Merge branch 'langgenius:main' into fix-doc-TOC-style

api/core/model_runtime/entities/message_entities.py

@@ -1,8 +1,8 @@
 from collections.abc import Sequence
 from enum import Enum, StrEnum
-from typing import Optional
+from typing import Any, Optional, Union
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, field_serializer, field_validator
 
 
 class PromptMessageRole(Enum):
@@ -135,6 +135,16 @@ class PromptMessage(BaseModel):
         """
         return not self.content
 
+    @field_serializer("content")
+    def serialize_content(
+        self, content: Optional[Union[str, Sequence[PromptMessageContent]]]
+    ) -> Optional[str | list[dict[str, Any] | PromptMessageContent] | Sequence[PromptMessageContent]]:
+        if content is None or isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            return [item.model_dump() if hasattr(item, "model_dump") else item for item in content]
+        return content
+
 
 class UserPromptMessage(PromptMessage):
     """

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -2,12 +2,12 @@ import array
 import json
 import re
 import uuid
-from contextlib import contextmanager
 from typing import Any
 
 import jieba.posseg as pseg  # type: ignore
 import numpy
 import oracledb
+from oracledb.connection import Connection
 from pydantic import BaseModel, model_validator
 
 from configs import dify_config
@@ -70,6 +70,7 @@ class OracleVector(BaseVector):
         super().__init__(collection_name)
         self.pool = self._create_connection_pool(config)
         self.table_name = f"embedding_{collection_name}"
+        self.config = config
 
     def get_type(self) -> str:
         return VectorType.ORACLE
@@ -107,16 +108,19 @@ class OracleVector(BaseVector):
                 outconverter=self.numpy_converter_out,
             )
 
+    def _get_connection(self) -> Connection:
+        connection = oracledb.connect(user=self.config.user, password=self.config.password, dsn=self.config.dsn)
+        return connection
+
     def _create_connection_pool(self, config: OracleVectorConfig):
         pool_params = {
             "user": config.user,
             "password": config.password,
             "dsn": config.dsn,
             "min": 1,
-            "max": 50,
+            "max": 5,
             "increment": 1,
         }
-
         if config.is_autonomous:
             pool_params.update(
                 {
@@ -125,22 +129,8 @@ class OracleVector(BaseVector):
                     "wallet_password": config.wallet_password,
                 }
             )
-
         return oracledb.create_pool(**pool_params)
 
-    @contextmanager
-    def _get_cursor(self):
-        conn = self.pool.acquire()
-        conn.inputtypehandler = self.input_type_handler
-        conn.outputtypehandler = self.output_type_handler
-        cur = conn.cursor()
-        try:
-            yield cur
-        finally:
-            cur.close()
-            conn.commit()
-            conn.close()
-
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
         dimension = len(embeddings[0])
         self._create_collection(dimension)
@@ -162,41 +152,68 @@ class OracleVector(BaseVector):
                         numpy.array(embeddings[i]),
                     )
                 )
-        # print(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)")
+        with self._get_connection() as conn:
-        with self._get_cursor() as cur:
+            conn.inputtypehandler = self.input_type_handler
-            cur.executemany(
+            conn.outputtypehandler = self.output_type_handler
-                f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values
+            # with conn.cursor() as cur:
-            )
+            #    cur.executemany(
+            #        f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values
+            #    )
+            # conn.commit()
+            for value in values:
+                with conn.cursor() as cur:
+                    try:
+                        cur.execute(
+                            f"""INSERT INTO {self.table_name} (id, text, meta, embedding) 
+                        VALUES (:1, :2, :3, :4)""",
+                            value,
+                        )
+                        conn.commit()
+                    except Exception as e:
+                        print(e)
+            conn.close()
         return pks
 
     def text_exists(self, id: str) -> bool:
-        with self._get_cursor() as cur:
+        with self._get_connection() as conn:
-            cur.execute(f"SELECT id FROM {self.table_name} WHERE id = '%s'" % (id,))
+            with conn.cursor() as cur:
-            return cur.fetchone() is not None
+                cur.execute(f"SELECT id FROM {self.table_name} WHERE id = '%s'" % (id,))
+                return cur.fetchone() is not None
+            conn.close()
 
