Merge remote-tracking branch 'origin/main' into feat/trigger

api/core/app/apps/base_app_queue_manager.py

@@ -1,9 +1,11 @@
+import logging
 import queue
 import time
 from abc import abstractmethod
 from enum import IntEnum, auto
 from typing import Any
 
+from redis.exceptions import RedisError
 from sqlalchemy.orm import DeclarativeMeta
 
 from configs import dify_config
@@ -18,6 +20,8 @@ from core.app.entities.queue_entities import (
 )
 from extensions.ext_redis import redis_client
 
+logger = logging.getLogger(__name__)
+
 
 class PublishFrom(IntEnum):
     APPLICATION_MANAGER = auto()
@@ -35,9 +39,8 @@ class AppQueueManager:
         self.invoke_from = invoke_from  # Public accessor for invoke_from
 
         user_prefix = "account" if self._invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end-user"
-        redis_client.setex(
+        self._task_belong_cache_key = AppQueueManager._generate_task_belong_cache_key(self._task_id)
-            AppQueueManager._generate_task_belong_cache_key(self._task_id), 1800, f"{user_prefix}-{self._user_id}"
+        redis_client.setex(self._task_belong_cache_key, 1800, f"{user_prefix}-{self._user_id}")
-        )
 
         q: queue.Queue[WorkflowQueueMessage | MessageQueueMessage | None] = queue.Queue()
 
@@ -79,9 +82,21 @@ class AppQueueManager:
         Stop listen to queue
         :return:
         """
+        self._clear_task_belong_cache()
         self._q.put(None)
 
-    def publish_error(self, e, pub_from: PublishFrom):
+    def _clear_task_belong_cache(self) -> None:
+        """
+        Remove the task belong cache key once listening is finished.
+        """
+        try:
+            redis_client.delete(self._task_belong_cache_key)
+        except RedisError:
+            logger.exception(
+                "Failed to clear task belong cache for task %s (key: %s)", self._task_id, self._task_belong_cache_key
+            )
+
+    def publish_error(self, e, pub_from: PublishFrom) -> None:
         """
         Publish error
         :param e: error

api/core/model_runtime/entities/message_entities.py

@@ -74,7 +74,7 @@ class TextPromptMessageContent(PromptMessageContent):
     Model class for text prompt message content.
     """
 
-    type: Literal[PromptMessageContentType.TEXT] = PromptMessageContentType.TEXT
+    type: Literal[PromptMessageContentType.TEXT] = PromptMessageContentType.TEXT  # type: ignore
     data: str
 
 
@@ -95,11 +95,11 @@ class MultiModalPromptMessageContent(PromptMessageContent):
 
 
 class VideoPromptMessageContent(MultiModalPromptMessageContent):
-    type: Literal[PromptMessageContentType.VIDEO] = PromptMessageContentType.VIDEO
+    type: Literal[PromptMessageContentType.VIDEO] = PromptMessageContentType.VIDEO  # type: ignore
 
 
 class AudioPromptMessageContent(MultiModalPromptMessageContent):
-    type: Literal[PromptMessageContentType.AUDIO] = PromptMessageContentType.AUDIO
+    type: Literal[PromptMessageContentType.AUDIO] = PromptMessageContentType.AUDIO  # type: ignore
 
 
 class ImagePromptMessageContent(MultiModalPromptMessageContent):
@@ -111,12 +111,12 @@ class ImagePromptMessageContent(MultiModalPromptMessageContent):
         LOW = auto()
         HIGH = auto()
 
-    type: Literal[PromptMessageContentType.IMAGE] = PromptMessageContentType.IMAGE
+    type: Literal[PromptMessageContentType.IMAGE] = PromptMessageContentType.IMAGE  # type: ignore
     detail: DETAIL = DETAIL.LOW
 
 
 class DocumentPromptMessageContent(MultiModalPromptMessageContent):
-    type: Literal[PromptMessageContentType.DOCUMENT] = PromptMessageContentType.DOCUMENT
+    type: Literal[PromptMessageContentType.DOCUMENT] = PromptMessageContentType.DOCUMENT  # type: ignore
 
