Resolve conflicts: Force the main code to overwrite everything in deploy/dev

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

api/configs/feature/__init__.py

@@ -959,6 +959,16 @@ class MailConfig(BaseSettings):
         default=None,
     )
 
+    ENABLE_TRIAL_APP: bool = Field(
+        description="Enable trial app",
+        default=False,
+    )
+
+    ENABLE_EXPLORE_BANNER: bool = Field(
+        description="Enable explore banner",
+        default=False,
+    )
+
 
 class RagEtlConfig(BaseSettings):
     """

api/controllers/console/__init__.py

@@ -107,10 +107,12 @@ from .datasets.rag_pipeline import (
 
 # Import explore controllers
 from .explore import (
+    banner,
     installed_app,
     parameter,
     recommended_app,
     saved_message,
+    trial,
 )
 
 # Import tag controllers
@@ -145,6 +147,7 @@ __all__ = [
     "apikey",
     "app",
     "audio",
+    "banner",
     "billing",
     "bp",
     "completion",
@@ -198,6 +201,7 @@ __all__ = [
     "statistic",
     "tags",
     "tool_providers",
+    "trial",
     "trigger_providers",
     "version",
     "website",

api/controllers/console/admin.py

@@ -15,7 +15,7 @@ from controllers.console.wraps import only_edition_cloud
 from core.db.session_factory import session_factory
 from extensions.ext_database import db
 from libs.token import extract_access_token
-from models.model import App, InstalledApp, RecommendedApp
+from models.model import App, ExporleBanner, InstalledApp, RecommendedApp, TrialApp
 
 P = ParamSpec("P")
 R = TypeVar("R")
@@ -32,6 +32,8 @@ class InsertExploreAppPayload(BaseModel):
     language: str = Field(...)
     category: str = Field(...)
     position: int = Field(...)
+    can_trial: bool = Field(default=False)
+    trial_limit: int = Field(default=0)
 
     @field_validator("language")
     @classmethod
@@ -39,11 +41,33 @@ class InsertExploreAppPayload(BaseModel):
         return supported_language(value)
 
 
+class InsertExploreBannerPayload(BaseModel):
+    category: str = Field(...)
+    title: str = Field(...)
+    description: str = Field(...)
+    img_src: str = Field(..., alias="img-src")
+    language: str = Field(default="en-US")
+    link: str = Field(...)
+    sort: int = Field(...)
+
+    @field_validator("language")
+    @classmethod
+    def validate_language(cls, value: str) -> str:
+        return supported_language(value)
+
+    model_config = {"populate_by_name": True}
+
+
 console_ns.schema_model(
     InsertExploreAppPayload.__name__,
     InsertExploreAppPayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
 )
 
+console_ns.schema_model(
+    InsertExploreBannerPayload.__name__,
+    InsertExploreBannerPayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
+
 
 def admin_required(view: Callable[P, R]):
     @wraps(view)
@@ -109,6 +133,20 @@ class InsertExploreAppListApi(Resource):
                 )
 
                 db.session.add(recommended_app)
+                if payload.can_trial:
+                    trial_app = db.session.execute(
+                        select(TrialApp).where(TrialApp.app_id == payload.app_id)
+                    ).scalar_one_or_none()
+                    if not trial_app:
+                        db.session.add(
+                            TrialApp(
+                                app_id=payload.app_id,
+                                tenant_id=app.tenant_id,
+                                trial_limit=payload.trial_limit,
+                            )
+                        )
+                    else:
+                        trial_app.trial_limit = payload.trial_limit
 
                 app.is_public = True
                 db.session.commit()
@@ -123,6 +161,20 @@ class InsertExploreAppListApi(Resource):
                 recommended_app.category = payload.category
                 recommended_app.position = payload.position
 
+                if payload.can_trial:
+                    trial_app = db.session.execute(
+                        select(TrialApp).where(TrialApp.app_id == payload.app_id)
+                    ).scalar_one_or_none()
+                    if not trial_app:
+                        db.session.add(
+                            TrialApp(
+                                app_id=payload.app_id,
+                                tenant_id=app.tenant_id,
+                                trial_limit=payload.trial_limit,
+                            )
+                        )
+                    else:
+                        trial_app.trial_limit = payload.trial_limit
                 app.is_public = True
 
                 db.session.commit()
@@ -168,7 +220,62 @@ class InsertExploreAppApi(Resource):
             for installed_app in installed_apps:
                 session.delete(installed_app)
 
+            trial_app = session.execute(
+                select(TrialApp).where(TrialApp.app_id == recommended_app.app_id)
+            ).scalar_one_or_none()
+            if trial_app:
+                session.delete(trial_app)
+
         db.session.delete(recommended_app)
         db.session.commit()
 
         return {"result": "success"}, 204
+
+
+@console_ns.route("/admin/insert-explore-banner")
+class InsertExploreBannerApi(Resource):
+    @console_ns.doc("insert_explore_banner")
+    @console_ns.doc(description="Insert an explore banner")
+    @console_ns.expect(console_ns.models[InsertExploreBannerPayload.__name__])
+    @console_ns.response(201, "Banner inserted successfully")
+    @only_edition_cloud
+    @admin_required
+    def post(self):
+        payload = InsertExploreBannerPayload.model_validate(console_ns.payload)
+
+        content = {
+            "category": payload.category,
+            "title": payload.title,
+            "description": payload.description,
+            "img-src": payload.img_src,
+        }
+
+        banner = ExporleBanner(
+            content=content,
+            link=payload.link,
+            sort=payload.sort,
+            language=payload.language,
+        )
+        db.session.add(banner)
+        db.session.commit()
+
+        return {"result": "success"}, 201
+
+
+@console_ns.route("/admin/insert-explore-banner/<uuid:banner_id>")
+class DeleteExploreBannerApi(Resource):
+    @console_ns.doc("delete_explore_banner")
+    @console_ns.doc(description="Delete an explore banner")
+    @console_ns.doc(params={"banner_id": "Banner ID to delete"})
+    @console_ns.response(204, "Banner deleted successfully")
+    @only_edition_cloud
+    @admin_required
+    def delete(self, banner_id):
+        banner = db.session.execute(select(ExporleBanner).where(ExporleBanner.id == banner_id)).scalar_one_or_none()
+        if not banner:
+            raise NotFound(f"Banner '{banner_id}' is not found")
+
+        db.session.delete(banner)
+        db.session.commit()
+
+        return {"result": "success"}, 204

api/controllers/console/app/annotation.py

@@ -272,6 +272,7 @@ class AnnotationExportApi(Resource):
     @account_initialization_required
     @edit_permission_required
     def get(self, app_id):
+
         app_id = str(app_id)
         annotation_list = AppAnnotationService.export_annotation_list_by_app_id(app_id)
         response_data = {"data": marshal(annotation_list, annotation_fields)}
@@ -359,7 +360,6 @@ class AnnotationBatchImportApi(Resource):
         file.seek(0, 2)  # Seek to end of file
         file_size = file.tell()
         file.seek(0)  # Reset to beginning
-
         max_size_bytes = dify_config.ANNOTATION_IMPORT_FILE_SIZE_LIMIT * 1024 * 1024
         if file_size > max_size_bytes:
             abort(

api/controllers/console/app/error.py

@@ -115,3 +115,9 @@ class InvokeRateLimitError(BaseHTTPException):
     error_code = "rate_limit_error"
     description = "Rate Limit Error"
     code = 429
+
+
+class NeedAddIdsError(BaseHTTPException):
+    error_code = "need_add_ids"
+    description = "Need to add ids."
+    code = 400

api/controllers/console/app/message.py

@@ -202,6 +202,7 @@ message_detail_model = console_ns.model(
         "status": fields.String,
         "error": fields.String,
         "parent_message_id": fields.String,
+        "generation_detail": fields.Raw,
     },
 )
 

api/controllers/console/app/wraps.py

@@ -23,6 +23,11 @@ def _load_app_model(app_id: str) -> App | None:
     return app_model
 
 
+def _load_app_model_with_trial(app_id: str) -> App | None:
+    app_model = db.session.query(App).where(App.id == app_id, App.status == "normal").first()
+    return app_model
+
+
 def get_app_model(view: Callable[P, R] | None = None, *, mode: Union[AppMode, list[AppMode], None] = None):
     def decorator(view_func: Callable[P1, R1]):
         @wraps(view_func)
@@ -62,3 +67,44 @@ def get_app_model(view: Callable[P, R] | None = None, *, mode: Union[AppMode, li
         return decorator
     else:
         return decorator(view)
+
+
+def get_app_model_with_trial(view: Callable[P, R] | None = None, *, mode: Union[AppMode, list[AppMode], None] = None):
+    def decorator(view_func: Callable[P, R]):
+        @wraps(view_func)
+        def decorated_view(*args: P.args, **kwargs: P.kwargs):
+            if not kwargs.get("app_id"):
+                raise ValueError("missing app_id in path parameters")
+
+            app_id = kwargs.get("app_id")
+            app_id = str(app_id)
+
+            del kwargs["app_id"]
+
+            app_model = _load_app_model_with_trial(app_id)
+
+            if not app_model:
+                raise AppNotFoundError()
+
+            app_mode = AppMode.value_of(app_model.mode)
+
+            if mode is not None:
+                if isinstance(mode, list):
+                    modes = mode
+                else:
+                    modes = [mode]
+
+                if app_mode not in modes:
+                    mode_values = {m.value for m in modes}
+                    raise AppNotFoundError(f"App mode is not in the supported list: {mode_values}")
+
+            kwargs["app_model"] = app_model
+
+            return view_func(*args, **kwargs)
+
+        return decorated_view
+
+    if view is None:
+        return decorator
+    else:
+        return decorator(view)

api/controllers/console/auth/oauth.py

@@ -161,10 +161,7 @@ class OAuthCallback(Resource):
             ip_address=extract_remote_ip(request),
         )
 
-        base_url = dify_config.CONSOLE_WEB_URL
+        response = redirect(f"{dify_config.CONSOLE_WEB_URL}?oauth_new_user={str(oauth_new_user).lower()}")
-        query_char = "&" if "?" in base_url else "?"
-        target_url = f"{base_url}{query_char}oauth_new_user={str(oauth_new_user).lower()}"
-        response = redirect(target_url)
 
         set_access_token_to_cookie(request, response, token_pair.access_token)
         set_refresh_token_to_cookie(request, response, token_pair.refresh_token)

api/controllers/console/datasets/datasets.py

@@ -146,6 +146,7 @@ class DatasetUpdatePayload(BaseModel):
     embedding_model: str | None = None
     embedding_model_provider: str | None = None
     retrieval_model: dict[str, Any] | None = None
+    summary_index_setting: dict[str, Any] | None = None
     partial_member_list: list[dict[str, str]] | None = None
     external_retrieval_model: dict[str, Any] | None = None
     external_knowledge_id: str | None = None

api/controllers/console/datasets/datasets_document.py

@@ -39,9 +39,10 @@ from fields.document_fields import (
 from libs.datetime_utils import naive_utc_now
 from libs.login import current_account_with_tenant, login_required
 from models import DatasetProcessRule, Document, DocumentSegment, UploadFile
-from models.dataset import DocumentPipelineExecutionLog
+from models.dataset import DocumentPipelineExecutionLog, DocumentSegmentSummary
 from services.dataset_service import DatasetService, DocumentService
 from services.entities.knowledge_entities.knowledge_entities import KnowledgeConfig, ProcessRule, RetrievalModel
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 from ..app.error import (
     ProviderModelCurrentlyNotSupportError,
@@ -104,6 +105,10 @@ class DocumentRenamePayload(BaseModel):
     name: str
 
 
+class GenerateSummaryPayload(BaseModel):
+    document_list: list[str]
+
+
 register_schema_models(
     console_ns,
     KnowledgeConfig,
@@ -111,6 +116,7 @@ register_schema_models(
     RetrievalModel,
     DocumentRetryPayload,
     DocumentRenamePayload,
+    GenerateSummaryPayload,
 )
 
 
@@ -295,6 +301,97 @@ class DatasetDocumentListApi(Resource):
 
         paginated_documents = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)
         documents = paginated_documents.items
+        
+        # Check if dataset has summary index enabled
+        has_summary_index = (
+            dataset.summary_index_setting
+            and dataset.summary_index_setting.get("enable") is True
+        )
+        
+        # Filter documents that need summary calculation
+        documents_need_summary = [doc for doc in documents if doc.need_summary is True]
+        document_ids_need_summary = [str(doc.id) for doc in documents_need_summary]
+        
+        # Calculate summary_index_status for documents that need summary (only if dataset summary index is enabled)
+        summary_status_map = {}
+        if has_summary_index and document_ids_need_summary:
+            # Get all segments for these documents (excluding qa_model and re_segment)
+            segments = (
+                db.session.query(DocumentSegment.id, DocumentSegment.document_id)
+                .where(
+                    DocumentSegment.document_id.in_(document_ids_need_summary),
+                    DocumentSegment.status != "re_segment",
+                    DocumentSegment.tenant_id == current_tenant_id,
+                )
+                .all()
+            )
+            
+            # Group segments by document_id
+            document_segments_map = {}
+            for segment in segments:
+                doc_id = str(segment.document_id)
+                if doc_id not in document_segments_map:
+                    document_segments_map[doc_id] = []
+                document_segments_map[doc_id].append(segment.id)
+            
+            # Get all summary records for these segments
+            all_segment_ids = [seg.id for seg in segments]
+            summaries = {}
+            if all_segment_ids:
+                summary_records = (
+                    db.session.query(DocumentSegmentSummary)
+                    .where(
+                        DocumentSegmentSummary.chunk_id.in_(all_segment_ids),
+                        DocumentSegmentSummary.dataset_id == dataset_id,
+                        DocumentSegmentSummary.enabled == True,  # Only count enabled summaries
+                    )
+                    .all()
+                )
+                summaries = {summary.chunk_id: summary.status for summary in summary_records}
+            
+            # Calculate summary_index_status for each document
+            for doc_id in document_ids_need_summary:
+                segment_ids = document_segments_map.get(doc_id, [])
+                if not segment_ids:
+                    # No segments, status is "GENERATING" (waiting to generate)
+                    summary_status_map[doc_id] = "GENERATING"
+                    continue
+                
+                # Count summary statuses for this document's segments
+                status_counts = {"completed": 0, "generating": 0, "error": 0, "not_started": 0}
+                for segment_id in segment_ids:
+                    status = summaries.get(segment_id, "not_started")
+                    if status in status_counts:
+                        status_counts[status] += 1
+                    else:
+                        status_counts["not_started"] += 1
+                
+                total_segments = len(segment_ids)
+                completed_count = status_counts["completed"]
+                generating_count = status_counts["generating"]
+                error_count = status_counts["error"]
+                
+                # Determine overall status (only three states: GENERATING, COMPLETED, ERROR)
+                if completed_count == total_segments:
+                    summary_status_map[doc_id] = "COMPLETED"
+                elif error_count > 0:
+                    # Has errors (even if some are completed or generating)
+                    summary_status_map[doc_id] = "ERROR"
+                elif generating_count > 0 or status_counts["not_started"] > 0:
+                    # Still generating or not started
+                    summary_status_map[doc_id] = "GENERATING"
+                else:
+                    # Default to generating
+                    summary_status_map[doc_id] = "GENERATING"
+        
+        # Add summary_index_status to each document
+        for document in documents:
+            if has_summary_index and document.need_summary is True:
+                document.summary_index_status = summary_status_map.get(str(document.id), "GENERATING")
+            else:
+                # Return null if summary index is not enabled or document doesn't need summary
+                document.summary_index_status = None
+        
         if fetch:
             for document in documents:
                 completed_segments = (
@@ -393,6 +490,7 @@ class DatasetDocumentListApi(Resource):
         return {"result": "success"}, 204
 
 
+
 @console_ns.route("/datasets/init")
 class DatasetInitApi(Resource):
     @console_ns.doc("init_dataset")
@@ -780,6 +878,7 @@ class DocumentApi(DocumentResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
         else:
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
@@ -815,6 +914,7 @@ class DocumentApi(DocumentResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
 
         return response, 200
@@ -1182,3 +1282,211 @@ class DocumentPipelineExecutionLogApi(DocumentResource):
             "input_data": log.input_data,
             "datasource_node_id": log.datasource_node_id,
         }, 200
+
+
+@console_ns.route("/datasets/<uuid:dataset_id>/documents/generate-summary")
+class DocumentGenerateSummaryApi(Resource):
+    @console_ns.doc("generate_summary_for_documents")
+    @console_ns.doc(description="Generate summary index for documents")
+    @console_ns.doc(params={"dataset_id": "Dataset ID"})
+    @console_ns.expect(console_ns.models[GenerateSummaryPayload.__name__])
+    @console_ns.response(200, "Summary generation started successfully")
+    @console_ns.response(400, "Invalid request or dataset configuration")
+    @console_ns.response(403, "Permission denied")
+    @console_ns.response(404, "Dataset not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @cloud_edition_billing_rate_limit_check("knowledge")
+    def post(self, dataset_id):
+        """
+        Generate summary index for specified documents.
+        
+        This endpoint checks if the dataset configuration supports summary generation
+        (indexing_technique must be 'high_quality' and summary_index_setting.enable must be true),
+        then asynchronously generates summary indexes for the provided documents.
+        """
+        current_user, _ = current_account_with_tenant()
+        dataset_id = str(dataset_id)
+        
+        # Get dataset
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        
+        # Check permissions
+        if not current_user.is_dataset_editor:
+            raise Forbidden()
+        
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+        
+        # Validate request payload
+        payload = GenerateSummaryPayload.model_validate(console_ns.payload or {})
+        document_list = payload.document_list
+        
+        if not document_list:
+            raise ValueError("document_list cannot be empty.")
+        
+        # Check if dataset configuration supports summary generation
+        if dataset.indexing_technique != "high_quality":
+            raise ValueError(
+                f"Summary generation is only available for 'high_quality' indexing technique. "
+                f"Current indexing technique: {dataset.indexing_technique}"
+            )
+        
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            raise ValueError(
+                "Summary index is not enabled for this dataset. "
+                "Please enable it in the dataset settings."
+            )
+        
+        # Verify all documents exist and belong to the dataset
+        documents = (
+            db.session.query(Document)
+            .filter(
+                Document.id.in_(document_list),
+                Document.dataset_id == dataset_id,
+            )
+            .all()
+        )
+        
+        if len(documents) != len(document_list):
+            found_ids = {doc.id for doc in documents}
+            missing_ids = set(document_list) - found_ids
+            raise NotFound(f"Some documents not found: {list(missing_ids)}")
+        
+        # Dispatch async tasks for each document
+        for document in documents:
+            # Skip qa_model documents as they don't generate summaries
+            if document.doc_form == "qa_model":
+                logger.info(
+                    f"Skipping summary generation for qa_model document {document.id}"
+                )
+                continue
+            
+            # Dispatch async task
+            generate_summary_index_task(dataset_id, document.id)
+            logger.info(
+                f"Dispatched summary generation task for document {document.id} in dataset {dataset_id}"
+            )
+        
+        return {"result": "success"}, 200
+
+
+@console_ns.route("/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/summary-status")
+class DocumentSummaryStatusApi(DocumentResource):
+    @console_ns.doc("get_document_summary_status")
+    @console_ns.doc(description="Get summary index generation status for a document")
+    @console_ns.doc(params={"dataset_id": "Dataset ID", "document_id": "Document ID"})
+    @console_ns.response(200, "Summary status retrieved successfully")
+    @console_ns.response(404, "Document not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, document_id):
+        """
+        Get summary index generation status for a document.
+        
+        Returns:
+        - total_segments: Total number of segments in the document
+        - summary_status: Dictionary with status counts
+          - completed: Number of summaries completed
+          - generating: Number of summaries being generated
+          - error: Number of summaries with errors
+          - not_started: Number of segments without summary records
+        - summaries: List of summary records with status and content preview
+        """
+        current_user, _ = current_account_with_tenant()
+        dataset_id = str(dataset_id)
+        document_id = str(document_id)
+        
+        # Get document
+        document = self.get_document(dataset_id, document_id)
+        
+        # Get dataset
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        
+        # Check permissions
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+        
+        # Get all segments for this document
+        segments = (
+            db.session.query(DocumentSegment)
+            .filter(
+                DocumentSegment.document_id == document_id,
+                DocumentSegment.dataset_id == dataset_id,
+                DocumentSegment.status == "completed",
+                DocumentSegment.enabled == True,
+            )
+            .all()
+        )
+        
+        total_segments = len(segments)
+        
+        # Get all summary records for these segments
+        segment_ids = [segment.id for segment in segments]
+        summaries = []
+        if segment_ids:
+            summaries = (
+                db.session.query(DocumentSegmentSummary)
+                .filter(
+                    DocumentSegmentSummary.document_id == document_id,
+                    DocumentSegmentSummary.dataset_id == dataset_id,
+                    DocumentSegmentSummary.chunk_id.in_(segment_ids),
+                    DocumentSegmentSummary.enabled == True,  # Only return enabled summaries
+                )
+                .all()
+            )
+        
+        # Create a mapping of chunk_id to summary
+        summary_map = {summary.chunk_id: summary for summary in summaries}
+        
+        # Count statuses
+        status_counts = {
+            "completed": 0,
+            "generating": 0,
+            "error": 0,
+            "not_started": 0,
+        }
+        
+        summary_list = []
+        for segment in segments:
+            summary = summary_map.get(segment.id)
+            if summary:
+                status = summary.status
+                status_counts[status] = status_counts.get(status, 0) + 1
+                summary_list.append({
+                    "segment_id": segment.id,
+                    "segment_position": segment.position,
+                    "status": summary.status,
+                    "summary_preview": summary.summary_content[:100] + "..." if summary.summary_content and len(summary.summary_content) > 100 else summary.summary_content,
+                    "error": summary.error,
+                    "created_at": int(summary.created_at.timestamp()) if summary.created_at else None,
+                    "updated_at": int(summary.updated_at.timestamp()) if summary.updated_at else None,
+                })
+            else:
+                status_counts["not_started"] += 1
+                summary_list.append({
+                    "segment_id": segment.id,
+                    "segment_position": segment.position,
+                    "status": "not_started",
+                    "summary_preview": None,
+                    "error": None,
+                    "created_at": None,
+                    "updated_at": None,
+                })
+        
+        return {
+            "total_segments": total_segments,
+            "summary_status": status_counts,
+            "summaries": summary_list,
+        }, 200

api/controllers/console/datasets/datasets_segments.py

@@ -32,7 +32,7 @@ from extensions.ext_redis import redis_client
 from fields.segment_fields import child_chunk_fields, segment_fields
 from libs.helper import escape_like_pattern
 from libs.login import current_account_with_tenant, login_required
-from models.dataset import ChildChunk, DocumentSegment
+from models.dataset import ChildChunk, DocumentSegment, DocumentSegmentSummary
 from models.model import UploadFile
 from services.dataset_service import DatasetService, DocumentService, SegmentService
 from services.entities.knowledge_entities.knowledge_entities import ChildChunkUpdateArgs, SegmentUpdateArgs
@@ -41,6 +41,23 @@ from services.errors.chunk import ChildChunkIndexingError as ChildChunkIndexingS
 from tasks.batch_create_segment_to_index_task import batch_create_segment_to_index_task
 
 
+def _get_segment_with_summary(segment, dataset_id):
+    """Helper function to marshal segment and add summary information."""
+    segment_dict = marshal(segment, segment_fields)
+    # Query summary for this segment (only enabled summaries)
+    summary = (
+        db.session.query(DocumentSegmentSummary)
+        .where(
+            DocumentSegmentSummary.chunk_id == segment.id,
+            DocumentSegmentSummary.dataset_id == dataset_id,
+            DocumentSegmentSummary.enabled == True,  # Only return enabled summaries
+        )
+        .first()
+    )
+    segment_dict["summary"] = summary.summary_content if summary else None
+    return segment_dict
+
+
 class SegmentListQuery(BaseModel):
     limit: int = Field(default=20, ge=1, le=100)
     status: list[str] = Field(default_factory=list)
@@ -63,6 +80,7 @@ class SegmentUpdatePayload(BaseModel):
     keywords: list[str] | None = None
     regenerate_child_chunks: bool = False
     attachment_ids: list[str] | None = None
+    summary: str | None = None  # Summary content for summary index
 
 
 class BatchImportPayload(BaseModel):
@@ -180,8 +198,34 @@ class DatasetDocumentSegmentListApi(Resource):
 
         segments = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)
 
+        # Query summaries for all segments in this page (batch query for efficiency)
+        segment_ids = [segment.id for segment in segments.items]
+        summaries = {}
+        if segment_ids:
+            summary_records = (
+                db.session.query(DocumentSegmentSummary)
+                .where(
+                    DocumentSegmentSummary.chunk_id.in_(segment_ids),
+                    DocumentSegmentSummary.dataset_id == dataset_id,
+                )
+                .all()
+            )
+            # Only include enabled summaries
+            summaries = {
+                summary.chunk_id: summary.summary_content 
+                for summary in summary_records 
+                if summary.enabled is True
+            }
+
+        # Add summary to each segment
+        segments_with_summary = []
+        for segment in segments.items:
+            segment_dict = marshal(segment, segment_fields)
+            segment_dict["summary"] = summaries.get(segment.id)
+            segments_with_summary.append(segment_dict)
+
         response = {
-            "data": marshal(segments.items, segment_fields),
+            "data": segments_with_summary,
             "limit": limit,
             "total": segments.total,
             "total_pages": segments.pages,
@@ -327,7 +371,7 @@ class DatasetDocumentSegmentAddApi(Resource):
         payload_dict = payload.model_dump(exclude_none=True)
         SegmentService.segment_create_args_validate(payload_dict, document)
         segment = SegmentService.create_segment(payload_dict, document, dataset)
-        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+        return {"data": _get_segment_with_summary(segment, dataset_id), "doc_form": document.doc_form}, 200
 
 
 @console_ns.route("/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>")
@@ -389,10 +433,12 @@ class DatasetDocumentSegmentUpdateApi(Resource):
         payload = SegmentUpdatePayload.model_validate(console_ns.payload or {})
         payload_dict = payload.model_dump(exclude_none=True)
         SegmentService.segment_create_args_validate(payload_dict, document)
+        
+        # Update segment (summary update with change detection is handled in SegmentService.update_segment)
         segment = SegmentService.update_segment(
             SegmentUpdateArgs.model_validate(payload.model_dump(exclude_none=True)), segment, document, dataset
         )
-        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+        return {"data": _get_segment_with_summary(segment, dataset_id), "doc_form": document.doc_form}, 200
 
     @setup_required
     @login_required

api/controllers/console/datasets/hit_testing.py

@@ -1,4 +1,4 @@
-from flask_restx import Resource
+from flask_restx import Resource, fields
 
 from controllers.common.schema import register_schema_model
 from libs.login import login_required
@@ -10,17 +10,56 @@ from ..wraps import (
     cloud_edition_billing_rate_limit_check,
     setup_required,
 )
+from fields.hit_testing_fields import (
+    child_chunk_fields,
+    document_fields,
+    files_fields,
+    hit_testing_record_fields,
+    segment_fields,
+)
 
 register_schema_model(console_ns, HitTestingPayload)
 