     def get_by_ids(self, ids: list[str]) -> list[Document]:
-        with self._get_cursor() as cur:
+        with self._get_connection() as conn:
-            cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
+            with conn.cursor() as cur:
-            docs = []
+                cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
-            for record in cur:
+                docs = []
-                docs.append(Document(page_content=record[1], metadata=record[0]))
+                for record in cur:
+                    docs.append(Document(page_content=record[1], metadata=record[0]))
+            self.pool.release(connection=conn)
+            conn.close()
         return docs
 
     def delete_by_ids(self, ids: list[str]) -> None:
         if not ids:
             return
-        with self._get_cursor() as cur:
+        with self._get_connection() as conn:
-            cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
+            with conn.cursor() as cur:
+                cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
+            conn.commit()
+            conn.close()
 
     def delete_by_metadata_field(self, key: str, value: str) -> None:
-        with self._get_cursor() as cur:
+        with self._get_connection() as conn:
-            cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
+            with conn.cursor() as cur:
+                cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
+            conn.commit()
+            conn.close()
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         """
         Search the nearest neighbors to a vector.
 
         :param query_vector: The input vector to search for similar items.
+        :param top_k: The number of nearest neighbors to return, default is 5.
         :return: List of Documents that are nearest to the query vector.
         """
         top_k = kwargs.get("top_k", 4)
@@ -205,20 +222,25 @@ class OracleVector(BaseVector):
         if document_ids_filter:
             document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
             where_clause = f"WHERE metadata->>'document_id' in ({document_ids})"
-        with self._get_cursor() as cur:
+        with self._get_connection() as conn:
-            cur.execute(
+            conn.inputtypehandler = self.input_type_handler
-                f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
+            conn.outputtypehandler = self.output_type_handler
-                f" {where_clause} ORDER BY distance fetch first {top_k} rows only",
+            with conn.cursor() as cur:
-                [numpy.array(query_vector)],
+                cur.execute(
-            )
+                    f"""SELECT meta, text, vector_distance(embedding,(select to_vector(:1) from dual),cosine) 
-            docs = []
+                    AS distance FROM {self.table_name} 
-            score_threshold = float(kwargs.get("score_threshold") or 0.0)
+                    {where_clause} ORDER BY distance fetch first {top_k} rows only""",
-            for record in cur:
+                    [numpy.array(query_vector)],
-                metadata, text, distance = record
+                )
-                score = 1 - distance
+                docs = []
-                metadata["score"] = score
+                score_threshold = float(kwargs.get("score_threshold") or 0.0)
-                if score > score_threshold:
+                for record in cur:
-                    docs.append(Document(page_content=text, metadata=metadata))
+                    metadata, text, distance = record
+                    score = 1 - distance
+                    metadata["score"] = score
+                    if score > score_threshold:
+                        docs.append(Document(page_content=text, metadata=metadata))
+            conn.close()
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@@ -228,7 +250,7 @@ class OracleVector(BaseVector):
 
         top_k = kwargs.get("top_k", 5)
         # just not implement fetch by score_threshold now, may be later
-        # score_threshold = float(kwargs.get("score_threshold") or 0.0)
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
         if len(query) > 0:
             # Check which language the query is in
             zh_pattern = re.compile("[\u4e00-\u9fa5]+")
@@ -239,7 +261,7 @@ class OracleVector(BaseVector):
                 words = pseg.cut(query)
                 current_entity = ""
                 for word, pos in words:
-                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名，ns: 地名，nt: 机构名
+                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名, ns: 地名, nt: 机构名
                         current_entity += word
                     else:
                         if current_entity:
@@ -260,30 +282,35 @@ class OracleVector(BaseVector):
                 for token in all_tokens:
                     if token not in stop_words:
                         entities.append(token)
-            with self._get_cursor() as cur:
+            with self._get_connection() as conn:
-                document_ids_filter = kwargs.get("document_ids_filter")
+                with conn.cursor() as cur:
-                where_clause = ""
+                    document_ids_filter = kwargs.get("document_ids_filter")
-                if document_ids_filter:
+                    where_clause = ""
-                    document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+                    if document_ids_filter:
-                    where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
+                        document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-                cur.execute(
+                        where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
-                    f"select meta, text, embedding FROM {self.table_name}"
+                    cur.execute(
-                    f"WHERE CONTAINS(text, :1, 1) > 0 {where_clause} "
+                        f"""select meta, text, embedding FROM {self.table_name} 
-                    f"order by score(1) desc fetch first {top_k} rows only",
+                    WHERE CONTAINS(text, :kk, 1) > 0  {where_clause}
-                    [" ACCUM ".join(entities)],
+                    order by score(1) desc fetch first {top_k} rows only""",
-                )
+                        kk=" ACCUM ".join(entities),
-                docs = []
+                    )
-                for record in cur:
+                    docs = []
-                    metadata, text, embedding = record
+                    for record in cur:
-                    docs.append(Document(page_content=text, vector=embedding, metadata=metadata))
+                        metadata, text, embedding = record
+                        docs.append(Document(page_content=text, vector=embedding, metadata=metadata))
+                conn.close()
             return docs
         else:
             return [Document(page_content="", metadata={})]
         return []
 