 
 PromptMessageContentUnionTypes = Annotated[

api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenizer.py

@@ -15,7 +15,7 @@ class GPT2Tokenizer:
         use gpt2 tokenizer to get num tokens
         """
         _tokenizer = GPT2Tokenizer.get_encoder()
-        tokens = _tokenizer.encode(text)
+        tokens = _tokenizer.encode(text)  # type: ignore
         return len(tokens)
 
     @staticmethod

api/core/model_runtime/utils/encoders.py

@@ -196,15 +196,15 @@ def jsonable_encoder(
             return encoder(obj)
 
     try:
-        data = dict(obj)
+        data = dict(obj)  # type: ignore
     except Exception as e:
         errors: list[Exception] = []
         errors.append(e)
         try:
-            data = vars(obj)
+            data = vars(obj)  # type: ignore
         except Exception as e:
             errors.append(e)
-            raise ValueError(errors) from e
+            raise ValueError(str(errors)) from e
     return jsonable_encoder(
         data,
         by_alias=by_alias,

api/core/ops/aliyun_trace/entities/aliyun_trace_entity.py

@@ -3,7 +3,8 @@ from dataclasses import dataclass
 from typing import Any
 
 from opentelemetry import trace as trace_api
-from opentelemetry.sdk.trace import Event, Status, StatusCode
+from opentelemetry.sdk.trace import Event
+from opentelemetry.trace import Status, StatusCode
 from pydantic import BaseModel, Field
 
 

api/core/ops/ops_trace_manager.py

@@ -155,7 +155,10 @@ class OpsTraceManager:
             if key in tracing_config:
                 if "*" in tracing_config[key]:
                     # If the key contains '*', retain the original value from the current config
-                    new_config[key] = current_trace_config.get(key, tracing_config[key])
+                    if current_trace_config:
+                        new_config[key] = current_trace_config.get(key, tracing_config[key])
+                    else:
+                        new_config[key] = tracing_config[key]
                 else:
                     # Otherwise, encrypt the key
                     new_config[key] = encrypt_token(tenant_id, tracing_config[key])

api/core/ops/weave_trace/weave_trace.py

@@ -62,7 +62,8 @@ class WeaveDataTrace(BaseTraceInstance):
         self,
     ):
         try:
-            project_url = f"https://wandb.ai/{self.weave_client._project_id()}"
+            project_identifier = f"{self.entity}/{self.project_name}" if self.entity else self.project_name
+            project_url = f"https://wandb.ai/{project_identifier}"
             return project_url
         except Exception as e:
             logger.debug("Weave get run url failed: %s", str(e))
@@ -424,7 +425,23 @@ class WeaveDataTrace(BaseTraceInstance):
             raise ValueError(f"Weave API check failed: {str(e)}")
 
     def start_call(self, run_data: WeaveTraceModel, parent_run_id: str | None = None):
-        call = self.weave_client.create_call(op=run_data.op, inputs=run_data.inputs, attributes=run_data.attributes)
+        inputs = run_data.inputs
+        if inputs is None:
+            inputs = {}
+        elif not isinstance(inputs, dict):
+            inputs = {"inputs": str(inputs)}
+
+        attributes = run_data.attributes
+        if attributes is None:
+            attributes = {}
+        elif not isinstance(attributes, dict):
+            attributes = {"attributes": str(attributes)}
+
+        call = self.weave_client.create_call(
+            op=run_data.op,
+            inputs=inputs,
+            attributes=attributes,
+        )
         self.calls[run_data.id] = call
         if parent_run_id:
             self.calls[run_data.id].parent_id = parent_run_id
@@ -432,6 +449,7 @@ class WeaveDataTrace(BaseTraceInstance):
     def finish_call(self, run_data: WeaveTraceModel):
         call = self.calls.get(run_data.id)
         if call:
-            self.weave_client.finish_call(call=call, output=run_data.outputs, exception=run_data.exception)
+            exception = Exception(run_data.exception) if run_data.exception else None
+            self.weave_client.finish_call(call=call, output=run_data.outputs, exception=exception)
         else:
             raise ValueError(f"Call with id {run_data.id} not found")

api/core/rag/datasource/retrieval_service.py

@@ -106,7 +106,9 @@ class RetrievalService:
         if exceptions:
             raise ValueError(";\n".join(exceptions))
 