 
+def _get_or_create_model(model_name: str, field_def):
+    """Get or create a flask_restx model to avoid dict type issues in Swagger."""
+    existing = console_ns.models.get(model_name)
+    if existing is None:
+        existing = console_ns.model(model_name, field_def)
+    return existing
+
+
+# Register models for flask_restx to avoid dict type issues in Swagger
+document_model = _get_or_create_model("HitTestingDocument", document_fields)
+
+segment_fields_copy = segment_fields.copy()
+segment_fields_copy["document"] = fields.Nested(document_model)
+segment_model = _get_or_create_model("HitTestingSegment", segment_fields_copy)
+
+child_chunk_model = _get_or_create_model("HitTestingChildChunk", child_chunk_fields)
+files_model = _get_or_create_model("HitTestingFile", files_fields)
+
+hit_testing_record_fields_copy = hit_testing_record_fields.copy()
+hit_testing_record_fields_copy["segment"] = fields.Nested(segment_model)
+hit_testing_record_fields_copy["child_chunks"] = fields.List(fields.Nested(child_chunk_model))
+hit_testing_record_fields_copy["files"] = fields.List(fields.Nested(files_model))
+hit_testing_record_model = _get_or_create_model("HitTestingRecord", hit_testing_record_fields_copy)
+
+# Response model for hit testing API
+hit_testing_response_fields = {
+    "query": fields.String,
+    "records": fields.List(fields.Nested(hit_testing_record_model)),
+}
+hit_testing_response_model = _get_or_create_model("HitTestingResponse", hit_testing_response_fields)
+
+
 @console_ns.route("/datasets/<uuid:dataset_id>/hit-testing")
 class HitTestingApi(Resource, DatasetsHitTestingBase):
     @console_ns.doc("test_dataset_retrieval")
     @console_ns.doc(description="Test dataset knowledge retrieval")
     @console_ns.doc(params={"dataset_id": "Dataset ID"})
     @console_ns.expect(console_ns.models[HitTestingPayload.__name__])
-    @console_ns.response(200, "Hit testing completed successfully")
+    @console_ns.response(200, "Hit testing completed successfully", model=hit_testing_response_model)
     @console_ns.response(404, "Dataset not found")
     @console_ns.response(400, "Invalid parameters")
     @setup_required

api/controllers/console/explore/banner.py

@@ -0,0 +1,43 @@
+from flask import request
+from flask_restx import Resource
+
+from controllers.console import api
+from controllers.console.explore.wraps import explore_banner_enabled
+from extensions.ext_database import db
+from models.model import ExporleBanner
+
+
+class BannerApi(Resource):
+    """Resource for banner list."""
+
+    @explore_banner_enabled
+    def get(self):
+        """Get banner list."""
+        language = request.args.get("language", "en-US")
+
+        # Build base query for enabled banners
+        base_query = db.session.query(ExporleBanner).where(ExporleBanner.status == "enabled")
+
+        # Try to get banners in the requested language
+        banners = base_query.where(ExporleBanner.language == language).order_by(ExporleBanner.sort).all()
+
+        # Fallback to en-US if no banners found and language is not en-US
+        if not banners and language != "en-US":
+            banners = base_query.where(ExporleBanner.language == "en-US").order_by(ExporleBanner.sort).all()
+        # Convert banners to serializable format
+        result = []
+        for banner in banners:
+            banner_data = {
+                "id": banner.id,
+                "content": banner.content,  # Already parsed as JSON by SQLAlchemy
+                "link": banner.link,
+                "sort": banner.sort,
+                "status": banner.status,
+                "created_at": banner.created_at.isoformat() if banner.created_at else None,
+            }
+            result.append(banner_data)
+
+        return result
+
+
+api.add_resource(BannerApi, "/explore/banners")

api/controllers/console/explore/error.py

@@ -29,3 +29,25 @@ class AppAccessDeniedError(BaseHTTPException):
     error_code = "access_denied"
     description = "App access denied."
     code = 403
+
+
+class TrialAppNotAllowed(BaseHTTPException):
+    """*403* `Trial App Not Allowed`
+
+    Raise if the user has reached the trial app limit.
+    """
+
+    error_code = "trial_app_not_allowed"
+    code = 403
+    description = "the app is not allowed to be trial."
+
+
+class TrialAppLimitExceeded(BaseHTTPException):
+    """*403* `Trial App Limit Exceeded`
+
+    Raise if the user has exceeded the trial app limit.
+    """
+
+    error_code = "trial_app_limit_exceeded"
+    code = 403
+    description = "The user has exceeded the trial app limit."

api/controllers/console/explore/recommended_app.py

@@ -29,6 +29,7 @@ recommended_app_fields = {
     "category": fields.String,
     "position": fields.Integer,
     "is_listed": fields.Boolean,
+    "can_trial": fields.Boolean,
 }
 
 recommended_app_list_fields = {

api/controllers/console/explore/trial.py

@@ -0,0 +1,512 @@
+import logging
+from typing import Any, cast
+
+from flask import request
+from flask_restx import Resource, marshal, marshal_with, reqparse
+from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
+
+import services
+from controllers.common.fields import Parameters as ParametersResponse
+from controllers.common.fields import Site as SiteResponse
+from controllers.console import api
+from controllers.console.app.error import (
+    AppUnavailableError,
+    AudioTooLargeError,
+    CompletionRequestError,
+    ConversationCompletedError,
+    NeedAddIdsError,
+    NoAudioUploadedError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderNotSupportSpeechToTextError,
+    ProviderQuotaExceededError,
+    UnsupportedAudioTypeError,
+)
+from controllers.console.app.wraps import get_app_model_with_trial
+from controllers.console.explore.error import (
+    AppSuggestedQuestionsAfterAnswerDisabledError,
+    NotChatAppError,
+    NotCompletionAppError,
+    NotWorkflowAppError,
+)
+from controllers.console.explore.wraps import TrialAppResource, trial_feature_enable
+from controllers.web.error import InvokeRateLimitError as InvokeRateLimitHttpError
+from core.app.app_config.common.parameters_mapping import get_parameters_from_feature_dict
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
+from core.model_runtime.errors.invoke import InvokeError
+from core.workflow.graph_engine.manager import GraphEngineManager
+from extensions.ext_database import db
+from fields.app_fields import app_detail_fields_with_site
+from fields.dataset_fields import dataset_fields
+from fields.workflow_fields import workflow_fields
+from libs import helper
+from libs.helper import uuid_value
+from libs.login import current_user
+from models import Account
+from models.account import TenantStatus
+from models.model import AppMode, Site
+from models.workflow import Workflow
+from services.app_generate_service import AppGenerateService
+from services.app_service import AppService
+from services.audio_service import AudioService
+from services.dataset_service import DatasetService
+from services.errors.audio import (
+    AudioTooLargeServiceError,
+    NoAudioUploadedServiceError,
+    ProviderNotSupportSpeechToTextServiceError,
+    UnsupportedAudioTypeServiceError,
+)
+from services.errors.conversation import ConversationNotExistsError
+from services.errors.llm import InvokeRateLimitError
+from services.errors.message import (
+    MessageNotExistsError,
+    SuggestedQuestionsAfterAnswerDisabledError,
+)
+from services.message_service import MessageService
+from services.recommended_app_service import RecommendedAppService
+
+logger = logging.getLogger(__name__)
+
+
+class TrialAppWorkflowRunApi(TrialAppResource):
+    def post(self, trial_app):
+        """
+        Run workflow
+        """
+        app_model = trial_app
+        if not app_model:
+            raise NotWorkflowAppError()
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, nullable=False, location="json")
+        parser.add_argument("files", type=list, required=False, location="json")
+        args = parser.parse_args()
+        assert current_user is not None
+        try:
+            app_id = app_model.id
+            user_id = current_user.id
+            response = AppGenerateService.generate(
+                app_model=app_model, user=current_user, args=args, invoke_from=InvokeFrom.EXPLORE, streaming=True
+            )
+            RecommendedAppService.add_trial_app_record(app_id, user_id)
+            return helper.compact_generate_response(response)
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except InvokeRateLimitError as ex:
+            raise InvokeRateLimitHttpError(ex.description)
+        except ValueError as e:
+            raise e
+        except Exception:
+            logger.exception("internal server error.")
+            raise InternalServerError()
+
+
+class TrialAppWorkflowTaskStopApi(TrialAppResource):
+    def post(self, trial_app, task_id: str):
+        """
+        Stop workflow task
+        """
+        app_model = trial_app
+        if not app_model:
+            raise NotWorkflowAppError()
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+        assert current_user is not None
+
+        # Stop using both mechanisms for backward compatibility
+        # Legacy stop flag mechanism (without user check)
+        AppQueueManager.set_stop_flag_no_user_check(task_id)
+
+        # New graph engine command channel mechanism
+        GraphEngineManager.send_stop_command(task_id)
+
+        return {"result": "success"}
+
+
+class TrialChatApi(TrialAppResource):
+    @trial_feature_enable
+    def post(self, trial_app):
+        app_model = trial_app
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, location="json")
+        parser.add_argument("query", type=str, required=True, location="json")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("conversation_id", type=uuid_value, location="json")
+        parser.add_argument("parent_message_id", type=uuid_value, required=False, location="json")
+        parser.add_argument("retriever_from", type=str, required=False, default="explore_app", location="json")
+        args = parser.parse_args()
+
+        args["auto_generate_name"] = False
+
+        try:
+            if not isinstance(current_user, Account):
+                raise ValueError("current_user must be an Account instance")
+
+            # Get IDs before they might be detached from session
+            app_id = app_model.id
+            user_id = current_user.id
+
+            response = AppGenerateService.generate(
+                app_model=app_model, user=current_user, args=args, invoke_from=InvokeFrom.EXPLORE, streaming=True
+            )
+            RecommendedAppService.add_trial_app_record(app_id, user_id)
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logger.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except InvokeRateLimitError as ex:
+            raise InvokeRateLimitHttpError(ex.description)
+        except ValueError as e:
+            raise e
+        except Exception:
+            logger.exception("internal server error.")
+            raise InternalServerError()
+
+
+class TrialMessageSuggestedQuestionApi(TrialAppResource):
+    @trial_feature_enable
+    def get(self, trial_app, message_id):
+        app_model = trial_app
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        message_id = str(message_id)
+
+        try:
+            if not isinstance(current_user, Account):
+                raise ValueError("current_user must be an Account instance")
+            questions = MessageService.get_suggested_questions_after_answer(
+                app_model=app_model, user=current_user, message_id=message_id, invoke_from=InvokeFrom.EXPLORE
+            )
+        except MessageNotExistsError:
+            raise NotFound("Message not found")
+        except ConversationNotExistsError:
+            raise NotFound("Conversation not found")
+        except SuggestedQuestionsAfterAnswerDisabledError:
+            raise AppSuggestedQuestionsAfterAnswerDisabledError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except Exception:
+            logger.exception("internal server error.")
+            raise InternalServerError()
+
+        return {"data": questions}
+
+
+class TrialChatAudioApi(TrialAppResource):
+    @trial_feature_enable
+    def post(self, trial_app):
+        app_model = trial_app
+
+        file = request.files["file"]
+
+        try:
+            if not isinstance(current_user, Account):
+                raise ValueError("current_user must be an Account instance")
+
+            # Get IDs before they might be detached from session
+            app_id = app_model.id
+            user_id = current_user.id
+
+            response = AudioService.transcript_asr(app_model=app_model, file=file, end_user=None)
+            RecommendedAppService.add_trial_app_record(app_id, user_id)
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logger.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logger.exception("internal server error.")
+            raise InternalServerError()
+
+
+class TrialChatTextApi(TrialAppResource):
+    @trial_feature_enable
+    def post(self, trial_app):
+        app_model = trial_app
+        try:
+            parser = reqparse.RequestParser()
+            parser.add_argument("message_id", type=str, required=False, location="json")
+            parser.add_argument("voice", type=str, location="json")
+            parser.add_argument("text", type=str, location="json")
+            parser.add_argument("streaming", type=bool, location="json")
+            args = parser.parse_args()
+
+            message_id = args.get("message_id", None)
+            text = args.get("text", None)
+            voice = args.get("voice", None)
+            if not isinstance(current_user, Account):
+                raise ValueError("current_user must be an Account instance")
+
+            # Get IDs before they might be detached from session
+            app_id = app_model.id
+            user_id = current_user.id
+
+            response = AudioService.transcript_tts(app_model=app_model, text=text, voice=voice, message_id=message_id)
+            RecommendedAppService.add_trial_app_record(app_id, user_id)
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logger.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logger.exception("internal server error.")
+            raise InternalServerError()
+
+
+class TrialCompletionApi(TrialAppResource):
+    @trial_feature_enable
+    def post(self, trial_app):
+        app_model = trial_app
+        if app_model.mode != "completion":
+            raise NotCompletionAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, location="json")
+        parser.add_argument("query", type=str, location="json", default="")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
+        parser.add_argument("retriever_from", type=str, required=False, default="explore_app", location="json")
+        args = parser.parse_args()
+
+        streaming = args["response_mode"] == "streaming"
+        args["auto_generate_name"] = False
+
+        try:
+            if not isinstance(current_user, Account):
+                raise ValueError("current_user must be an Account instance")
+
+            # Get IDs before they might be detached from session
+            app_id = app_model.id
+            user_id = current_user.id
+
+            response = AppGenerateService.generate(
+                app_model=app_model, user=current_user, args=args, invoke_from=InvokeFrom.EXPLORE, streaming=streaming
+            )
+
+            RecommendedAppService.add_trial_app_record(app_id, user_id)
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logger.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception:
+            logger.exception("internal server error.")
+            raise InternalServerError()
+
+
+class TrialSitApi(Resource):
+    """Resource for trial app sites."""
+
+    @trial_feature_enable
+    @get_app_model_with_trial
+    def get(self, app_model):
+        """Retrieve app site info.
+
+        Returns the site configuration for the application including theme, icons, and text.
+        """
+        site = db.session.query(Site).where(Site.app_id == app_model.id).first()
+
+        if not site:
+            raise Forbidden()
+
+        assert app_model.tenant
+        if app_model.tenant.status == TenantStatus.ARCHIVE:
+            raise Forbidden()
+
+        return SiteResponse.model_validate(site).model_dump(mode="json")
+
+
+class TrialAppParameterApi(Resource):
+    """Resource for app variables."""
+
+    @trial_feature_enable
+    @get_app_model_with_trial
+    def get(self, app_model):
+        """Retrieve app parameters."""
+
+        if app_model is None:
+            raise AppUnavailableError()
+
+        if app_model.mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
+            workflow = app_model.workflow
+            if workflow is None:
+                raise AppUnavailableError()
+
+            features_dict = workflow.features_dict
+            user_input_form = workflow.user_input_form(to_old_structure=True)
+        else:
+            app_model_config = app_model.app_model_config
+            if app_model_config is None:
+                raise AppUnavailableError()
+
+            features_dict = app_model_config.to_dict()
+
+            user_input_form = features_dict.get("user_input_form", [])
+
+        parameters = get_parameters_from_feature_dict(features_dict=features_dict, user_input_form=user_input_form)
+        return ParametersResponse.model_validate(parameters).model_dump(mode="json")
+
+
+class AppApi(Resource):
+    @trial_feature_enable
+    @get_app_model_with_trial
+    @marshal_with(app_detail_fields_with_site)
+    def get(self, app_model):
+        """Get app detail"""
+
+        app_service = AppService()
+        app_model = app_service.get_app(app_model)
+
+        return app_model
+
+
+class AppWorkflowApi(Resource):
+    @trial_feature_enable
+    @get_app_model_with_trial
+    @marshal_with(workflow_fields)
+    def get(self, app_model):
+        """Get workflow detail"""
+        if not app_model.workflow_id:
+            raise AppUnavailableError()
+
+        workflow = (
+            db.session.query(Workflow)
+            .where(
+                Workflow.id == app_model.workflow_id,
+            )
+            .first()
+        )
+        return workflow
+
+
+class DatasetListApi(Resource):
+    @trial_feature_enable
+    @get_app_model_with_trial
+    def get(self, app_model):
+        page = request.args.get("page", default=1, type=int)
+        limit = request.args.get("limit", default=20, type=int)
+        ids = request.args.getlist("ids")
+
+        tenant_id = app_model.tenant_id
+        if ids:
+            datasets, total = DatasetService.get_datasets_by_ids(ids, tenant_id)
+        else:
+            raise NeedAddIdsError()
+
+        data = cast(list[dict[str, Any]], marshal(datasets, dataset_fields))
+
+        response = {"data": data, "has_more": len(datasets) == limit, "limit": limit, "total": total, "page": page}
+        return response
+
+
+api.add_resource(TrialChatApi, "/trial-apps/<uuid:app_id>/chat-messages", endpoint="trial_app_chat_completion")
+
+api.add_resource(
+    TrialMessageSuggestedQuestionApi,
+    "/trial-apps/<uuid:app_id>/messages/<uuid:message_id>/suggested-questions",
+    endpoint="trial_app_suggested_question",
+)
+
+api.add_resource(TrialChatAudioApi, "/trial-apps/<uuid:app_id>/audio-to-text", endpoint="trial_app_audio")
+api.add_resource(TrialChatTextApi, "/trial-apps/<uuid:app_id>/text-to-audio", endpoint="trial_app_text")
+
+api.add_resource(TrialCompletionApi, "/trial-apps/<uuid:app_id>/completion-messages", endpoint="trial_app_completion")
+
+api.add_resource(TrialSitApi, "/trial-apps/<uuid:app_id>/site")
+
+api.add_resource(TrialAppParameterApi, "/trial-apps/<uuid:app_id>/parameters", endpoint="trial_app_parameters")
+
+api.add_resource(AppApi, "/trial-apps/<uuid:app_id>", endpoint="trial_app")
+
+api.add_resource(TrialAppWorkflowRunApi, "/trial-apps/<uuid:app_id>/workflows/run", endpoint="trial_app_workflow_run")
+api.add_resource(TrialAppWorkflowTaskStopApi, "/trial-apps/<uuid:app_id>/workflows/tasks/<string:task_id>/stop")
+
+api.add_resource(AppWorkflowApi, "/trial-apps/<uuid:app_id>/workflows", endpoint="trial_app_workflow")
+api.add_resource(DatasetListApi, "/trial-apps/<uuid:app_id>/datasets", endpoint="trial_app_datasets")

api/controllers/console/explore/wraps.py

@@ -2,14 +2,15 @@ from collections.abc import Callable
 from functools import wraps
 from typing import Concatenate, ParamSpec, TypeVar
 
+from flask import abort
 from flask_restx import Resource
 from werkzeug.exceptions import NotFound
 
-from controllers.console.explore.error import AppAccessDeniedError
+from controllers.console.explore.error import AppAccessDeniedError, TrialAppLimitExceeded, TrialAppNotAllowed
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
 from libs.login import current_account_with_tenant, login_required
-from models import InstalledApp
+from models import AccountTrialAppRecord, App, InstalledApp, TrialApp
 from services.enterprise.enterprise_service import EnterpriseService
 from services.feature_service import FeatureService
 
@@ -71,6 +72,61 @@ def user_allowed_to_access_app(view: Callable[Concatenate[InstalledApp, P], R] |
     return decorator
 
 
+def trial_app_required(view: Callable[Concatenate[App, P], R] | None = None):
+    def decorator(view: Callable[Concatenate[App, P], R]):
+        @wraps(view)
+        def decorated(app_id: str, *args: P.args, **kwargs: P.kwargs):
+            current_user, _ = current_account_with_tenant()
+
+            trial_app = db.session.query(TrialApp).where(TrialApp.app_id == str(app_id)).first()
+
+            if trial_app is None:
+                raise TrialAppNotAllowed()
+            app = trial_app.app
+
+            if app is None:
+                raise TrialAppNotAllowed()
+
+            account_trial_app_record = (
+                db.session.query(AccountTrialAppRecord)
+                .where(AccountTrialAppRecord.account_id == current_user.id, AccountTrialAppRecord.app_id == app_id)
+                .first()
+            )
+            if account_trial_app_record:
+                if account_trial_app_record.count >= trial_app.trial_limit:
+                    raise TrialAppLimitExceeded()
+
+            return view(app, *args, **kwargs)
+
+        return decorated
+
+    if view:
+        return decorator(view)
+    return decorator
+
+
+def trial_feature_enable(view: Callable[..., R]) -> Callable[..., R]:
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        features = FeatureService.get_system_features()
+        if not features.enable_trial_app:
+            abort(403, "Trial app feature is not enabled.")
+        return view(*args, **kwargs)
+
+    return decorated
+
+
+def explore_banner_enabled(view: Callable[..., R]) -> Callable[..., R]:
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        features = FeatureService.get_system_features()
+        if not features.enable_explore_banner:
+            abort(403, "Explore banner feature is not enabled.")
+        return view(*args, **kwargs)
+
+    return decorated
+
+
 class InstalledAppResource(Resource):
     # must be reversed if there are multiple decorators
 
@@ -80,3 +136,13 @@ class InstalledAppResource(Resource):
         account_initialization_required,
         login_required,
     ]
+
+
+class TrialAppResource(Resource):
+    # must be reversed if there are multiple decorators
+
+    method_decorators = [
+        trial_app_required,
+        account_initialization_required,
+        login_required,
+    ]

api/controllers/console/wraps.py

@@ -358,14 +358,12 @@ def annotation_import_rate_limit(view: Callable[P, R]):
     def decorated(*args: P.args, **kwargs: P.kwargs):
         _, current_tenant_id = current_account_with_tenant()
         current_time = int(time.time() * 1000)
-
         # Check per-minute rate limit
         minute_key = f"annotation_import_rate_limit:{current_tenant_id}:1min"
         redis_client.zadd(minute_key, {current_time: current_time})
         redis_client.zremrangebyscore(minute_key, 0, current_time - 60000)
         minute_count = redis_client.zcard(minute_key)
         redis_client.expire(minute_key, 120)  # 2 minutes TTL
-
         if minute_count > dify_config.ANNOTATION_IMPORT_RATE_LIMIT_PER_MINUTE:
             abort(
                 429,
@@ -379,7 +377,6 @@ def annotation_import_rate_limit(view: Callable[P, R]):
         redis_client.zremrangebyscore(hour_key, 0, current_time - 3600000)
         hour_count = redis_client.zcard(hour_key)
         redis_client.expire(hour_key, 7200)  # 2 hours TTL
-
         if hour_count > dify_config.ANNOTATION_IMPORT_RATE_LIMIT_PER_HOUR:
             abort(
                 429,

api/core/agent/agent_app_runner.py

@@ -0,0 +1,380 @@
+import logging
+from collections.abc import Generator
+from copy import deepcopy
+from typing import Any
+
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.entities import AgentEntity, AgentLog, AgentResult
+from core.agent.patterns.strategy_factory import StrategyFactory
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
+from core.file import file_manager
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageContentType,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
+from core.tools.__base.tool import Tool
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
+from models.model import Message
+
+logger = logging.getLogger(__name__)
+
+
+class AgentAppRunner(BaseAgentRunner):
+    def _create_tool_invoke_hook(self, message: Message):
+        """
+        Create a tool invoke hook that uses ToolEngine.agent_invoke.
+        This hook handles file creation and returns proper meta information.
+        """
+        # Get trace manager from app generate entity
+        trace_manager = self.application_generate_entity.trace_manager
+
+        def tool_invoke_hook(
+            tool: Tool, tool_args: dict[str, Any], tool_name: str
+        ) -> tuple[str, list[str], ToolInvokeMeta]:
+            """Hook that uses agent_invoke for proper file and meta handling."""
+            tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                tool=tool,
+                tool_parameters=tool_args,
+                user_id=self.user_id,
+                tenant_id=self.tenant_id,
+                message=message,
+                invoke_from=self.application_generate_entity.invoke_from,
+                agent_tool_callback=self.agent_callback,
+                trace_manager=trace_manager,
+                app_id=self.application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                conversation_id=self.conversation.id,
+            )
+
+            # Publish files and track IDs
+            for message_file_id in message_files:
+                self.queue_manager.publish(
+                    QueueMessageFileEvent(message_file_id=message_file_id),
+                    PublishFrom.APPLICATION_MANAGER,
+                )
+                self._current_message_file_ids.append(message_file_id)
+
+            return tool_invoke_response, message_files, tool_invoke_meta
+
+        return tool_invoke_hook
+
+    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
+        """
+        Run Agent application
+        """
+        self.query = query
+        app_generate_entity = self.application_generate_entity
+
+        app_config = self.app_config
+        assert app_config is not None, "app_config is required"
+        assert app_config.agent is not None, "app_config.agent is required"
+
+        # convert tools into ModelRuntime Tool format
+        tool_instances, _ = self._init_prompt_tools()
+
+        assert app_config.agent
+
+        # Create tool invoke hook for agent_invoke
+        tool_invoke_hook = self._create_tool_invoke_hook(message)
+
+        # Get instruction for ReAct strategy
+        instruction = self.app_config.prompt_template.simple_prompt_template or ""
+
+        # Use factory to create appropriate strategy
+        strategy = StrategyFactory.create_strategy(
+            model_features=self.model_features,
+            model_instance=self.model_instance,
+            tools=list(tool_instances.values()),
+            files=list(self.files),
+            max_iterations=app_config.agent.max_iteration,
+            context=self.build_execution_context(),
+            agent_strategy=self.config.strategy,
+            tool_invoke_hook=tool_invoke_hook,
+            instruction=instruction,
+        )
+
+        # Initialize state variables
+        current_agent_thought_id = None
+        has_published_thought = False
+        current_tool_name: str | None = None
+        self._current_message_file_ids: list[str] = []
+
+        # organize prompt messages
+        prompt_messages = self._organize_prompt_messages()
+
+        # Run strategy
+        generator = strategy.run(
+            prompt_messages=prompt_messages,
+            model_parameters=app_generate_entity.model_conf.parameters,
+            stop=app_generate_entity.model_conf.stop,
+            stream=True,
+        )
+
+        # Consume generator and collect result
+        result: AgentResult | None = None
+        try:
+            while True:
+                try:
+                    output = next(generator)
+                except StopIteration as e:
+                    # Generator finished, get the return value
+                    result = e.value
+                    break
+
+                if isinstance(output, LLMResultChunk):
+                    # Handle LLM chunk
+                    if current_agent_thought_id and not has_published_thought:
+                        self.queue_manager.publish(
+                            QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                            PublishFrom.APPLICATION_MANAGER,
+                        )
+                        has_published_thought = True
+
+                    yield output
+
+                elif isinstance(output, AgentLog):
+                    # Handle Agent Log using log_type for type-safe dispatch
+                    if output.status == AgentLog.LogStatus.START:
+                        if output.log_type == AgentLog.LogType.ROUND:
+                            # Start of a new round
+                            message_file_ids: list[str] = []
+                            current_agent_thought_id = self.create_agent_thought(
+                                message_id=message.id,
+                                message="",
+                                tool_name="",
+                                tool_input="",
+                                messages_ids=message_file_ids,
+                            )
+                            has_published_thought = False
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call start - extract data from structured fields
+                            current_tool_name = output.data.get("tool_name", "")
+                            tool_input = output.data.get("tool_args", {})
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=current_tool_name,
+                                tool_input=tool_input,
+                                thought=None,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                    elif output.status == AgentLog.LogStatus.SUCCESS:
+                        if output.log_type == AgentLog.LogType.THOUGHT:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            thought_text = output.data.get("thought")
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=thought_text,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call finished
+                            tool_output = output.data.get("output")
+                            # Get meta from strategy output (now properly populated)
+                            tool_meta = output.data.get("meta")
+
+                            # Wrap tool_meta with tool_name as key (required by agent_service)
+                            if tool_meta and current_tool_name:
+                                tool_meta = {current_tool_name: tool_meta}
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=None,
+                                observation=tool_output,
+                                tool_invoke_meta=tool_meta,
+                                answer=None,
+                                messages_ids=self._current_message_file_ids,
+                            )
+                            # Clear message file ids after saving
+                            self._current_message_file_ids = []
+                            current_tool_name = None
+
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.ROUND:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Round finished - save LLM usage and answer
+                            llm_usage = output.metadata.get(AgentLog.LogMetadata.LLM_USAGE)
+                            llm_result = output.data.get("llm_result")
+                            final_answer = output.data.get("final_answer")
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=llm_result,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=final_answer,
+                                messages_ids=[],
+                                llm_usage=llm_usage,
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+        except Exception:
+            # Re-raise any other exceptions
+            raise
+
+        # Process final result
+        if isinstance(result, AgentResult):
+            final_answer = result.text
+            usage = result.usage or LLMUsage.empty_usage()
+
+            # Publish end event
+            self.queue_manager.publish(
+                QueueMessageEndEvent(
+                    llm_result=LLMResult(
+                        model=self.model_instance.model,
+                        prompt_messages=prompt_messages,
+                        message=AssistantPromptMessage(content=final_answer),
+                        usage=usage,
+                        system_fingerprint="",
+                    )
+                ),
+                PublishFrom.APPLICATION_MANAGER,
+            )
+
+    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Initialize system message
+        """
+        if not prompt_template:
+            return prompt_messages or []
+
+        prompt_messages = prompt_messages or []
+
+        if prompt_messages and isinstance(prompt_messages[0], SystemPromptMessage):
+            prompt_messages[0] = SystemPromptMessage(content=prompt_template)
+            return prompt_messages
+
+        if not prompt_messages:
+            return [SystemPromptMessage(content=prompt_template)]
+
+        prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
+        return prompt_messages
+
+    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Organize user query
+        """
+        if self.files:
+            # get image detail config
+            image_detail_config = (
+                self.application_generate_entity.file_upload_config.image_config.detail
+                if (
+                    self.application_generate_entity.file_upload_config
+                    and self.application_generate_entity.file_upload_config.image_config
+                )
+                else None
+            )
+            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+            for file in self.files:
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(
+                        file,
+                        image_detail_config=image_detail_config,
+                    )
+                )
+            prompt_message_contents.append(TextPromptMessageContent(data=query))
+
+            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+        else:
+            prompt_messages.append(UserPromptMessage(content=query))
+
+        return prompt_messages
+
+    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        As for now, gpt supports both fc and vision at the first iteration.
+        We need to remove the image messages from the prompt messages at the first iteration.
+        """
+        prompt_messages = deepcopy(prompt_messages)
+
+        for prompt_message in prompt_messages:
+            if isinstance(prompt_message, UserPromptMessage):
+                if isinstance(prompt_message.content, list):
+                    prompt_message.content = "\n".join(
+                        [
+                            content.data
+                            if content.type == PromptMessageContentType.TEXT
+                            else "[image]"
+                            if content.type == PromptMessageContentType.IMAGE
+                            else "[file]"
+                            for content in prompt_message.content
+                        ]
+                    )
+
+        return prompt_messages
+
+    def _organize_prompt_messages(self):
+        # For ReAct strategy, use the agent prompt template
+        if self.config.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT and self.config.prompt:
+            prompt_template = self.config.prompt.first_prompt
+        else:
+            prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
+
+        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
+        query_prompt_messages = self._organize_user_query(self.query or "", [])
+
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory,
+        ).get_prompt()
+
+        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
+        if len(self._current_thoughts) != 0:
+            # clear messages after the first iteration
+            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
+        return prompt_messages

api/core/agent/base_agent_runner.py

@@ -6,7 +6,7 @@ from typing import Union, cast
 
 from sqlalchemy import select
 
-from core.agent.entities import AgentEntity, AgentToolEntity
+from core.agent.entities import AgentEntity, AgentToolEntity, ExecutionContext
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager
@@ -116,9 +116,20 @@ class BaseAgentRunner(AppRunner):
         features = model_schema.features if model_schema and model_schema.features else []
         self.stream_tool_call = ModelFeature.STREAM_TOOL_CALL in features
         self.files = application_generate_entity.files if ModelFeature.VISION in features else []
+        self.model_features = features
         self.query: str | None = ""
         self._current_thoughts: list[PromptMessage] = []
 