     def delete(self) -> None:
-        with self._get_cursor() as cur:
+        with self._get_connection() as conn:
-            cur.execute(f"DROP TABLE IF EXISTS {self.table_name} cascade constraints")
+            with conn.cursor() as cur:
+                cur.execute(f"DROP TABLE IF EXISTS {self.table_name} cascade constraints")
+            conn.commit()
+            conn.close()
 
     def _create_collection(self, dimension: int):
         cache_key = f"vector_indexing_{self._collection_name}"
@@ -293,11 +320,14 @@ class OracleVector(BaseVector):
             if redis_client.get(collection_exist_cache_key):
                 return
 
-            with self._get_cursor() as cur:
+            with self._get_connection() as conn:
-                cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name))
+                with conn.cursor() as cur:
-            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+                    cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name))
-            with self._get_cursor() as cur:
+                redis_client.set(collection_exist_cache_key, 1, ex=3600)
-                cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
+                with conn.cursor() as cur:
+                    cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
+                conn.commit()
+                conn.close()
 
 
 class OracleVectorFactory(AbstractVectorFactory):

api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py

@@ -94,7 +94,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                         "title": item.metadata.get("title"),
                         "content": item.page_content,
                     }
-                context_list.append(source)
+                    context_list.append(source)
             for hit_callback in self.hit_callbacks:
                 hit_callback.return_retriever_resource_info(context_list)
 

api/models/workflow.py

@@ -245,6 +245,13 @@ class Workflow(Base):
 
     @property
     def tool_published(self) -> bool:
+        """
+        DEPRECATED: This property is not accurate for determining if a workflow is published as a tool.
+        It only checks if there's a WorkflowToolProvider for the app, not if this specific workflow version
+        is the one being used by the tool.
+
+        For accurate checking, use a direct query with tenant_id, app_id, and version.
+        """
         from models.tools import WorkflowToolProvider
 
         return (

api/pyproject.toml

@@ -178,7 +178,7 @@ vdb = [
     "couchbase~=4.3.0",
     "elasticsearch==8.14.0",
     "opensearch-py==2.4.0",
-    "oracledb~=2.2.1",
+    "oracledb==3.0.0",
     "pgvecto-rs[sqlalchemy]~=0.2.1",
     "pgvector==0.2.5",
     "pymilvus~=2.5.0",

api/services/workflow_service.py

@@ -28,6 +28,7 @@ from extensions.ext_database import db
 from models.account import Account
 from models.enums import CreatedByRole
 from models.model import App, AppMode
+from models.tools import WorkflowToolProvider
 from models.workflow import (
     Workflow,
     WorkflowNodeExecution,
@@ -523,8 +524,19 @@ class WorkflowService:
             # Cannot delete a workflow that's currently in use by an app
             raise WorkflowInUseError(f"Cannot delete workflow that is currently in use by app '{app.name}'")
 
-        # Check if this workflow is published as a tool
+        # Don't use workflow.tool_published as it's not accurate for specific workflow versions
-        if workflow.tool_published:
+        # Check if there's a tool provider using this specific workflow version
+        tool_provider = (
+            session.query(WorkflowToolProvider)
+            .filter(
+                WorkflowToolProvider.tenant_id == workflow.tenant_id,
+                WorkflowToolProvider.app_id == workflow.app_id,
+                WorkflowToolProvider.version == workflow.version,
+            )
+            .first()
+        )
+
+        if tool_provider:
             # Cannot delete a workflow that's published as a tool
             raise WorkflowInUseError("Cannot delete workflow that is published as a tool")
 

api/tests/unit_tests/services/workflow/test_workflow_deletion.py

@@ -40,6 +40,10 @@ def workflow_setup():
 
