Thread(target=self._generate_worker, kwargs={
             'flask_app': current_app._get_current_object(),
             'application_generate_entity': application_generate_entity,
             'queue_manager': queue_manager,
-            'conversation_id': conversation.id,
             'message_id': message.id,
-            'user': user,
-            'context': contextvars.copy_context()
+            'context': contextvars.copy_context(),
         })
 
         worker_thread.start()
@@ -242,7 +303,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream
+            stream=stream,
         )
 
         return AdvancedChatAppGenerateResponseConverter.convert(
@@ -253,9 +314,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
     def _generate_worker(self, flask_app: Flask,
                          application_generate_entity: AdvancedChatAppGenerateEntity,
                          queue_manager: AppQueueManager,
-                         conversation_id: str,
                          message_id: str,
-                         user: Account,
                          context: contextvars.Context) -> None:
         """
         Generate worker in a new thread.
@@ -282,8 +341,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                         user_id=application_generate_entity.user_id
                     )
                 else:
-                    # get conversation and message
-                    conversation = self._get_conversation(conversation_id)
+                    # get message
                     message = self._get_message(message_id)
 
                     # chatbot app
@@ -291,7 +349,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                     runner.run(
                         application_generate_entity=application_generate_entity,
                         queue_manager=queue_manager,
-                        conversation=conversation,
                         message=message
                     )
             except GenerateTaskStoppedException:
@@ -314,14 +371,17 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             finally:
                 db.session.close()
 
-    def _handle_advanced_chat_response(self, application_generate_entity: AdvancedChatAppGenerateEntity,
-                                       workflow: Workflow,
-                                       queue_manager: AppQueueManager,
-                                       conversation: Conversation,
-                                       message: Message,
-                                       user: Union[Account, EndUser],
-                                       stream: bool = False) \
-            -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
+    def _handle_advanced_chat_response(
+        self,
+        *,
+        application_generate_entity: AdvancedChatAppGenerateEntity,
+        workflow: Workflow,
+        queue_manager: AppQueueManager,
+        conversation: Conversation,
+        message: Message,
+        user: Union[Account, EndUser],
+        stream: bool = False,
+    ) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
         """
         Handle response.
         :param application_generate_entity: application generate entity
@@ -341,7 +401,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream
+            stream=stream,
         )
 
         try:

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

@@ -4,9 +4,6 @@ import time
 from collections.abc import Mapping
 from typing import Any, Optional, cast
 
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
 from core.app.apps.advanced_chat.workflow_event_trigger_callback import WorkflowEventTriggerCallback
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -19,13 +16,10 @@ from core.app.entities.app_invoke_entities import (
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent, QueueStopEvent, QueueTextChunkEvent
 from core.moderation.base import ModerationException
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.node_entities import SystemVariable
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
-from models.model import App, Conversation, EndUser, Message
-from models.workflow import ConversationVariable, Workflow
+from models import App, Message, Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -39,7 +33,6 @@ class AdvancedChatAppRunner(AppRunner):
         self,
         application_generate_entity: AdvancedChatAppGenerateEntity,
         queue_manager: AppQueueManager,
-        conversation: Conversation,
         message: Message,
     ) -> None:
         """
@@ -63,15 +56,6 @@ class AdvancedChatAppRunner(AppRunner):
 
         inputs = application_generate_entity.inputs
         query = application_generate_entity.query
-        files = application_generate_entity.files
-
-        user_id = None
-        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
-            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
-            if end_user:
-                user_id = end_user.session_id
-        else:
-            user_id = application_generate_entity.user_id
 
         # moderation
         if self.handle_input_moderation(
@@ -103,38 +87,6 @@ class AdvancedChatAppRunner(AppRunner):
         if bool(os.environ.get('DEBUG', 'False').lower() == 'true'):
             workflow_callbacks.append(WorkflowLoggingCallback())
 
-        # Init conversation variables
-        stmt = select(ConversationVariable).where(
-            ConversationVariable.app_id == conversation.app_id, ConversationVariable.conversation_id == conversation.id
-        )
-        with Session(db.engine) as session:
-            conversation_variables = session.scalars(stmt).all()
-            if not conversation_variables:
-                conversation_variables = [
-                    ConversationVariable.from_variable(
-                        app_id=conversation.app_id, conversation_id=conversation.id, variable=variable
-                    )
-                    for variable in workflow.conversation_variables
-                ]
-                session.add_all(conversation_variables)
-                session.commit()
-            # Convert database entities to variables
-            conversation_variables = [item.to_variable() for item in conversation_variables]
-
-        # Create a variable pool.
-        system_inputs = {
-            SystemVariable.QUERY: query,
-            SystemVariable.FILES: files,
-            SystemVariable.CONVERSATION_ID: conversation.id,
-            SystemVariable.USER_ID: user_id,
-        }
-        variable_pool = VariablePool(
-            system_variables=system_inputs,
-            user_inputs=inputs,
-            environment_variables=workflow.environment_variables,
-            conversation_variables=conversation_variables,
-        )
-
         # RUN WORKFLOW
         workflow_engine_manager = WorkflowEngineManager()
         workflow_engine_manager.run_workflow(
@@ -146,7 +98,6 @@ class AdvancedChatAppRunner(AppRunner):
             invoke_from=application_generate_entity.invoke_from,
             callbacks=workflow_callbacks,
             call_depth=application_generate_entity.call_depth,
-            variable_pool=variable_pool,
         )
 
     def single_iteration_run(
@@ -155,7 +106,7 @@ class AdvancedChatAppRunner(AppRunner):
         """
         Single iteration run
         """
-        app_record: App = db.session.query(App).filter(App.id == app_id).first()
+        app_record = db.session.query(App).filter(App.id == app_id).first()
         if not app_record:
             raise ValueError('App not found')
 

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

@@ -4,6 +4,7 @@ import time
 from collections.abc import Generator
 from typing import Any, Optional, Union, cast
 
+import contexts
 from constants.tts_auto_play_timeout import TTS_AUTO_PLAY_TIMEOUT, TTS_AUTO_PLAY_YIELD_CPU_TIME
 from core.app.apps.advanced_chat.app_generator_tts_publisher import AppGeneratorTTSPublisher, AudioTrunk
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -47,7 +48,8 @@ from core.file.file_obj import FileVar
 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.node_entities import NodeType, SystemVariable
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.answer.answer_node import AnswerNode
 from core.workflow.nodes.answer.entities import TextGenerateRouteChunk, VarGenerateRouteChunk
 from events.message_event import message_was_created
@@ -71,6 +73,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
     _application_generate_entity: AdvancedChatAppGenerateEntity
     _workflow: Workflow
     _user: Union[Account, EndUser]
+    # Deprecated
     _workflow_system_variables: dict[SystemVariable, Any]
     _iteration_nested_relations: dict[str, list[str]]
 
@@ -81,7 +84,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             conversation: Conversation,
             message: Message,
             user: Union[Account, EndUser],
-            stream: bool
+            stream: bool,
     ) -> None:
         """
         Initialize AdvancedChatAppGenerateTaskPipeline.
@@ -103,11 +106,12 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         self._workflow = workflow
         self._conversation = conversation
         self._message = message
+        # Deprecated
         self._workflow_system_variables = {
             SystemVariable.QUERY: message.query,
             SystemVariable.FILES: application_generate_entity.files,
             SystemVariable.CONVERSATION_ID: conversation.id,
-            SystemVariable.USER_ID: user_id
+            SystemVariable.USER_ID: user_id,
         }
 
         self._task_state = AdvancedChatTaskState(
@@ -613,7 +617,9 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
                 if route_chunk_node_id == 'sys':
                     # system variable
-                    value = self._workflow_system_variables.get(SystemVariable.value_of(value_selector[1]))
+                    value = contexts.workflow_variable_pool.get().get(value_selector)
+                    if value:
+                        value = value.text
                 elif route_chunk_node_id in self._iteration_nested_relations:
                     # it's a iteration variable
                     if not self._iteration_state or route_chunk_node_id not in self._iteration_state.current_iterations:

api/core/app/apps/message_based_app_generator.py

@@ -258,7 +258,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
 
         return introduction
 
-    def _get_conversation(self, conversation_id: str) -> Conversation:
+    def _get_conversation(self, conversation_id: str):
         """
         Get conversation by conversation id
         :param conversation_id: conversation id
@@ -270,6 +270,9 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             .first()
         )
 
+        if not conversation:
+            raise ConversationNotExistsError()
+
         return conversation
 
     def _get_message(self, message_id: str) -> Message:

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

@@ -11,8 +11,8 @@ from core.app.entities.app_invoke_entities import (
     WorkflowAppGenerateEntity,
 )
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db