+    def build_execution_context(self) -> ExecutionContext:
+        """Build execution context."""
+        return ExecutionContext(
+            user_id=self.user_id,
+            app_id=self.app_config.app_id,
+            conversation_id=self.conversation.id,
+            message_id=self.message.id,
+            tenant_id=self.tenant_id,
+        )
+
     def _repack_app_generate_entity(
         self, app_generate_entity: AgentChatAppGenerateEntity
     ) -> AgentChatAppGenerateEntity:

api/core/agent/cot_agent_runner.py

@@ -1,437 +0,0 @@
-import json
-import logging
-from abc import ABC, abstractmethod
-from collections.abc import Generator, Mapping, Sequence
-from typing import Any
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.agent.entities import AgentScratchpadUnit
-from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    PromptMessageTool,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.ops.ops_trace_manager import TraceQueueManager
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.__base.tool import Tool
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from core.workflow.nodes.agent.exc import AgentMaxIterationError
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class CotAgentRunner(BaseAgentRunner, ABC):
-    _is_first_iteration = True
-    _ignore_observation_providers = ["wenxin"]
-    _historic_prompt_messages: list[PromptMessage]
-    _agent_scratchpad: list[AgentScratchpadUnit]
-    _instruction: str
-    _query: str
-    _prompt_messages_tools: Sequence[PromptMessageTool]
-
-    def run(
-        self,
-        message: Message,
-        query: str,
-        inputs: Mapping[str, str],
-    ) -> Generator:
-        """
-        Run Cot agent application
-        """
-
-        app_generate_entity = self.application_generate_entity
-        self._repack_app_generate_entity(app_generate_entity)
-        self._init_react_state(query)
-
-        trace_manager = app_generate_entity.trace_manager
-
-        # check model mode
-        if "Observation" not in app_generate_entity.model_conf.stop:
-            if app_generate_entity.model_conf.provider not in self._ignore_observation_providers:
-                app_generate_entity.model_conf.stop.append("Observation")
-
-        app_config = self.app_config
-        assert app_config.agent
-
-        # init instruction
-        inputs = inputs or {}
-        instruction = app_config.prompt_template.simple_prompt_template or ""
-        self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-        self._prompt_messages_tools = prompt_messages_tools
-
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-        agent_thought_id = ""  # Initialize agent_thought_id
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            # continue to run until there is not any tool call
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                self._prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            if iteration_step > 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=[],
-                stop=app_generate_entity.model_conf.stop,
-                stream=True,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            usage_dict: dict[str, LLMUsage | None] = {}
-            react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
-            scratchpad = AgentScratchpadUnit(
-                agent_response="",
-                thought="",
-                action_str="",
-                observation="",
-                action=None,
-            )
-
-            # publish agent thought if it's first iteration
-            if iteration_step == 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            for chunk in react_chunks:
-                if isinstance(chunk, AgentScratchpadUnit.Action):
-                    action = chunk
-                    # detect action
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += json.dumps(chunk.model_dump())
-                    scratchpad.action_str = json.dumps(chunk.model_dump())
-                    scratchpad.action = action
-                else:
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += chunk
-                    assert scratchpad.thought is not None
-                    scratchpad.thought += chunk
-                    yield LLMResultChunk(
-                        model=self.model_config.model,
-                        prompt_messages=prompt_messages,
-                        system_fingerprint="",
-                        delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
-                    )
-
-            assert scratchpad.thought is not None
-            scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
-            self._agent_scratchpad.append(scratchpad)
-
-            # Check if max iteration is reached and model still wants to call tools
-            if iteration_step == max_iteration_steps and scratchpad.action:
-                if scratchpad.action.action_name.lower() != "final answer":
-                    raise AgentMaxIterationError(app_config.agent.max_iteration)
-
-            # get llm usage
-            if "usage" in usage_dict:
-                if usage_dict["usage"] is not None:
-                    increase_usage(llm_usage, usage_dict["usage"])
-            else:
-                usage_dict["usage"] = LLMUsage.empty_usage()
-
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
-                tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
-                tool_invoke_meta={},
-                thought=scratchpad.thought or "",
-                observation="",
-                answer=scratchpad.agent_response or "",
-                messages_ids=[],
-                llm_usage=usage_dict["usage"],
-            )
-
-            if not scratchpad.is_final():
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            if not scratchpad.action:
-                # failed to extract action, return final answer directly
-                final_answer = ""
-            else:
-                if scratchpad.action.action_name.lower() == "final answer":
-                    # action is final answer, return final answer directly
-                    try:
-                        if isinstance(scratchpad.action.action_input, dict):
-                            final_answer = json.dumps(scratchpad.action.action_input, ensure_ascii=False)
-                        elif isinstance(scratchpad.action.action_input, str):
-                            final_answer = scratchpad.action.action_input
-                        else:
-                            final_answer = f"{scratchpad.action.action_input}"
-                    except TypeError:
-                        final_answer = f"{scratchpad.action.action_input}"
-                else:
-                    function_call_state = True
-                    # action is tool call, invoke tool
-                    tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
-                        action=scratchpad.action,
-                        tool_instances=tool_instances,
-                        message_file_ids=message_file_ids,
-                        trace_manager=trace_manager,
-                    )
-                    scratchpad.observation = tool_invoke_response
-                    scratchpad.agent_response = tool_invoke_response
-
-                    self.save_agent_thought(
-                        agent_thought_id=agent_thought_id,
-                        tool_name=scratchpad.action.action_name,
-                        tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
-                        thought=scratchpad.thought or "",
-                        observation={scratchpad.action.action_name: tool_invoke_response},
-                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
-                        answer=scratchpad.agent_response,
-                        messages_ids=message_file_ids,
-                        llm_usage=usage_dict["usage"],
-                    )
-
-                    self.queue_manager.publish(
-                        QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                    )
-
-                # update prompt tool message
-                for prompt_tool in self._prompt_messages_tools:
-                    self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        yield LLMResultChunk(
-            model=model_instance.model,
-            prompt_messages=prompt_messages,
-            delta=LLMResultChunkDelta(
-                index=0, message=AssistantPromptMessage(content=final_answer), usage=llm_usage["usage"]
-            ),
-            system_fingerprint="",
-        )
-
-        # save agent thought
-        self.save_agent_thought(
-            agent_thought_id=agent_thought_id,
-            tool_name="",
-            tool_input={},
-            tool_invoke_meta={},
-            thought=final_answer,
-            observation={},
-            answer=final_answer,
-            messages_ids=[],
-        )
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def _handle_invoke_action(
-        self,
-        action: AgentScratchpadUnit.Action,
-        tool_instances: Mapping[str, Tool],
-        message_file_ids: list[str],
-        trace_manager: TraceQueueManager | None = None,
-    ) -> tuple[str, ToolInvokeMeta]:
-        """
-        handle invoke action
-        :param action: action
-        :param tool_instances: tool instances
-        :param message_file_ids: message file ids
-        :param trace_manager: trace manager
-        :return: observation, meta
-        """
-        # action is tool call, invoke tool
-        tool_call_name = action.action_name
-        tool_call_args = action.action_input
-        tool_instance = tool_instances.get(tool_call_name)
-
-        if not tool_instance:
-            answer = f"there is not a tool named {tool_call_name}"
-            return answer, ToolInvokeMeta.error_instance(answer)
-
-        if isinstance(tool_call_args, str):
-            try:
-                tool_call_args = json.loads(tool_call_args)
-            except json.JSONDecodeError:
-                pass
-
-        # invoke tool
-        tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-            tool=tool_instance,
-            tool_parameters=tool_call_args,
-            user_id=self.user_id,
-            tenant_id=self.tenant_id,
-            message=self.message,
-            invoke_from=self.application_generate_entity.invoke_from,
-            agent_tool_callback=self.agent_callback,
-            trace_manager=trace_manager,
-        )
-
-        # publish files
-        for message_file_id in message_files:
-            # publish message file
-            self.queue_manager.publish(
-                QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-            )
-            # add message file ids
-            message_file_ids.append(message_file_id)
-
-        return tool_invoke_response, tool_invoke_meta
-
-    def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
-        """
-        convert dict to action
-        """
-        return AgentScratchpadUnit.Action(action_name=action["action"], action_input=action["action_input"])
-
-    def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: Mapping[str, Any]) -> str:
-        """
-        fill in inputs from external data tools
-        """
-        for key, value in inputs.items():
-            try:
-                instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
-            except Exception:
-                continue
-
-        return instruction
-
-    def _init_react_state(self, query):
-        """
-        init agent scratchpad
-        """
-        self._query = query
-        self._agent_scratchpad = []
-        self._historic_prompt_messages = self._organize_historic_prompt_messages()
-
-    @abstractmethod
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        organize prompt messages
-        """
-
-    def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
-        """
-        format assistant message
-        """
-        message = ""
-        for scratchpad in agent_scratchpad:
-            if scratchpad.is_final():
-                message += f"Final Answer: {scratchpad.agent_response}"
-            else:
-                message += f"Thought: {scratchpad.thought}\n\n"
-                if scratchpad.action_str:
-                    message += f"Action: {scratchpad.action_str}\n\n"
-                if scratchpad.observation:
-                    message += f"Observation: {scratchpad.observation}\n\n"
-
-        return message
-
-    def _organize_historic_prompt_messages(
-        self, current_session_messages: list[PromptMessage] | None = None
-    ) -> list[PromptMessage]:
-        """
-        organize historic prompt messages
-        """
-        result: list[PromptMessage] = []
-        scratchpads: list[AgentScratchpadUnit] = []
-        current_scratchpad: AgentScratchpadUnit | None = None
-
-        for message in self.history_prompt_messages:
-            if isinstance(message, AssistantPromptMessage):
-                if not current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad = AgentScratchpadUnit(
-                        agent_response=message.content,
-                        thought=message.content or "I am thinking about how to help you",
-                        action_str="",
-                        action=None,
-                        observation=None,
-                    )
-                    scratchpads.append(current_scratchpad)
-                if message.tool_calls:
-                    try:
-                        current_scratchpad.action = AgentScratchpadUnit.Action(
-                            action_name=message.tool_calls[0].function.name,
-                            action_input=json.loads(message.tool_calls[0].function.arguments),
-                        )
-                        current_scratchpad.action_str = json.dumps(current_scratchpad.action.to_dict())
-                    except Exception:
-                        logger.exception("Failed to parse tool call from assistant message")
-            elif isinstance(message, ToolPromptMessage):
-                if current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad.observation = message.content
-                else:
-                    raise NotImplementedError("expected str type")
-            elif isinstance(message, UserPromptMessage):
-                if scratchpads:
-                    result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-                    scratchpads = []
-                    current_scratchpad = None
-
-                result.append(message)
-
-        if scratchpads:
-            result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-
-        historic_prompts = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=current_session_messages or [],
-            history_messages=result,
-            memory=self.memory,
-        ).get_prompt()
-        return historic_prompts

api/core/agent/cot_chat_agent_runner.py

@@ -1,118 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotChatAgentRunner(CotAgentRunner):
-    def _organize_system_prompt(self) -> SystemPromptMessage:
-        """
-        Organize system prompt
-        """
-        assert self.app_config.agent
-        assert self.app_config.agent.prompt
-
-        prompt_entity = self.app_config.agent.prompt
-        if not prompt_entity:
-            raise ValueError("Agent prompt configuration is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return SystemPromptMessage(content=system_prompt)
-
-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize
-        """
-        # organize system prompt
-        system_message = self._organize_system_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        if not agent_scratchpad:
-            assistant_messages = []
-        else:
-            assistant_message = AssistantPromptMessage(content="")
-            assistant_message.content = ""  # FIXME: type check tell mypy that assistant_message.content is str
-            for unit in agent_scratchpad:
-                if unit.is_final():
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Final Answer: {unit.agent_response}"
-                else:
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Thought: {unit.thought}\n\n"
-                    if unit.action_str:
-                        assistant_message.content += f"Action: {unit.action_str}\n\n"
-                    if unit.observation:
-                        assistant_message.content += f"Observation: {unit.observation}\n\n"
-
-            assistant_messages = [assistant_message]
-
-        # query messages
-        query_messages = self._organize_user_query(self._query, [])
-
-        if assistant_messages:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages(
-                [system_message, *query_messages, *assistant_messages, UserPromptMessage(content="continue")]
-            )
-            messages = [
-                system_message,
-                *historic_messages,
-                *query_messages,
-                *assistant_messages,
-                UserPromptMessage(content="continue"),
-            ]
-        else:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages([system_message, *query_messages])
-            messages = [system_message, *historic_messages, *query_messages]
-
-        # join all messages
-        return messages

api/core/agent/cot_completion_agent_runner.py

@@ -1,87 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotCompletionAgentRunner(CotAgentRunner):
-    def _organize_instruction_prompt(self) -> str:
-        """
-        Organize instruction prompt
-        """
-        if self.app_config.agent is None:
-            raise ValueError("Agent configuration is not set")
-        prompt_entity = self.app_config.agent.prompt
-        if prompt_entity is None:
-            raise ValueError("prompt entity is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return system_prompt
-
-    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] | None = None) -> str:
-        """
-        Organize historic prompt
-        """
-        historic_prompt_messages = self._organize_historic_prompt_messages(current_session_messages)
-        historic_prompt = ""
-
-        for message in historic_prompt_messages:
-            if isinstance(message, UserPromptMessage):
-                historic_prompt += f"Question: {message.content}\n\n"
-            elif isinstance(message, AssistantPromptMessage):
-                if isinstance(message.content, str):
-                    historic_prompt += message.content + "\n\n"
-                elif isinstance(message.content, list):
-                    for content in message.content:
-                        if not isinstance(content, TextPromptMessageContent):
-                            continue
-                        historic_prompt += content.data
-
-        return historic_prompt
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize prompt messages
-        """
-        # organize system prompt
-        system_prompt = self._organize_instruction_prompt()
-
-        # organize historic prompt messages
-        historic_prompt = self._organize_historic_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        assistant_prompt = ""
-        for unit in agent_scratchpad or []:
-            if unit.is_final():
-                assistant_prompt += f"Final Answer: {unit.agent_response}"
-            else:
-                assistant_prompt += f"Thought: {unit.thought}\n\n"
-                if unit.action_str:
-                    assistant_prompt += f"Action: {unit.action_str}\n\n"
-                if unit.observation:
-                    assistant_prompt += f"Observation: {unit.observation}\n\n"
-
-        # query messages
-        query_prompt = f"Question: {self._query}"
-
-        # join all messages
-        prompt = (
-            system_prompt.replace("{{historic_messages}}", historic_prompt)
-            .replace("{{agent_scratchpad}}", assistant_prompt)
-            .replace("{{query}}", query_prompt)
-        )
-
-        return [UserPromptMessage(content=prompt)]

api/core/agent/entities.py

@@ -1,3 +1,5 @@
+import uuid
+from collections.abc import Mapping
 from enum import StrEnum
 from typing import Any, Union
 
@@ -92,3 +94,96 @@ class AgentInvokeMessage(ToolInvokeMessage):
     """
 
     pass
+
+
+class ExecutionContext(BaseModel):
+    """Execution context containing trace and audit information.
+
+    This context carries all the IDs and metadata that are not part of
+    the core business logic but needed for tracing, auditing, and
+    correlation purposes.
+    """
+
+    user_id: str | None = None
+    app_id: str | None = None
+    conversation_id: str | None = None
+    message_id: str | None = None
+    tenant_id: str | None = None
+
+    @classmethod
+    def create_minimal(cls, user_id: str | None = None) -> "ExecutionContext":
+        """Create a minimal context with only essential fields."""
+        return cls(user_id=user_id)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for passing to legacy code."""
+        return {
+            "user_id": self.user_id,
+            "app_id": self.app_id,
+            "conversation_id": self.conversation_id,
+            "message_id": self.message_id,
+            "tenant_id": self.tenant_id,
+        }
+
+    def with_updates(self, **kwargs) -> "ExecutionContext":
+        """Create a new context with updated fields."""
+        data = self.to_dict()
+        data.update(kwargs)
+
+        return ExecutionContext(
+            user_id=data.get("user_id"),
+            app_id=data.get("app_id"),
+            conversation_id=data.get("conversation_id"),
+            message_id=data.get("message_id"),
+            tenant_id=data.get("tenant_id"),
+        )
+
+
+class AgentLog(BaseModel):
+    """
+    Agent Log.
+    """
+
+    class LogType(StrEnum):
+        """Type of agent log entry."""
+
+        ROUND = "round"  # A complete iteration round
+        THOUGHT = "thought"  # LLM thinking/reasoning
+        TOOL_CALL = "tool_call"  # Tool invocation
+
+    class LogMetadata(StrEnum):
+        STARTED_AT = "started_at"
+        FINISHED_AT = "finished_at"
+        ELAPSED_TIME = "elapsed_time"
+        TOTAL_PRICE = "total_price"
+        TOTAL_TOKENS = "total_tokens"
+        PROVIDER = "provider"
+        CURRENCY = "currency"
+        LLM_USAGE = "llm_usage"
+        ICON = "icon"
+        ICON_DARK = "icon_dark"
+
+    class LogStatus(StrEnum):
+        START = "start"
+        ERROR = "error"
+        SUCCESS = "success"
+
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="The id of the log")
+    label: str = Field(..., description="The label of the log")
+    log_type: LogType = Field(..., description="The type of the log")
+    parent_id: str | None = Field(default=None, description="Leave empty for root log")
+    error: str | None = Field(default=None, description="The error message")
+    status: LogStatus = Field(..., description="The status of the log")
+    data: Mapping[str, Any] = Field(..., description="Detailed log data")
+    metadata: Mapping[LogMetadata, Any] = Field(default={}, description="The metadata of the log")
+
+
+class AgentResult(BaseModel):
+    """
+    Agent execution result.
+    """
+
+    text: str = Field(default="", description="The generated text")
+    files: list[Any] = Field(default_factory=list, description="Files produced during execution")
+    usage: Any | None = Field(default=None, description="LLM usage statistics")
+    finish_reason: str | None = Field(default=None, description="Reason for completion")

api/core/agent/patterns/README.md

@@ -0,0 +1,55 @@
+# Agent Patterns
+
+A unified agent pattern module that powers both Agent V2 workflow nodes and agent applications. Strategies share a common execution contract while adapting to model capabilities and tool availability.
+
+## Overview
+
+The module applies a strategy pattern around LLM/tool orchestration. `StrategyFactory` auto-selects the best implementation based on model features or an explicit agent strategy, and each strategy streams logs and usage consistently.
+
+## Key Features
+
+- **Dual strategies**
+  - `FunctionCallStrategy`: uses native LLM function/tool calling when the model exposes `TOOL_CALL`, `MULTI_TOOL_CALL`, or `STREAM_TOOL_CALL`.
+  - `ReActStrategy`: ReAct (reasoning + acting) flow driven by `CotAgentOutputParser`, used when function calling is unavailable or explicitly requested.
+- **Explicit or auto selection**
+  - `StrategyFactory.create_strategy` prefers an explicit `AgentEntity.Strategy` (FUNCTION_CALLING or CHAIN_OF_THOUGHT).
+  - Otherwise it falls back to function calling when tool-call features exist, or ReAct when they do not.
+- **Unified execution contract**
+  - `AgentPattern.run` yields streaming `AgentLog` entries and `LLMResultChunk` data, returning an `AgentResult` with text, files, usage, and `finish_reason`.
+  - Iterations are configurable and hard-capped at 99 rounds; the last round forces a final answer by withholding tools.
+- **Tool handling and hooks**
+  - Tools convert to `PromptMessageTool` objects before invocation.
+  - Optional `tool_invoke_hook` lets callers override tool execution (e.g., agent apps) while workflow runs use `ToolEngine.generic_invoke`.
+  - Tool outputs support text, links, JSON, variables, blobs, retriever resources, and file attachments; `target=="self"` files are reloaded into model context, others are returned as outputs.
+- **File-aware arguments**
+  - Tool args accept `[File: <id>]` or `[Files: <id1, id2>]` placeholders that resolve to `File` objects before invocation, enabling models to reference uploaded files safely.
+- **ReAct prompt shaping**
+  - System prompts replace `{{instruction}}`, `{{tools}}`, and `{{tool_names}}` placeholders.
+  - Adds `Observation` to stop sequences and appends scratchpad text so the model sees prior Thought/Action/Observation history.
+- **Observability and accounting**
+  - Standardized `AgentLog` entries for rounds, model thoughts, and tool calls, including usage aggregation (`LLMUsage`) across streaming and non-streaming paths.
+
+## Architecture
+
+```
+agent/patterns/
+├── base.py              # Shared utilities: logging, usage, tool invocation, file handling
+├── function_call.py     # Native function-calling loop with tool execution
+├── react.py             # ReAct loop with CoT parsing and scratchpad wiring
+└── strategy_factory.py  # Strategy selection by model features or explicit override
+```
+
+## Usage
+
+- For auto-selection:
+  - Call `StrategyFactory.create_strategy(model_features, model_instance, context, tools, files, ...)` and run the returned strategy with prompt messages and model params.
+- For explicit behavior:
+  - Pass `agent_strategy=AgentEntity.Strategy.FUNCTION_CALLING` to force native calls (falls back to ReAct if unsupported), or `CHAIN_OF_THOUGHT` to force ReAct.
+- Both strategies stream chunks and logs; collect the generator output until it returns an `AgentResult`.
+
+## Integration Points
+
+- **Model runtime**: delegates to `ModelInstance.invoke_llm` for both streaming and non-streaming calls.
+- **Tool system**: defaults to `ToolEngine.generic_invoke`, with `tool_invoke_hook` for custom callers.
+- **Files**: flows through `File` objects for tool inputs/outputs and model-context attachments.
+- **Execution context**: `ExecutionContext` fields (user/app/conversation/message) propagate to tool invocations and logging.