 def test_delete_workflow_success(workflow_setup):
     # Setup mocks
+
+    # Mock the tool provider query to return None (not published as a tool)
+    workflow_setup["session"].query.return_value.filter.return_value.first.return_value = None
+
     workflow_setup["session"].scalar = MagicMock(
         side_effect=[workflow_setup["workflow"], None]
     )  # Return workflow first, then None for app
@@ -97,7 +101,12 @@ def test_delete_workflow_in_use_by_app_error(workflow_setup):
 
 def test_delete_workflow_published_as_tool_error(workflow_setup):
     # Setup mocks
-    workflow_setup["workflow"].tool_published = True
+    from models.tools import WorkflowToolProvider
+
+    # Mock the tool provider query
+    mock_tool_provider = MagicMock(spec=WorkflowToolProvider)
+    workflow_setup["session"].query.return_value.filter.return_value.first.return_value = mock_tool_provider
+
     workflow_setup["session"].scalar = MagicMock(
         side_effect=[workflow_setup["workflow"], None]
     )  # Return workflow first, then None for app

api/uv.lock

@@ -1471,7 +1471,7 @@ vdb = [
     { name = "couchbase", specifier = "~=4.3.0" },
     { name = "elasticsearch", specifier = "==8.14.0" },
     { name = "opensearch-py", specifier = "==2.4.0" },
-    { name = "oracledb", specifier = "~=2.2.1" },
+    { name = "oracledb", specifier = "==3.0.0" },
     { name = "pgvecto-rs", extras = ["sqlalchemy"], specifier = "~=0.2.1" },
     { name = "pgvector", specifier = "==0.2.5" },
     { name = "pymilvus", specifier = "~=2.5.0" },
@@ -3600,23 +3600,23 @@ wheels = [
 
 [[package]]
 name = "oracledb"
-version = "2.2.1"
+version = "3.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cryptography" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/bf/39/712f797b75705c21148fa1d98651f63c2e5cc6876e509a0a9e2f5b406572/oracledb-3.0.0.tar.gz", hash = "sha256:64dc86ee5c032febc556798b06e7b000ef6828bb0252084f6addacad3363db85", size = 840431 }
-wheels = [
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 ]
 
 [[package]]

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

@@ -29,6 +29,8 @@ import { useChildSegmentListKey, useSegmentListKey } from '@/service/knowledge/u
 import useEditDocumentMetadata from '../metadata/hooks/use-edit-dataset-metadata'
 import DatasetMetadataDrawer from '../metadata/metadata-dataset/dataset-metadata-drawer'
 import StatusWithAction from '../common/document-status-with-action/status-with-action'
+import { LanguagesSupported } from '@/i18n/language'
+import { getLocaleOnClient } from '@/i18n'
 
 const FolderPlusIcon = ({ className }: React.SVGProps<SVGElement>) => {
   return <svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg" className={className ?? ''}>
@@ -98,7 +100,7 @@ const Documents: FC<IDocumentsProps> = ({ datasetId }) => {
   const isDataSourceWeb = dataset?.data_source_type === DataSourceType.WEB
   const isDataSourceFile = dataset?.data_source_type === DataSourceType.FILE
   const embeddingAvailable = !!dataset?.embedding_available
-
+  const locale = getLocaleOnClient()
   const debouncedSearchValue = useDebounce(searchValue, { wait: 500 })
 
   const { data: documentsRes, isFetching: isListLoading } = useDocumentList({
@@ -260,7 +262,12 @@ const Documents: FC<IDocumentsProps> = ({ datasetId }) => {
           <a
             className='flex items-center text-text-accent'
             target='_blank'
-            href='https://docs.dify.ai/guides/knowledge-base/integrate-knowledge-within-application'>
+            href={
+              locale === LanguagesSupported[1]
+                ? 'https://docs.dify.ai/v/zh-hans/guides/knowledge-base/integrate-knowledge-within-application'
+                : 'https://docs.dify.ai/guides/knowledge-base/integrate-knowledge-within-application'
+            }
+            >
             <span>{t('datasetDocuments.list.learnMore')}</span>
             <RiExternalLinkLine className='h-3 w-3' />
           </a>

web/app/components/workflow/nodes/_base/components/variable/utils.ts

@@ -57,7 +57,14 @@ const inputVarTypeToVarType = (type: InputVarType): VarType => {
   } as any)[type] || VarType.string
 }
 