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

@@ -42,7 +42,8 @@ from core.app.entities.task_entities import (
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.workflow_cycle_manage import WorkflowCycleManage
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.node_entities import NodeType, SystemVariable
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.end.end_node import EndNode
 from extensions.ext_database import db
 from models.account import Account
@@ -530,4 +531,3 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 node.get('id') for node in nodes if node.get('data', {}).get('iteration_id') == iteration_id
             ] for iteration_id in iteration_ids
         }
-    

api/core/app/segments/__init__.py

@@ -2,7 +2,6 @@ from .segment_group import SegmentGroup
 from .segments import (
     ArrayAnySegment,
     ArraySegment,
-    FileSegment,
     FloatSegment,
     IntegerSegment,
     NoneSegment,
@@ -13,11 +12,9 @@ from .segments import (
 from .types import SegmentType
 from .variables import (
     ArrayAnyVariable,
-    ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
     ArrayStringVariable,
-    FileVariable,
     FloatVariable,
     IntegerVariable,
     NoneVariable,
@@ -32,7 +29,6 @@ __all__ = [
     'FloatVariable',
     'ObjectVariable',
     'SecretVariable',
-    'FileVariable',
     'StringVariable',
     'ArrayAnyVariable',
     'Variable',
@@ -45,11 +41,9 @@ __all__ = [
     'FloatSegment',
     'ObjectSegment',
     'ArrayAnySegment',
-    'FileSegment',
     'StringSegment',
     'ArrayStringVariable',
     'ArrayNumberVariable',
     'ArrayObjectVariable',
-    'ArrayFileVariable',
     'ArraySegment',
 ]

api/core/app/segments/factory.py

@@ -2,12 +2,10 @@ from collections.abc import Mapping
 from typing import Any
 
 from configs import dify_config
-from core.file.file_obj import FileVar
 
 from .exc import VariableError
 from .segments import (
     ArrayAnySegment,
-    FileSegment,
     FloatSegment,
     IntegerSegment,
     NoneSegment,
@@ -17,11 +15,9 @@ from .segments import (
 )
 from .types import SegmentType
 from .variables import (
-    ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
     ArrayStringVariable,
-    FileVariable,
     FloatVariable,
     IntegerVariable,
     ObjectVariable,
@@ -49,8 +45,6 @@ def build_variable_from_mapping(mapping: Mapping[str, Any], /) -> Variable:
             result = FloatVariable.model_validate(mapping)
         case SegmentType.NUMBER if not isinstance(value, float | int):
             raise VariableError(f'invalid number value {value}')
-        case SegmentType.FILE:
-            result = FileVariable.model_validate(mapping)
         case SegmentType.OBJECT if isinstance(value, dict):
             result = ObjectVariable.model_validate(mapping)
         case SegmentType.ARRAY_STRING if isinstance(value, list):
@@ -59,10 +53,6 @@ def build_variable_from_mapping(mapping: Mapping[str, Any], /) -> Variable:
             result = ArrayNumberVariable.model_validate(mapping)
         case SegmentType.ARRAY_OBJECT if isinstance(value, list):
             result = ArrayObjectVariable.model_validate(mapping)
-        case SegmentType.ARRAY_FILE if isinstance(value, list):
-            mapping = dict(mapping)
-            mapping['value'] = [{'value': v} for v in value]
-            result = ArrayFileVariable.model_validate(mapping)
         case _:
             raise VariableError(f'not supported value type {value_type}')
     if result.size > dify_config.MAX_VARIABLE_SIZE:
@@ -83,6 +73,4 @@ def build_segment(value: Any, /) -> Segment:
         return ObjectSegment(value=value)
     if isinstance(value, list):
         return ArrayAnySegment(value=value)
-    if isinstance(value, FileVar):
-        return FileSegment(value=value)
     raise ValueError(f'not supported value {value}')

api/core/app/segments/segments.py

@@ -5,8 +5,6 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, field_validator
 
-from core.file.file_obj import FileVar
-
 from .types import SegmentType
 
 
@@ -78,14 +76,7 @@ class IntegerSegment(Segment):
     value: int
 
 
-class FileSegment(Segment):
-    value_type: SegmentType = SegmentType.FILE
-    # TODO: embed FileVar in this model.
-    value: FileVar
 
-    @property
-    def markdown(self) -> str:
-        return self.value.to_markdown()
 
 
 class ObjectSegment(Segment):
@@ -130,7 +121,3 @@ class ArrayObjectSegment(ArraySegment):
     value_type: SegmentType = SegmentType.ARRAY_OBJECT
     value: Sequence[Mapping[str, Any]]
 
-
-class ArrayFileSegment(ArraySegment):
-    value_type: SegmentType = SegmentType.ARRAY_FILE
-    value: Sequence[FileSegment]

api/core/app/segments/types.py

@@ -10,8 +10,6 @@ class SegmentType(str, Enum):
     ARRAY_STRING = 'array[string]'
     ARRAY_NUMBER = 'array[number]'
     ARRAY_OBJECT = 'array[object]'
-    ARRAY_FILE = 'array[file]'
     OBJECT = 'object'
-    FILE = 'file'
 
     GROUP = 'group'

api/core/app/segments/variables.py

@@ -4,11 +4,9 @@ from core.helper import encrypter
 
 from .segments import (
     ArrayAnySegment,
-    ArrayFileSegment,
     ArrayNumberSegment,
     ArrayObjectSegment,
     ArrayStringSegment,
-    FileSegment,
     FloatSegment,
     IntegerSegment,
     NoneSegment,
@@ -44,10 +42,6 @@ class IntegerVariable(IntegerSegment, Variable):
     pass
 
 
-class FileVariable(FileSegment, Variable):
-    pass
-
-
 class ObjectVariable(ObjectSegment, Variable):
     pass
 
@@ -68,9 +62,6 @@ class ArrayObjectVariable(ArrayObjectSegment, Variable):
     pass
 
 
-class ArrayFileVariable(ArrayFileSegment, Variable):
-    pass
-
 
 class SecretVariable(StringVariable):
     value_type: SegmentType = SegmentType.SECRET

api/core/app/task_pipeline/workflow_cycle_state_manager.py

@@ -2,7 +2,7 @@ from typing import Any, Union
 
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import AdvancedChatTaskState, WorkflowTaskState
-from core.workflow.entities.node_entities import SystemVariable
+from core.workflow.enums import SystemVariable
 from models.account import Account
 from models.model import EndUser
 from models.workflow import Workflow

api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml

@@ -0,0 +1,81 @@
+model: farui-plus
+label:
+  en_US: farui-plus
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 12288
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    type: float
+    default: 0.3
+    min: 0.0
+    max: 2.0
+    help:
+      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
+      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 2000
+    min: 1
+    max: 2000
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
+  - name: top_p
+    use_template: top_p
+    type: float
+    default: 0.8
+    min: 0.1
+    max: 0.9
+    help:
+      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
+      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
+  - name: top_k
+    type: int
+    min: 0
+    max: 99
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    help:
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
+  - name: seed
+    required: false
+    type: int
+    default: 1234
+    label:
+      zh_Hans: 随机种子
+      en_US: Random seed
+    help:
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
+  - name: repetition_penalty
+    required: false
+    type: float
+    default: 1.1
+    label:
+      en_US: Repetition penalty
+    help:
+      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: enable_search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.02'
+  output: '0.02'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml

@@ -2,3 +2,8 @@ model: text-embedding-v1
 model_type: text-embedding
 model_properties:
   context_size: 2048
+  max_chunks: 25
+pricing:
+  input: "0.0007"
+  unit: "0.001"
+  currency: RMB

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml

@@ -2,3 +2,8 @@ model: text-embedding-v2
 model_type: text-embedding
 model_properties:
   context_size: 2048
+  max_chunks: 25
+pricing:
+  input: "0.0007"
+  unit: "0.001"
+  currency: RMB

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

@@ -2,6 +2,7 @@ import time
 from typing import Optional
 
 import dashscope
+import numpy as np
 
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import (
@@ -21,11 +22,11 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
     """
 
     def _invoke(
-            self,
-            model: str,
-            credentials: dict,
-            texts: list[str],
-            user: Optional[str] = None,
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -37,16 +38,44 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
         :return: embeddings result
         """
         credentials_kwargs = self._to_credential_kwargs(credentials)
-        embeddings, embedding_used_tokens = self.embed_documents(
-            credentials_kwargs=credentials_kwargs,
-            model=model,
-            texts=texts
-        )
 
+        context_size = self._get_context_size(model, credentials)
+        max_chunks = self._get_max_chunks(model, credentials)
+        inputs = []
+        indices = []
+        used_tokens = 0
+
+        for i, text in enumerate(texts):
+
+            # Here token count is only an approximation based on the GPT2 tokenizer
+            num_tokens = self._get_num_tokens_by_gpt2(text)
+
+            if num_tokens >= context_size:
+                cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
+                # if num tokens is larger than context length, only use the start
+                inputs.append(text[0:cutoff])
+            else:
+                inputs.append(text)
+            indices += [i]
+
+        batched_embeddings = []
+        _iter = range(0, len(inputs), max_chunks)
+
+        for i in _iter:
+            embeddings_batch, embedding_used_tokens = self.embed_documents(
+                credentials_kwargs=credentials_kwargs,
+                model=model,
+                texts=inputs[i : i + max_chunks],
+            )
+            used_tokens += embedding_used_tokens
+            batched_embeddings += embeddings_batch
+
+        # calc usage
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, tokens=used_tokens
+        )
         return TextEmbeddingResult(
-            embeddings=embeddings,
-            usage=self._calc_response_usage(model, credentials_kwargs, embedding_used_tokens),
-            model=model
+            embeddings=batched_embeddings, usage=usage, model=model
         )
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
@@ -79,12 +108,16 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             credentials_kwargs = self._to_credential_kwargs(credentials)
 
             # call embedding model
-            self.embed_documents(credentials_kwargs=credentials_kwargs, model=model, texts=["ping"])
+            self.embed_documents(
+                credentials_kwargs=credentials_kwargs, model=model, texts=["ping"]
+            )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
     @staticmethod
-    def embed_documents(credentials_kwargs: dict, model: str, texts: list[str]) -> tuple[list[list[float]], int]:
+    def embed_documents(
+        credentials_kwargs: dict, model: str, texts: list[str]
+    ) -> tuple[list[list[float]], int]:
         """Call out to Tongyi's embedding endpoint.
 