api/core/agent/patterns/__init__.py

@@ -0,0 +1,19 @@
+"""Agent patterns module.
+
+This module provides different strategies for agent execution:
+- FunctionCallStrategy: Uses native function/tool calling
+- ReActStrategy: Uses ReAct (Reasoning + Acting) approach
+- StrategyFactory: Factory for creating strategies based on model features
+"""
+
+from .base import AgentPattern
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+from .strategy_factory import StrategyFactory
+
+__all__ = [
+    "AgentPattern",
+    "FunctionCallStrategy",
+    "ReActStrategy",
+    "StrategyFactory",
+]

api/core/agent/patterns/base.py

@@ -0,0 +1,474 @@
+"""Base class for agent strategies."""
+
+from __future__ import annotations
+
+import json
+import re
+import time
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator
+from typing import TYPE_CHECKING, Any
+
+from core.agent.entities import AgentLog, AgentResult, ExecutionContext
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import TextPromptMessageContent
+from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMeta
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+# Type alias for tool invoke hook
+# Returns: (response_content, message_file_ids, tool_invoke_meta)
+ToolInvokeHook = Callable[["Tool", dict[str, Any], str], tuple[str, list[str], ToolInvokeMeta]]
+
+
+class AgentPattern(ABC):
+    """Base class for agent execution strategies."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+    ):
+        """Initialize the agent strategy."""
+        self.model_instance = model_instance
+        self.tools = tools
+        self.context = context
+        self.max_iterations = min(max_iterations, 99)  # Cap at 99 iterations
+        self.workflow_call_depth = workflow_call_depth
+        self.files: list[File] = files
+        self.tool_invoke_hook = tool_invoke_hook
+
+    @abstractmethod
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the agent strategy."""
+        pass
+
+    def _accumulate_usage(self, total_usage: dict[str, Any], delta_usage: LLMUsage) -> None:
+        """Accumulate LLM usage statistics."""
+        if not total_usage.get("usage"):
+            # Create a copy to avoid modifying the original
+            total_usage["usage"] = LLMUsage(
+                prompt_tokens=delta_usage.prompt_tokens,
+                prompt_unit_price=delta_usage.prompt_unit_price,
+                prompt_price_unit=delta_usage.prompt_price_unit,
+                prompt_price=delta_usage.prompt_price,
+                completion_tokens=delta_usage.completion_tokens,
+                completion_unit_price=delta_usage.completion_unit_price,
+                completion_price_unit=delta_usage.completion_price_unit,
+                completion_price=delta_usage.completion_price,
+                total_tokens=delta_usage.total_tokens,
+                total_price=delta_usage.total_price,
+                currency=delta_usage.currency,
+                latency=delta_usage.latency,
+            )
+        else:
+            current: LLMUsage = total_usage["usage"]
+            current.prompt_tokens += delta_usage.prompt_tokens
+            current.completion_tokens += delta_usage.completion_tokens
+            current.total_tokens += delta_usage.total_tokens
+            current.prompt_price += delta_usage.prompt_price
+            current.completion_price += delta_usage.completion_price
+            current.total_price += delta_usage.total_price
+
+    def _extract_content(self, content: Any) -> str:
+        """Extract text content from message content."""
+        if isinstance(content, list):
+            # Content items are PromptMessageContentUnionTypes
+            text_parts = []
+            for c in content:
+                # Check if it's a TextPromptMessageContent (which has data attribute)
+                if isinstance(c, TextPromptMessageContent):
+                    text_parts.append(c.data)
+            return "".join(text_parts)
+        return str(content)
+
+    def _has_tool_calls(self, chunk: LLMResultChunk) -> bool:
+        """Check if chunk contains tool calls."""
+        # LLMResultChunk always has delta attribute
+        return bool(chunk.delta.message and chunk.delta.message.tool_calls)
+
+    def _has_tool_calls_result(self, result: LLMResult) -> bool:
+        """Check if result contains tool calls (non-streaming)."""
+        # LLMResult always has message attribute
+        return bool(result.message and result.message.tool_calls)
+
+    def _extract_tool_calls(self, chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from streaming chunk."""
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        if chunk.delta.message and chunk.delta.message.tool_calls:
+            for tool_call in chunk.delta.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_tool_calls_result(self, result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from non-streaming result."""
+        tool_calls = []
+        if result.message and result.message.tool_calls:
+            for tool_call in result.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_text_from_message(self, message: PromptMessage) -> str:
+        """Extract text content from a prompt message."""
+        # PromptMessage always has content attribute
+        content = message.content
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            # Extract text from content list
+            text_parts = []
+            for item in content:
+                if isinstance(item, TextPromptMessageContent):
+                    text_parts.append(item.data)
+            return " ".join(text_parts)
+        return ""
+
+    def _get_tool_metadata(self, tool_instance: Tool) -> dict[AgentLog.LogMetadata, Any]:
+        """Get metadata for a tool including provider and icon info."""
+        from core.tools.tool_manager import ToolManager
+
+        metadata: dict[AgentLog.LogMetadata, Any] = {}
+        if tool_instance.entity and tool_instance.entity.identity:
+            identity = tool_instance.entity.identity
+            if identity.provider:
+                metadata[AgentLog.LogMetadata.PROVIDER] = identity.provider
+
+            # Get icon using ToolManager for proper URL generation
+            tenant_id = self.context.tenant_id
+            if tenant_id and identity.provider:
+                try:
+                    provider_type = tool_instance.tool_provider_type()
+                    icon = ToolManager.get_tool_icon(tenant_id, provider_type, identity.provider)
+                    if isinstance(icon, str):
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                    elif isinstance(icon, dict):
+                        # Handle icon dict with background/content or light/dark variants
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                except Exception:
+                    # Fallback to identity.icon if ToolManager fails
+                    if identity.icon:
+                        metadata[AgentLog.LogMetadata.ICON] = identity.icon
+            elif identity.icon:
+                metadata[AgentLog.LogMetadata.ICON] = identity.icon
+        return metadata
+
+    def _create_log(
+        self,
+        label: str,
+        log_type: AgentLog.LogType,
+        status: AgentLog.LogStatus,
+        data: dict[str, Any] | None = None,
+        parent_id: str | None = None,
+        extra_metadata: dict[AgentLog.LogMetadata, Any] | None = None,
+    ) -> AgentLog:
+        """Create a new AgentLog with standard metadata."""
+        metadata: dict[AgentLog.LogMetadata, Any] = {
+            AgentLog.LogMetadata.STARTED_AT: time.perf_counter(),
+        }
+        if extra_metadata:
+            metadata.update(extra_metadata)
+
+        return AgentLog(
+            label=label,
+            log_type=log_type,
+            status=status,
+            data=data or {},
+            parent_id=parent_id,
+            metadata=metadata,
+        )
+
+    def _finish_log(
+        self,
+        log: AgentLog,
+        data: dict[str, Any] | None = None,
+        usage: LLMUsage | None = None,
+    ) -> AgentLog:
+        """Finish an AgentLog by updating its status and metadata."""
+        log.status = AgentLog.LogStatus.SUCCESS
+
+        if data is not None:
+            log.data = data
+
+        # Calculate elapsed time
+        started_at = log.metadata.get(AgentLog.LogMetadata.STARTED_AT, time.perf_counter())
+        finished_at = time.perf_counter()
+
+        # Update metadata
+        log.metadata = {
+            **log.metadata,
+            AgentLog.LogMetadata.FINISHED_AT: finished_at,
+            # Calculate elapsed time in seconds
+            AgentLog.LogMetadata.ELAPSED_TIME: round(finished_at - started_at, 4),
+        }
+
+        # Add usage information if provided
+        if usage:
+            log.metadata.update(
+                {
+                    AgentLog.LogMetadata.TOTAL_PRICE: usage.total_price,
+                    AgentLog.LogMetadata.CURRENCY: usage.currency,
+                    AgentLog.LogMetadata.TOTAL_TOKENS: usage.total_tokens,
+                    AgentLog.LogMetadata.LLM_USAGE: usage,
+                }
+            )
+
+        return log
+
+    def _replace_file_references(self, tool_args: dict[str, Any]) -> dict[str, Any]:
+        """
+        Replace file references in tool arguments with actual File objects.
+
+        Args:
+            tool_args: Dictionary of tool arguments
+
+        Returns:
+            Updated tool arguments with file references replaced
+        """
+        # Process each argument in the dictionary
+        processed_args: dict[str, Any] = {}
+        for key, value in tool_args.items():
+            processed_args[key] = self._process_file_reference(value)
+        return processed_args
+
+    def _process_file_reference(self, data: Any) -> Any:
+        """
+        Recursively process data to replace file references.
+        Supports both single file [File: file_id] and multiple files [Files: file_id1, file_id2, ...].
+
+        Args:
+            data: The data to process (can be dict, list, str, or other types)
+
+        Returns:
+            Processed data with file references replaced
+        """
+        single_file_pattern = re.compile(r"^\[File:\s*([^\]]+)\]$")
+        multiple_files_pattern = re.compile(r"^\[Files:\s*([^\]]+)\]$")
+
+        if isinstance(data, dict):
+            # Process dictionary recursively
+            return {key: self._process_file_reference(value) for key, value in data.items()}
+        elif isinstance(data, list):
+            # Process list recursively
+            return [self._process_file_reference(item) for item in data]
+        elif isinstance(data, str):
+            # Check for single file pattern [File: file_id]
+            single_match = single_file_pattern.match(data.strip())
+            if single_match:
+                file_id = single_match.group(1).strip()
+                # Find the file in self.files
+                for file in self.files:
+                    if file.id and str(file.id) == file_id:
+                        return file
+                # If file not found, return original value
+                return data
+
+            # Check for multiple files pattern [Files: file_id1, file_id2, ...]
+            multiple_match = multiple_files_pattern.match(data.strip())
+            if multiple_match:
+                file_ids_str = multiple_match.group(1).strip()
+                # Split by comma and strip whitespace
+                file_ids = [fid.strip() for fid in file_ids_str.split(",")]
+
+                # Find all matching files
+                matched_files: list[File] = []
+                for file_id in file_ids:
+                    for file in self.files:
+                        if file.id and str(file.id) == file_id:
+                            matched_files.append(file)
+                            break
+
+                # Return list of files if any were found, otherwise return original
+                return matched_files or data
+
+            return data
+        else:
+            # Return other types as-is
+            return data
+
+    def _create_text_chunk(self, text: str, prompt_messages: list[PromptMessage]) -> LLMResultChunk:
+        """Create a text chunk for streaming."""
+        return LLMResultChunk(
+            model=self.model_instance.model,
+            prompt_messages=prompt_messages,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=AssistantPromptMessage(content=text),
+                usage=None,
+            ),
+            system_fingerprint="",
+        )
+
+    def _invoke_tool(
+        self,
+        tool_instance: Tool,
+        tool_args: dict[str, Any],
+        tool_name: str,
+    ) -> tuple[str, list[File], ToolInvokeMeta | None]:
+        """
+        Invoke a tool and collect its response.
+
+        Args:
+            tool_instance: The tool instance to invoke
+            tool_args: Tool arguments
+            tool_name: Name of the tool
+
+        Returns:
+            Tuple of (response_content, tool_files, tool_invoke_meta)
+        """
+        # Process tool_args to replace file references with actual File objects
+        tool_args = self._replace_file_references(tool_args)
+
+        # If a tool invoke hook is set, use it instead of generic_invoke
+        if self.tool_invoke_hook:
+            response_content, _, tool_invoke_meta = self.tool_invoke_hook(tool_instance, tool_args, tool_name)
+            # Note: message_file_ids are stored in DB, we don't convert them to File objects here
+            # The caller (AgentAppRunner) handles file publishing
+            return response_content, [], tool_invoke_meta
+
+        # Default: use generic_invoke for workflow scenarios
+        # Import here to avoid circular import
+        from core.tools.tool_engine import DifyWorkflowCallbackHandler, ToolEngine
+
+        tool_response = ToolEngine().generic_invoke(
+            tool=tool_instance,
+            tool_parameters=tool_args,
+            user_id=self.context.user_id or "",
+            workflow_tool_callback=DifyWorkflowCallbackHandler(),
+            workflow_call_depth=self.workflow_call_depth,
+            app_id=self.context.app_id,
+            conversation_id=self.context.conversation_id,
+            message_id=self.context.message_id,
+        )
+
+        # Collect response and files
+        response_content = ""
+        tool_files: list[File] = []
+
+        for response in tool_response:
+            if response.type == ToolInvokeMessage.MessageType.TEXT:
+                assert isinstance(response.message, ToolInvokeMessage.TextMessage)
+                response_content += response.message.text
+
+            elif response.type == ToolInvokeMessage.MessageType.LINK:
+                # Handle link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Link: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE:
+                # Handle image URL messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK:
+                # Handle image link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BINARY_LINK:
+                # Handle binary file link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    filename = response.meta.get("filename", "file") if response.meta else "file"
+                    response_content += f"[File: {filename} - {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.JSON:
+                # Handle JSON messages
+                if isinstance(response.message, ToolInvokeMessage.JsonMessage):
+                    response_content += json.dumps(response.message.json_object, ensure_ascii=False, indent=2)
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB:
+                # Handle blob messages - convert to text representation
+                if isinstance(response.message, ToolInvokeMessage.BlobMessage):
+                    mime_type = (
+                        response.meta.get("mime_type", "application/octet-stream")
+                        if response.meta
+                        else "application/octet-stream"
+                    )
+                    size = len(response.message.blob)
+                    response_content += f"[Binary data: {mime_type}, size: {size} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.VARIABLE:
+                # Handle variable messages
+                if isinstance(response.message, ToolInvokeMessage.VariableMessage):
+                    var_name = response.message.variable_name
+                    var_value = response.message.variable_value
+                    if isinstance(var_value, str):
+                        response_content += var_value
+                    else:
+                        response_content += f"[Variable {var_name}: {json.dumps(var_value, ensure_ascii=False)}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB_CHUNK:
+                # Handle blob chunk messages - these are parts of a larger blob
+                if isinstance(response.message, ToolInvokeMessage.BlobChunkMessage):
+                    response_content += f"[Blob chunk {response.message.sequence}: {len(response.message.blob)} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.RETRIEVER_RESOURCES:
+                # Handle retriever resources messages
+                if isinstance(response.message, ToolInvokeMessage.RetrieverResourceMessage):
+                    response_content += response.message.context
+
+            elif response.type == ToolInvokeMessage.MessageType.FILE:
+                # Extract file from meta
+                if response.meta and "file" in response.meta:
+                    file = response.meta["file"]
+                    if isinstance(file, File):
+                        # Check if file is for model or tool output
+                        if response.meta.get("target") == "self":
+                            # File is for model - add to files for next prompt
+                            self.files.append(file)
+                            response_content += f"File '{file.filename}' has been loaded into your context."
+                        else:
+                            # File is tool output
+                            tool_files.append(file)
+
+        return response_content, tool_files, None
+
+    def _find_tool_by_name(self, tool_name: str) -> Tool | None:
+        """Find a tool instance by its name."""
+        for tool in self.tools:
+            if tool.entity.identity.name == tool_name:
+                return tool
+        return None
+
+    def _convert_tools_to_prompt_format(self) -> list[PromptMessageTool]:
+        """Convert tools to prompt message format."""
+        prompt_tools: list[PromptMessageTool] = []
+        for tool in self.tools:
+            prompt_tools.append(tool.to_prompt_message_tool())
+        return prompt_tools
+
+    def _update_usage_with_empty(self, llm_usage: dict[str, Any]) -> None:
+        """Initialize usage tracking with empty usage if not set."""
+        if "usage" not in llm_usage or llm_usage["usage"] is None:
+            llm_usage["usage"] = LLMUsage.empty_usage()

api/core/agent/patterns/function_call.py

@@ -0,0 +1,299 @@
+"""Function Call strategy implementation."""
+
+import json
+from collections.abc import Generator
+from typing import Any, Union
+
+from core.agent.entities import AgentLog, AgentResult
+from core.file import File
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageTool,
+    ToolPromptMessage,
+)
+from core.tools.entities.tool_entities import ToolInvokeMeta
+
+from .base import AgentPattern
+
+
+class FunctionCallStrategy(AgentPattern):
+    """Function Call strategy using model's native tool calling capability."""
+
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the function call agent strategy."""
+        # Convert tools to prompt format
+        prompt_tools: list[PromptMessageTool] = self._convert_tools_to_prompt_format()
+
+        # Initialize tracking
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        function_call_state: bool = True
+        total_usage: dict[str, LLMUsage | None] = {"usage": None}
+        messages: list[PromptMessage] = list(prompt_messages)  # Create mutable copy
+        final_text: str = ""
+        finish_reason: str | None = None
+        output_files: list[File] = []  # Track files produced by tools
+
+        while function_call_state and iteration_step <= max_iterations:
+            function_call_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+            # On last iteration, remove tools to force final answer
+            current_tools: list[PromptMessageTool] = [] if iteration_step == max_iterations else prompt_tools
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, LLMUsage | None] = {"usage": None}
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages,
+                model_parameters=model_parameters,
+                tools=current_tools,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id,
+                callbacks=[],
+            )
+
+            # Process response
+            tool_calls, response_content, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log
+            )
+            messages.append(self._create_assistant_message(response_content, tool_calls))
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update final text if no tool calls (this is likely the final answer)
+            if not tool_calls:
+                final_text = response_content
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Process tool calls
+            tool_outputs: dict[str, str] = {}
+            if tool_calls:
+                function_call_state = True
+                # Execute tools
+                for tool_call_id, tool_name, tool_args in tool_calls:
+                    tool_response, tool_files, _ = yield from self._handle_tool_call(
+                        tool_name, tool_args, tool_call_id, messages, round_log
+                    )
+                    tool_outputs[tool_name] = tool_response
+                    # Track files produced by tools
+                    output_files.extend(tool_files)
+            yield self._finish_log(
+                round_log,
+                data={
+                    "llm_result": response_content,
+                    "tool_calls": [
+                        {"name": tc[1], "args": tc[2], "output": tool_outputs.get(tc[1], "")} for tc in tool_calls
+                    ]
+                    if tool_calls
+                    else [],
+                    "final_answer": final_text if not function_call_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text,
+            files=output_files,
+            usage=total_usage.get("usage") or LLMUsage.empty_usage(),
+            finish_reason=finish_reason,
+        )
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, LLMUsage | None],
+        start_log: AgentLog,
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[list[tuple[str, str, dict[str, Any]]], str, str | None],
+    ]:
+        """Handle LLM response chunks and extract tool calls and content.
+
+        Returns a tuple of (tool_calls, response_content, finish_reason).
+        """
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        response_content: str = ""
+        finish_reason: str | None = None
+        if isinstance(chunks, Generator):
+            # Streaming response
+            for chunk in chunks:
+                # Extract tool calls
+                if self._has_tool_calls(chunk):
+                    tool_calls.extend(self._extract_tool_calls(chunk))
+
+                # Extract content
+                if chunk.delta.message and chunk.delta.message.content:
+                    response_content += self._extract_content(chunk.delta.message.content)
+
+                # Track usage
+                if chunk.delta.usage:
+                    self._accumulate_usage(llm_usage, chunk.delta.usage)
+
+                # Capture finish reason
+                if chunk.delta.finish_reason:
+                    finish_reason = chunk.delta.finish_reason
+
+                yield chunk
+        else:
+            # Non-streaming response
+            result: LLMResult = chunks
+
+            if self._has_tool_calls_result(result):
+                tool_calls.extend(self._extract_tool_calls_result(result))
+
+            if result.message and result.message.content:
+                response_content += self._extract_content(result.message.content)
+
+            if result.usage:
+                self._accumulate_usage(llm_usage, result.usage)
+
+            # Convert to streaming format
+            yield LLMResultChunk(
+                model=result.model,
+                prompt_messages=result.prompt_messages,
+                delta=LLMResultChunkDelta(index=0, message=result.message, usage=result.usage),
+            )
+        yield self._finish_log(
+            start_log,
+            data={
+                "result": response_content,
+            },
+            usage=llm_usage.get("usage"),
+        )
+        return tool_calls, response_content, finish_reason
+
+    def _create_assistant_message(
+        self, content: str, tool_calls: list[tuple[str, str, dict[str, Any]]] | None = None
+    ) -> AssistantPromptMessage:
+        """Create assistant message with tool calls."""
+        if tool_calls is None:
+            return AssistantPromptMessage(content=content)
+        return AssistantPromptMessage(
+            content=content or "",
+            tool_calls=[
+                AssistantPromptMessage.ToolCall(
+                    id=tc[0],
+                    type="function",
+                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tc[1], arguments=json.dumps(tc[2])),
+                )
+                for tc in tool_calls
+            ],
+        )
+
+    def _handle_tool_call(
+        self,
+        tool_name: str,
+        tool_args: dict[str, Any],
+        tool_call_id: str,
+        messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File], ToolInvokeMeta | None]]:
+        """Handle a single tool call and return response with files and meta."""
+        # Find tool
+        tool_instance = self._find_tool_by_name(tool_name)
+        if not tool_instance:
+            raise ValueError(f"Tool {tool_name} not found")
+
+        # Get tool metadata (provider, icon, etc.)
+        tool_metadata = self._get_tool_metadata(tool_instance)
+
+        # Create tool call log
+        tool_call_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_call_id": tool_call_id,
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_call_log
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args, tool_name)
+
+            yield self._finish_log(
+                tool_call_log,
+                data={
+                    **tool_call_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+            final_content = response_content or "Tool executed successfully"
+            # Add tool response to messages
+            messages.append(
+                ToolPromptMessage(
+                    content=final_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return response_content, tool_files, tool_invoke_meta
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_call_log.status = AgentLog.LogStatus.ERROR
+            tool_call_log.error = error_message
+            tool_call_log.data = {
+                **tool_call_log.data,
+                "error": error_message,
+            }
+            yield tool_call_log
+
+            # Add error message to conversation
+            error_content = f"Tool execution failed: {error_message}"
+            messages.append(
+                ToolPromptMessage(
+                    content=error_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return error_content, [], None

api/core/agent/patterns/react.py

@@ -0,0 +1,418 @@
+"""ReAct strategy implementation."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Generator
+from typing import TYPE_CHECKING, Any, Union
+
+from core.agent.entities import AgentLog, AgentResult, AgentScratchpadUnit, ExecutionContext
+from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    SystemPromptMessage,
+)
+
+from .base import AgentPattern, ToolInvokeHook
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class ReActStrategy(AgentPattern):
+    """ReAct strategy using reasoning and acting approach."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ):
+        """Initialize the ReAct strategy with instruction support."""
+        super().__init__(
+            model_instance=model_instance,
+            tools=tools,
+            context=context,
+            max_iterations=max_iterations,
+            workflow_call_depth=workflow_call_depth,
+            files=files,
+            tool_invoke_hook=tool_invoke_hook,
+        )
+        self.instruction = instruction
+
+    def run(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict[str, Any],
+        stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the ReAct agent strategy."""
+        # Initialize tracking
+        agent_scratchpad: list[AgentScratchpadUnit] = []
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        react_state: bool = True
+        total_usage: dict[str, Any] = {"usage": None}
+        output_files: list[File] = []  # Track files produced by tools
+        final_text: str = ""
+        finish_reason: str | None = None
+
+        # Add "Observation" to stop sequences
+        if "Observation" not in stop:
+            stop = stop.copy()
+            stop.append("Observation")
+
+        while react_state and iteration_step <= max_iterations:
+            react_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+
+            # Build prompt with/without tools based on iteration
+            include_tools = iteration_step < max_iterations
+            current_messages = self._build_prompt_with_react_format(
+                prompt_messages, agent_scratchpad, include_tools, self.instruction
+            )
+
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, Any] = {"usage": None}
+
+            # Use current messages directly (files are handled by base class if needed)
+            messages_to_use = current_messages
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages_to_use,
+                model_parameters=model_parameters,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id or "",
+                callbacks=[],
+            )
+
+            # Process response
+            scratchpad, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log, current_messages
+            )
+            agent_scratchpad.append(scratchpad)
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Check if we have an action to execute
+            if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
+                react_state = True
+                # Execute tool
+                observation, tool_files = yield from self._handle_tool_call(
+                    scratchpad.action, current_messages, round_log
+                )
+                scratchpad.observation = observation
+                # Track files produced by tools
+                output_files.extend(tool_files)
+
+                # Add observation to scratchpad for display
+                yield self._create_text_chunk(f"\nObservation: {observation}\n", current_messages)
+            else:
+                # Extract final answer
+                if scratchpad.action and scratchpad.action.action_input:
+                    final_answer = scratchpad.action.action_input
+                    if isinstance(final_answer, dict):
+                        final_answer = json.dumps(final_answer, ensure_ascii=False)
+                    final_text = str(final_answer)
+                elif scratchpad.thought:
+                    # If no action but we have thought, use thought as final answer
+                    final_text = scratchpad.thought
+
+            yield self._finish_log(
+                round_log,
+                data={
+                    "thought": scratchpad.thought,
+                    "action": scratchpad.action_str if scratchpad.action else None,
+                    "observation": scratchpad.observation or None,
+                    "final_answer": final_text if not react_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text, files=output_files, usage=total_usage.get("usage"), finish_reason=finish_reason
+        )
+
+    def _build_prompt_with_react_format(
+        self,
+        original_messages: list[PromptMessage],
+        agent_scratchpad: list[AgentScratchpadUnit],
+        include_tools: bool = True,
+        instruction: str = "",
+    ) -> list[PromptMessage]:
+        """Build prompt messages with ReAct format."""
+        # Copy messages to avoid modifying original
+        messages = list(original_messages)
+
+        # Find and update the system prompt that should already exist
+        system_prompt_found = False
+        for i, msg in enumerate(messages):
+            if isinstance(msg, SystemPromptMessage):
+                system_prompt_found = True
+                # The system prompt from frontend already has the template, just replace placeholders
+
+                # Format tools
+                tools_str = ""
+                tool_names = []
+                if include_tools and self.tools:
+                    # Convert tools to prompt message tools format
+                    prompt_tools = [tool.to_prompt_message_tool() for tool in self.tools]
+                    tool_names = [tool.name for tool in prompt_tools]
+
+                    # Format tools as JSON for comprehensive information
+                    from core.model_runtime.utils.encoders import jsonable_encoder
+
+                    tools_str = json.dumps(jsonable_encoder(prompt_tools), indent=2)
+                    tool_names_str = ", ".join(f'"{name}"' for name in tool_names)
+                else:
+                    tools_str = "No tools available"
+                    tool_names_str = ""
+
+                # Replace placeholders in the existing system prompt
+                updated_content = msg.content
+                assert isinstance(updated_content, str)
+                updated_content = updated_content.replace("{{instruction}}", instruction)
+                updated_content = updated_content.replace("{{tools}}", tools_str)
+                updated_content = updated_content.replace("{{tool_names}}", tool_names_str)
+
+                # Create new SystemPromptMessage with updated content
+                messages[i] = SystemPromptMessage(content=updated_content)
+                break
+
+        # If no system prompt found, that's unexpected but add scratchpad anyway
+        if not system_prompt_found:
+            # This shouldn't happen if frontend is working correctly
+            pass
+
+        # Format agent scratchpad
+        scratchpad_str = ""
+        if agent_scratchpad:
+            scratchpad_parts: list[str] = []
+            for unit in agent_scratchpad:
+                if unit.thought:
+                    scratchpad_parts.append(f"Thought: {unit.thought}")
+                if unit.action_str:
+                    scratchpad_parts.append(f"Action:\n```\n{unit.action_str}\n```")
+                if unit.observation:
+                    scratchpad_parts.append(f"Observation: {unit.observation}")
+            scratchpad_str = "\n".join(scratchpad_parts)
+
+        # If there's a scratchpad, append it to the last message
+        if scratchpad_str:
+            messages.append(AssistantPromptMessage(content=scratchpad_str))
+
+        return messages
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, Any],
+        model_log: AgentLog,
+        current_messages: list[PromptMessage],
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[AgentScratchpadUnit, str | None],
+    ]:
+        """Handle LLM response chunks and extract action/thought.
+
+        Returns a tuple of (scratchpad_unit, finish_reason).
+        """
+        usage_dict: dict[str, Any] = {}
+
+        # Convert non-streaming to streaming format if needed
+        if isinstance(chunks, LLMResult):
+            # Create a generator from the LLMResult
+            def result_to_chunks() -> Generator[LLMResultChunk, None, None]:
+                yield LLMResultChunk(
+                    model=chunks.model,
+                    prompt_messages=chunks.prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=chunks.message,
+                        usage=chunks.usage,
+                        finish_reason=None,  # LLMResult doesn't have finish_reason, only streaming chunks do
+                    ),
+                    system_fingerprint=chunks.system_fingerprint or "",
+                )
+
+            streaming_chunks = result_to_chunks()
+        else:
+            streaming_chunks = chunks
+
+        react_chunks = CotAgentOutputParser.handle_react_stream_output(streaming_chunks, usage_dict)
+
+        # Initialize scratchpad unit
+        scratchpad = AgentScratchpadUnit(
+            agent_response="",
+            thought="",
+            action_str="",
+            observation="",
+            action=None,
+        )
+
+        finish_reason: str | None = None
+
+        # Process chunks
+        for chunk in react_chunks:
+            if isinstance(chunk, AgentScratchpadUnit.Action):
+                # Action detected
+                action_str = json.dumps(chunk.model_dump())
+                scratchpad.agent_response = (scratchpad.agent_response or "") + action_str
+                scratchpad.action_str = action_str
+                scratchpad.action = chunk
+
+                yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
+            else:
+                # Text chunk
+                chunk_text = str(chunk)
+                scratchpad.agent_response = (scratchpad.agent_response or "") + chunk_text
+                scratchpad.thought = (scratchpad.thought or "") + chunk_text
+
+                yield self._create_text_chunk(chunk_text, current_messages)
+
+        # Update usage
+        if usage_dict.get("usage"):
+            if llm_usage.get("usage"):
+                self._accumulate_usage(llm_usage, usage_dict["usage"])
+            else:
+                llm_usage["usage"] = usage_dict["usage"]
+
+        # Clean up thought
+        scratchpad.thought = (scratchpad.thought or "").strip() or "I am thinking about how to help you"
+
+        # Finish model log
+        yield self._finish_log(
+            model_log,
+            data={
+                "thought": scratchpad.thought,
+                "action": scratchpad.action_str if scratchpad.action else None,
+            },
+            usage=llm_usage.get("usage"),
+        )
+
+        return scratchpad, finish_reason
+
+    def _handle_tool_call(
+        self,
+        action: AgentScratchpadUnit.Action,
+        prompt_messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File]]]:
+        """Handle tool call and return observation with files."""
+        tool_name = action.action_name
+        tool_args: dict[str, Any] | str = action.action_input
+
+        # Find tool instance first to get metadata
+        tool_instance = self._find_tool_by_name(tool_name)
+        tool_metadata = self._get_tool_metadata(tool_instance) if tool_instance else {}
+
+        # Start tool log with tool metadata
+        tool_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_log
+
+        if not tool_instance:
+            # Finish tool log with error
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "error": f"Tool {tool_name} not found",
+                },
+            )
+            return f"Tool {tool_name} not found", []
+
+        # Ensure tool_args is a dict
+        tool_args_dict: dict[str, Any]
+        if isinstance(tool_args, str):
+            try:
+                tool_args_dict = json.loads(tool_args)
+            except json.JSONDecodeError:
+                tool_args_dict = {"input": tool_args}
+        elif not isinstance(tool_args, dict):
+            tool_args_dict = {"input": str(tool_args)}
+        else:
+            tool_args_dict = tool_args
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args_dict, tool_name)
+
+            # Finish tool log
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+
+            return response_content or "Tool executed successfully", tool_files
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_log.status = AgentLog.LogStatus.ERROR
+            tool_log.error = error_message
+            tool_log.data = {
+                **tool_log.data,
+                "error": error_message,
+            }
+            yield tool_log
+
+            return f"Tool execution failed: {error_message}", []