+        # Deduplicate documents for hybrid search to avoid duplicate chunks
         if retrieval_method == RetrievalMethod.HYBRID_SEARCH.value:
+            all_documents = cls._deduplicate_documents(all_documents)
             data_post_processor = DataPostProcessor(
                 str(dataset.tenant_id), reranking_mode, reranking_model, weights, False
             )
@@ -143,6 +145,40 @@ class RetrievalService:
         )
         return all_documents
 
+    @classmethod
+    def _deduplicate_documents(cls, documents: list[Document]) -> list[Document]:
+        """Deduplicate documents based on doc_id to avoid duplicate chunks in hybrid search."""
+        if not documents:
+            return documents
+
+        unique_documents = []
+        seen_doc_ids = set()
+
+        for document in documents:
+            # For dify provider documents, use doc_id for deduplication
+            if document.provider == "dify" and document.metadata is not None and "doc_id" in document.metadata:
+                doc_id = document.metadata["doc_id"]
+                if doc_id not in seen_doc_ids:
+                    seen_doc_ids.add(doc_id)
+                    unique_documents.append(document)
+                # If duplicate, keep the one with higher score
+                elif "score" in document.metadata:
+                    # Find existing document with same doc_id and compare scores
+                    for i, existing_doc in enumerate(unique_documents):
+                        if (
+                            existing_doc.metadata
+                            and existing_doc.metadata.get("doc_id") == doc_id
+                            and existing_doc.metadata.get("score", 0) < document.metadata.get("score", 0)
+                        ):
+                            unique_documents[i] = document
+                            break
+            else:
+                # For non-dify documents, use content-based deduplication
+                if document not in unique_documents:
+                    unique_documents.append(document)
+
+        return unique_documents
+
     @classmethod
     def _get_dataset(cls, dataset_id: str) -> Dataset | None:
         with Session(db.engine) as session:

api/core/workflow/node_events/node.py

@@ -20,6 +20,7 @@ class ModelInvokeCompletedEvent(NodeEventBase):
     usage: LLMUsage
     finish_reason: str | None = None
     reasoning_content: str | None = None
+    structured_output: dict | None = None
 
 
 class RunRetryEvent(NodeEventBase):

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

@@ -87,7 +87,7 @@ class Executor:
                 node_data.authorization.config.api_key
             ).text
 
-        self.url: str = node_data.url
+        self.url = node_data.url
         self.method = node_data.method
         self.auth = node_data.authorization
         self.timeout = timeout
@@ -349,11 +349,10 @@ class Executor:
             "timeout": (self.timeout.connect, self.timeout.read, self.timeout.write),
             "ssl_verify": self.ssl_verify,
             "follow_redirects": True,
-            "max_retries": self.max_retries,
         }
         # request_args = {k: v for k, v in request_args.items() if v is not None}
         try:
-            response: httpx.Response = _METHOD_MAP[method_lc](**request_args)
+            response: httpx.Response = _METHOD_MAP[method_lc](**request_args, max_retries=self.max_retries)
         except (ssrf_proxy.MaxRetriesExceededError, httpx.RequestError) as e:
             raise HttpRequestNodeError(str(e)) from e
         # FIXME: fix type ignore, this maybe httpx type issue

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

@@ -165,6 +165,8 @@ class HttpRequestNode(Node):
             body_type = typed_node_data.body.type
             data = typed_node_data.body.data
             match body_type:
+                case "none":
+                    pass
                 case "binary":
                     if len(data) != 1:
                         raise RequestBodyError("invalid body data, should have only one item")

api/core/workflow/nodes/if_else/if_else_node.py

@@ -83,7 +83,7 @@ class IfElseNode(Node):
             else:
                 # TODO: Update database then remove this
                 # Fallback to old structure if cases are not defined
-                input_conditions, group_result, final_result = _should_not_use_old_function(  # ty: ignore [deprecated]
+                input_conditions, group_result, final_result = _should_not_use_old_function(  # pyright: ignore [reportDeprecated]
                     condition_processor=condition_processor,
                     variable_pool=self.graph_runtime_state.variable_pool,
                     conditions=self._node_data.conditions or [],