         Args:
@@ -102,7 +135,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
                 api_key=credentials_kwargs["dashscope_api_key"],
                 model=model,
                 input=text,
-                text_type="document"
+                text_type="document",
             )
             data = response.output["embeddings"][0]
             embeddings.append(data["embedding"])
@@ -111,7 +144,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
 
     def _calc_response_usage(
-            self, model: str, credentials: dict, tokens: int
+        self, model: str, credentials: dict, tokens: int
     ) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -125,7 +158,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             model=model,
             credentials=credentials,
             price_type=PriceType.INPUT,
-            tokens=tokens
+            tokens=tokens,
         )
 
         # transform usage
@@ -136,7 +169,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage

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

@@ -1 +1 @@
-- soloar-1-mini-chat
+- solar-1-mini-chat

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py

@@ -0,0 +1,191 @@
+import json
+from typing import Any
+
+import requests
+from elasticsearch import Elasticsearch
+from flask import current_app
+from pydantic import BaseModel, model_validator
+
+from core.rag.datasource.entity.embedding import Embeddings
+from core.rag.datasource.vdb.vector_base import BaseVector
+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.models.document import Document
+from models.dataset import Dataset
+
+
+class ElasticSearchConfig(BaseModel):
+    host: str
+    port: str
+    username: str
+    password: str
+
+    @model_validator(mode='before')
+    def validate_config(cls, values: dict) -> dict:
+        if not values['host']:
+            raise ValueError("config HOST is required")
+        if not values['port']:
+            raise ValueError("config PORT is required")
+        if not values['username']:
+            raise ValueError("config USERNAME is required")
+        if not values['password']:
+            raise ValueError("config PASSWORD is required")
+        return values
+
+
+class ElasticSearchVector(BaseVector):
+    def __init__(self, index_name: str, config: ElasticSearchConfig, attributes: list):
+        super().__init__(index_name.lower())
+        self._client = self._init_client(config)
+        self._attributes = attributes
+
+    def _init_client(self, config: ElasticSearchConfig) -> Elasticsearch:
+        try:
+            client = Elasticsearch(
+                hosts=f'{config.host}:{config.port}',
+                basic_auth=(config.username, config.password),
+                request_timeout=100000,
+                retry_on_timeout=True,
+                max_retries=10000,
+            )
+        except requests.exceptions.ConnectionError:
+            raise ConnectionError("Vector database connection error")
+
+        return client
+
+    def get_type(self) -> str:
+        return 'elasticsearch'
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        uuids = self._get_uuids(documents)
+        texts = [d.page_content for d in documents]
+        metadatas = [d.metadata for d in documents]
+
+        if not self._client.indices.exists(index=self._collection_name):
+            dim = len(embeddings[0])
+            mapping = {
+                "properties": {
+                    "text": {
+                        "type": "text"
+                    },
+                    "vector": {
+                        "type": "dense_vector",
+                        "index": True,
+                        "dims": dim,
+                        "similarity": "l2_norm"
+                    },
+                }
+            }
+            self._client.indices.create(index=self._collection_name, mappings=mapping)
+
+        added_ids = []
+        for i, text in enumerate(texts):
+            self._client.index(index=self._collection_name,
+                               id=uuids[i],
+                               document={
+                                   "text": text,
+                                   "vector": embeddings[i] if embeddings[i] else None,
+                                   "metadata": metadatas[i] if metadatas[i] else {},
+                               })
+            added_ids.append(uuids[i])
+
+        self._client.indices.refresh(index=self._collection_name)
+        return uuids
+
+    def text_exists(self, id: str) -> bool:
+        return self._client.exists(index=self._collection_name, id=id).__bool__()
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        for id in ids:
+            self._client.delete(index=self._collection_name, id=id)
+
+    def delete_by_metadata_field(self, key: str, value: str) -> None:
+        query_str = {
+            'query': {
+                'match': {
+                    f'metadata.{key}': f'{value}'
+                }
+            }
+        }
+        results = self._client.search(index=self._collection_name, body=query_str)
+        ids = [hit['_id'] for hit in results['hits']['hits']]
+        if ids:
+            self.delete_by_ids(ids)
+
+    def delete(self) -> None:
+        self._client.indices.delete(index=self._collection_name)
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        query_str = {
+            "query": {
+                "script_score": {
+                    "query": {
+                        "match_all": {}
+                    },
+                    "script": {
+                        "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
+                        "params": {
+                            "query_vector": query_vector
+                        }
+                    }
+                }
+            }
+        }
+
+        results = self._client.search(index=self._collection_name, body=query_str)
+
+        docs_and_scores = []
+        for hit in results['hits']['hits']:
+            docs_and_scores.append(
+                (Document(page_content=hit['_source']['text'], metadata=hit['_source']['metadata']), hit['_score']))
+
+        docs = []
+        for doc, score in docs_and_scores:
+            score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+            if score > score_threshold:
+                doc.metadata['score'] = score
+            docs.append(doc)
+
+        # Sort the documents by score in descending order
+        docs = sorted(docs, key=lambda x: x.metadata['score'], reverse=True)
+
+        return docs
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        query_str = {
+            "match": {
+                "text": query
+            }
+        }
+        results = self._client.search(index=self._collection_name, query=query_str)
+        docs = []
+        for hit in results['hits']['hits']:
+            docs.append(Document(page_content=hit['_source']['text'], metadata=hit['_source']['metadata']))
+
+        return docs
+
+    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        return self.add_texts(texts, embeddings, **kwargs)
+
+
+class ElasticSearchVectorFactory(AbstractVectorFactory):
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> ElasticSearchVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            collection_name = class_prefix
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id)
+            dataset.index_struct = json.dumps(
+                self.gen_index_struct_dict(VectorType.ELASTICSEARCH, collection_name))
+
+        config = current_app.config
+        return ElasticSearchVector(
+            index_name=collection_name,
+            config=ElasticSearchConfig(
+                host=config.get('ELASTICSEARCH_HOST'),
+                port=config.get('ELASTICSEARCH_PORT'),
+                username=config.get('ELASTICSEARCH_USERNAME'),
+                password=config.get('ELASTICSEARCH_PASSWORD'),
+            ),
+            attributes=[]
+        )

api/core/rag/datasource/vdb/myscale/myscale_vector.py

@@ -93,7 +93,7 @@ class MyScaleVector(BaseVector):
 
     @staticmethod
     def escape_str(value: Any) -> str:
-        return "".join(f"\\{c}" if c in ("\\", "'") else c for c in str(value))
+        return "".join(" " if c in ("\\", "'") else c for c in str(value))
 
     def text_exists(self, id: str) -> bool:
         results = self._client.query(f"SELECT id FROM {self._config.database}.{self._collection_name} WHERE id='{id}'")
@@ -118,7 +118,7 @@ class MyScaleVector(BaseVector):
         return self._search(f"distance(vector, {str(query_vector)})", self._vec_order, **kwargs)
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        return self._search(f"TextSearch(text, '{query}')", SortOrder.DESC, **kwargs)
+        return self._search(f"TextSearch('enable_nlq=false')(text, '{query}')", SortOrder.DESC, **kwargs)
 
     def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)

api/core/rag/datasource/vdb/vector_factory.py

@@ -71,6 +71,9 @@ class Vector:
             case VectorType.RELYT:
                 from core.rag.datasource.vdb.relyt.relyt_vector import RelytVectorFactory
                 return RelytVectorFactory
+            case VectorType.ELASTICSEARCH:
+                from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
+                return ElasticSearchVectorFactory
             case VectorType.TIDB_VECTOR:
                 from core.rag.datasource.vdb.tidb_vector.tidb_vector import TiDBVectorFactory
                 return TiDBVectorFactory

api/core/rag/datasource/vdb/vector_type.py

@@ -15,3 +15,4 @@ class VectorType(str, Enum):
     OPENSEARCH = 'opensearch'
     TENCENT = 'tencent'
     ORACLE = 'oracle'
+    ELASTICSEARCH = 'elasticsearch'

api/core/tools/entities/tool_entities.py

@@ -103,8 +103,8 @@ class ToolInvokeMessage(BaseModel):
     """
         plain text, image url or link url
     """
-    message: Union[str, bytes, dict] = None
-    meta: dict[str, Any] = None
+    message: str | bytes | dict | None = None
+    meta: dict[str, Any] | None = None
     save_as: str = ''
 
 class ToolInvokeMessageBinary(BaseModel):