api/core/agent/patterns/strategy_factory.py

@@ -0,0 +1,107 @@
+"""Strategy factory for creating agent strategies."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.agent.entities import AgentEntity, ExecutionContext
+from core.file.models import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.model_entities import ModelFeature
+
+from .base import AgentPattern, ToolInvokeHook
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class StrategyFactory:
+    """Factory for creating agent strategies based on model features."""
+
+    # Tool calling related features
+    TOOL_CALL_FEATURES = {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL}
+
+    @staticmethod
+    def create_strategy(
+        model_features: list[ModelFeature],
+        model_instance: ModelInstance,
+        context: ExecutionContext,
+        tools: list[Tool],
+        files: list[File],
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        agent_strategy: AgentEntity.Strategy | None = None,
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ) -> AgentPattern:
+        """
+        Create an appropriate strategy based on model features.
+
+        Args:
+            model_features: List of model features/capabilities
+            model_instance: Model instance to use
+            context: Execution context containing trace/audit information
+            tools: Available tools
+            files: Available files
+            max_iterations: Maximum iterations for the strategy
+            workflow_call_depth: Depth of workflow calls
+            agent_strategy: Optional explicit strategy override
+            tool_invoke_hook: Optional hook for custom tool invocation (e.g., agent_invoke)
+            instruction: Optional instruction for ReAct strategy
+
+        Returns:
+            AgentStrategy instance
+        """
+        # If explicit strategy is provided and it's Function Calling, try to use it if supported
+        if agent_strategy == AgentEntity.Strategy.FUNCTION_CALLING:
+            if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+                return FunctionCallStrategy(
+                    model_instance=model_instance,
+                    context=context,
+                    tools=tools,
+                    files=files,
+                    max_iterations=max_iterations,
+                    workflow_call_depth=workflow_call_depth,
+                    tool_invoke_hook=tool_invoke_hook,
+                )
+            # Fallback to ReAct if FC is requested but not supported
+
+        # If explicit strategy is Chain of Thought (ReAct)
+        if agent_strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )
+
+        # Default auto-selection logic
+        if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+            # Model supports native function calling
+            return FunctionCallStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+            )
+        else:
+            # Use ReAct strategy for models without function calling
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )

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

@@ -4,6 +4,7 @@ import re
 import time
 from collections.abc import Callable, Generator, Mapping
 from contextlib import contextmanager
+from dataclasses import dataclass, field
 from threading import Thread
 from typing import Any, Union
 
@@ -19,6 +20,7 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
+    ChunkType,
     MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
@@ -70,13 +72,122 @@ from core.workflow.runtime import GraphRuntimeState
 from core.workflow.system_variable import SystemVariable
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models import Account, Conversation, EndUser, Message, MessageFile
+from models import Account, Conversation, EndUser, LLMGenerationDetail, Message, MessageFile
 from models.enums import CreatorUserRole
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class StreamEventBuffer:
+    """
+    Buffer for recording stream events in order to reconstruct the generation sequence.
+    Records the exact order of text chunks, thoughts, and tool calls as they stream.
+    """
+
+    # Accumulated reasoning content (each thought block is a separate element)
+    reasoning_content: list[str] = field(default_factory=list)
+    # Current reasoning buffer (accumulates until we see a different event type)
+    _current_reasoning: str = ""
+    # Tool calls with their details
+    tool_calls: list[dict] = field(default_factory=list)
+    # Tool call ID to index mapping for updating results
+    _tool_call_id_map: dict[str, int] = field(default_factory=dict)
+    # Sequence of events in stream order
+    sequence: list[dict] = field(default_factory=list)
+    # Current position in answer text
+    _content_position: int = 0
+    # Track last event type to detect transitions
+    _last_event_type: str | None = None
+
+    def _flush_current_reasoning(self) -> None:
+        """Flush accumulated reasoning to the list and add to sequence."""
+        if self._current_reasoning.strip():
+            self.reasoning_content.append(self._current_reasoning.strip())
+            self.sequence.append({"type": "reasoning", "index": len(self.reasoning_content) - 1})
+            self._current_reasoning = ""
+
+    def record_text_chunk(self, text: str) -> None:
+        """Record a text chunk event."""
+        if not text:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        text_len = len(text)
+        start_pos = self._content_position
+
+        # If last event was also content, extend it; otherwise create new
+        if self.sequence and self.sequence[-1].get("type") == "content":
+            self.sequence[-1]["end"] = start_pos + text_len
+        else:
+            self.sequence.append({"type": "content", "start": start_pos, "end": start_pos + text_len})
+
+        self._content_position += text_len
+        self._last_event_type = "content"
+
+    def record_thought_chunk(self, text: str) -> None:
+        """Record a thought/reasoning chunk event."""
+        if not text:
+            return
+
+        # Accumulate thought content
+        self._current_reasoning += text
+        self._last_event_type = "thought"
+
+    def record_tool_call(self, tool_call_id: str, tool_name: str, tool_arguments: str) -> None:
+        """Record a tool call event."""
+        if not tool_call_id:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        # Check if this tool call already exists (we might get multiple chunks)
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            # Update arguments if provided
+            if tool_arguments:
+                self.tool_calls[idx]["arguments"] = tool_arguments
+        else:
+            # New tool call
+            tool_call = {
+                "id": tool_call_id or "",
+                "name": tool_name or "",
+                "arguments": tool_arguments or "",
+                "result": "",
+                "elapsed_time": None,
+            }
+            self.tool_calls.append(tool_call)
+            idx = len(self.tool_calls) - 1
+            self._tool_call_id_map[tool_call_id] = idx
+            self.sequence.append({"type": "tool_call", "index": idx})
+
+        self._last_event_type = "tool_call"
+
+    def record_tool_result(self, tool_call_id: str, result: str, tool_elapsed_time: float | None = None) -> None:
+        """Record a tool result event (update existing tool call)."""
+        if not tool_call_id:
+            return
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            self.tool_calls[idx]["result"] = result
+            self.tool_calls[idx]["elapsed_time"] = tool_elapsed_time
+
+    def finalize(self) -> None:
+        """Finalize the buffer, flushing any pending data."""
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+    def has_data(self) -> bool:
+        """Check if there's any meaningful data recorded."""
+        return bool(self.reasoning_content or self.tool_calls or self.sequence)
+
+
 class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
@@ -144,6 +255,8 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._workflow_run_id: str = ""
         self._draft_var_saver_factory = draft_var_saver_factory
         self._graph_runtime_state: GraphRuntimeState | None = None
+        # Stream event buffer for recording generation sequence
+        self._stream_buffer = StreamEventBuffer()
         self._seed_graph_runtime_state_from_queue_manager()
 
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
@@ -383,7 +496,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         queue_message: Union[WorkflowQueueMessage, MessageQueueMessage] | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
-        """Handle text chunk events."""
+        """Handle text chunk events and record to stream buffer for sequence reconstruction."""
         delta_text = event.text
         if delta_text is None:
             return
@@ -405,9 +518,52 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        self._task_state.answer += delta_text
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else ""
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else ""
+        tool_arguments = tool_call.arguments if tool_call and tool_call.arguments else ""
+        tool_files = tool_result.files if tool_result else []
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+        # Record stream event based on chunk type
+        chunk_type = event.chunk_type or ChunkType.TEXT
+        match chunk_type:
+            case ChunkType.TEXT:
+                self._stream_buffer.record_text_chunk(delta_text)
+                self._task_state.answer += delta_text
+            case ChunkType.THOUGHT:
+                # Reasoning should not be part of final answer text
+                self._stream_buffer.record_thought_chunk(delta_text)
+            case ChunkType.TOOL_CALL:
+                self._stream_buffer.record_tool_call(
+                    tool_call_id=tool_call_id,
+                    tool_name=tool_name,
+                    tool_arguments=tool_arguments,
+                )
+            case ChunkType.TOOL_RESULT:
+                self._stream_buffer.record_tool_result(
+                    tool_call_id=tool_call_id,
+                    result=delta_text,
+                    tool_elapsed_time=tool_elapsed_time,
+                )
+                self._task_state.answer += delta_text
+            case _:
+                pass
         yield self._message_cycle_manager.message_to_stream_response(
-            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
+            answer=delta_text,
+            message_id=self._message_id,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type.value if event.chunk_type else None,
+            tool_call_id=tool_call_id or None,
+            tool_name=tool_name or None,
+            tool_arguments=tool_arguments or None,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
         )
 
     def _handle_iteration_start_event(
@@ -775,6 +931,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
 
         # If there are assistant files, remove markdown image links from answer
         answer_text = self._task_state.answer
+        answer_text = self._strip_think_blocks(answer_text)
         if self._recorded_files:
             # Remove markdown image links since we're storing files separately
             answer_text = re.sub(r"!\[.*?\]\(.*?\)", "", answer_text).strip()
@@ -826,6 +983,54 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
+        # Save generation detail (reasoning/tool calls/sequence) from stream buffer
+        self._save_generation_detail(session=session, message=message)
+
+    @staticmethod
+    def _strip_think_blocks(text: str) -> str:
+        """Remove <think>...</think> blocks (including their content) from text."""
+        if not text or "<think" not in text.lower():
+            return text
+
+        clean_text = re.sub(r"<think[^>]*>.*?</think>", "", text, flags=re.IGNORECASE | re.DOTALL)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+        return clean_text
+
+    def _save_generation_detail(self, *, session: Session, message: Message) -> None:
+        """
+        Save LLM generation detail for Chatflow using stream event buffer.
+        The buffer records the exact order of events as they streamed,
+        allowing accurate reconstruction of the generation sequence.
+        """
+        # Finalize the stream buffer to flush any pending data
+        self._stream_buffer.finalize()
+
+        # Only save if there's meaningful data
+        if not self._stream_buffer.has_data():
+            return
+
+        reasoning_content = self._stream_buffer.reasoning_content
+        tool_calls = self._stream_buffer.tool_calls
+        sequence = self._stream_buffer.sequence
+
+        # Check if generation detail already exists for this message
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_content) if reasoning_content else None
+            existing.tool_calls = json.dumps(tool_calls) if tool_calls else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_content) if reasoning_content else None,
+                tool_calls=json.dumps(tool_calls) if tool_calls else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
     def _seed_graph_runtime_state_from_queue_manager(self) -> None:
         """Bootstrap the cached runtime state from the queue manager when present."""
         candidate = self._base_task_pipeline.queue_manager.graph_runtime_state

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

@@ -3,10 +3,8 @@ from typing import cast
 
 from sqlalchemy import select
 
-from core.agent.cot_chat_agent_runner import CotChatAgentRunner
+from core.agent.agent_app_runner import AgentAppRunner
-from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
 from core.agent.entities import AgentEntity
-from core.agent.fc_agent_runner import FunctionCallAgentRunner
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.apps.base_app_runner import AppRunner
@@ -14,8 +12,7 @@ from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
-from core.model_runtime.entities.llm_entities import LLMMode
+from core.model_runtime.entities.model_entities import ModelFeature
-from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationError
 from extensions.ext_database import db
@@ -194,22 +191,7 @@ class AgentChatAppRunner(AppRunner):
             raise ValueError("Message not found")
         db.session.close()
 
-        runner_cls: type[FunctionCallAgentRunner] | type[CotChatAgentRunner] | type[CotCompletionAgentRunner]
+        runner = AgentAppRunner(
-        # start agent runner
-        if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
-            # check LLM mode
-            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT:
-                runner_cls = CotChatAgentRunner
-            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION:
-                runner_cls = CotCompletionAgentRunner
-            else:
-                raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")
-        elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
-            runner_cls = FunctionCallAgentRunner
-        else:
-            raise ValueError(f"Invalid agent strategy: {agent_entity.strategy}")
-
-        runner = runner_cls(
             tenant_id=app_config.tenant_id,
             application_generate_entity=application_generate_entity,
             conversation=conversation_result,

api/core/app/apps/common/workflow_response_converter.py

@@ -671,7 +671,7 @@ class WorkflowResponseConverter:
             task_id=task_id,
             data=AgentLogStreamResponse.Data(
                 node_execution_id=event.node_execution_id,
-                id=event.id,
+                message_id=event.id,
                 parent_id=event.parent_id,
                 label=event.label,
                 error=event.error,

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

@@ -13,6 +13,7 @@ from core.app.apps.common.workflow_response_converter import WorkflowResponseCon
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
     AppQueueEvent,
+    ChunkType,
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
@@ -483,11 +484,33 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if delta_text is None:
             return
 
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else None
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else None
+        tool_arguments = tool_call.arguments if tool_call else None
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_files = tool_result.files if tool_result else []
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+
         # only publish tts message at text chunk streaming
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        yield self._text_chunk_to_stream_response(delta_text, from_variable_selector=event.from_variable_selector)
+        yield self._text_chunk_to_stream_response(
+            text=delta_text,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type,
+            tool_call_id=tool_call_id,
+            tool_name=tool_name,
+            tool_arguments=tool_arguments,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
+        )
 
     def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
         """Handle agent log events."""
@@ -650,16 +673,61 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         session.add(workflow_app_log)
 
     def _text_chunk_to_stream_response(
-        self, text: str, from_variable_selector: list[str] | None = None
+        self,
+        text: str,
+        from_variable_selector: list[str] | None = None,
+        chunk_type: ChunkType | None = None,
+        tool_call_id: str | None = None,
+        tool_name: str | None = None,
+        tool_arguments: str | None = None,
+        tool_files: list[str] | None = None,
+        tool_error: str | None = None,
+        tool_elapsed_time: float | None = None,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
     ) -> TextChunkStreamResponse:
         """
         Handle completed event.
         :param text: text
         :return:
         """
+        from core.app.entities.task_entities import ChunkType as ResponseChunkType
+
+        response_chunk_type = ResponseChunkType(chunk_type.value) if chunk_type else ResponseChunkType.TEXT
+
+        data = TextChunkStreamResponse.Data(
+            text=text,
+            from_variable_selector=from_variable_selector,
+            chunk_type=response_chunk_type,
+        )
+
+        if response_chunk_type == ResponseChunkType.TOOL_CALL:
+            data = data.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+        elif response_chunk_type == ResponseChunkType.TOOL_RESULT:
+            data = data.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_files": tool_files,
+                    "tool_error": tool_error,
+                    "tool_elapsed_time": tool_elapsed_time,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+
         response = TextChunkStreamResponse(
             task_id=self._application_generate_entity.task_id,
-            data=TextChunkStreamResponse.Data(text=text, from_variable_selector=from_variable_selector),
+            data=data,
         )
 
         return response

api/core/app/apps/workflow_app_runner.py

@@ -463,12 +463,20 @@ class WorkflowBasedAppRunner:
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
+            from core.app.entities.queue_entities import ChunkType as QueueChunkType
+
+            if event.is_final and not event.chunk:
+                return
+
             self._publish_event(
                 QueueTextChunkEvent(
                     text=event.chunk,
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    chunk_type=QueueChunkType(event.chunk_type.value),
+                    tool_call=event.tool_call,
+                    tool_result=event.tool_result,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):

api/core/app/entities/llm_generation_entities.py

@@ -0,0 +1,70 @@
+"""
+LLM Generation Detail entities.
+
+Defines the structure for storing and transmitting LLM generation details
+including reasoning content, tool calls, and their sequence.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class ContentSegment(BaseModel):
+    """Represents a content segment in the generation sequence."""
+
+    type: Literal["content"] = "content"
+    start: int = Field(..., description="Start position in the text")
+    end: int = Field(..., description="End position in the text")
+
+
+class ReasoningSegment(BaseModel):
+    """Represents a reasoning segment in the generation sequence."""
+
+    type: Literal["reasoning"] = "reasoning"
+    index: int = Field(..., description="Index into reasoning_content array")
+
+
+class ToolCallSegment(BaseModel):
+    """Represents a tool call segment in the generation sequence."""
+
+    type: Literal["tool_call"] = "tool_call"
+    index: int = Field(..., description="Index into tool_calls array")
+
+
+SequenceSegment = ContentSegment | ReasoningSegment | ToolCallSegment
+
+
+class ToolCallDetail(BaseModel):
+    """Represents a tool call with its arguments and result."""
+
+    id: str = Field(default="", description="Unique identifier for the tool call")
+    name: str = Field(..., description="Name of the tool")
+    arguments: str = Field(default="", description="JSON string of tool arguments")
+    result: str = Field(default="", description="Result from the tool execution")
+    elapsed_time: float | None = Field(default=None, description="Elapsed time in seconds")
+
+
+class LLMGenerationDetailData(BaseModel):
+    """
+    Domain model for LLM generation detail.
+
+    Contains the structured data for reasoning content, tool calls,
+    and their display sequence.
+    """
+
+    reasoning_content: list[str] = Field(default_factory=list, description="List of reasoning segments")
+    tool_calls: list[ToolCallDetail] = Field(default_factory=list, description="List of tool call details")
+    sequence: list[SequenceSegment] = Field(default_factory=list, description="Display order of segments")
+
+    def is_empty(self) -> bool:
+        """Check if there's any meaningful generation detail."""
+        return not self.reasoning_content and not self.tool_calls
+
+    def to_response_dict(self) -> dict:
+        """Convert to dictionary for API response."""
+        return {
+            "reasoning_content": self.reasoning_content,
+            "tool_calls": [tc.model_dump() for tc in self.tool_calls],
+            "sequence": [seg.model_dump() for seg in self.sequence],
+        }

api/core/app/entities/queue_entities.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit
+from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes import NodeType
 
@@ -177,6 +177,17 @@ class QueueLoopCompletedEvent(AppQueueEvent):
     error: str | None = None
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
@@ -191,6 +202,16 @@ class QueueTextChunkEvent(AppQueueEvent):
     in_loop_id: str | None = None
     """loop id if node is in loop"""
 
+    # Extended fields for Agent/Tool streaming
+    chunk_type: ChunkType = ChunkType.TEXT
+    """type of the chunk"""
+
+    # Tool streaming payloads
+    tool_call: ToolCall | None = None
+    """structured tool call info"""
+    tool_result: ToolResult | None = None
+    """structured tool result info"""
+
 
 class QueueAgentMessageEvent(AppQueueEvent):
     """

api/core/app/entities/task_entities.py

@@ -113,6 +113,38 @@ class MessageStreamResponse(StreamResponse):
     answer: str
     from_variable_selector: list[str] | None = None
 
+    # Extended fields for Agent/Tool streaming (imported at runtime to avoid circular import)
+    chunk_type: str | None = None
+    """type of the chunk: text, tool_call, tool_result, thought"""
+
+    # Tool call fields (when chunk_type == "tool_call")
+    tool_call_id: str | None = None
+    """unique identifier for this tool call"""
+    tool_name: str | None = None
+    """name of the tool being called"""
+    tool_arguments: str | None = None
+    """accumulated tool arguments JSON"""
+
+    # Tool result fields (when chunk_type == "tool_result")
+    tool_files: list[str] | None = None
+    """file IDs produced by tool"""
+    tool_error: str | None = None
+    """error message if tool failed"""
+    tool_elapsed_time: float | None = None
+    """elapsed time spent executing the tool"""
+    tool_icon: str | dict | None = None
+    """icon of the tool"""
+    tool_icon_dark: str | dict | None = None
+    """dark theme icon of the tool"""
+
+    def model_dump(self, *args, **kwargs) -> dict[str, object]:
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump(*args, **kwargs)
+
+    def model_dump_json(self, *args, **kwargs) -> str:
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump_json(*args, **kwargs)
+
 
 class MessageAudioStreamResponse(StreamResponse):
     """
@@ -582,6 +614,17 @@ class LoopNodeCompletedStreamResponse(StreamResponse):
     data: Data
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class TextChunkStreamResponse(StreamResponse):
     """
     TextChunkStreamResponse entity
@@ -595,6 +638,36 @@ class TextChunkStreamResponse(StreamResponse):
         text: str
         from_variable_selector: list[str] | None = None
 
+        # Extended fields for Agent/Tool streaming
+        chunk_type: ChunkType = ChunkType.TEXT
+        """type of the chunk"""
+
+        # Tool call fields (when chunk_type == TOOL_CALL)
+        tool_call_id: str | None = None
+        """unique identifier for this tool call"""
+        tool_name: str | None = None
+        """name of the tool being called"""
+        tool_arguments: str | None = None
+        """accumulated tool arguments JSON"""
+
+        # Tool result fields (when chunk_type == TOOL_RESULT)
+        tool_files: list[str] | None = None
+        """file IDs produced by tool"""
+        tool_error: str | None = None
+        """error message if tool failed"""
+
+        # Tool elapsed time fields (when chunk_type == TOOL_RESULT)
+        tool_elapsed_time: float | None = None
+        """elapsed time spent executing the tool"""
+
+        def model_dump(self, *args, **kwargs) -> dict[str, object]:
+            kwargs.setdefault("exclude_none", True)
+            return super().model_dump(*args, **kwargs)
+
+        def model_dump_json(self, *args, **kwargs) -> str:
+            kwargs.setdefault("exclude_none", True)
+            return super().model_dump_json(*args, **kwargs)
+
     event: StreamEvent = StreamEvent.TEXT_CHUNK
     data: Data
 
@@ -743,7 +816,7 @@ class AgentLogStreamResponse(StreamResponse):
         """
 
         node_execution_id: str
-        id: str
+        message_id: str
         label: str
         parent_id: str | None = None
         error: str | None = None

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -1,4 +1,5 @@
 import logging
+import re
 import time
 from collections.abc import Generator
 from threading import Thread
@@ -58,7 +59,7 @@ from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.model import AppMode, Conversation, Message, MessageAgentThought
+from models.model import AppMode, Conversation, LLMGenerationDetail, Message, MessageAgentThought
 
 logger = logging.getLogger(__name__)
 
@@ -68,6 +69,8 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
+    _THINK_PATTERN = re.compile(r"<think[^>]*>(.*?)</think>", re.IGNORECASE | re.DOTALL)
+
     _task_state: EasyUITaskState
     _application_generate_entity: Union[ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity]
 
@@ -409,11 +412,136 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
                 )
             )
 
+        # Save LLM generation detail if there's reasoning_content
+        self._save_generation_detail(session=session, message=message, llm_result=llm_result)
+
         message_was_created.send(
             message,
             application_generate_entity=self._application_generate_entity,
         )
 
+    def _save_generation_detail(self, *, session: Session, message: Message, llm_result: LLMResult) -> None:
+        """
+        Save LLM generation detail for Completion/Chat/Agent-Chat applications.
+        For Agent-Chat, also merges MessageAgentThought records.
+        """
+        import json
+
+        reasoning_list: list[str] = []
+        tool_calls_list: list[dict] = []
+        sequence: list[dict] = []
+        answer = message.answer or ""
+
+        # Check if this is Agent-Chat mode by looking for agent thoughts
+        agent_thoughts = (
+            session.query(MessageAgentThought)
+            .filter_by(message_id=message.id)
+            .order_by(MessageAgentThought.position.asc())
+            .all()
+        )
+
+        if agent_thoughts:
+            # Agent-Chat mode: merge MessageAgentThought records
+            content_pos = 0
+            cleaned_answer_parts: list[str] = []
+            for thought in agent_thoughts:
+                # Add thought/reasoning
+                if thought.thought:
+                    reasoning_text = thought.thought
+                    if "<think" in reasoning_text.lower():
+                        clean_text, extracted_reasoning = self._split_reasoning_from_answer(reasoning_text)
+                        if extracted_reasoning:
+                            reasoning_text = extracted_reasoning
+                            thought.thought = clean_text or extracted_reasoning
+                    reasoning_list.append(reasoning_text)
+                    sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
+
+                # Add tool calls
+                if thought.tool:
+                    tool_calls_list.append(
+                        {
+                            "name": thought.tool,
+                            "arguments": thought.tool_input or "",
+                            "result": thought.observation or "",
+                        }
+                    )
+                    sequence.append({"type": "tool_call", "index": len(tool_calls_list) - 1})
+
+                # Add answer content if present
+                if thought.answer:
+                    content_text = thought.answer
+                    if "<think" in content_text.lower():
+                        clean_answer, extracted_reasoning = self._split_reasoning_from_answer(content_text)
+                        if extracted_reasoning:
+                            reasoning_list.append(extracted_reasoning)
+                            sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
+                        content_text = clean_answer
+                        thought.answer = clean_answer or content_text
+
+                    if content_text:
+                        start = content_pos
+                        end = content_pos + len(content_text)
+                        sequence.append({"type": "content", "start": start, "end": end})
+                        content_pos = end
+                        cleaned_answer_parts.append(content_text)
+
+            if cleaned_answer_parts:
+                merged_answer = "".join(cleaned_answer_parts)
+                message.answer = merged_answer
+                llm_result.message.content = merged_answer
+        else:
+            # Completion/Chat mode: use reasoning_content from llm_result
+            reasoning_content = llm_result.reasoning_content
+            if not reasoning_content and answer:
+                # Extract reasoning from <think> blocks and clean the final answer
+                clean_answer, reasoning_content = self._split_reasoning_from_answer(answer)
+                if reasoning_content:
+                    answer = clean_answer
+                    llm_result.message.content = clean_answer
+                    llm_result.reasoning_content = reasoning_content
+                    message.answer = clean_answer
+            if reasoning_content:
+                reasoning_list = [reasoning_content]
+                # Content comes first, then reasoning
+                if answer:
+                    sequence.append({"type": "content", "start": 0, "end": len(answer)})
+                sequence.append({"type": "reasoning", "index": 0})
+
+        # Only save if there's meaningful generation detail
+        if not reasoning_list and not tool_calls_list:
+            return
+
+        # Check if generation detail already exists
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_list) if reasoning_list else None
+            existing.tool_calls = json.dumps(tool_calls_list) if tool_calls_list else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_list) if reasoning_list else None,
+                tool_calls=json.dumps(tool_calls_list) if tool_calls_list else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
+    @classmethod
+    def _split_reasoning_from_answer(cls, text: str) -> tuple[str, str]:
+        """
+        Extract reasoning segments from <think> blocks and return (clean_text, reasoning).
+        """
+        matches = cls._THINK_PATTERN.findall(text)
+        reasoning_content = "\n".join(match.strip() for match in matches) if matches else ""
+
+        clean_text = cls._THINK_PATTERN.sub("", text)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+
+        return clean_text, reasoning_content or ""
+
     def _handle_stop(self, event: QueueStopEvent):
         """
         Handle stop.