api/core/workflow/nodes/knowledge_index/knowledge_index_node.py

@@ -136,6 +136,11 @@ class KnowledgeIndexNode(Node):
         document = db.session.query(Document).filter_by(id=document_id.value).first()
         if not document:
             raise KnowledgeIndexNodeError(f"Document {document_id.value} not found.")
+        doc_id_value = document.id
+        ds_id_value = dataset.id
+        dataset_name_value = dataset.name
+        document_name_value = document.name
+        created_at_value = document.created_at
         # chunk nodes by chunk size
         indexing_start_at = time.perf_counter()
         index_processor = IndexProcessorFactory(dataset.chunk_structure).init_index_processor()
@@ -161,16 +166,16 @@ class KnowledgeIndexNode(Node):
         document.word_count = (
             db.session.query(func.sum(DocumentSegment.word_count))
             .where(
-                DocumentSegment.document_id == document.id,
+                DocumentSegment.document_id == doc_id_value,
-                DocumentSegment.dataset_id == dataset.id,
+                DocumentSegment.dataset_id == ds_id_value,
             )
             .scalar()
         )
         db.session.add(document)
         # update document segment status
         db.session.query(DocumentSegment).where(
-            DocumentSegment.document_id == document.id,
+            DocumentSegment.document_id == doc_id_value,
-            DocumentSegment.dataset_id == dataset.id,
+            DocumentSegment.dataset_id == ds_id_value,
         ).update(
             {
                 DocumentSegment.status: "completed",
@@ -182,13 +187,13 @@ class KnowledgeIndexNode(Node):
         db.session.commit()
 
         return {
-            "dataset_id": dataset.id,
+            "dataset_id": ds_id_value,
-            "dataset_name": dataset.name,
+            "dataset_name": dataset_name_value,
             "batch": batch.value,
-            "document_id": document.id,
+            "document_id": doc_id_value,
-            "document_name": document.name,
+            "document_name": document_name_value,
-            "created_at": document.created_at.timestamp(),
+            "created_at": created_at_value.timestamp(),
-            "display_status": document.indexing_status,
+            "display_status": "completed",
         }
 
     def _get_preview_output(self, chunk_structure: str, chunks: Any) -> Mapping[str, Any]:

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -107,7 +107,7 @@ class KnowledgeRetrievalNode(Node):
             graph_runtime_state=graph_runtime_state,
         )
         # LLM file outputs, used for MultiModal outputs.
-        self._file_outputs: list[File] = []
+        self._file_outputs = []
 
         if llm_file_saver is None:
             llm_file_saver = FileSaverImpl(

api/core/workflow/nodes/list_operator/node.py

@@ -161,6 +161,8 @@ class ListOperatorNode(Node):
             elif isinstance(variable, ArrayFileSegment):
                 if isinstance(condition.value, str):
                     value = self.graph_runtime_state.variable_pool.convert_template(condition.value).text
+                elif isinstance(condition.value, bool):
+                    raise ValueError(f"File filter expects a string value, got {type(condition.value)}")
                 else:
                     value = condition.value
                 filter_func = _get_file_filter_func(

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

@@ -46,7 +46,7 @@ class LLMFileSaver(tp.Protocol):
             dot (`.`). For example, `.py` and `.tar.gz` are both valid values, while `py`
             and `tar.gz` are not.
         """
-        pass
+        raise NotImplementedError()
 
     def save_remote_url(self, url: str, file_type: FileType) -> File:
         """save_remote_url saves the file from a remote url returned by LLM.
@@ -56,7 +56,7 @@ class LLMFileSaver(tp.Protocol):
         :param url: the url of the file.
         :param file_type: the file type of the file, check `FileType` enum for reference.
         """
-        pass
+        raise NotImplementedError()
 
 
 EngineFactory: tp.TypeAlias = tp.Callable[[], Engine]