-const structTypeToVarType = (type: Type): VarType => {
+const structTypeToVarType = (type: Type, isArray?: boolean): VarType => {
+  if (isArray) {
+    return ({
+      [Type.string]: VarType.arrayString,
+      [Type.number]: VarType.arrayNumber,
+      [Type.object]: VarType.arrayObject,
+    } as any)[type] || VarType.string
+  }
   return ({
     [Type.string]: VarType.string,
     [Type.number]: VarType.number,
@@ -82,9 +89,12 @@ const findExceptVarInStructuredProperties = (properties: Record<string, StructFi
     Object.keys(properties).forEach((key) => {
       const item = properties[key]
       const isObj = item.type === Type.object
+      const isArray = item.type === Type.array
+      const arrayType = item.items?.type
+
       if (!isObj && !filterVar({
         variable: key,
-        type: structTypeToVarType(item.type),
+        type: structTypeToVarType(isArray ? arrayType! : item.type, isArray),
       }, [key])) {
         delete properties[key]
         return
@@ -103,9 +113,11 @@ const findExceptVarInStructuredOutput = (structuredOutput: StructuredOutput, fil
     Object.keys(properties).forEach((key) => {
       const item = properties[key]
       const isObj = item.type === Type.object
+      const isArray = item.type === Type.array
+      const arrayType = item.items?.type
       if (!isObj && !filterVar({
         variable: key,
-        type: structTypeToVarType(item.type),
+        type: structTypeToVarType(isArray ? arrayType! : item.type, isArray),
       }, [key])) {
         delete properties[key]
         return
@@ -1314,9 +1326,12 @@ const varToValueSelectorList = (v: Var, parentValueSelector: ValueSelector, res:
   }
   if (isStructuredOutput) {
     Object.keys((v.children as StructuredOutput)?.schema?.properties || {}).forEach((key) => {
+      const type = (v.children as StructuredOutput)?.schema?.properties[key].type
+      const isArray = type === Type.array
+      const arrayType = (v.children as StructuredOutput)?.schema?.properties[key].items?.type
       varToValueSelectorList({
         variable: key,
-        type: structTypeToVarType((v.children as StructuredOutput)?.schema?.properties[key].type),
+        type: structTypeToVarType(isArray ? arrayType! : type, isArray),
       }, [...parentValueSelector, v.variable], res)
     })
   }

web/app/components/workflow/nodes/llm/types.ts

@@ -25,6 +25,9 @@ export enum Type {
   boolean = 'boolean',
   object = 'object',
   array = 'array',
+  arrayString = 'array[string]',
+  arrayNumber = 'array[number]',
+  arrayObject = 'array[object]',
 }
 
 export enum ArrayType {

web/app/components/workflow/panel/chat-variable-panel/components/variable-modal.tsx

@@ -123,7 +123,7 @@ const ChatVariableModal = ({
       case ChatVarType.Number:
         return value || 0
       case ChatVarType.Object:
-        return formatValueFromObject(objectValue)
+        return editInJSON ? value : formatValueFromObject(objectValue)
       case ChatVarType.ArrayString:
       case ChatVarType.ArrayNumber:
       case ChatVarType.ArrayObject:

web/i18n/ja-JP/plugin.ts

@@ -80,8 +80,8 @@ const translation = {
     configureApp: 'アプリを設定する',
     endpointDeleteContent: '{{name}}を削除しますか？',
     actionNum: '{{num}} {{action}} が含まれています',
-    endpointsDocLink: '文書を表示する',
+    endpointsDocLink: 'ドキュメントを表示する',
-    switchVersion: 'スイッチ版',
+    switchVersion: 'バージョンの切り替え',
   },
   debugInfo: {
     title: 'デバッグ',
@@ -134,7 +134,7 @@ const translation = {
     install: 'インストール',
     dropPluginToInstall: 'プラグインパッケージをここにドロップしてインストールします',
     installPlugin: 'プラグインをインストールする',
-    back: 'バック',
+    back: '戻る',
     uploadingPackage: '{{packageName}}をアップロード中...',
   },
   installFromGitHub: {
@@ -191,7 +191,7 @@ const translation = {
     installingWithError: '{{installingLength}}個のプラグインをインストール中、{{successLength}}件成功、{{errorLength}}件失敗',
     installing: '{{installingLength}}個のプラグインをインストール中、0個完了。',
   },
-  from: 'から',
+  from: 'インストール元',
   install: '{{num}} インストール',
   installAction: 'インストール',
   installFrom: 'インストール元',