api/core/tools/provider/builtin/gitlab/_assets/gitlab.svg

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="utf-8"?><!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="24" height="25" viewBox="0 0 24 25" xmlns="http://www.w3.org/2000/svg" fill="none"><path fill="#FC6D26" d="M14.975 8.904L14.19 6.55l-1.552-4.67a.268.268 0 00-.255-.18.268.268 0 00-.254.18l-1.552 4.667H5.422L3.87 1.879a.267.267 0 00-.254-.179.267.267 0 00-.254.18l-1.55 4.667-.784 2.357a.515.515 0 00.193.583l6.78 4.812 6.778-4.812a.516.516 0 00.196-.583z"/><path fill="#E24329" d="M8 14.296l2.578-7.75H5.423L8 14.296z"/><path fill="#FC6D26" d="M8 14.296l-2.579-7.75H1.813L8 14.296z"/><path fill="#FCA326" d="M1.81 6.549l-.784 2.354a.515.515 0 00.193.583L8 14.3 1.81 6.55z"/><path fill="#E24329" d="M1.812 6.549h3.612L3.87 1.882a.268.268 0 00-.254-.18.268.268 0 00-.255.18L1.812 6.549z"/><path fill="#FC6D26" d="M8 14.296l2.578-7.75h3.614L8 14.296z"/><path fill="#FCA326" d="M14.19 6.549l.783 2.354a.514.514 0 01-.193.583L8 14.296l6.188-7.747h.001z"/><path fill="#E24329" d="M14.19 6.549H10.58l1.551-4.667a.267.267 0 01.255-.18c.115 0 .217.073.254.18l1.552 4.667z"/></svg>

api/core/tools/provider/builtin/gitlab/gitlab.py

@@ -0,0 +1,34 @@
+from typing import Any
+
+import requests
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class GitlabProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
+        try:
+            if 'access_tokens' not in credentials or not credentials.get('access_tokens'):
+                raise ToolProviderCredentialValidationError("Gitlab Access Tokens is required.")
+            
+            if 'site_url' not in credentials or not credentials.get('site_url'):
+                site_url = 'https://gitlab.com'
+            else:
+                site_url = credentials.get('site_url')
+
+            try:
+                headers = {
+                    "Content-Type": "application/vnd.text+json",
+                    "Authorization": f"Bearer {credentials.get('access_tokens')}",
+                }
+
+                response = requests.get(
+                    url= f"{site_url}/api/v4/user",
+                    headers=headers)
+                if response.status_code != 200:
+                    raise ToolProviderCredentialValidationError((response.json()).get('message'))
+            except Exception as e:
+                raise ToolProviderCredentialValidationError("Gitlab Access Tokens and Api Version is invalid. {}".format(e))
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))

api/core/tools/provider/builtin/gitlab/gitlab.yaml

@@ -0,0 +1,38 @@
+identity:
+  author: Leo.Wang
+  name: gitlab
+  label:
+    en_US: Gitlab
+    zh_Hans: Gitlab
+  description:
+    en_US: Gitlab plugin for commit
+    zh_Hans: 用于获取Gitlab commit的插件
+  icon: gitlab.svg
+credentials_for_provider:
+  access_tokens:
+    type: secret-input
+    required: true
+    label:
+      en_US: Gitlab access token
+      zh_Hans: Gitlab access token
+    placeholder:
+      en_US: Please input your Gitlab access token
+      zh_Hans: 请输入你的 Gitlab access token
+    help:
+      en_US: Get your Gitlab access token from Gitlab
+      zh_Hans: 从 Gitlab 获取您的 access token
+    url: https://docs.gitlab.com/16.9/ee/api/oauth2.html
+  site_url:
+    type: text-input
+    required: false
+    default: 'https://gitlab.com'
+    label:
+      en_US: Gitlab site url
+      zh_Hans: Gitlab site url
+    placeholder:
+      en_US: Please input your Gitlab site url
+      zh_Hans: 请输入你的 Gitlab site url
+    help:
+      en_US: Find your Gitlab url
+      zh_Hans: 找到你的Gitlab url
+    url: https://gitlab.com/help

api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.py

@@ -0,0 +1,101 @@
+import json
+from datetime import datetime, timedelta
+from typing import Any, Union
+
+import requests
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class GitlabCommitsTool(BuiltinTool):
+    def _invoke(self, 
+                user_id: str,
+                tool_parameters: dict[str, Any]
+        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+
+        project = tool_parameters.get('project', '')
+        employee = tool_parameters.get('employee', '')
+        start_time = tool_parameters.get('start_time', '')
+        end_time = tool_parameters.get('end_time', '')
+
+        if not project:
+            return self.create_text_message('Project is required')
+
+        if not start_time:
+            start_time = (datetime.utcnow() - timedelta(days=1)).isoformat()
+        if not end_time:
+            end_time = datetime.utcnow().isoformat()
+
+        access_token = self.runtime.credentials.get('access_tokens')
+        site_url = self.runtime.credentials.get('site_url')
+
+        if 'access_tokens' not in self.runtime.credentials or not self.runtime.credentials.get('access_tokens'):
+            return self.create_text_message("Gitlab API Access Tokens is required.")
+        if 'site_url' not in self.runtime.credentials or not self.runtime.credentials.get('site_url'):
+            site_url = 'https://gitlab.com'
+        
+        # Get commit content
+        result = self.fetch(user_id, site_url, access_token, project, employee, start_time, end_time)
+
+        return self.create_text_message(json.dumps(result, ensure_ascii=False))
+    
+    def fetch(self,user_id: str, site_url: str, access_token: str, project: str, employee: str = None, start_time: str = '', end_time: str = '') -> list[dict[str, Any]]:
+        domain = site_url
+        headers = {"PRIVATE-TOKEN": access_token}
+        results = []
+
+        try:
+            # Get all of projects
+            url = f"{domain}/api/v4/projects"
+            response = requests.get(url, headers=headers)
+            response.raise_for_status()
+            projects = response.json()
+
+            filtered_projects = [p for p in projects if project == "*" or p['name'] == project]
+
+            for project in filtered_projects:
+                project_id = project['id']
+                project_name = project['name']
+                print(f"Project: {project_name}")
+
+                # Get all of proejct commits
+                commits_url = f"{domain}/api/v4/projects/{project_id}/repository/commits"
+                params = {
+                    'since': start_time,
+                    'until': end_time
+                }
+                if employee:
+                    params['author'] = employee
+
+                commits_response = requests.get(commits_url, headers=headers, params=params)
+                commits_response.raise_for_status()
+                commits = commits_response.json()
+
+                for commit in commits:
+                    commit_sha = commit['id']
+                    print(f"\tCommit SHA: {commit_sha}")
+
+                    diff_url = f"{domain}/api/v4/projects/{project_id}/repository/commits/{commit_sha}/diff"
+                    diff_response = requests.get(diff_url, headers=headers)
+                    diff_response.raise_for_status()
+                    diffs = diff_response.json()
+                    
+                    for diff in diffs:
+                        # Caculate code lines of changed
+                        added_lines = diff['diff'].count('\n+')
+                        removed_lines = diff['diff'].count('\n-')
+                        total_changes = added_lines + removed_lines
+
+                        if total_changes > 1:
+                            final_code = ''.join([line[1:] for line in diff['diff'].split('\n') if line.startswith('+') and not line.startswith('+++')])
+                            results.append({
+                                "project": project_name,
+                                "commit_sha": commit_sha,
+                                "diff": final_code
+                            })
+                            print(f"Commit code:{final_code}")
+        except requests.RequestException as e:
+            print(f"Error fetching data from GitLab: {e}")
+        
+        return results

api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.yaml

@@ -0,0 +1,56 @@
+identity:
+  name: gitlab_commits
+  author: Leo.Wang
+  label:
+    en_US: Gitlab Commits
+    zh_Hans: Gitlab代码提交内容
+description:
+  human:
+    en_US: A tool for query gitlab commits. Input should be a exists username.
+    zh_Hans: 一个用于查询gitlab代码提交记录的的工具，输入的内容应该是一个已存在的用户名或者项目名。
+  llm: A tool for query gitlab commits. Input should be a exists username or project.
+parameters:
+  - name: employee
+    type: string
+    required: false
+    label:
+      en_US: employee
+      zh_Hans: 员工用户名
+    human_description:
+      en_US: employee
+      zh_Hans: 员工用户名
+    llm_description: employee for gitlab
+    form: llm
+  - name: project
+    type: string
+    required: true
+    label:
+      en_US: project
+      zh_Hans: 项目名
+    human_description:
+      en_US: project
+      zh_Hans: 项目名
+    llm_description: project for gitlab
+    form: llm
+  - name: start_time
+    type: string
+    required: false
+    label:
+      en_US: start_time
+      zh_Hans: 开始时间
+    human_description:
+      en_US: start_time
+      zh_Hans: 开始时间
+    llm_description: start_time for gitlab
+    form: llm
+  - name: end_time
+    type: string
+    required: false
+    label:
+      en_US: end_time
+      zh_Hans: 结束时间
+    human_description:
+      en_US: end_time
+      zh_Hans: 结束时间
+    llm_description: end_time for gitlab
+    form: llm

api/core/workflow/entities/node_entities.py

@@ -4,13 +4,14 @@ from typing import Any, Optional
 
 from pydantic import BaseModel
 
-from models.workflow import WorkflowNodeExecutionStatus
+from models import WorkflowNodeExecutionStatus
 
 
 class NodeType(Enum):
     """
     Node Types.
     """
+
     START = 'start'
     END = 'end'
     ANSWER = 'answer'
@@ -44,33 +45,11 @@ class NodeType(Enum):
         raise ValueError(f'invalid node type value {value}')
 