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

@@ -23,6 +23,7 @@ from core.model_runtime.entities.llm_entities import (
     LLMResult,
     LLMResultChunk,
     LLMResultChunkWithStructuredOutput,
+    LLMResultWithStructuredOutput,
     LLMStructuredOutput,
     LLMUsage,
 )
@@ -127,7 +128,7 @@ class LLMNode(Node):
             graph_runtime_state=graph_runtime_state,
         )
         # LLM file outputs, used for MultiModal outputs.
-        self._file_outputs: list[File] = []
+        self._file_outputs = []
 
         if llm_file_saver is None:
             llm_file_saver = FileSaverImpl(
@@ -165,6 +166,7 @@ class LLMNode(Node):
         node_inputs: dict[str, Any] = {}
         process_data: dict[str, Any] = {}
         result_text = ""
+        clean_text = ""
         usage = LLMUsage.empty_usage()
         finish_reason = None
         reasoning_content = None
@@ -278,6 +280,13 @@ class LLMNode(Node):
                         # Extract clean text from <think> tags
                         clean_text, _ = LLMNode._split_reasoning(result_text, self._node_data.reasoning_format)
 
+                    # Process structured output if available from the event.
+                    structured_output = (
+                        LLMStructuredOutput(structured_output=event.structured_output)
+                        if event.structured_output
+                        else None
+                    )
+
                     # deduct quota
                     llm_utils.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
                     break
@@ -1048,7 +1057,7 @@ class LLMNode(Node):
     @staticmethod
     def handle_blocking_result(
         *,
-        invoke_result: LLMResult,
+        invoke_result: LLMResult | LLMResultWithStructuredOutput,
         saver: LLMFileSaver,
         file_outputs: list["File"],
         reasoning_format: Literal["separated", "tagged"] = "tagged",
@@ -1079,6 +1088,8 @@ class LLMNode(Node):
             finish_reason=None,
             # Reasoning content for workflow variables and downstream nodes
             reasoning_content=reasoning_content,
+            # Pass structured output if enabled
+            structured_output=getattr(invoke_result, "structured_output", None),
         )
 
     @staticmethod

api/core/workflow/nodes/parameter_extractor/prompts.py

@@ -179,6 +179,6 @@ CHAT_EXAMPLE = [
                 "required": ["food"],
             },
         },
-        "assistant": {"text": "I need to output a valid JSON object.", "json": {"result": "apple pie"}},
+        "assistant": {"text": "I need to output a valid JSON object.", "json": {"food": "apple pie"}},
     },
 ]

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -68,7 +68,7 @@ class QuestionClassifierNode(Node):
             graph_runtime_state=graph_runtime_state,
         )
         # LLM file outputs, used for MultiModal outputs.
-        self._file_outputs: list[File] = []
+        self._file_outputs = []
 
         if llm_file_saver is None:
             llm_file_saver = FileSaverImpl(
@@ -111,9 +111,9 @@ class QuestionClassifierNode(Node):
         query = variable.value if variable else None
         variables = {"query": query}
         # fetch model config
-        model_instance, model_config = LLMNode._fetch_model_config(
+        model_instance, model_config = llm_utils.fetch_model_config(
-            node_data_model=node_data.model,
             tenant_id=self.tenant_id,
+            node_data_model=node_data.model,
         )
         # fetch memory
         memory = llm_utils.fetch_memory(

api/core/workflow/workflow_entry.py

@@ -416,4 +416,8 @@ class WorkflowEntry:
 
             # append variable and value to variable pool
             if variable_node_id != ENVIRONMENT_VARIABLE_NODE_ID:
+                # In single run, the input_value is set as the LLM's structured output value within the variable_pool.
+                if len(variable_key_list) == 2 and variable_key_list[0] == "structured_output":
+                    input_value = {variable_key_list[1]: input_value}
+                    variable_key_list = variable_key_list[0:1]
                 variable_pool.add([variable_node_id] + variable_key_list, input_value)

api/pyrightconfig.json

@@ -6,9 +6,6 @@
     "migrations/",
     "core/rag",
     "extensions",
-    "core/ops",
-    "core/model_runtime",
-    "core/workflow/nodes",
     "core/app/app_config/easy_ui_based_app/dataset"
   ],
   "typeCheckingMode": "strict",
@@ -37,4 +34,4 @@
   "reportAttributeAccessIssue": "hint",
   "pythonVersion": "3.11",
   "pythonPlatform": "All"
-}
+}

api/schedule/check_upgradable_plugin_task.py

@@ -52,7 +52,8 @@ def check_upgradable_plugin_task():
                 strategy.include_plugins,
             )
 