api/core/app/task_pipeline/message_cycle_manager.py

@@ -232,15 +232,31 @@ class MessageCycleManager:
         answer: str,
         message_id: str,
         from_variable_selector: list[str] | None = None,
+        chunk_type: str | None = None,
+        tool_call_id: str | None = None,
+        tool_name: str | None = None,
+        tool_arguments: str | None = None,
+        tool_files: list[str] | None = None,
+        tool_error: str | None = None,
+        tool_elapsed_time: float | None = None,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
         event_type: StreamEvent | None = None,
     ) -> MessageStreamResponse:
         """
         Message to stream response.
         :param answer: answer
         :param message_id: message id
+        :param from_variable_selector: from variable selector
+        :param chunk_type: type of the chunk (text, function_call, tool_result, thought)
+        :param tool_call_id: unique identifier for this tool call
+        :param tool_name: name of the tool being called
+        :param tool_arguments: accumulated tool arguments JSON
+        :param tool_files: file IDs produced by tool
+        :param tool_error: error message if tool failed
         :return:
         """
-        return MessageStreamResponse(
+        response = MessageStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=message_id,
             answer=answer,
@@ -248,6 +264,35 @@ class MessageCycleManager:
             event=event_type or StreamEvent.MESSAGE,
         )
 
+        if chunk_type:
+            response = response.model_copy(update={"chunk_type": chunk_type})
+
+        if chunk_type == "tool_call":
+            response = response.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+        elif chunk_type == "tool_result":
+            response = response.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_files": tool_files,
+                    "tool_error": tool_error,
+                    "tool_elapsed_time": tool_elapsed_time,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+
+        return response
+
     def message_replace_to_stream_response(self, answer: str, reason: str = "") -> MessageReplaceStreamResponse:
         """
         Message replace to stream response.

api/core/callback_handler/index_tool_callback_handler.py

@@ -5,7 +5,6 @@ from sqlalchemy import select
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.models.document import Document
@@ -90,6 +89,8 @@ class DatasetIndexToolCallbackHandler:
     # TODO(-LAN-): Improve type check
     def return_retriever_resource_info(self, resource: Sequence[RetrievalSourceMetadata]):
         """Handle return_retriever_resource_info."""
+        from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
+
         self._queue_manager.publish(
             QueueRetrieverResourcesEvent(retriever_resources=resource), PublishFrom.APPLICATION_MANAGER
         )

api/core/entities/knowledge_entities.py

@@ -3,6 +3,7 @@ from pydantic import BaseModel, Field, field_validator
 
 class PreviewDetail(BaseModel):
     content: str
+    summary: str | None = None
     child_chunks: list[str] | None = None
 
 

api/core/indexing_runner.py

@@ -311,14 +311,18 @@ class IndexingRunner:
         qa_preview_texts: list[QAPreviewDetail] = []
 
         total_segments = 0
+        # doc_form represents the segmentation method (general, parent-child, QA)
         index_type = doc_form
         index_processor = IndexProcessorFactory(index_type).init_index_processor()
+        # one extract_setting is one source document
         for extract_setting in extract_settings:
             # extract
             processing_rule = DatasetProcessRule(
                 mode=tmp_processing_rule["mode"], rules=json.dumps(tmp_processing_rule["rules"])
             )
+            # Extract document content
             text_docs = index_processor.extract(extract_setting, process_rule_mode=tmp_processing_rule["mode"])
+            # Cleaning and segmentation
             documents = index_processor.transform(
                 text_docs,
                 current_user=None,
@@ -361,6 +365,12 @@ class IndexingRunner:
 
         if doc_form and doc_form == "qa_model":
             return IndexingEstimate(total_segments=total_segments * 20, qa_preview=qa_preview_texts, preview=[])
+
+        # Generate summary preview
+        summary_index_setting = tmp_processing_rule["summary_index_setting"] if "summary_index_setting" in tmp_processing_rule else None
+        if summary_index_setting and summary_index_setting.get('enable') and preview_texts:
+            preview_texts = index_processor.generate_summary_preview(tenant_id, preview_texts, summary_index_setting)
+
         return IndexingEstimate(total_segments=total_segments, preview=preview_texts)
 
     def _extract(

api/core/llm_generator/prompts.py

@@ -434,3 +434,6 @@ INSTRUCTION_GENERATE_TEMPLATE_PROMPT = """The output of this prompt is not as ex
 You should edit the prompt according to the IDEAL OUTPUT."""
 
 INSTRUCTION_GENERATE_TEMPLATE_CODE = """Please fix the errors in the {{#error_message#}}."""
+
+DEFAULT_GENERATOR_SUMMARY_PROMPT = """
+You are a helpful assistant that summarizes long pieces of text into concise summaries. Given the following text, generate a brief summary that captures the main points and key information. The summary should be clear, concise, and written in complete sentences. """

api/core/rag/datasource/retrieval_service.py

@@ -392,6 +392,69 @@ class RetrievalService:
             records = []
             include_segment_ids = set()
             segment_child_map = {}
+            segment_file_map = {}
+            segment_summary_map = {}  # Map segment_id to summary content
+            summary_segment_ids = set()  # Track segments retrieved via summary
+            with Session(bind=db.engine, expire_on_commit=False) as session:
+                # Process documents
+                for document in documents:
+                    segment_id = None
+                    attachment_info = None
+                    child_chunk = None
+                    document_id = document.metadata.get("document_id")
+                    if document_id not in dataset_documents:
+                        continue
+
+                    dataset_document = dataset_documents[document_id]
+                    if not dataset_document:
+                        continue
+
+                    if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
+                        # Handle parent-child documents
+                        if document.metadata.get("doc_type") == DocType.IMAGE:
+                            attachment_info_dict = cls.get_segment_attachment_info(
+                                dataset_document.dataset_id,
+                                dataset_document.tenant_id,
+                                document.metadata.get("doc_id") or "",
+                                session,
+                            )
+                            if attachment_info_dict:
+                                attachment_info = attachment_info_dict["attachment_info"]
+                                segment_id = attachment_info_dict["segment_id"]
+                        else:
+                            # Check if this is a summary document
+                            is_summary = document.metadata.get("is_summary", False)
+                            if is_summary:
+                                # For summary documents, find the original chunk via original_chunk_id
+                                original_chunk_id = document.metadata.get("original_chunk_id")
+                                if not original_chunk_id:
+                                    continue
+                                segment_id = original_chunk_id
+                                # Track that this segment was retrieved via summary
+                                summary_segment_ids.add(segment_id)
+                            else:
+                                # For normal documents, find by child chunk index_node_id
+                                child_index_node_id = document.metadata.get("doc_id")
+                                child_chunk_stmt = select(ChildChunk).where(ChildChunk.index_node_id == child_index_node_id)
+                                child_chunk = session.scalar(child_chunk_stmt)
+
+                                if not child_chunk:
+                                    continue
+                                segment_id = child_chunk.segment_id
+
+                        if not segment_id:
+                            continue
+
+                        segment = (
+                            session.query(DocumentSegment)
+                            .where(
+                                DocumentSegment.dataset_id == dataset_document.dataset_id,
+                                DocumentSegment.enabled == True,
+                                DocumentSegment.status == "completed",
+                                DocumentSegment.id == segment_id,
+                            )
+                            .first()
+                        )
 
             valid_dataset_documents = {}
             image_doc_ids: list[Any] = []
@@ -507,7 +570,47 @@ class RetrievalService:
                                 max_score = max(
                                     max_score, file_document.metadata.get("score", 0.0) if file_document else 0.0
                                 )
+                                segment = session.scalar(document_segment_stmt)
+                                if segment:
+                                    segment_file_map[segment.id] = [attachment_info]
+                        else:
+                            # Check if this is a summary document
+                            is_summary = document.metadata.get("is_summary", False)
+                            if is_summary:
+                                # For summary documents, find the original chunk via original_chunk_id
+                                original_chunk_id = document.metadata.get("original_chunk_id")
+                                if not original_chunk_id:
+                                    continue
+                                # Track that this segment was retrieved via summary
+                                summary_segment_ids.add(original_chunk_id)
+                                document_segment_stmt = select(DocumentSegment).where(
+                                    DocumentSegment.dataset_id == dataset_document.dataset_id,
+                                    DocumentSegment.enabled == True,
+                                    DocumentSegment.status == "completed",
+                                    DocumentSegment.id == original_chunk_id,
+                                )
+                                segment = session.scalar(document_segment_stmt)
+                            else:
+                                # For normal documents, find by index_node_id
+                                index_node_id = document.metadata.get("doc_id")
+                                if not index_node_id:
+                                    continue
+                                document_segment_stmt = select(DocumentSegment).where(
+                                    DocumentSegment.dataset_id == dataset_document.dataset_id,
+                                    DocumentSegment.enabled == True,
+                                    DocumentSegment.status == "completed",
+                                    DocumentSegment.index_node_id == index_node_id,
+                                )
+                                segment = session.scalar(document_segment_stmt)
 
+                        if not segment:
+                            continue
+                        if segment.id not in include_segment_ids:
+                            include_segment_ids.add(segment.id)
+                            record = {
+                                "segment": segment,
+                                "score": document.metadata.get("score"),  # type: ignore
+                            }
                             map_detail = {
                                 "max_score": max_score,
                                 "child_chunks": child_chunk_details,
@@ -542,6 +645,23 @@ class RetrievalService:
                 if record["segment"].id in attachment_map:
                     record["files"] = attachment_map[record["segment"].id]  # type: ignore[assignment]
 
+            # Batch query summaries for segments retrieved via summary (only enabled summaries)
+            if summary_segment_ids:
+                from models.dataset import DocumentSegmentSummary
+
+                summaries = (
+                    session.query(DocumentSegmentSummary)
+                    .filter(
+                        DocumentSegmentSummary.chunk_id.in_(summary_segment_ids),
+                        DocumentSegmentSummary.status == "completed",
+                        DocumentSegmentSummary.enabled == True,  # Only retrieve enabled summaries
+                    )
+                    .all()
+                )
+                for summary in summaries:
+                    if summary.summary_content:
+                        segment_summary_map[summary.chunk_id] = summary.summary_content
+
             result: list[RetrievalSegments] = []
             for record in records:
                 # Extract segment
@@ -576,9 +696,16 @@ class RetrievalService:
                     else None
                 )
 
+                # Extract summary if this segment was retrieved via summary
+                summary_content = segment_summary_map.get(segment.id)
+
                 # Create RetrievalSegments object
                 retrieval_segment = RetrievalSegments(
-                    segment=segment, child_chunks=child_chunks_list, score=score, files=files
+                    segment=segment,
+                    child_chunks=child_chunks_list,
+                    score=score,
+                    files=files,
+                    summary=summary_content
                 )
                 result.append(retrieval_segment)
 

api/core/rag/embedding/retrieval.py

@@ -20,3 +20,4 @@ class RetrievalSegments(BaseModel):
     child_chunks: list[RetrievalChildChunk] | None = None
     score: float | None = None
     files: list[dict[str, str | int]] | None = None
+    summary: str | None = None  # Summary content if retrieved via summary index

api/core/rag/index_processor/index_processor_base.py

@@ -13,6 +13,7 @@ from urllib.parse import unquote, urlparse
 import httpx
 
 from configs import dify_config
+from core.entities.knowledge_entities import PreviewDetail
 from core.helper import ssrf_proxy
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.index_processor.constant.doc_type import DocType
@@ -45,6 +46,15 @@ class BaseIndexProcessor(ABC):
     def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         raise NotImplementedError
 
+    @abstractmethod
+    def generate_summary_preview(self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict) -> list[PreviewDetail]:
+        """
+        For each segment in preview_texts, generate a summary using LLM and attach it to the segment.
+        The summary can be stored in a new attribute, e.g., summary.
+        This method should be implemented by subclasses.
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def load(
         self,

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -1,9 +1,13 @@
 """Paragraph index processor."""
 
+import logging
 import uuid
 from collections.abc import Mapping
 from typing import Any
 
+logger = logging.getLogger(__name__)
+
+from core.entities.knowledge_entities import PreviewDetail
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -17,12 +21,19 @@ from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import AttachmentDocument, Document, MultimodalGeneralStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
+from extensions.ext_database import db
 from libs import helper
 from models.account import Account
-from models.dataset import Dataset, DatasetProcessRule
+from models.dataset import Dataset, DatasetProcessRule, DocumentSegment
 from models.dataset import Document as DatasetDocument
 from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import Rule
+from services.summary_index_service import SummaryIndexService
+from core.llm_generator.prompts import DEFAULT_GENERATOR_SUMMARY_PROMPT
+from core.model_runtime.entities.message_entities import UserPromptMessage
+from core.model_runtime.entities.model_entities import ModelType
+from core.provider_manager import ProviderManager
+from core.model_manager import ModelInstance
 
 
 class ParagraphIndexProcessor(BaseIndexProcessor):
@@ -108,6 +119,29 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
                 keyword.add_texts(documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             if node_ids:
@@ -227,3 +261,70 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
             }
         else:
             raise ValueError("Chunks is not a list")
+
+    def generate_summary_preview(self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict) -> list[PreviewDetail]:
+        """
+        For each segment, concurrently call generate_summary to generate a summary
+        and write it to the summary attribute of PreviewDetail.
+        """
+        import concurrent.futures
+        from flask import current_app
+
+        # Capture Flask app context for worker threads
+        flask_app = None
+        try:
+            flask_app = current_app._get_current_object()  # type: ignore
+        except RuntimeError:
+            logger.warning("No Flask application context available, summary generation may fail")
+
+        def process(preview: PreviewDetail) -> None:
+            """Generate summary for a single preview item."""
+            try:
+                if flask_app:
+                    # Ensure Flask app context in worker thread
+                    with flask_app.app_context():
+                        summary = self.generate_summary(tenant_id, preview.content, summary_index_setting)
+                        preview.summary = summary
+                else:
+                    # Fallback: try without app context (may fail)
+                    summary = self.generate_summary(tenant_id, preview.content, summary_index_setting)
+                    preview.summary = summary
+            except Exception as e:
+                logger.error(f"Failed to generate summary for preview: {str(e)}")
+                # Don't fail the entire preview if summary generation fails
+                preview.summary = None
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            list(executor.map(process, preview_texts))
+        return preview_texts
+
+    @staticmethod
+    def generate_summary(tenant_id: str, text: str, summary_index_setting: dict = None) -> str:
+        """
+        Generate summary for the given text using ModelInstance.invoke_llm and the default or custom summary prompt.
+        """
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            raise ValueError("summary_index_setting is required and must be enabled to generate summary.")
+
+        model_name = summary_index_setting.get("model_name")
+        model_provider_name = summary_index_setting.get("model_provider_name")
+        summary_prompt = summary_index_setting.get("summary_prompt")
+
+        # Import default summary prompt
+        if not summary_prompt:
+            summary_prompt = DEFAULT_GENERATOR_SUMMARY_PROMPT
+
+        prompt = f"{summary_prompt}\n{text}"
+
+        provider_manager = ProviderManager()
+        provider_model_bundle = provider_manager.get_provider_model_bundle(tenant_id, model_provider_name, ModelType.LLM)
+        model_instance = ModelInstance(provider_model_bundle, model_name)
+        prompt_messages = [UserPromptMessage(content=prompt)]
+
+        result = model_instance.invoke_llm(
+            prompt_messages=prompt_messages,
+            model_parameters={},
+            stream=False
+        )
+
+        return getattr(result.message, "content", "")

api/core/rag/index_processor/processor/parent_child_index_processor.py

@@ -25,6 +25,7 @@ from models.dataset import ChildChunk, Dataset, DatasetProcessRule, DocumentSegm
 from models.dataset import Document as DatasetDocument
 from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import ParentMode, Rule
+from services.summary_index_service import SummaryIndexService
 
 
 class ParentChildIndexProcessor(BaseIndexProcessor):
@@ -135,6 +136,29 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
         # node_ids is segment's node_ids
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         if dataset.indexing_technique == "high_quality":
             delete_child_chunks = kwargs.get("delete_child_chunks") or False
             precomputed_child_node_ids = kwargs.get("precomputed_child_node_ids")

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -25,9 +25,10 @@ from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
 from models.account import Account
-from models.dataset import Dataset
+from models.dataset import Dataset, DocumentSegment
 from models.dataset import Document as DatasetDocument
 from services.entities.knowledge_entities.knowledge_entities import Rule
+from services.summary_index_service import SummaryIndexService
 
 logger = logging.getLogger(__name__)
 
@@ -144,6 +145,30 @@ class QAIndexProcessor(BaseIndexProcessor):
                 vector.create_multimodal(multimodal_documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Note: qa_model doesn't generate summaries, but we clean them for completeness
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         vector = Vector(dataset)
         if node_ids:
             vector.delete_by_ids(node_ids)

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -29,6 +29,7 @@ from models import (
     Account,
     CreatorUserRole,
     EndUser,
+    LLMGenerationDetail,
     WorkflowNodeExecutionModel,
     WorkflowNodeExecutionTriggeredFrom,
 )
@@ -457,6 +458,113 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             session.merge(db_model)
             session.flush()
 
+            # Save LLMGenerationDetail for LLM nodes with successful execution
+            if (
+                domain_model.node_type == NodeType.LLM
+                and domain_model.status == WorkflowNodeExecutionStatus.SUCCEEDED
+                and domain_model.outputs is not None
+            ):
+                self._save_llm_generation_detail(session, domain_model)
+
+    def _save_llm_generation_detail(self, session, execution: WorkflowNodeExecution) -> None:
+        """
+        Save LLM generation detail for LLM nodes.
+        Extracts reasoning_content, tool_calls, and sequence from outputs and metadata.
+        """
+        outputs = execution.outputs or {}
+        metadata = execution.metadata or {}
+
+        reasoning_list = self._extract_reasoning(outputs)
+        tool_calls_list = self._extract_tool_calls(metadata.get(WorkflowNodeExecutionMetadataKey.AGENT_LOG))
+
+        if not reasoning_list and not tool_calls_list:
+            return
+
+        sequence = self._build_generation_sequence(outputs.get("text", ""), reasoning_list, tool_calls_list)
+        self._upsert_generation_detail(session, execution, reasoning_list, tool_calls_list, sequence)
+
+    def _extract_reasoning(self, outputs: Mapping[str, Any]) -> list[str]:
+        """Extract reasoning_content as a clean list of non-empty strings."""
+        reasoning_content = outputs.get("reasoning_content")
+        if isinstance(reasoning_content, str):
+            trimmed = reasoning_content.strip()
+            return [trimmed] if trimmed else []
+        if isinstance(reasoning_content, list):
+            return [item.strip() for item in reasoning_content if isinstance(item, str) and item.strip()]
+        return []
+
+    def _extract_tool_calls(self, agent_log: Any) -> list[dict[str, str]]:
+        """Extract tool call records from agent logs."""
+        if not agent_log or not isinstance(agent_log, list):
+            return []
+
+        tool_calls: list[dict[str, str]] = []
+        for log in agent_log:
+            log_data = log.data if hasattr(log, "data") else (log.get("data", {}) if isinstance(log, dict) else {})
+            tool_name = log_data.get("tool_name")
+            if tool_name and str(tool_name).strip():
+                tool_calls.append(
+                    {
+                        "id": log_data.get("tool_call_id", ""),
+                        "name": tool_name,
+                        "arguments": json.dumps(log_data.get("tool_args", {})),
+                        "result": str(log_data.get("output", "")),
+                    }
+                )
+        return tool_calls
+
+    def _build_generation_sequence(
+        self, text: str, reasoning_list: list[str], tool_calls_list: list[dict[str, str]]
+    ) -> list[dict[str, Any]]:
+        """Build a simple content/reasoning/tool_call sequence."""
+        sequence: list[dict[str, Any]] = []
+        if text:
+            sequence.append({"type": "content", "start": 0, "end": len(text)})
+        for index in range(len(reasoning_list)):
+            sequence.append({"type": "reasoning", "index": index})
+        for index in range(len(tool_calls_list)):
+            sequence.append({"type": "tool_call", "index": index})
+        return sequence
+
+    def _upsert_generation_detail(
+        self,
+        session,
+        execution: WorkflowNodeExecution,
+        reasoning_list: list[str],
+        tool_calls_list: list[dict[str, str]],
+        sequence: list[dict[str, Any]],
+    ) -> None:
+        """Insert or update LLMGenerationDetail with serialized fields."""
+        existing = (
+            session.query(LLMGenerationDetail)
+            .filter_by(
+                workflow_run_id=execution.workflow_execution_id,
+                node_id=execution.node_id,
+            )
+            .first()
+        )
+
+        reasoning_json = json.dumps(reasoning_list) if reasoning_list else None
+        tool_calls_json = json.dumps(tool_calls_list) if tool_calls_list else None
+        sequence_json = json.dumps(sequence) if sequence else None
+
+        if existing:
+            existing.reasoning_content = reasoning_json
+            existing.tool_calls = tool_calls_json
+            existing.sequence = sequence_json
+            return
+
+        generation_detail = LLMGenerationDetail(
+            tenant_id=self._tenant_id,
+            app_id=self._app_id,
+            workflow_run_id=execution.workflow_execution_id,
+            node_id=execution.node_id,
+            reasoning_content=reasoning_json,
+            tool_calls=tool_calls_json,
+            sequence=sequence_json,
+        )
+        session.add(generation_detail)
+
     def get_db_models_by_workflow_run(
         self,
         workflow_run_id: str,

api/core/tools/__base/tool.py

@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Any
 if TYPE_CHECKING:
     from models.model import File
 
+from core.model_runtime.entities.message_entities import PromptMessageTool
 from core.tools.__base.tool_runtime import ToolRuntime
 from core.tools.entities.tool_entities import (
     ToolEntity,
@@ -154,6 +155,60 @@ class Tool(ABC):
 
         return parameters
 
+    def to_prompt_message_tool(self) -> PromptMessageTool:
+        message_tool = PromptMessageTool(
+            name=self.entity.identity.name,
+            description=self.entity.description.llm if self.entity.description else "",
+            parameters={
+                "type": "object",
+                "properties": {},
+                "required": [],
+            },
+        )
+
+        parameters = self.get_merged_runtime_parameters()
+        for parameter in parameters:
+            if parameter.form != ToolParameter.ToolParameterForm.LLM:
+                continue
+
+            parameter_type = parameter.type.as_normal_type()
+            if parameter.type in {
+                ToolParameter.ToolParameterType.SYSTEM_FILES,
+                ToolParameter.ToolParameterType.FILE,
+                ToolParameter.ToolParameterType.FILES,
+            }:
+                # Determine the description based on parameter type
+                if parameter.type == ToolParameter.ToolParameterType.FILE:
+                    file_format_desc = " Input the file id with format: [File: file_id]."
+                else:
+                    file_format_desc = "Input the file id with format: [Files: file_id1, file_id2, ...]. "
+
+                message_tool.parameters["properties"][parameter.name] = {
+                    "type": "string",
+                    "description": (parameter.llm_description or "") + file_format_desc,
+                }
+                continue
+            enum = []
+            if parameter.type == ToolParameter.ToolParameterType.SELECT:
+                enum = [option.value for option in parameter.options] if parameter.options else []
+
+            message_tool.parameters["properties"][parameter.name] = (
+                {
+                    "type": parameter_type,
+                    "description": parameter.llm_description or "",
+                }
+                if parameter.input_schema is None
+                else parameter.input_schema
+            )
+
+            if len(enum) > 0:
+                message_tool.parameters["properties"][parameter.name]["enum"] = enum
+
+            if parameter.required:
+                message_tool.parameters["required"].append(parameter.name)
+
+        return message_tool
+
     def create_image_message(
         self,
         image: str,

api/core/workflow/entities/__init__.py

@@ -1,11 +1,16 @@
 from .agent import AgentNodeStrategyInit
 from .graph_init_params import GraphInitParams
+from .tool_entities import ToolCall, ToolCallResult, ToolResult, ToolResultStatus
 from .workflow_execution import WorkflowExecution
 from .workflow_node_execution import WorkflowNodeExecution
 
 __all__ = [
     "AgentNodeStrategyInit",
     "GraphInitParams",
+    "ToolCall",
+    "ToolCallResult",
+    "ToolResult",
+    "ToolResultStatus",
     "WorkflowExecution",
     "WorkflowNodeExecution",
 ]

api/core/workflow/entities/tool_entities.py

@@ -0,0 +1,39 @@
+from enum import StrEnum
+
+from pydantic import BaseModel, Field
+
+from core.file import File
+
+
+class ToolResultStatus(StrEnum):
+    SUCCESS = "success"
+    ERROR = "error"
+
+
+class ToolCall(BaseModel):
+    id: str | None = Field(default=None, description="Unique identifier for this tool call")
+    name: str | None = Field(default=None, description="Name of the tool being called")
+    arguments: str | None = Field(default=None, description="Accumulated tool arguments JSON")
+    icon: str | dict | None = Field(default=None, description="Icon of the tool")
+    icon_dark: str | dict | None = Field(default=None, description="Dark theme icon of the tool")
+
+
+class ToolResult(BaseModel):
+    id: str | None = Field(default=None, description="Identifier of the tool call this result belongs to")
+    name: str | None = Field(default=None, description="Name of the tool")
+    output: str | None = Field(default=None, description="Tool output text, error or success message")
+    files: list[str] = Field(default_factory=list, description="File produced by tool")
+    status: ToolResultStatus | None = Field(default=ToolResultStatus.SUCCESS, description="Tool execution status")
+    elapsed_time: float | None = Field(default=None, description="Elapsed seconds spent executing the tool")
+    icon: str | dict | None = Field(default=None, description="Icon of the tool")
+    icon_dark: str | dict | None = Field(default=None, description="Dark theme icon of the tool")
+
+
+class ToolCallResult(BaseModel):
+    id: str | None = Field(default=None, description="Identifier for the tool call")
+    name: str | None = Field(default=None, description="Name of the tool")
+    arguments: str | None = Field(default=None, description="Accumulated tool arguments JSON")
+    output: str | None = Field(default=None, description="Tool output text, error or success message")
+    files: list[File] = Field(default_factory=list, description="File produced by tool")
+    status: ToolResultStatus = Field(default=ToolResultStatus.SUCCESS, description="Tool execution status")
+    elapsed_time: float | None = Field(default=None, description="Elapsed seconds spent executing the tool")

api/core/workflow/enums.py

@@ -251,6 +251,8 @@ class WorkflowNodeExecutionMetadataKey(StrEnum):
     ERROR_STRATEGY = "error_strategy"  # node in continue on error mode return the field
     LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
     DATASOURCE_INFO = "datasource_info"
+    LLM_CONTENT_SEQUENCE = "llm_content_sequence"
+    LLM_TRACE = "llm_trace"
     COMPLETED_REASON = "completed_reason"  # completed reason for loop node
 
 

api/core/workflow/graph_engine/response_coordinator/coordinator.py

@@ -16,7 +16,13 @@ from pydantic import BaseModel, Field
 
 from core.workflow.enums import NodeExecutionType, NodeState
 from core.workflow.graph import Graph
-from core.workflow.graph_events import NodeRunStreamChunkEvent, NodeRunSucceededEvent
+from core.workflow.graph_events import (
+    ChunkType,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+    ToolCall,
+    ToolResult,
+)
 from core.workflow.nodes.base.template import TextSegment, VariableSegment
 from core.workflow.runtime import VariablePool
 
@@ -321,11 +327,24 @@ class ResponseStreamCoordinator:
         selector: Sequence[str],
         chunk: str,
         is_final: bool = False,
+        chunk_type: ChunkType = ChunkType.TEXT,
+        tool_call: ToolCall | None = None,
+        tool_result: ToolResult | None = None,
     ) -> NodeRunStreamChunkEvent:
         """Create a stream chunk event with consistent structure.
 