 
-class SystemVariable(Enum):
-    """
-    System Variables.
-    """
-    QUERY = 'query'
-    FILES = 'files'
-    CONVERSATION_ID = 'conversation_id'
-    USER_ID = 'user_id'
-
-    @classmethod
-    def value_of(cls, value: str) -> 'SystemVariable':
-        """
-        Get value of given system variable.
-
-        :param value: system variable value
-        :return: system variable
-        """
-        for system_variable in cls:
-            if system_variable.value == value:
-                return system_variable
-        raise ValueError(f'invalid system variable value {value}')
-
-
 class NodeRunMetadataKey(Enum):
     """
     Node Run Metadata Key.
     """
+
     TOTAL_TOKENS = 'total_tokens'
     TOTAL_PRICE = 'total_price'
     CURRENCY = 'currency'
@@ -83,6 +62,7 @@ class NodeRunResult(BaseModel):
     """
     Node Run Result.
     """
+
     status: WorkflowNodeExecutionStatus = WorkflowNodeExecutionStatus.RUNNING
 
     inputs: Optional[Mapping[str, Any]] = None  # node inputs

api/core/workflow/entities/variable_pool.py

@@ -6,7 +6,7 @@ from typing_extensions import deprecated
 
 from core.app.segments import Segment, Variable, factory
 from core.file.file_obj import FileVar
-from core.workflow.entities.node_entities import SystemVariable
+from core.workflow.enums import SystemVariable
 
 VariableValue = Union[str, int, float, dict, list, FileVar]
 

api/core/workflow/enums.py

@@ -0,0 +1,25 @@
+from enum import Enum
+
+
+class SystemVariable(str, Enum):
+    """
+    System Variables.
+    """
+    QUERY = 'query'
+    FILES = 'files'
+    CONVERSATION_ID = 'conversation_id'
+    USER_ID = 'user_id'
+    DIALOGUE_COUNT = 'dialogue_count'
+
+    @classmethod
+    def value_of(cls, value: str):
+        """
+        Get value of given system variable.
+
+        :param value: system variable value
+        :return: system variable
+        """
+        for system_variable in cls:
+            if system_variable.value == value:
+                return system_variable
+        raise ValueError(f'invalid system variable value {value}')

api/core/workflow/nodes/http_request/http_request_node.py

@@ -133,9 +133,6 @@ class HttpRequestNode(BaseNode):
         """
         files = []
         mimetype, file_binary = response.extract_file()
-        # if not image, return directly
-        if 'image' not in mimetype:
-            return files
 
         if mimetype:
             # extract filename from url

api/core/workflow/nodes/llm/llm_node.py

@@ -23,8 +23,9 @@ from core.model_runtime.utils.encoders import jsonable_encoder
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType, SystemVariable
+from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.llm.entities import (
     LLMNodeChatModelMessage,

api/core/workflow/nodes/tool/tool_node.py

@@ -2,19 +2,20 @@ from collections.abc import Mapping, Sequence
 from os import path
 from typing import Any, cast
 
-from core.app.segments import parser
+from core.app.segments import ArrayAnyVariable, parser
 from core.callback_handler.workflow_tool_callback_handler import DifyWorkflowCallbackHandler
 from core.file.file_obj import FileTransferMethod, FileType, FileVar
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter
 from core.tools.tool_engine import ToolEngine
 from core.tools.tool_manager import ToolManager
 from core.tools.utils.message_transformer import ToolFileMessageTransformer
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType, SystemVariable
+from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.tool.entities import ToolNodeData
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
-from models.workflow import WorkflowNodeExecutionStatus
+from models import WorkflowNodeExecutionStatus
 
 
 class ToolNode(BaseNode):
@@ -140,9 +141,9 @@ class ToolNode(BaseNode):
         return result
 
     def _fetch_files(self, variable_pool: VariablePool) -> list[FileVar]:
-        # FIXME: ensure this is a ArrayVariable contains FileVariable.
         variable = variable_pool.get(['sys', SystemVariable.FILES.value])
-        return [file_var.value for file_var in variable.value] if variable else []
+        assert isinstance(variable, ArrayAnyVariable)
+        return list(variable.value) if variable else []
 
     def _convert_tool_messages(self, messages: list[ToolInvokeMessage]):
         """

api/core/workflow/workflow_engine_manager.py

@@ -3,6 +3,7 @@ import time
 from collections.abc import Mapping, Sequence
 from typing import Any, Optional, cast
 
+import contexts
 from configs import dify_config
 from core.app.apps.base_app_queue_manager import GenerateTaskStoppedException
 from core.app.entities.app_invoke_entities import InvokeFrom
@@ -97,16 +98,16 @@ class WorkflowEngineManager:
         invoke_from: InvokeFrom,
         callbacks: Sequence[WorkflowCallback],
         call_depth: int = 0,
-        variable_pool: VariablePool,
+        variable_pool: VariablePool | None = None,
     ) -> None:
         """
         :param workflow: Workflow instance
         :param user_id: user id
         :param user_from: user from
-        :param user_inputs: user variables inputs
-        :param system_inputs: system inputs, like: query, files
+        :param invoke_from: invoke from
         :param callbacks: workflow callbacks
         :param call_depth: call depth
+        :param variable_pool: variable pool
         """
         # fetch workflow graph
         graph = workflow.graph_dict
@@ -128,6 +129,8 @@ class WorkflowEngineManager:
             raise ValueError('Max workflow call depth {} reached.'.format(workflow_call_max_depth))
 
         # init workflow run state
+        if not variable_pool:
+            variable_pool = contexts.workflow_variable_pool.get()
         workflow_run_state = WorkflowRunState(
             workflow=workflow,
             start_at=time.perf_counter(),

api/libs/bearer_data_source.py

@@ -2,10 +2,10 @@
 from abc import abstractmethod
 
 import requests
-from api.models.source import DataSourceBearerBinding
 from flask_login import current_user
 
 from extensions.ext_database import db
+from models.source import DataSourceBearerBinding
 
 
 class BearerDataSource:

api/migrations/versions/2024_08_14_1354-8782057ff0dc_add_conversations_dialogue_count.py

@@ -0,0 +1,33 @@
+"""add conversations.dialogue_count
+
+Revision ID: 8782057ff0dc
+Revises: 63a83fcf12ba
+Create Date: 2024-08-14 13:54:25.161324
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+import models as models
+
+# revision identifiers, used by Alembic.
+revision = '8782057ff0dc'
+down_revision = '63a83fcf12ba'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('conversations', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('dialogue_count', sa.Integer(), server_default='0', nullable=False))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('conversations', schema=None) as batch_op:
+        batch_op.drop_column('dialogue_count')
+
+    # ### end Alembic commands ###

api/models/__init__.py

@@ -1,10 +1,10 @@
 from enum import Enum
 
-from .model import AppMode
+from .model import App, AppMode, Message
 from .types import StringUUID
-from .workflow import ConversationVariable, WorkflowNodeExecutionStatus
+from .workflow import ConversationVariable, Workflow, WorkflowNodeExecutionStatus
 
-__all__ = ['ConversationVariable', 'StringUUID', 'AppMode', 'WorkflowNodeExecutionStatus']
+__all__ = ['ConversationVariable', 'StringUUID', 'AppMode', 'WorkflowNodeExecutionStatus', 'Workflow', 'App', 'Message']
 
 
 class CreatedByRole(Enum):

api/models/model.py

@@ -7,6 +7,7 @@ from typing import Optional
 from flask import request
 from flask_login import UserMixin
 from sqlalchemy import Float, func, text
+from sqlalchemy.orm import Mapped, mapped_column
 
 from configs import dify_config
 from core.file.tool_file_parser import ToolFileParser
@@ -512,12 +513,12 @@ class Conversation(db.Model):
     from_account_id = db.Column(StringUUID)
     read_at = db.Column(db.DateTime)
     read_account_id = db.Column(StringUUID)
+    dialogue_count: Mapped[int] = mapped_column(default=0)
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
     updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
 
     messages = db.relationship("Message", backref="conversation", lazy='select', passive_deletes="all")
-    message_annotations = db.relationship("MessageAnnotation", backref="conversation", lazy='select',
-                                          passive_deletes="all")
+    message_annotations = db.relationship("MessageAnnotation", backref="conversation", lazy='select', passive_deletes="all")
 
     is_deleted = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
 

api/poetry.lock

@@ -2100,6 +2100,44 @@ primp = ">=0.5.5"
 dev = ["mypy (>=1.11.0)", "pytest (>=8.3.1)", "pytest-asyncio (>=0.23.8)", "ruff (>=0.5.5)"]
 lxml = ["lxml (>=5.2.2)"]
 