-        if batch_interval_time > 0.0001:  # if lower than 1ms, skip
+        # Only sleep if batch_interval_time > 0.0001 AND current batch is not the last one
+        if batch_interval_time > 0.0001 and i + MAX_CONCURRENT_CHECK_TASKS < total_strategies:
             time.sleep(batch_interval_time)
 
     end_at = time.perf_counter()

api/services/dataset_service.py

@@ -93,7 +93,7 @@ logger = logging.getLogger(__name__)
 class DatasetService:
     @staticmethod
     def get_datasets(page, per_page, tenant_id=None, user=None, search=None, tag_ids=None, include_all=False):
-        query = select(Dataset).where(Dataset.tenant_id == tenant_id).order_by(Dataset.created_at.desc())
+        query = select(Dataset).where(Dataset.tenant_id == tenant_id).order_by(Dataset.created_at.desc(), Dataset.id)
 
         if user:
             # get permitted dataset ids

web/app/components/explore/app-list/index.tsx

@@ -152,23 +152,20 @@ const Apps = ({
       <div className={cn(
         'mt-6 flex items-center justify-between px-12',
       )}>
-        <>
+        <Category
-          <Category
+          list={categories}
-            list={categories}
+          value={currCategory}
-            value={currCategory}
+          onChange={setCurrCategory}
-            onChange={setCurrCategory}
+          allCategoriesEn={allCategoriesEn}
-            allCategoriesEn={allCategoriesEn}
+        />
-          />
-        </>
         <Input
           showLeftIcon
           showClearIcon
-          wrapperClassName='w-[200px]'
+          wrapperClassName='w-[200px] self-start'
           value={keywords}
           onChange={e => handleKeywordsChange(e.target.value)}
           onClear={() => handleKeywordsChange('')}
         />
-
       </div>
 
       <div className={cn(

web/i18n/fa-IR/workflow.ts

@@ -941,9 +941,9 @@ const translation = {
       chunkIsRequired: 'ساختار تکه ای مورد نیاز است',
       chooseChunkStructure: 'یک ساختار تکه ای را انتخاب کنید',
       chunksInput: 'تکه‌ها',
-      chunksInputTip: 'متغیر ورودی گره پایگاه دانش چانک‌ها است. نوع متغیر یک شیء با یک طرح JSON خاص است که باید با ساختار چانک انتخاب شده سازگار باشد.',
+      chunksInputTip: 'متغیر ورودی گره پایگاه دانش تکه‌ها است. نوع متغیر یک شیء با یک طرح JSON خاص است که باید با ساختار تکه انتخاب شده سازگار باشد.',
       embeddingModelIsRequired: 'مدل جاسازی مورد نیاز است',
-      chunksVariableIsRequired: 'متغیر Chunks الزامی است',
+      chunksVariableIsRequired: 'متغیر تکه‌ها الزامی است',
       rerankingModelIsRequired: 'مدل رتبه‌بندی مجدد مورد نیاز است',
     },
   },

web/i18n/hi-IN/workflow.ts

@@ -961,8 +961,8 @@ const translation = {
       aboutRetrieval: 'पुनर्प्राप्ति विधि के बारे में।',
       chooseChunkStructure: 'एक चंक संरचना चुनें',
       chunksInput: 'टुकड़े',
-      chunksInputTip: 'ज्ञान आधार नोड का इनपुट वेरिएबल चंक्स है। वेरिएबल प्रकार एक ऑब्जेक्ट है जिसमें एक विशेष JSON स्कीमा है जो चयनित चंक संरचना के साथ सुसंगत होना चाहिए।',
+      chunksInputTip: 'ज्ञान आधार नोड का इनपुट वेरिएबल टुकड़े है। वेरिएबल प्रकार एक ऑब्जेक्ट है जिसमें एक विशेष JSON स्कीमा है जो चयनित चंक संरचना के साथ सुसंगत होना चाहिए।',
-      chunksVariableIsRequired: 'Chunks चर आवश्यक है',
+      chunksVariableIsRequired: 'टुकड़े चर आवश्यक है',
       embeddingModelIsRequired: 'एम्बेडिंग मॉडल आवश्यक है',
       rerankingModelIsRequired: 'पुनः क्रमांकन मॉडल की आवश्यकता है',
     },