         For selectors with special prefixes (sys, env, conversation), we use the
         active response node's information since these are not actual node IDs.
+
+        Args:
+            node_id: The node ID to attribute the event to
+            execution_id: The execution ID for this node
+            selector: The variable selector
+            chunk: The chunk content
+            is_final: Whether this is the final chunk
+            chunk_type: The semantic type of the chunk being streamed
+            tool_call: Structured data for tool_call chunks
+            tool_result: Structured data for tool_result chunks
         """
         # Check if this is a special selector that doesn't correspond to a node
         if selector and selector[0] not in self._graph.nodes and self._active_session:
@@ -338,6 +357,9 @@ class ResponseStreamCoordinator:
                 selector=selector,
                 chunk=chunk,
                 is_final=is_final,
+                chunk_type=chunk_type,
+                tool_call=tool_call,
+                tool_result=tool_result,
             )
 
         # Standard case: selector refers to an actual node
@@ -349,6 +371,9 @@ class ResponseStreamCoordinator:
             selector=selector,
             chunk=chunk,
             is_final=is_final,
+            chunk_type=chunk_type,
+            tool_call=tool_call,
+            tool_result=tool_result,
         )
 
     def _process_variable_segment(self, segment: VariableSegment) -> tuple[Sequence[NodeRunStreamChunkEvent], bool]:
@@ -356,6 +381,8 @@ class ResponseStreamCoordinator:
 
         Handles both regular node selectors and special system selectors (sys, env, conversation).
         For special selectors, we attribute the output to the active response node.
+
+        For object-type variables, automatically streams all child fields that have stream events.
         """
         events: list[NodeRunStreamChunkEvent] = []
         source_selector_prefix = segment.selector[0] if segment.selector else ""
@@ -364,60 +391,81 @@ class ResponseStreamCoordinator:
         # Determine which node to attribute the output to
         # For special selectors (sys, env, conversation), use the active response node
         # For regular selectors, use the source node
-        if self._active_session and source_selector_prefix not in self._graph.nodes:
+        active_session = self._active_session
-            # Special selector - use active response node
+        special_selector = bool(active_session and source_selector_prefix not in self._graph.nodes)
-            output_node_id = self._active_session.node_id
+        output_node_id = active_session.node_id if special_selector and active_session else source_selector_prefix
-        else:
-            # Regular node selector
-            output_node_id = source_selector_prefix
         execution_id = self._get_or_create_execution_id(output_node_id)
 
-        # Stream all available chunks
+        # Check if there's a direct stream for this selector
-        while self._has_unread_stream(segment.selector):
+        has_direct_stream = (
-            if event := self._pop_stream_chunk(segment.selector):
+            tuple(segment.selector) in self._stream_buffers or tuple(segment.selector) in self._closed_streams
-                # For special selectors, we need to update the event to use
+        )
-                # the active response node's information
-                if self._active_session and source_selector_prefix not in self._graph.nodes:
-                    response_node = self._graph.nodes[self._active_session.node_id]
-                    # Create a new event with the response node's information
-                    # but keep the original selector
-                    updated_event = NodeRunStreamChunkEvent(
-                        id=execution_id,
-                        node_id=response_node.id,
-                        node_type=response_node.node_type,
-                        selector=event.selector,  # Keep original selector
-                        chunk=event.chunk,
-                        is_final=event.is_final,
-                    )
-                    events.append(updated_event)
-                else:
-                    # Regular node selector - use event as is
-                    events.append(event)
 
-        # Check if this is the last chunk by looking ahead
+        stream_targets = [segment.selector] if has_direct_stream else sorted(self._find_child_streams(segment.selector))
-        stream_closed = self._is_stream_closed(segment.selector)
-        # Check if stream is closed to determine if segment is complete
-        if stream_closed:
-            is_complete = True
 
-        elif value := self._variable_pool.get(segment.selector):
+        if stream_targets:
-            # Process scalar value
+            all_complete = True
-            is_last_segment = bool(
+
-                self._active_session and self._active_session.index == len(self._active_session.template.segments) - 1
+            for target_selector in stream_targets:
-            )
+                while self._has_unread_stream(target_selector):
-            events.append(
+                    if event := self._pop_stream_chunk(target_selector):
-                self._create_stream_chunk_event(
+                        events.append(
-                    node_id=output_node_id,
+                            self._rewrite_stream_event(
-                    execution_id=execution_id,
+                                event=event,
-                    selector=segment.selector,
+                                output_node_id=output_node_id,
-                    chunk=value.markdown,
+                                execution_id=execution_id,
-                    is_final=is_last_segment,
+                                special_selector=bool(special_selector),
+                            )
+                        )
+
+                if not self._is_stream_closed(target_selector):
+                    all_complete = False
+
+            is_complete = all_complete
+
+        # Fallback: check if scalar value exists in variable pool
+        if not is_complete and not has_direct_stream:
+            if value := self._variable_pool.get(segment.selector):
+                # Process scalar value
+                is_last_segment = bool(
+                    self._active_session
+                    and self._active_session.index == len(self._active_session.template.segments) - 1
                 )
-            )
+                events.append(
-            is_complete = True
+                    self._create_stream_chunk_event(
+                        node_id=output_node_id,
+                        execution_id=execution_id,
+                        selector=segment.selector,
+                        chunk=value.markdown,
+                        is_final=is_last_segment,
+                    )
+                )
+                is_complete = True
 
         return events, is_complete
 
+    def _rewrite_stream_event(
+        self,
+        event: NodeRunStreamChunkEvent,
+        output_node_id: str,
+        execution_id: str,
+        special_selector: bool,
+    ) -> NodeRunStreamChunkEvent:
+        """Rewrite event to attribute to active response node when selector is special."""
+        if not special_selector:
+            return event
+
+        return self._create_stream_chunk_event(
+            node_id=output_node_id,
+            execution_id=execution_id,
+            selector=event.selector,
+            chunk=event.chunk,
+            is_final=event.is_final,
+            chunk_type=event.chunk_type,
+            tool_call=event.tool_call,
+            tool_result=event.tool_result,
+        )
+
     def _process_text_segment(self, segment: TextSegment) -> Sequence[NodeRunStreamChunkEvent]:
         """Process a text segment. Returns (events, is_complete)."""
         assert self._active_session is not None
@@ -513,6 +561,36 @@ class ResponseStreamCoordinator:
 
     # ============= Internal Stream Management Methods =============
 
+    def _find_child_streams(self, parent_selector: Sequence[str]) -> list[tuple[str, ...]]:
+        """Find all child stream selectors that are descendants of the parent selector.
+
+        For example, if parent_selector is ['llm', 'generation'], this will find:
+        - ['llm', 'generation', 'content']
+        - ['llm', 'generation', 'tool_calls']
+        - ['llm', 'generation', 'tool_results']
+        - ['llm', 'generation', 'thought']
+
+        Args:
+            parent_selector: The parent selector to search for children
+
+        Returns:
+            List of child selector tuples found in stream buffers or closed streams
+        """
+        parent_key = tuple(parent_selector)
+        parent_len = len(parent_key)
+        child_streams: set[tuple[str, ...]] = set()
+
+        # Search in both active buffers and closed streams
+        all_selectors = set(self._stream_buffers.keys()) | self._closed_streams
+
+        for selector_key in all_selectors:
+            # Check if this selector is a direct child of the parent
+            # Direct child means: len(child) == len(parent) + 1 and child starts with parent
+            if len(selector_key) == parent_len + 1 and selector_key[:parent_len] == parent_key:
+                child_streams.add(selector_key)
+
+        return sorted(child_streams)
+
     def _append_stream_chunk(self, selector: Sequence[str], event: NodeRunStreamChunkEvent) -> None:
         """
         Append a stream chunk to the internal buffer.

api/core/workflow/graph_events/__init__.py

@@ -36,6 +36,7 @@ from .loop import (
 
 # Node events
 from .node import (
+    ChunkType,
     NodeRunExceptionEvent,
     NodeRunFailedEvent,
     NodeRunPauseRequestedEvent,
@@ -44,10 +45,13 @@ from .node import (
     NodeRunStartedEvent,
     NodeRunStreamChunkEvent,
     NodeRunSucceededEvent,
+    ToolCall,
+    ToolResult,
 )
 
 __all__ = [
     "BaseGraphEvent",
+    "ChunkType",
     "GraphEngineEvent",
     "GraphNodeEventBase",
     "GraphRunAbortedEvent",
@@ -73,4 +77,6 @@ __all__ = [
     "NodeRunStartedEvent",
     "NodeRunStreamChunkEvent",
     "NodeRunSucceededEvent",
+    "ToolCall",
+    "ToolResult",
 ]

api/core/workflow/graph_events/node.py

@@ -1,10 +1,11 @@
 from collections.abc import Sequence
 from datetime import datetime
+from enum import StrEnum
 
 from pydantic import Field
 
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit
+from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
 from core.workflow.entities.pause_reason import PauseReason
 
 from .base import GraphNodeEventBase
@@ -21,13 +22,39 @@ class NodeRunStartedEvent(GraphNodeEventBase):
     provider_id: str = ""
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class NodeRunStreamChunkEvent(GraphNodeEventBase):
-    # Spec-compliant fields
+    """Stream chunk event for workflow node execution."""
+
+    # Base fields
     selector: Sequence[str] = Field(
         ..., description="selector identifying the output location (e.g., ['nodeA', 'text'])"
     )
     chunk: str = Field(..., description="the actual chunk content")
     is_final: bool = Field(default=False, description="indicates if this is the last chunk")
+    chunk_type: ChunkType = Field(default=ChunkType.TEXT, description="type of the chunk")
+
+    # Tool call fields (when chunk_type == TOOL_CALL)
+    tool_call: ToolCall | None = Field(
+        default=None,
+        description="structured payload for tool_call chunks",
+    )
+
+    # Tool result fields (when chunk_type == TOOL_RESULT)
+    tool_result: ToolResult | None = Field(
+        default=None,
+        description="structured payload for tool_result chunks",
+    )
 
 
 class NodeRunRetrieverResourceEvent(GraphNodeEventBase):

api/core/workflow/node_events/__init__.py

@@ -13,16 +13,21 @@ from .loop import (
     LoopSucceededEvent,
 )
 from .node import (
+    ChunkType,
     ModelInvokeCompletedEvent,
     PauseRequestedEvent,
     RunRetrieverResourceEvent,
     RunRetryEvent,
     StreamChunkEvent,
     StreamCompletedEvent,
+    ThoughtChunkEvent,
+    ToolCallChunkEvent,
+    ToolResultChunkEvent,
 )
 
 __all__ = [
     "AgentLogEvent",
+    "ChunkType",
     "IterationFailedEvent",
     "IterationNextEvent",
     "IterationStartedEvent",
@@ -39,4 +44,7 @@ __all__ = [
     "RunRetryEvent",
     "StreamChunkEvent",
     "StreamCompletedEvent",
+    "ThoughtChunkEvent",
+    "ToolCallChunkEvent",
+    "ToolResultChunkEvent",
 ]

api/core/workflow/node_events/node.py

@@ -1,11 +1,13 @@
 from collections.abc import Sequence
 from datetime import datetime
+from enum import StrEnum
 
 from pydantic import Field
 
 from core.file import File
 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
+from core.workflow.entities import ToolCall, ToolResult
 from core.workflow.entities.pause_reason import PauseReason
 from core.workflow.node_events import NodeRunResult
 
@@ -32,13 +34,60 @@ class RunRetryEvent(NodeEventBase):
     start_at: datetime = Field(..., description="Retry start time")
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class StreamChunkEvent(NodeEventBase):
-    # Spec-compliant fields
+    """Base stream chunk event - normal text streaming output."""
+
     selector: Sequence[str] = Field(
         ..., description="selector identifying the output location (e.g., ['nodeA', 'text'])"
     )
     chunk: str = Field(..., description="the actual chunk content")
     is_final: bool = Field(default=False, description="indicates if this is the last chunk")
+    chunk_type: ChunkType = Field(default=ChunkType.TEXT, description="type of the chunk")
+    tool_call: ToolCall | None = Field(default=None, description="structured payload for tool_call chunks")
+    tool_result: ToolResult | None = Field(default=None, description="structured payload for tool_result chunks")
+
+
+class ToolCallChunkEvent(StreamChunkEvent):
+    """Tool call streaming event - tool call arguments streaming output."""
+
+    chunk_type: ChunkType = Field(default=ChunkType.TOOL_CALL, frozen=True)
+    tool_call: ToolCall | None = Field(default=None, description="structured tool call payload")
+
+
+class ToolResultChunkEvent(StreamChunkEvent):
+    """Tool result event - tool execution result."""
+
+    chunk_type: ChunkType = Field(default=ChunkType.TOOL_RESULT, frozen=True)
+    tool_result: ToolResult | None = Field(default=None, description="structured tool result payload")
+
+
+class ThoughtStartChunkEvent(StreamChunkEvent):
+    """Agent thought start streaming event - Agent thinking process (ReAct)."""
+
+    chunk_type: ChunkType = Field(default=ChunkType.THOUGHT_START, frozen=True)
+
+
+class ThoughtEndChunkEvent(StreamChunkEvent):
+    """Agent thought end streaming event - Agent thinking process (ReAct)."""
+
+    chunk_type: ChunkType = Field(default=ChunkType.THOUGHT_END, frozen=True)
+
+
+class ThoughtChunkEvent(StreamChunkEvent):
+    """Agent thought streaming event - Agent thinking process (ReAct)."""
+
+    chunk_type: ChunkType = Field(default=ChunkType.THOUGHT, frozen=True)
 
 
 class StreamCompletedEvent(NodeEventBase):

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

@@ -48,6 +48,9 @@ from core.workflow.node_events import (
     RunRetrieverResourceEvent,
     StreamChunkEvent,
     StreamCompletedEvent,
+    ThoughtChunkEvent,
+    ToolCallChunkEvent,
+    ToolResultChunkEvent,
 )
 from core.workflow.runtime import GraphRuntimeState
 from libs.datetime_utils import naive_utc_now
@@ -564,6 +567,8 @@ class Node(Generic[NodeDataT]):
 
     @_dispatch.register
     def _(self, event: StreamChunkEvent) -> NodeRunStreamChunkEvent:
+        from core.workflow.graph_events import ChunkType
+
         return NodeRunStreamChunkEvent(
             id=self.execution_id,
             node_id=self._node_id,
@@ -571,6 +576,60 @@ class Node(Generic[NodeDataT]):
             selector=event.selector,
             chunk=event.chunk,
             is_final=event.is_final,
+            chunk_type=ChunkType(event.chunk_type.value),
+            tool_call=event.tool_call,
+            tool_result=event.tool_result,
+        )
+
+    @_dispatch.register
+    def _(self, event: ToolCallChunkEvent) -> NodeRunStreamChunkEvent:
+        from core.workflow.graph_events import ChunkType
+
+        return NodeRunStreamChunkEvent(
+            id=self._node_execution_id,
+            node_id=self._node_id,
+            node_type=self.node_type,
+            selector=event.selector,
+            chunk=event.chunk,
+            is_final=event.is_final,
+            chunk_type=ChunkType.TOOL_CALL,
+            tool_call=event.tool_call,
+        )
+
+    @_dispatch.register
+    def _(self, event: ToolResultChunkEvent) -> NodeRunStreamChunkEvent:
+        from core.workflow.entities import ToolResult, ToolResultStatus
+        from core.workflow.graph_events import ChunkType
+
+        tool_result = event.tool_result or ToolResult()
+        status: ToolResultStatus = tool_result.status or ToolResultStatus.SUCCESS
+        tool_result = tool_result.model_copy(
+            update={"status": status, "files": tool_result.files or []},
+        )
+
+        return NodeRunStreamChunkEvent(
+            id=self._node_execution_id,
+            node_id=self._node_id,
+            node_type=self.node_type,
+            selector=event.selector,
+            chunk=event.chunk,
+            is_final=event.is_final,
+            chunk_type=ChunkType.TOOL_RESULT,
+            tool_result=tool_result,
+        )
+
+    @_dispatch.register
+    def _(self, event: ThoughtChunkEvent) -> NodeRunStreamChunkEvent:
+        from core.workflow.graph_events import ChunkType
+
+        return NodeRunStreamChunkEvent(
+            id=self._node_execution_id,
+            node_id=self._node_id,
+            node_type=self.node_type,
+            selector=event.selector,
+            chunk=event.chunk,
+            is_final=event.is_final,
+            chunk_type=ChunkType.THOUGHT,
         )
 
     @_dispatch.register

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

@@ -62,6 +62,21 @@ class DocumentExtractorNode(Node[DocumentExtractorNodeData]):
         inputs = {"variable_selector": variable_selector}
         process_data = {"documents": value if isinstance(value, list) else [value]}
 
+        # Ensure storage_key is loaded for File objects
+        files_to_check = value if isinstance(value, list) else [value]
+        files_needing_storage_key = [
+            f for f in files_to_check
+            if isinstance(f, File) and not f.storage_key and f.related_id
+        ]
+        if files_needing_storage_key:
+            from factories.file_factory import StorageKeyLoader
+            from extensions.ext_database import db
+            from sqlalchemy.orm import Session
+            
+            with Session(bind=db.engine) as session:
+                storage_key_loader = StorageKeyLoader(session, tenant_id=self.tenant_id)
+                storage_key_loader.load_storage_keys(files_needing_storage_key)
+
         try:
             if isinstance(value, list):
                 extracted_text_list = list(map(_extract_text_from_file, value))
@@ -415,6 +430,15 @@ def _download_file_content(file: File) -> bytes:
             response.raise_for_status()
             return response.content
         else:
+            # Check if storage_key is set
+            if not file.storage_key:
+                raise FileDownloadError(f"File storage_key is missing for file: {file.filename}")
+            
+            # Check if file exists before downloading
+            from extensions.ext_storage import storage
+            if not storage.exists(file.storage_key):
+                raise FileDownloadError(f"File not found in storage: {file.storage_key}")
+            
             return file_manager.download(file)
     except Exception as e:
         raise FileDownloadError(f"Error downloading file: {str(e)}") from e

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

@@ -158,3 +158,5 @@ class KnowledgeIndexNodeData(BaseNodeData):
     type: str = "knowledge-index"
     chunk_structure: str
     index_chunk_variable_selector: list[str]
+    indexing_technique: str | None = None
+    summary_index_setting: dict | None = None

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

@@ -1,9 +1,11 @@
+import concurrent.futures
 import datetime
 import logging
 import time
 from collections.abc import Mapping
 from typing import Any
 
+from flask import current_app
 from sqlalchemy import func, select
 
 from core.app.entities.app_invoke_entities import InvokeFrom
@@ -16,7 +18,9 @@ from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.base.template import Template
 from core.workflow.runtime import VariablePool
 from extensions.ext_database import db
-from models.dataset import Dataset, Document, DocumentSegment
+from models.dataset import Dataset, Document, DocumentSegment, DocumentSegmentSummary
+from services.summary_index_service import SummaryIndexService
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 from .entities import KnowledgeIndexNodeData
 from .exc import (
@@ -67,7 +71,18 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
         # index knowledge
         try:
             if is_preview:
-                outputs = self._get_preview_output(node_data.chunk_structure, chunks)
+                # Preview mode: generate summaries for chunks directly without saving to database
+                # Format preview and generate summaries on-the-fly
+                # Get indexing_technique and summary_index_setting from node_data (workflow graph config)
+                # or fallback to dataset if not available in node_data
+                indexing_technique = node_data.indexing_technique or dataset.indexing_technique
+                summary_index_setting = node_data.summary_index_setting or dataset.summary_index_setting
+                
+                outputs = self._get_preview_output_with_summaries(
+                    node_data.chunk_structure, chunks, dataset=dataset,
+                    indexing_technique=indexing_technique,
+                    summary_index_setting=summary_index_setting
+                )
                 return NodeRunResult(
                     status=WorkflowNodeExecutionStatus.SUCCEEDED,
                     inputs=variables,
@@ -163,6 +178,9 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
 
         db.session.commit()
 
+        # Generate summary index if enabled
+        self._handle_summary_index_generation(dataset, document, variable_pool)
+
         return {
             "dataset_id": ds_id_value,
             "dataset_name": dataset_name_value,
@@ -173,9 +191,269 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
             "display_status": "completed",
         }
 
-    def _get_preview_output(self, chunk_structure: str, chunks: Any) -> Mapping[str, Any]:
+    def _handle_summary_index_generation(
+        self,
+        dataset: Dataset,
+        document: Document,
+        variable_pool: VariablePool,
+    ) -> None:
+        """
+        Handle summary index generation based on mode (debug/preview or production).
+
+        Args:
+            dataset: Dataset containing the document
+            document: Document to generate summaries for
+            variable_pool: Variable pool to check invoke_from
+        """
+        # Only generate summary index for high_quality indexing technique
+        if dataset.indexing_technique != "high_quality":
+            return
+
+        # Check if summary index is enabled
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            return
+
+        # Skip qa_model documents
+        if document.doc_form == "qa_model":
+            return
+
+        # Determine if in preview/debug mode
+        invoke_from = variable_pool.get(["sys", SystemVariableKey.INVOKE_FROM])
+        is_preview = invoke_from and invoke_from.value == InvokeFrom.DEBUGGER
+
+        # Determine if only parent chunks should be processed
+        only_parent_chunks = dataset.chunk_structure == "parent_child_index"
+
+        if is_preview:
+            try:
+                # Query segments that need summary generation
+                query = db.session.query(DocumentSegment).filter_by(
+                    dataset_id=dataset.id,
+                    document_id=document.id,
+                    status="completed",
+                    enabled=True,
+                )
+                segments = query.all()
+
+                if not segments:
+                    logger.info(f"No segments found for document {document.id}")
+                    return
+
+                # Filter segments based on mode
+                segments_to_process = []
+                for segment in segments:
+                    # Skip if summary already exists
+                    existing_summary = (
+                        db.session.query(DocumentSegmentSummary)
+                        .filter_by(chunk_id=segment.id, dataset_id=dataset.id, status="completed")
+                        .first()
+                    )
+                    if existing_summary:
+                        continue
+
+                    # For parent-child mode, all segments are parent chunks, so process all
+                    segments_to_process.append(segment)
+
+                if not segments_to_process:
+                    logger.info(f"No segments need summary generation for document {document.id}")
+                    return
+
+                # Use ThreadPoolExecutor for concurrent generation
+                flask_app = current_app._get_current_object()  # type: ignore
+                max_workers = min(10, len(segments_to_process))  # Limit to 10 workers
+
+                def process_segment(segment: DocumentSegment) -> None:
+                    """Process a single segment in a thread with Flask app context."""
+                    with flask_app.app_context():
+                        try:
+                            SummaryIndexService.generate_and_vectorize_summary(
+                                segment, dataset, summary_index_setting
+                            )
+                        except Exception as e:
+                            logger.error(f"Failed to generate summary for segment {segment.id}: {str(e)}")
+                            # Continue processing other segments
+
+                with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+                    futures = [
+                        executor.submit(process_segment, segment) for segment in segments_to_process
+                    ]
+                    # Wait for all tasks to complete
+                    concurrent.futures.wait(futures, timeout=300)
+
+                logger.info(
+                    f"Successfully generated summary index for {len(segments_to_process)} segments "
+                    f"in document {document.id}"
+                )
+            except Exception as e:
+                logger.exception(f"Failed to generate summary index for document {document.id}: {str(e)}")
+                # Don't fail the entire indexing process if summary generation fails
+        else:
+            # Production mode: asynchronous generation
+            logger.info(f"Queuing summary index generation task for document {document.id} (production mode)")
+            try:
+                generate_summary_index_task.delay(dataset.id, document.id, None)
+                logger.info(f"Summary index generation task queued for document {document.id}")
+            except Exception as e:
+                logger.exception(f"Failed to queue summary index generation task for document {document.id}: {str(e)}")
+                # Don't fail the entire indexing process if task queuing fails
+
+    def _get_preview_output_with_summaries(
+        self, chunk_structure: str, chunks: Any, dataset: Dataset,
+        indexing_technique: str | None = None,
+        summary_index_setting: dict | None = None
+    ) -> Mapping[str, Any]:
+        """
+        Generate preview output with summaries for chunks in preview mode.
+        This method generates summaries on-the-fly without saving to database.
+        
+        Args:
+            chunk_structure: Chunk structure type
+            chunks: Chunks to generate preview for
+            dataset: Dataset object (for tenant_id)
+            indexing_technique: Indexing technique from node config or dataset
+            summary_index_setting: Summary index setting from node config or dataset
+        """
         index_processor = IndexProcessorFactory(chunk_structure).init_index_processor()
-        return index_processor.format_preview(chunks)
+        preview_output = index_processor.format_preview(chunks)
+        
+        # Check if summary index is enabled
+        if indexing_technique != "high_quality":
+            return preview_output
+        
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            return preview_output
+        
+        # Generate summaries for chunks
+        if "preview" in preview_output and isinstance(preview_output["preview"], list):
+            chunk_count = len(preview_output["preview"])
+            logger.info(
+                f"Generating summaries for {chunk_count} chunks in preview mode "
+                f"(dataset: {dataset.id})"
+            )
+            # Use ParagraphIndexProcessor's generate_summary method
+            from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+            
+            # Get Flask app for application context in worker threads
+            flask_app = None
+            try:
+                flask_app = current_app._get_current_object()  # type: ignore
+            except RuntimeError:
+                logger.warning("No Flask application context available, summary generation may fail")
+            
+            def generate_summary_for_chunk(preview_item: dict) -> None:
+                """Generate summary for a single chunk."""
+                if "content" in preview_item:
+                    try:
+                        # Set Flask application context in worker thread
+                        if flask_app:
+                            with flask_app.app_context():
+                                summary = ParagraphIndexProcessor.generate_summary(
+                                    tenant_id=dataset.tenant_id,
+                                    text=preview_item["content"],
+                                    summary_index_setting=summary_index_setting,
+                                )
+                                if summary:
+                                    preview_item["summary"] = summary
+                        else:
+                            # Fallback: try without app context (may fail)
+                            summary = ParagraphIndexProcessor.generate_summary(
+                                tenant_id=dataset.tenant_id,
+                                text=preview_item["content"],
+                                summary_index_setting=summary_index_setting,
+                            )
+                            if summary:
+                                preview_item["summary"] = summary
+                    except Exception as e:
+                        logger.error(f"Failed to generate summary for chunk: {str(e)}")
+                        # Don't fail the entire preview if summary generation fails
+            
+            # Generate summaries concurrently using ThreadPoolExecutor
+            # Set a reasonable timeout to prevent hanging (60 seconds per chunk, max 5 minutes total)
+            timeout_seconds = min(300, 60 * len(preview_output["preview"]))
+            with concurrent.futures.ThreadPoolExecutor(max_workers=min(10, len(preview_output["preview"]))) as executor:
+                futures = [
+                    executor.submit(generate_summary_for_chunk, preview_item)
+                    for preview_item in preview_output["preview"]
+                ]
+                # Wait for all tasks to complete with timeout
+                done, not_done = concurrent.futures.wait(futures, timeout=timeout_seconds)
+                
+                # Cancel tasks that didn't complete in time
+                if not_done:
+                    logger.warning(
+                        f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s. "
+                        "Cancelling remaining tasks..."
+                    )
+                    for future in not_done:
+                        future.cancel()
+                    # Wait a bit for cancellation to take effect
+                    concurrent.futures.wait(not_done, timeout=5)
+            
+            completed_count = sum(1 for item in preview_output["preview"] if item.get("summary") is not None)
+            logger.info(
+                f"Completed summary generation for preview chunks: {completed_count}/{len(preview_output['preview'])} succeeded"
+            )
+        
+        return preview_output
+
+    def _get_preview_output(
+        self, chunk_structure: str, chunks: Any, dataset: Dataset | None = None, variable_pool: VariablePool | None = None
+    ) -> Mapping[str, Any]:
+        index_processor = IndexProcessorFactory(chunk_structure).init_index_processor()
+        preview_output = index_processor.format_preview(chunks)
+        
+        # If dataset is provided, try to enrich preview with summaries
+        if dataset and variable_pool:
+            document_id = variable_pool.get(["sys", SystemVariableKey.DOCUMENT_ID])
+            if document_id:
+                document = db.session.query(Document).filter_by(id=document_id.value).first()
+                if document:
+                    # Query summaries for this document
+                    summaries = (
+                        db.session.query(DocumentSegmentSummary)
+                        .filter_by(
+                            dataset_id=dataset.id,
+                            document_id=document.id,
+                            status="completed",
+                            enabled=True,
+                        )
+                        .all()
+                    )
+                    
+                    if summaries:
+                        # Create a map of segment content to summary for matching
+                        # Use content matching as chunks in preview might not be indexed yet
+                        summary_by_content = {}
+                        for summary in summaries:
+                            segment = (
+                                db.session.query(DocumentSegment)
+                                .filter_by(id=summary.chunk_id, dataset_id=dataset.id)
+                                .first()
+                            )
+                            if segment:
+                                # Normalize content for matching (strip whitespace)
+                                normalized_content = segment.content.strip()
+                                summary_by_content[normalized_content] = summary.summary_content
+                        
+                        # Enrich preview with summaries by content matching
+                        if "preview" in preview_output and isinstance(preview_output["preview"], list):
+                            matched_count = 0
+                            for preview_item in preview_output["preview"]:
+                                if "content" in preview_item:
+                                    # Normalize content for matching
+                                    normalized_chunk_content = preview_item["content"].strip()
+                                    if normalized_chunk_content in summary_by_content:
+                                        preview_item["summary"] = summary_by_content[normalized_chunk_content]
+                                        matched_count += 1
+                            
+                            if matched_count > 0:
+                                logger.info(
+                                    f"Enriched preview with {matched_count} existing summaries "
+                                    f"(dataset: {dataset.id}, document: {document.id})"
+                                )
+        
+        return preview_output
 