+[[package]]
+name = "elastic-transport"
+version = "8.15.0"
+description = "Transport classes and utilities shared among Python Elastic client libraries"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "elastic_transport-8.15.0-py3-none-any.whl", hash = "sha256:d7080d1dada2b4eee69e7574f9c17a76b42f2895eff428e562f94b0360e158c0"},
+    {file = "elastic_transport-8.15.0.tar.gz", hash = "sha256:85d62558f9baafb0868c801233a59b235e61d7b4804c28c2fadaa866b6766233"},
+]
+
+[package.dependencies]
+certifi = "*"
+urllib3 = ">=1.26.2,<3"
+
+[package.extras]
+develop = ["aiohttp", "furo", "httpx", "opentelemetry-api", "opentelemetry-sdk", "orjson", "pytest", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "pytest-mock", "requests", "respx", "sphinx (>2)", "sphinx-autodoc-typehints", "trustme"]
+
+[[package]]
+name = "elasticsearch"
+version = "8.14.0"
+description = "Python client for Elasticsearch"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "elasticsearch-8.14.0-py3-none-any.whl", hash = "sha256:cef8ef70a81af027f3da74a4f7d9296b390c636903088439087b8262a468c130"},
+    {file = "elasticsearch-8.14.0.tar.gz", hash = "sha256:aa2490029dd96f4015b333c1827aa21fd6c0a4d223b00dfb0fe933b8d09a511b"},
+]
+
+[package.dependencies]
+elastic-transport = ">=8.13,<9"
+
+[package.extras]
+async = ["aiohttp (>=3,<4)"]
+orjson = ["orjson (>=3)"]
+requests = ["requests (>=2.4.0,!=2.32.2,<3.0.0)"]
+vectorstore-mmr = ["numpy (>=1)", "simsimd (>=3)"]
+
 [[package]]
 name = "emoji"
 version = "2.12.1"
@@ -9546,4 +9584,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "2b822039247a445f72e04e967aef84f841781e2789b70071acad022f36ba26a5"
+content-hash = "05dfa6b9bce9ed8ac21caf58eff1596f146080ab2ab6987924b189be673c22cf"

api/pyproject.toml

@@ -181,6 +181,7 @@ zhipuai = "1.0.7"
 rank-bm25 = "~0.2.2"
 openpyxl = "^3.1.5"
 kaleido = "0.2.1"
+elasticsearch = "8.14.0"
 
 ############################################################
 # Tool dependencies required by tool implementations

api/services/app_dsl_service.py

@@ -13,9 +13,9 @@ from services.workflow_service import WorkflowService
 
 logger = logging.getLogger(__name__)
 
-current_dsl_version = "0.1.0"
+current_dsl_version = "0.1.1"
 dsl_to_dify_version_mapping: dict[str, str] = {
-    "0.1.0": "0.6.0",  # dsl version -> from dify version
+    "0.1.1": "0.6.0",  # dsl version -> from dify version
 }
 
 

api/tests/integration_tests/model_runtime/__mock/huggingface_tei.py

@@ -1,5 +1,4 @@
-
-from api.core.model_runtime.model_providers.huggingface_tei.tei_helper import TeiModelExtraParameter
+from core.model_runtime.model_providers.huggingface_tei.tei_helper import TeiModelExtraParameter
 
 
 class MockTEIClass:

api/tests/integration_tests/model_runtime/huggingface_tei/test_embeddings.py

@@ -1,12 +1,12 @@
 import os
 
 import pytest
-from api.core.model_runtime.model_providers.huggingface_tei.text_embedding.text_embedding import TeiHelper
 
 from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.huggingface_tei.text_embedding.text_embedding import (
     HuggingfaceTeiTextEmbeddingModel,
+    TeiHelper,
 )
 from tests.integration_tests.model_runtime.__mock.huggingface_tei import MockTEIClass
 

api/tests/integration_tests/vdb/elasticsearch/test_elasticsearch.py

@@ -0,0 +1,25 @@
+from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import ElasticSearchConfig, ElasticSearchVector
+from tests.integration_tests.vdb.test_vector_store import (
+    AbstractVectorTest,
+    setup_mock_redis,
+)
+
+
+class ElasticSearchVectorTest(AbstractVectorTest):
+    def __init__(self):
+        super().__init__()
+        self.attributes = ['doc_id', 'dataset_id', 'document_id', 'doc_hash']
+        self.vector = ElasticSearchVector(
+            index_name=self.collection_name.lower(),
+            config=ElasticSearchConfig(
+                host='http://localhost',
+                port='9200',
+                username='elastic',
+                password='elastic'
+            ),
+            attributes=self.attributes
+        )
+
+
+def test_elasticsearch_vector(setup_mock_redis):
+    ElasticSearchVectorTest().run_all_tests()

api/tests/integration_tests/workflow/nodes/test_llm.py

@@ -10,8 +10,8 @@ from core.entities.provider_entities import CustomConfiguration, CustomProviderC
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers import ModelProviderFactory
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.nodes.llm.llm_node import LLMNode
 from extensions.ext_database import db

api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py

@@ -12,8 +12,8 @@ from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
 from extensions.ext_database import db

api/tests/unit_tests/core/app/segments/test_factory.py

@@ -3,12 +3,9 @@ from uuid import uuid4
 import pytest
 
 from core.app.segments import (
-    ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
     ArrayStringVariable,
-    FileSegment,
-    FileVariable,
     FloatVariable,
     IntegerVariable,
     ObjectSegment,
@@ -149,83 +146,6 @@ def test_array_object_variable():
     assert isinstance(variable.value[1]['key2'], int)
 
 
-def test_file_variable():
-    mapping = {
-        'id': str(uuid4()),
-        'value_type': 'file',
-        'name': 'test_file',
-        'description': 'Description of the variable.',
-        'value': {
-            'id': str(uuid4()),
-            'tenant_id': 'tenant_id',
-            'type': 'image',
-            'transfer_method': 'local_file',
-            'url': 'url',
-            'related_id': 'related_id',
-            'extra_config': {
-                'image_config': {
-                    'width': 100,
-                    'height': 100,
-                },
-            },
-            'filename': 'filename',
-            'extension': 'extension',
-            'mime_type': 'mime_type',
-        },
-    }
-    variable = factory.build_variable_from_mapping(mapping)
-    assert isinstance(variable, FileVariable)
-
-
-def test_array_file_variable():
-    mapping = {
-        'id': str(uuid4()),
-        'value_type': 'array[file]',
-        'name': 'test_array_file',
-        'description': 'Description of the variable.',
-        'value': [
-            {
-                'id': str(uuid4()),
-                'tenant_id': 'tenant_id',
-                'type': 'image',
-                'transfer_method': 'local_file',
-                'url': 'url',
-                'related_id': 'related_id',
-                'extra_config': {
-                    'image_config': {
-                        'width': 100,
-                        'height': 100,
-                    },
-                },
-                'filename': 'filename',
-                'extension': 'extension',
-                'mime_type': 'mime_type',
-            },
-            {
-                'id': str(uuid4()),
-                'tenant_id': 'tenant_id',
-                'type': 'image',
-                'transfer_method': 'local_file',
-                'url': 'url',
-                'related_id': 'related_id',
-                'extra_config': {
-                    'image_config': {
-                        'width': 100,
-                        'height': 100,
-                    },
-                },
-                'filename': 'filename',
-                'extension': 'extension',
-                'mime_type': 'mime_type',
-            },
-        ],
-    }
-    variable = factory.build_variable_from_mapping(mapping)
-    assert isinstance(variable, ArrayFileVariable)
-    assert isinstance(variable.value[0], FileSegment)
-    assert isinstance(variable.value[1], FileSegment)
-
-
 def test_variable_cannot_large_than_5_kb():
     with pytest.raises(VariableError):
         factory.build_variable_from_mapping(

api/tests/unit_tests/core/app/segments/test_segment.py

@@ -1,7 +1,7 @@
 from core.app.segments import SecretVariable, StringSegment, parser
 from core.helper import encrypter
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 
 
 def test_segment_group_to_text():

api/tests/unit_tests/core/workflow/nodes/test_answer.py

@@ -1,8 +1,8 @@
 from unittest.mock import MagicMock
 
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.answer.answer_node import AnswerNode
 from core.workflow.nodes.base_node import UserFrom
 from extensions.ext_database import db

api/tests/unit_tests/core/workflow/nodes/test_if_else.py

@@ -1,8 +1,8 @@
 from unittest.mock import MagicMock
 
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.nodes.if_else.if_else_node import IfElseNode
 from extensions.ext_database import db

api/tests/unit_tests/core/workflow/nodes/test_variable_assigner.py

@@ -3,8 +3,8 @@ from uuid import uuid4
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.segments import ArrayStringVariable, StringVariable
-from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.nodes.variable_assigner import VariableAssignerNode, WriteMode
 

dev/pytest/pytest_vdb.sh

@@ -7,4 +7,5 @@ pytest api/tests/integration_tests/vdb/chroma \
   api/tests/integration_tests/vdb/pgvector \
   api/tests/integration_tests/vdb/qdrant \
   api/tests/integration_tests/vdb/weaviate \
+  api/tests/integration_tests/vdb/elasticsearch \
   api/tests/integration_tests/vdb/test_vector_store.py

docker-legacy/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.6.16
+    image: langgenius/dify-api:0.7.0
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -169,6 +169,11 @@ services:
       CHROMA_DATABASE: default_database
       CHROMA_AUTH_PROVIDER: chromadb.auth.token_authn.TokenAuthClientProvider
       CHROMA_AUTH_CREDENTIALS: xxxxxx
+      # ElasticSearch Config
+      ELASTICSEARCH_HOST: 127.0.0.1
+      ELASTICSEARCH_PORT: 9200
+      ELASTICSEARCH_USERNAME: elastic
+      ELASTICSEARCH_PASSWORD: elastic
       # Mail configuration, support: resend, smtp
       MAIL_TYPE: ''
       # default send from email address, if not specified
@@ -224,7 +229,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.6.16
+    image: langgenius/dify-api:0.7.0
     restart: always
     environment:
       CONSOLE_WEB_URL: ''
@@ -371,6 +376,11 @@ services:
       CHROMA_DATABASE: default_database
       CHROMA_AUTH_PROVIDER: chromadb.auth.token_authn.TokenAuthClientProvider
       CHROMA_AUTH_CREDENTIALS: xxxxxx
+      # ElasticSearch Config
+      ELASTICSEARCH_HOST: 127.0.0.1
+      ELASTICSEARCH_PORT: 9200
+      ELASTICSEARCH_USERNAME: elastic
+      ELASTICSEARCH_PASSWORD: elastic
       # Notion import configuration, support public and internal
       NOTION_INTEGRATION_TYPE: public
       NOTION_CLIENT_SECRET: you-client-secret
@@ -390,7 +400,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.6.16
+    image: langgenius/dify-web:0.7.0
     restart: always
     environment:
       # The base URL of console application api server, refers to the Console base URL of WEB service if console domain is