web/i18n/sl-SI/workflow.ts

@@ -950,7 +950,7 @@ const translation = {
       chunksInput: 'Kosi',
       chunksInputTip: 'Vhodna spremenljivka vozlišča podatkovne baze je Chunks. Tip spremenljivke je objekt s specifično JSON shemo, ki mora biti skladna z izbrano strukturo kosov.',
       chunksVariableIsRequired: 'Spremenljivka Chunks je obvezna',
-      embeddingModelIsRequired: 'Zahtuje se vgrajevalni model',
+      embeddingModelIsRequired: 'Zahteva se vgrajevalni model',
       rerankingModelIsRequired: 'Potreben je model za ponovno razvrščanje',
     },
   },

web/i18n/uk-UA/workflow.ts

@@ -944,7 +944,7 @@ const translation = {
       chunksInputTip: 'Вхідна змінна вузла бази знань - це Частини. Тип змінної - об\'єкт з певною JSON-схемою, яка повинна відповідати вибраній структурі частин.',
       chunksVariableIsRequired: 'Змінна chunks є обов\'язковою',
       embeddingModelIsRequired: 'Потрібна модель вбудовування',
-      rerankingModelIsRequired: 'Потрібна модель перенавчання',
+      rerankingModelIsRequired: 'Потрібна модель повторного ранжування',
     },
   },
   tracing: {

web/package.json

@@ -2,7 +2,7 @@
   "name": "dify-web",
   "version": "1.9.1",
   "private": true,
-  "packageManager": "pnpm@10.16.0",
+  "packageManager": "pnpm@10.17.1",
   "engines": {
     "node": ">=v22.11.0"
   },
@@ -39,13 +39,12 @@
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "preinstall": "npx only-allow pnpm",
-    "analyze": "ANALYZE=true pnpm build"
+    "analyze": "ANALYZE=true pnpm build",
+    "knip": "knip"
   },
   "dependencies": {
-    "@babel/runtime": "^7.22.3",
     "@dagrejs/dagre": "^1.1.4",
     "@emoji-mart/data": "^1.2.1",
-    "@eslint/compat": "^1.2.4",
     "@floating-ui/react": "^0.26.25",
     "@formatjs/intl-localematcher": "^0.5.6",
     "@headlessui/react": "2.2.1",
@@ -63,7 +62,6 @@
     "@octokit/request-error": "^6.1.5",
     "@remixicon/react": "^4.5.0",
     "@sentry/react": "^8.54.0",
-    "@sentry/utils": "^8.54.0",
     "@svgdotjs/svg.js": "^3.2.4",
     "@tailwindcss/typography": "^0.5.15",
     "@tanstack/react-form": "^1.3.3",
@@ -76,7 +74,6 @@
     "cmdk": "^1.1.1",
     "copy-to-clipboard": "^3.3.3",
     "cron-parser": "^5.4.0",
-    "crypto-js": "^4.2.0",
     "dayjs": "^1.11.13",
     "decimal.js": "^10.4.3",
     "dompurify": "^3.2.4",
@@ -92,7 +89,6 @@
     "js-audio-recorder": "^1.0.7",
     "js-cookie": "^3.0.5",
     "jsonschema": "^1.5.0",
-    "jwt-decode": "^4.0.0",
     "katex": "^0.16.21",
     "ky": "^1.7.2",
     "lamejs": "^1.2.1",
@@ -113,12 +109,9 @@
     "react-18-input-autosize": "^3.0.0",
     "react-dom": "19.1.1",
     "react-easy-crop": "^5.1.0",
-    "react-error-boundary": "^4.1.2",
-    "react-headless-pagination": "^1.1.6",
     "react-hook-form": "^7.53.1",
     "react-hotkeys-hook": "^4.6.1",
     "react-i18next": "^15.1.0",
-    "react-infinite-scroll-component": "^6.1.0",
     "react-markdown": "^9.0.1",
     "react-multi-email": "^1.0.25",
     "react-papaparse": "^4.4.0",
@@ -127,11 +120,8 @@
     "react-sortablejs": "^6.1.4",
     "react-syntax-highlighter": "^15.6.1",
     "react-textarea-autosize": "^8.5.8",
-    "react-tooltip": "5.8.3",
     "react-window": "^1.8.10",
-    "react-window-infinite-loader": "^1.0.9",
     "reactflow": "^11.11.3",
-    "recordrtc": "^5.6.2",
     "rehype-katex": "^7.0.1",
     "rehype-raw": "^7.0.0",
     "remark-breaks": "^4.0.0",
@@ -139,9 +129,7 @@
     "remark-math": "^6.0.0",
     "scheduler": "^0.26.0",
     "semver": "^7.6.3",
-    "server-only": "^0.0.1",
     "sharp": "^0.33.2",
-    "shave": "^5.0.4",
     "sortablejs": "^1.15.0",
     "swr": "^2.3.0",
     "tailwind-merge": "^2.5.4",
@@ -154,13 +142,8 @@
   },
   "devDependencies": {
     "@antfu/eslint-config": "^5.0.0",
-    "@babel/core": "^7.28.3",
-    "@babel/preset-env": "^7.28.3",
     "@chromatic-com/storybook": "^3.1.0",
     "@eslint-react/eslint-plugin": "^1.15.0",
-    "@eslint/eslintrc": "^3.1.0",
-    "@eslint/js": "^9.36.0",
-    "@faker-js/faker": "^9.0.3",
     "@happy-dom/jest-environment": "^17.4.4",
     "@mdx-js/loader": "^3.1.0",
     "@mdx-js/react": "^3.1.0",
@@ -173,14 +156,13 @@
     "@storybook/addon-links": "8.5.0",
     "@storybook/addon-onboarding": "8.5.0",
     "@storybook/addon-themes": "8.5.0",
-    "@storybook/blocks": "8.5.0",
     "@storybook/nextjs": "8.5.0",
     "@storybook/react": "8.5.0",
     "@storybook/test": "8.5.0",
     "@testing-library/dom": "^10.4.0",
     "@testing-library/jest-dom": "^6.8.0",
     "@testing-library/react": "^16.0.1",
-    "@types/crypto-js": "^4.2.2",
+    "@babel/core": "^7.28.3",
     "@types/dagre": "^0.7.52",
     "@types/jest": "^29.5.13",
     "@types/js-cookie": "^3.0.6",
@@ -193,18 +175,14 @@
     "@types/react-slider": "^1.3.6",
     "@types/react-syntax-highlighter": "^15.5.13",
     "@types/react-window": "^1.8.8",
-    "@types/react-window-infinite-loader": "^1.0.9",
-    "@types/recordrtc": "^5.6.14",
     "@types/semver": "^7.5.8",
     "@types/sortablejs": "^1.15.1",
     "@types/uuid": "^10.0.0",
     "autoprefixer": "^10.4.20",
-    "babel-loader": "^10.0.0",
     "bing-translate-api": "^4.0.2",
     "code-inspector-plugin": "1.2.9",
     "cross-env": "^7.0.3",
     "eslint": "^9.35.0",
-    "eslint-config-next": "15.5.0",
     "eslint-plugin-oxlint": "^1.6.0",
     "eslint-plugin-react-hooks": "^5.1.0",
     "eslint-plugin-react-refresh": "^0.4.19",
@@ -214,6 +192,7 @@
     "globals": "^15.11.0",
     "husky": "^9.1.6",
     "jest": "^29.7.0",
+    "knip": "^5.64.1",
     "lint-staged": "^15.2.10",
     "lodash": "^4.17.21",
     "magicast": "^0.3.4",
@@ -221,10 +200,9 @@
     "sass": "^1.92.1",
     "storybook": "8.5.0",
     "tailwindcss": "^3.4.14",
-    "ts-node": "^10.9.2",
     "typescript": "^5.8.3",
-    "typescript-eslint": "^8.38.0",
+    "uglify-js": "^3.19.3",
-    "uglify-js": "^3.19.3"
+    "babel-loader": "^9.2.1"
   },
   "resolutions": {
     "@types/react": "19.1.11",