docker/docker-compose.yaml

@@ -125,6 +125,10 @@ x-shared-env: &shared-api-worker-env
   CHROMA_DATABASE: ${CHROMA_DATABASE:-default_database}
   CHROMA_AUTH_PROVIDER: ${CHROMA_AUTH_PROVIDER:-chromadb.auth.token_authn.TokenAuthClientProvider}
   CHROMA_AUTH_CREDENTIALS: ${CHROMA_AUTH_CREDENTIALS:-}
+  ELASTICSEARCH_HOST: ${ELASTICSEARCH_HOST:-127.0.0.1}
+  ELASTICSEARCH_PORT: ${ELASTICSEARCH_PORT:-9200}
+  ELASTICSEARCH_USERNAME: ${ELASTICSEARCH_USERNAME:-elastic}
+  ELASTICSEARCH_PASSWORD: ${ELASTICSEARCH_PASSWORD:-elastic}
   # AnalyticDB configuration
   ANALYTICDB_KEY_ID: ${ANALYTICDB_KEY_ID:-}
   ANALYTICDB_KEY_SECRET: ${ANALYTICDB_KEY_SECRET:-}
@@ -187,7 +191,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.6.16
+    image: langgenius/dify-api:0.7.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -207,7 +211,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.6.16
+    image: langgenius/dify-api:0.7.0
     restart: always
     environment:
       # Use the shared environment variables.
@@ -226,7 +230,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.6.16
+    image: langgenius/dify-web:0.7.0
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}
@@ -583,7 +587,7 @@ services:
   # MyScale vector database
   myscale:
     container_name: myscale
-    image: myscale/myscaledb:1.6
+    image: myscale/myscaledb:1.6.4
     profiles:
       - myscale
     restart: always
@@ -595,6 +599,27 @@ services:
     ports:
       - "${MYSCALE_PORT:-8123}:${MYSCALE_PORT:-8123}"
 
+  elasticsearch:
+    image: docker.elastic.co/elasticsearch/elasticsearch:8.14.3
+    container_name: elasticsearch
+    profiles:
+      - elasticsearch
+    restart: always
+    environment:
+      - "ELASTIC_PASSWORD=${ELASTICSEARCH_USERNAME:-elastic}"
+      - "cluster.name=dify-es-cluster"
+      - "node.name=dify-es0"
+      - "discovery.type=single-node"
+      - "xpack.security.http.ssl.enabled=false"
+      - "xpack.license.self_generated.type=trial"
+    ports:
+      - "${ELASTICSEARCH_PORT:-9200}:${ELASTICSEARCH_PORT:-9200}"
+    healthcheck:
+      test: ["CMD", "curl", "-s", "http://localhost:9200/_cluster/health?pretty"]
+      interval: 30s
+      timeout: 10s
+      retries: 50
+
   # unstructured .
   # (if used, you need to set ETL_TYPE to Unstructured in the api & worker service.)
   unstructured:

web/app/(commonLayout)/datasets/template/template.en.mdx

@@ -922,6 +922,9 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
       <Property name='dataset_id' type='string' key='dataset_id'>
         Knowledge ID
       </Property>
+      <Property name='document_id' type='string' key='document_id'>
+        Document ID
+      </Property>
       <Property name='segment_id' type='string' key='segment_id'>
         Document Segment ID
       </Property>
@@ -965,6 +968,9 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
       <Property name='dataset_id' type='string' key='dataset_id'>
         Knowledge ID
       </Property>
+      <Property name='document_id' type='string' key='document_id'>
+        Document ID
+      </Property>
       <Property name='segment_id' type='string' key='segment_id'>
         Document Segment ID
       </Property>

web/app/(commonLayout)/datasets/template/template.zh.mdx

@@ -922,6 +922,9 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
       <Property name='dataset_id' type='string' key='dataset_id'>
         知识库 ID
       </Property>
+      <Property name='document_id' type='string' key='document_id'>
+        文档 ID
+      </Property>
       <Property name='segment_id' type='string' key='segment_id'>
         文档分段ID
       </Property>
@@ -965,6 +968,9 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
       <Property name='dataset_id' type='string' key='dataset_id'>
         知识库 ID
       </Property>
+      <Property name='document_id' type='string' key='document_id'>
+        文档 ID
+      </Property>
       <Property name='segment_id' type='string' key='segment_id'>
         文档分段ID
       </Property>

web/app/components/base/markdown.tsx

@@ -1,4 +1,5 @@
 import ReactMarkdown from 'react-markdown'
+import ReactEcharts from 'echarts-for-react'
 import 'katex/dist/katex.min.css'
 import RemarkMath from 'remark-math'
 import RemarkBreaks from 'remark-breaks'
@@ -30,6 +31,7 @@ const capitalizationLanguageNameMap: Record<string, string> = {
   mermaid: 'Mermaid',
   markdown: 'MarkDown',
   makefile: 'MakeFile',
+  echarts: 'ECharts',
 }
 const getCorrectCapitalizationLanguageName = (language: string) => {
   if (!language)
@@ -107,6 +109,14 @@ const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props }
   const match = /language-(\w+)/.exec(className || '')
   const language = match?.[1]
   const languageShowName = getCorrectCapitalizationLanguageName(language || '')
+  let chartData = JSON.parse(String('{"title":{"text":"Something went wrong."}}').replace(/\n$/, ''))
+  if (language === 'echarts') {
+    try {
+      chartData = JSON.parse(String(children).replace(/\n$/, ''))
+    }
+    catch (error) {
+    }
+  }
 
   // Use `useMemo` to ensure that `SyntaxHighlighter` only re-renders when necessary
   return useMemo(() => {
@@ -136,19 +146,25 @@ const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props }
           </div>
           {(language === 'mermaid' && isSVG)
             ? (<Flowchart PrimitiveCode={String(children).replace(/\n$/, '')} />)
-            : (<SyntaxHighlighter
-              {...props}
-              style={atelierHeathLight}
-              customStyle={{
-                paddingLeft: 12,
-                backgroundColor: '#fff',
-              }}
-              language={match[1]}
-              showLineNumbers
-              PreTag="div"
-            >
-              {String(children).replace(/\n$/, '')}
-            </SyntaxHighlighter>)}
+            : (
+              (language === 'echarts')
+                ? (<div style={{ minHeight: '250px', minWidth: '250px' }}><ReactEcharts
+                  option={chartData}
+                >
+                </ReactEcharts></div>)
+                : (<SyntaxHighlighter
+                  {...props}
+                  style={atelierHeathLight}
+                  customStyle={{
+                    paddingLeft: 12,
+                    backgroundColor: '#fff',
+                  }}
+                  language={match[1]}
+                  showLineNumbers
+                  PreTag="div"
+                >
+                  {String(children).replace(/\n$/, '')}
+                </SyntaxHighlighter>))}
         </div>
       )
       : (

web/app/components/tools/edit-custom-collection-modal/index.tsx

@@ -329,36 +329,36 @@ const EditCustomCollectionModal: FC<Props> = ({
                 <Button variant='primary' onClick={handleSave}>{t('common.operation.save')}</Button>
               </div>
             </div>
+            {showEmojiPicker && <EmojiPicker
+              onSelect={(icon, icon_background) => {
+                setEmoji({ content: icon, background: icon_background })
+                setShowEmojiPicker(false)
+              }}
+              onClose={() => {
+                setShowEmojiPicker(false)
+              }}
+            />}
+            {credentialsModalShow && (
+              <ConfigCredentials
+                positionCenter={isAdd}
+                credential={credential}
+                onChange={setCredential}
+                onHide={() => setCredentialsModalShow(false)}
+              />)
+            }
+            {isShowTestApi && (
+              <TestApi
+                positionCenter={isAdd}
+                tool={currTool as CustomParamSchema}
+                customCollection={customCollection}
+                onHide={() => setIsShowTestApi(false)}
+              />
+            )}
           </div>
         }
         isShowMask={true}
         clickOutsideNotOpen={true}
       />
-      {showEmojiPicker && <EmojiPicker
-        onSelect={(icon, icon_background) => {
-          setEmoji({ content: icon, background: icon_background })
-          setShowEmojiPicker(false)
-        }}
-        onClose={() => {
-          setShowEmojiPicker(false)
-        }}
-      />}
-      {credentialsModalShow && (
-        <ConfigCredentials
-          positionCenter={isAdd}
-          credential={credential}
-          onChange={setCredential}
-          onHide={() => setCredentialsModalShow(false)}
-        />)
-      }
-      {isShowTestApi && (
-        <TestApi
-          positionCenter={isAdd}
-          tool={currTool as CustomParamSchema}
-          customCollection={customCollection}
-          onHide={() => setIsShowTestApi(false)}
-        />
-      )}
     </>
 
   )

web/app/components/workflow/nodes/_base/hooks/use-available-var-list.ts

@@ -7,12 +7,16 @@ import {
 import type { ValueSelector, Var } from '@/app/components/workflow/types'
 type Params = {
   onlyLeafNodeVar?: boolean
+  hideEnv?: boolean
+  hideChatVar?: boolean
   filterVar: (payload: Var, selector: ValueSelector) => boolean
 }
 
 const useAvailableVarList = (nodeId: string, {
   onlyLeafNodeVar,
   filterVar,
+  hideEnv,
+  hideChatVar,
 }: Params = {
   onlyLeafNodeVar: false,
   filterVar: () => true,
@@ -32,6 +36,8 @@ const useAvailableVarList = (nodeId: string, {
     beforeNodes: availableNodes,
     isChatMode,
     filterVar,
+    hideEnv,
+    hideChatVar,
   })
 
   return {

web/app/components/workflow/nodes/answer/panel.tsx

@@ -23,6 +23,8 @@ const Panel: FC<NodePanelProps<AnswerNodeType>> = ({
 
   const { availableVars, availableNodesWithParent } = useAvailableVarList(id, {
     onlyLeafNodeVar: false,
+    hideChatVar: true,
+    hideEnv: true,
     filterVar,
   })
 

web/app/components/workflow/run/index.tsx

@@ -62,30 +62,25 @@ const RunPanel: FC<RunProps> = ({ hideResult, activeTab = 'RESULT', runID, getRe
   const formatNodeList = useCallback((list: NodeTracing[]) => {
     const allItems = list.reverse()
     const result: NodeTracing[] = []
-    let iterationIndexInfos: {
-      start: number
-      end: number
-    }[] = []
+    let iterationIndex = 0
     allItems.forEach((item) => {
-      const { node_type, index, execution_metadata } = item
+      const { node_type, execution_metadata } = item
       if (node_type !== BlockEnum.Iteration) {
-        let isInIteration = false
-        let isIterationFirstNode = false
-        iterationIndexInfos.forEach(({ start, end }) => {
-          if (index >= start && index < end) {
-            if (index === start)
-              isIterationFirstNode = true
+        const isInIteration = !!execution_metadata?.iteration_id
 
-            isInIteration = true
-          }
-        })
         if (isInIteration) {
           const iterationDetails = result[result.length - 1].details!
-          if (isIterationFirstNode)
-            iterationDetails!.push([item])
+          const currentIterationIndex = execution_metadata?.iteration_index
+          const isIterationFirstNode = iterationIndex !== currentIterationIndex || iterationDetails.length === 0
 
-          else
+          if (isIterationFirstNode) {
+            iterationDetails!.push([item])
+            iterationIndex = currentIterationIndex!
+          }
+
+          else {
             iterationDetails[iterationDetails.length - 1].push(item)
+          }
 
           return
         }
@@ -95,26 +90,6 @@ const RunPanel: FC<RunProps> = ({ hideResult, activeTab = 'RESULT', runID, getRe
         return
       }
 
-      const { steps_boundary } = execution_metadata
-      iterationIndexInfos = []
-      steps_boundary.forEach((boundary, index) => {
-        if (index === 0) {
-          iterationIndexInfos.push({
-            start: boundary,
-            end: 0,
-          })
-        }
-        else if (index === steps_boundary.length - 1) {
-          iterationIndexInfos[iterationIndexInfos.length - 1].end = boundary
-        }
-        else {
-          iterationIndexInfos[iterationIndexInfos.length - 1].end = boundary
-          iterationIndexInfos.push({
-            start: boundary,
-            end: 0,
-          })
-        }
-      })
       result.push({
         ...item,
         details: [],

web/app/components/workflow/run/node.tsx

@@ -123,7 +123,7 @@ const NodePanel: FC<Props> = ({
                 <div
                   className='flex items-center h-[34px] justify-between px-3 bg-gray-100 border-[0.5px] border-gray-200 rounded-lg cursor-pointer'
                   onClick={handleOnShowIterationDetail}>
-                  <div className='leading-[18px] text-[13px] font-medium text-gray-700'>{t('workflow.nodes.iteration.iteration', { count: nodeInfo.metadata?.iterator_length || (nodeInfo.execution_metadata?.steps_boundary?.length - 1) })}</div>
+                  <div className='leading-[18px] text-[13px] font-medium text-gray-700'>{t('workflow.nodes.iteration.iteration', { count: nodeInfo.metadata?.iterator_length })}</div>
                   {justShowIterationNavArrow
                     ? (
                       <RiArrowRightSLine className='w-3.5 h-3.5 text-gray-500' />

web/i18n/ja-JP/workflow.ts

@@ -94,11 +94,38 @@ const translation = {
     },
     export: {
       title: 'シークレット環境変数をエクスポートしますか？',
-      checkbox: 'シクレート値をエクスポート',
+      checkbox: 'シークレット値をエクスポート',
       ignore: 'DSLをエクスポート',
-      export: 'シクレート値を含むDSLをエクスポート',
+      export: 'シークレット値を含むDSLをエクスポート',
     },
   },
+  chatVariable: {
+    panelTitle: '会話変数',
+    panelDescription: '会話変数は、LLMが記憶すべき対話情報を保存するために使用されます。この情報には、対話の履歴、アップロードされたファイル、ユーザーの好みなどが含まれます。読み書きが可能です。',
+    docLink: '詳しくはドキュメントをご覧ください。',
+    button: '変数を追加',
+    modal: {
+      title: '会話変数を追加',
+      editTitle: '会話変数を編集',
+      name: '名前',
+      namePlaceholder: '変数名前',
+      type: 'タイプ',
+      value: 'デフォルト値',
+      valuePlaceholder: 'デフォルト値、設定しない場合は空白にしでください',
+      description: '説明',
+      descriptionPlaceholder: '変数の説明',
+      editInJSON: 'JSONで編集する',
+      oneByOne: '次々に追加する',
+      editInForm: 'フォームで編集',
+      arrayValue: '値',
+      addArrayValue: '値を追加',
+      objectKey: 'キー',
+      objectType: 'タイプ',
+      objectValue: 'デフォルト値',
+    },
+    storedContent: '保存されたコンテンツ',
+    updatedAt: '更新日は',
+  },
   changeHistory: {
     title: '変更履歴',
     placeholder: 'まだ何も変更していません',
@@ -149,6 +176,7 @@ const translation = {
   tabs: {
     'searchBlock': 'ブロックを検索',
     'blocks': 'ブロック',
+    'searchTool': '検索ツール',
     'tools': 'ツール',
     'allTool': 'すべて',
     'workflowTool': 'ワークフロー',
@@ -171,8 +199,9 @@ const translation = {
     'code': 'コード',
     'template-transform': 'テンプレート',
     'http-request': 'HTTPリクエスト',
-    'variable-assigner': '変数代入',
+    'variable-assigner': '変数代入器',
     'variable-aggregator': '変数集約器',
+    'assigner': '変数代入',
     'iteration-start': 'イテレーション開始',
     'iteration': 'イテレーション',
     'parameter-extractor': 'パラメーター抽出',
@@ -189,6 +218,7 @@ const translation = {
     'template-transform': 'Jinjaテンプレート構文を使用してデータを文字列に変換します',
     'http-request': 'HTTPプロトコル経由でサーバーリクエストを送信できます',
     'variable-assigner': '複数のブランチの変数を1つの変数に集約し、下流のノードに対して統一された設定を行います。',
+    'assigner': '変数代入ノードは、書き込み可能な変数（例えば、会話変数）に値を割り当てるために使用されます。',
     'variable-aggregator': '複数のブランチの変数を1つの変数に集約し、下流のノードに対して統一された設定を行います。',
     'iteration': 'リストオブジェクトに対して複数のステップを実行し、すべての結果が出力されるまで繰り返します。',
     'parameter-extractor': '自然言語からツールの呼び出しやHTTPリクエストのための構造化されたパラメーターを抽出するためにLLMを使用します。',
@@ -215,6 +245,7 @@ const translation = {
     checklistResolved: 'すべての問題が解決されました',
     organizeBlocks: 'ブロックを整理',
     change: '変更',
+    optional: '(オプション)',
   },
   nodes: {
     common: {
@@ -406,6 +437,17 @@ const translation = {
       },
       setAssignVariable: '代入された変数を設定',
     },
+    assigner: {
+      'assignedVariable': '代入された変数',
+      'writeMode': '書き込みモード',
+      'writeModeTip': '代入された変数が配列の場合, 末尾に追記モードを追加する。',
+      'over-write': '上書き',
+      'append': '追記',
+      'plus': 'プラス',
+      'clear': 'クリア',
+      'setVariable': '変数を設定する',
+      'variable': '変数',
+    },
     tool: {
       toAuthorize: '承認するには',
       inputVars: '入力変数',

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "dify-web",
-  "version": "0.6.16",
+  "version": "0.7.0",
   "private": true,
   "engines": {
     "node": ">=18.17.0"

web/types/workflow.ts

@@ -24,7 +24,8 @@ export type NodeTracing = {
     total_tokens: number
     total_price: number
     currency: string
-    steps_boundary: number[]
+    iteration_id?: string
+    iteration_index?: number
   }
   metadata: {
     iterator_length: number