     @classmethod
     def version(cls) -> str:

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

@@ -3,6 +3,7 @@ from .entities import (
     LLMNodeCompletionModelPromptTemplate,
     LLMNodeData,
     ModelConfig,
+    ToolMetadata,
     VisionConfig,
 )
 from .node import LLMNode
@@ -13,5 +14,6 @@ __all__ = [
     "LLMNodeCompletionModelPromptTemplate",
     "LLMNodeData",
     "ModelConfig",
+    "ToolMetadata",
     "VisionConfig",
 ]

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

@@ -1,10 +1,17 @@
+import re
 from collections.abc import Mapping, Sequence
 from typing import Any, Literal
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
+from core.agent.entities import AgentLog, AgentResult
+from core.file import File
 from core.model_runtime.entities import ImagePromptMessageContent, LLMMode
+from core.model_runtime.entities.llm_entities import LLMUsage
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
+from core.tools.entities.tool_entities import ToolProviderType
+from core.workflow.entities import ToolCall, ToolCallResult
+from core.workflow.node_events import AgentLogEvent
 from core.workflow.nodes.base import BaseNodeData
 from core.workflow.nodes.base.entities import VariableSelector
 
@@ -58,6 +65,268 @@ class LLMNodeCompletionModelPromptTemplate(CompletionModelPromptTemplate):
     jinja2_text: str | None = None
 
 
+class ToolMetadata(BaseModel):
+    """
+    Tool metadata for LLM node with tool support.
+
+    Defines the essential fields needed for tool configuration,
+    particularly the 'type' field to identify tool provider type.
+    """
+
+    # Core fields
+    enabled: bool = True
+    type: ToolProviderType = Field(..., description="Tool provider type: builtin, api, mcp, workflow")
+    provider_name: str = Field(..., description="Tool provider name/identifier")
+    tool_name: str = Field(..., description="Tool name")
+
+    # Optional fields
+    plugin_unique_identifier: str | None = Field(None, description="Plugin unique identifier for plugin tools")
+    credential_id: str | None = Field(None, description="Credential ID for tools requiring authentication")
+
+    # Configuration fields
+    parameters: dict[str, Any] = Field(default_factory=dict, description="Tool parameters")
+    settings: dict[str, Any] = Field(default_factory=dict, description="Tool settings configuration")
+    extra: dict[str, Any] = Field(default_factory=dict, description="Extra tool configuration like custom description")
+
+
+class ModelTraceSegment(BaseModel):
+    """Model invocation trace segment with token usage and output."""
+
+    text: str | None = Field(None, description="Model output text content")
+    reasoning: str | None = Field(None, description="Reasoning/thought content from model")
+    tool_calls: list[ToolCall] = Field(default_factory=list, description="Tool calls made by the model")
+
+
+class ToolTraceSegment(BaseModel):
+    """Tool invocation trace segment with call details and result."""
+
+    id: str | None = Field(default=None, description="Unique identifier for this tool call")
+    name: str | None = Field(default=None, description="Name of the tool being called")
+    arguments: str | None = Field(default=None, description="Accumulated tool arguments JSON")
+    output: str | None = Field(default=None, description="Tool call result")
+
+
+class LLMTraceSegment(BaseModel):
+    """
+    Streaming trace segment for LLM tool-enabled runs.
+
+    Represents alternating model and tool invocations in sequence:
+    model -> tool -> model -> tool -> ...
+
+    Each segment records its execution duration.
+    """
+
+    type: Literal["model", "tool"]
+    duration: float = Field(..., description="Execution duration in seconds")
+    usage: LLMUsage | None = Field(default=None, description="Token usage statistics for this model call")
+    output: ModelTraceSegment | ToolTraceSegment = Field(..., description="Output of the segment")
+
+    # Common metadata for both model and tool segments
+    provider: str | None = Field(default=None, description="Model or tool provider identifier")
+    name: str | None = Field(default=None, description="Name of the model or tool")
+    icon: str | None = Field(default=None, description="Icon for the provider")
+    icon_dark: str | None = Field(default=None, description="Dark theme icon for the provider")
+    error: str | None = Field(default=None, description="Error message if segment failed")
+    status: Literal["success", "error"] | None = Field(default=None, description="Tool execution status")
+
+
+class LLMGenerationData(BaseModel):
+    """Generation data from LLM invocation with tools.
+
+    For multi-turn tool calls like: thought1 -> text1 -> tool_call1 -> thought2 -> text2 -> tool_call2
+    - reasoning_contents: [thought1, thought2, ...] - one element per turn
+    - tool_calls: [{id, name, arguments, result}, ...] - all tool calls with results
+    """
+
+    text: str = Field(..., description="Accumulated text content from all turns")
+    reasoning_contents: list[str] = Field(default_factory=list, description="Reasoning content per turn")
+    tool_calls: list[ToolCallResult] = Field(default_factory=list, description="Tool calls with results")
+    sequence: list[dict[str, Any]] = Field(default_factory=list, description="Ordered segments for rendering")
+    usage: LLMUsage = Field(..., description="LLM usage statistics")
+    finish_reason: str | None = Field(None, description="Finish reason from LLM")
+    files: list[File] = Field(default_factory=list, description="Generated files")
+    trace: list[LLMTraceSegment] = Field(default_factory=list, description="Streaming trace in emitted order")
+
+
+class ThinkTagStreamParser:
+    """Lightweight state machine to split streaming chunks by <think> tags."""
+
+    _START_PATTERN = re.compile(r"<think(?:\s[^>]*)?>", re.IGNORECASE)
+    _END_PATTERN = re.compile(r"</think>", re.IGNORECASE)
+    _START_PREFIX = "<think"
+    _END_PREFIX = "</think"
+
+    def __init__(self):
+        self._buffer = ""
+        self._in_think = False
+
+    @staticmethod
+    def _suffix_prefix_len(text: str, prefix: str) -> int:
+        """Return length of the longest suffix of `text` that is a prefix of `prefix`."""
+        max_len = min(len(text), len(prefix) - 1)
+        for i in range(max_len, 0, -1):
+            if text[-i:].lower() == prefix[:i].lower():
+                return i
+        return 0
+
+    def process(self, chunk: str) -> list[tuple[str, str]]:
+        """
+        Split incoming chunk into ('thought' | 'text', content) tuples.
+        Content excludes the <think> tags themselves and handles split tags across chunks.
+        """
+        parts: list[tuple[str, str]] = []
+        self._buffer += chunk
+
+        while self._buffer:
+            if self._in_think:
+                end_match = self._END_PATTERN.search(self._buffer)
+                if end_match:
+                    thought_text = self._buffer[: end_match.start()]
+                    if thought_text:
+                        parts.append(("thought", thought_text))
+                    parts.append(("thought_end", ""))
+                    self._buffer = self._buffer[end_match.end() :]
+                    self._in_think = False
+                    continue
+
+                hold_len = self._suffix_prefix_len(self._buffer, self._END_PREFIX)
+                emit = self._buffer[: len(self._buffer) - hold_len]
+                if emit:
+                    parts.append(("thought", emit))
+                self._buffer = self._buffer[-hold_len:] if hold_len > 0 else ""
+                break
+
+            start_match = self._START_PATTERN.search(self._buffer)
+            if start_match:
+                prefix = self._buffer[: start_match.start()]
+                if prefix:
+                    parts.append(("text", prefix))
+                self._buffer = self._buffer[start_match.end() :]
+                parts.append(("thought_start", ""))
+                self._in_think = True
+                continue
+
+            hold_len = self._suffix_prefix_len(self._buffer, self._START_PREFIX)
+            emit = self._buffer[: len(self._buffer) - hold_len]
+            if emit:
+                parts.append(("text", emit))
+            self._buffer = self._buffer[-hold_len:] if hold_len > 0 else ""
+            break
+
+        cleaned_parts: list[tuple[str, str]] = []
+        for kind, content in parts:
+            # Extra safeguard: strip any stray tags that slipped through.
+            content = self._START_PATTERN.sub("", content)
+            content = self._END_PATTERN.sub("", content)
+            if content or kind in {"thought_start", "thought_end"}:
+                cleaned_parts.append((kind, content))
+
+        return cleaned_parts
+
+    def flush(self) -> list[tuple[str, str]]:
+        """Flush remaining buffer when the stream ends."""
+        if not self._buffer:
+            return []
+        kind = "thought" if self._in_think else "text"
+        content = self._buffer
+        # Drop dangling partial tags instead of emitting them
+        if content.lower().startswith(self._START_PREFIX) or content.lower().startswith(self._END_PREFIX):
+            content = ""
+        self._buffer = ""
+        if not content and not self._in_think:
+            return []
+        # Strip any complete tags that might still be present.
+        content = self._START_PATTERN.sub("", content)
+        content = self._END_PATTERN.sub("", content)
+
+        result: list[tuple[str, str]] = []
+        if content:
+            result.append((kind, content))
+        if self._in_think:
+            result.append(("thought_end", ""))
+            self._in_think = False
+        return result
+
+
+class StreamBuffers(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    think_parser: ThinkTagStreamParser = Field(default_factory=ThinkTagStreamParser)
+    pending_thought: list[str] = Field(default_factory=list)
+    pending_content: list[str] = Field(default_factory=list)
+    pending_tool_calls: list[ToolCall] = Field(default_factory=list)
+    current_turn_reasoning: list[str] = Field(default_factory=list)
+    reasoning_per_turn: list[str] = Field(default_factory=list)
+
+
+class TraceState(BaseModel):
+    trace_segments: list[LLMTraceSegment] = Field(default_factory=list)
+    tool_trace_map: dict[str, LLMTraceSegment] = Field(default_factory=dict)
+    tool_call_index_map: dict[str, int] = Field(default_factory=dict)
+    model_segment_start_time: float | None = Field(default=None, description="Start time for current model segment")
+    pending_usage: LLMUsage | None = Field(default=None, description="Pending usage for current model segment")
+
+
+class AggregatedResult(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    text: str = ""
+    files: list[File] = Field(default_factory=list)
+    usage: LLMUsage = Field(default_factory=LLMUsage.empty_usage)
+    finish_reason: str | None = None
+
+
+class AgentContext(BaseModel):
+    agent_logs: list[AgentLogEvent] = Field(default_factory=list)
+    agent_result: AgentResult | None = None
+
+
+class ToolOutputState(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    stream: StreamBuffers = Field(default_factory=StreamBuffers)
+    trace: TraceState = Field(default_factory=TraceState)
+    aggregate: AggregatedResult = Field(default_factory=AggregatedResult)
+    agent: AgentContext = Field(default_factory=AgentContext)
+
+
+class ToolLogPayload(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    tool_name: str = ""
+    tool_call_id: str = ""
+    tool_args: dict[str, Any] = Field(default_factory=dict)
+    tool_output: Any = None
+    tool_error: Any = None
+    files: list[Any] = Field(default_factory=list)
+    meta: dict[str, Any] = Field(default_factory=dict)
+
+    @classmethod
+    def from_log(cls, log: AgentLog) -> "ToolLogPayload":
+        data = log.data or {}
+        return cls(
+            tool_name=data.get("tool_name", ""),
+            tool_call_id=data.get("tool_call_id", ""),
+            tool_args=data.get("tool_args") or {},
+            tool_output=data.get("output"),
+            tool_error=data.get("error"),
+            files=data.get("files") or [],
+            meta=data.get("meta") or {},
+        )
+
+    @classmethod
+    def from_mapping(cls, data: Mapping[str, Any]) -> "ToolLogPayload":
+        return cls(
+            tool_name=data.get("tool_name", ""),
+            tool_call_id=data.get("tool_call_id", ""),
+            tool_args=data.get("tool_args") or {},
+            tool_output=data.get("output"),
+            tool_error=data.get("error"),
+            files=data.get("files") or [],
+            meta=data.get("meta") or {},
+        )
+
+
 class LLMNodeData(BaseNodeData):
     model: ModelConfig
     prompt_template: Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
@@ -86,6 +355,10 @@ class LLMNodeData(BaseNodeData):
         ),
     )
 
+    # Tool support
+    tools: Sequence[ToolMetadata] = Field(default_factory=list)
+    max_iterations: int | None = Field(default=None, description="Maximum number of iterations for the LLM node")
+
     @field_validator("prompt_config", mode="before")
     @classmethod
     def convert_none_prompt_config(cls, v: Any):

api/extensions/ext_celery.py

@@ -102,6 +102,8 @@ def init_app(app: DifyApp) -> Celery:
     imports = [
         "tasks.async_workflow_tasks",  # trigger workers
         "tasks.trigger_processing_tasks",  # async trigger processing
+        "tasks.generate_summary_index_task",  # summary index generation
+        "tasks.regenerate_summary_index_task",  # summary index regeneration
     ]
     day = dify_config.CELERY_BEAT_SCHEDULER_TIME
 

api/fields/conversation_fields.py

@@ -169,6 +169,7 @@ class MessageDetail(ResponseModel):
     status: str
     error: str | None = None
     parent_message_id: str | None = None
+    generation_detail: JSONValue | None = Field(default=None, validation_alias="generation_detail_dict")
 
     @field_validator("inputs", mode="before")
     @classmethod

api/fields/dataset_fields.py

@@ -39,6 +39,14 @@ dataset_retrieval_model_fields = {
     "score_threshold_enabled": fields.Boolean,
     "score_threshold": fields.Float,
 }
+
+dataset_summary_index_fields = {
+    "enable": fields.Boolean,
+    "model_name": fields.String,
+    "model_provider_name": fields.String,
+    "summary_prompt": fields.String,
+}
+
 external_retrieval_model_fields = {
     "top_k": fields.Integer,
     "score_threshold": fields.Float,
@@ -83,6 +91,7 @@ dataset_detail_fields = {
     "embedding_model_provider": fields.String,
     "embedding_available": fields.Boolean,
     "retrieval_model_dict": fields.Nested(dataset_retrieval_model_fields),
+    "summary_index_setting": fields.Nested(dataset_summary_index_fields),
     "tags": fields.List(fields.Nested(tag_fields)),
     "doc_form": fields.String,
     "external_knowledge_info": fields.Nested(external_knowledge_info_fields),

api/fields/document_fields.py

@@ -33,6 +33,8 @@ document_fields = {
     "hit_count": fields.Integer,
     "doc_form": fields.String,
     "doc_metadata": fields.List(fields.Nested(document_metadata_fields), attribute="doc_metadata_details"),
+    "summary_index_status": fields.String,  # Summary index generation status: "waiting", "generating", "completed", "partial_error", or null if not enabled
+    "need_summary": fields.Boolean,  # Whether this document needs summary index generation
 }
 
 document_with_segments_fields = {
@@ -60,6 +62,8 @@ document_with_segments_fields = {
     "completed_segments": fields.Integer,
     "total_segments": fields.Integer,
     "doc_metadata": fields.List(fields.Nested(document_metadata_fields), attribute="doc_metadata_details"),
+    "summary_index_status": fields.String,  # Summary index generation status: "waiting", "generating", "completed", "partial_error", or null if not enabled
+    "need_summary": fields.Boolean,  # Whether this document needs summary index generation
 }
 
 dataset_and_document_fields = {

api/fields/hit_testing_fields.py

@@ -58,4 +58,5 @@ hit_testing_record_fields = {
     "score": fields.Float,
     "tsne_position": fields.Raw,
     "files": fields.List(fields.Nested(files_fields)),
+    "summary": fields.String,  # Summary content if retrieved via summary index
 }

api/fields/message_fields.py

@@ -59,6 +59,7 @@ class MessageListItem(ResponseModel):
     message_files: list[MessageFile]
     status: str
     error: str | None = None
+    generation_detail: JSONValueType | None = Field(default=None, validation_alias="generation_detail_dict")
 
     @field_validator("inputs", mode="before")
     @classmethod

api/fields/segment_fields.py

@@ -49,4 +49,5 @@ segment_fields = {
     "stopped_at": TimestampField,
     "child_chunks": fields.List(fields.Nested(child_chunk_fields)),
     "attachments": fields.List(fields.Nested(attachment_fields)),
+    "summary": fields.String,  # Summary content for the segment
 }

api/fields/workflow_run_fields.py

@@ -81,6 +81,7 @@ workflow_run_detail_fields = {
     "inputs": fields.Raw(attribute="inputs_dict"),
     "status": fields.String,
     "outputs": fields.Raw(attribute="outputs_dict"),
+    "outputs_as_generation": fields.Boolean,
     "error": fields.String,
     "elapsed_time": fields.Float,
     "total_tokens": fields.Integer,
@@ -129,6 +130,7 @@ workflow_run_node_execution_fields = {
     "inputs_truncated": fields.Boolean,
     "outputs_truncated": fields.Boolean,
     "process_data_truncated": fields.Boolean,
+    "generation_detail": fields.Raw,
 }
 
 workflow_run_node_execution_list_fields = {

api/migrations/versions/2025_12_10_1504-8a7f2ad7c23e_add_workflow_runs_created_at_idx.py

@@ -0,0 +1,29 @@
+"""Add index on workflow_runs.created_at
+
+Revision ID: 8a7f2ad7c23e
+Revises: d57accd375ae
+Create Date: 2025-12-10 15:04:00.000000
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "8a7f2ad7c23e"
+down_revision = "d57accd375ae"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    with op.batch_alter_table("workflow_runs", schema=None) as batch_op:
+        batch_op.create_index(
+            batch_op.f("workflow_runs_created_at_idx"),
+            ["created_at"],
+            unique=False,
+        )
+
+
+def downgrade():
+    with op.batch_alter_table("workflow_runs", schema=None) as batch_op:
+        batch_op.drop_index(batch_op.f("workflow_runs_created_at_idx"))

api/migrations/versions/2025_12_15_1614-6bb0832495f0_alter_table_pipeline_recommended_.py

@@ -0,0 +1,64 @@
+"""Alter table pipeline_recommended_plugins add column type
+
+Revision ID: 6bb0832495f0
+Revises: 7bb281b7a422
+Create Date: 2025-12-15 16:14:38.482072
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = '6bb0832495f0'
+down_revision = '7bb281b7a422'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('app_triggers', schema=None) as batch_op:
+        batch_op.alter_column('provider_name',
+               existing_type=sa.VARCHAR(length=255),
+               nullable=False,
+               existing_server_default=sa.text("''::character varying"))
+
+    with op.batch_alter_table('operation_logs', schema=None) as batch_op:
+        batch_op.alter_column('content',
+               existing_type=postgresql.JSON(astext_type=sa.Text()),
+               nullable=False)
+
+    with op.batch_alter_table('pipeline_recommended_plugins', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('type', sa.String(length=50), nullable=True))
+
+    with op.batch_alter_table('providers', schema=None) as batch_op:
+        batch_op.alter_column('quota_used',
+               existing_type=sa.BIGINT(),
+               nullable=False)
+
+    # ### end Alembic commands ###
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('providers', schema=None) as batch_op:
+        batch_op.alter_column('quota_used',
+               existing_type=sa.BIGINT(),
+               nullable=True)
+
+    with op.batch_alter_table('pipeline_recommended_plugins', schema=None) as batch_op:
+        batch_op.drop_column('type')
+
+    with op.batch_alter_table('operation_logs', schema=None) as batch_op:
+        batch_op.alter_column('content',
+               existing_type=postgresql.JSON(astext_type=sa.Text()),
+               nullable=True)
+
+    with op.batch_alter_table('app_triggers', schema=None) as batch_op:
+        batch_op.alter_column('provider_name',
+               existing_type=sa.VARCHAR(length=255),
+               nullable=True,
+               existing_server_default=sa.text("''::character varying"))
+
+    # ### end Alembic commands ###

api/migrations/versions/2025_12_16_1424-2536f83803a8_add_type_column_not_null_default_tool.py

@@ -0,0 +1,33 @@
+"""add type column not null default tool
+
+Revision ID: 2536f83803a8
+Revises: 6bb0832495f0
+Create Date: 2025-12-16 14:24:40.740253
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = '2536f83803a8'
+down_revision = '6bb0832495f0'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('pipeline_recommended_plugins', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('type', sa.String(length=50), nullable=False, server_default='tool'))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('pipeline_recommended_plugins', schema=None) as batch_op:
+        batch_op.drop_column('type')
+
+    # ### end Alembic commands ###

api/migrations/versions/2025_12_30_1617-85c8b4a64f53_add_llm_generation_detail_table.py

@@ -0,0 +1,46 @@
+"""add llm generation detail table.
+
+Revision ID: 85c8b4a64f53
+Revises: 7bb281b7a422
+Create Date: 2025-12-10 16:17:46.597669
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = '85c8b4a64f53'
+down_revision = '7df29de0f6be'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('llm_generation_details',
+    sa.Column('id', models.types.StringUUID(), nullable=False),
+    sa.Column('tenant_id', models.types.StringUUID(), nullable=False),
+    sa.Column('app_id', models.types.StringUUID(), nullable=False),
+    sa.Column('message_id', models.types.StringUUID(), nullable=True),
+    sa.Column('workflow_run_id', models.types.StringUUID(), nullable=True),
+    sa.Column('node_id', sa.String(length=255), nullable=True),
+    sa.Column('reasoning_content', models.types.LongText(), nullable=True),
+    sa.Column('tool_calls', models.types.LongText(), nullable=True),
+    sa.Column('sequence', models.types.LongText(), nullable=True),
+    sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.CheckConstraint('(message_id IS NOT NULL AND workflow_run_id IS NULL AND node_id IS NULL) OR (message_id IS NULL AND workflow_run_id IS NOT NULL AND node_id IS NOT NULL)', name=op.f('llm_generation_details_ck_llm_generation_detail_assoc_mode_check')),
+    sa.PrimaryKeyConstraint('id', name='llm_generation_detail_pkey'),
+    sa.UniqueConstraint('message_id', name=op.f('llm_generation_details_message_id_key'))
+    )
+    with op.batch_alter_table('llm_generation_details', schema=None) as batch_op:
+        batch_op.create_index('idx_llm_generation_detail_message', ['message_id'], unique=False)
+        batch_op.create_index('idx_llm_generation_detail_workflow', ['workflow_run_id', 'node_id'], unique=False)
+
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table('llm_generation_details')
+    # ### end Alembic commands ###

api/migrations/versions/2026_01_09_1110-f9f6d18a37f9_add_table_explore_banner_and_trial.py

@@ -0,0 +1,73 @@
+"""add table explore banner and trial
+
+Revision ID: f9f6d18a37f9
+Revises: 7df29de0f6be
+Create Date: 2026-01-09 11:10:18.079355
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = 'f9f6d18a37f9'
+down_revision = '7df29de0f6be'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('account_trial_app_records',
+    sa.Column('id', models.types.StringUUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
+    sa.Column('account_id', models.types.StringUUID(), nullable=False),
+    sa.Column('app_id', models.types.StringUUID(), nullable=False),
+    sa.Column('count', sa.Integer(), nullable=False),
+    sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.PrimaryKeyConstraint('id', name='user_trial_app_pkey'),
+    sa.UniqueConstraint('account_id', 'app_id', name='unique_account_trial_app_record')
+    )
+    with op.batch_alter_table('account_trial_app_records', schema=None) as batch_op:
+        batch_op.create_index('account_trial_app_record_account_id_idx', ['account_id'], unique=False)
+        batch_op.create_index('account_trial_app_record_app_id_idx', ['app_id'], unique=False)
+
+    op.create_table('exporle_banners',
+    sa.Column('id', models.types.StringUUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
+    sa.Column('content', sa.JSON(), nullable=False),
+    sa.Column('link', sa.String(length=255), nullable=False),
+    sa.Column('sort', sa.Integer(), nullable=False),
+    sa.Column('status', sa.String(length=255), server_default=sa.text("'enabled'::character varying"), nullable=False),
+    sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.Column('language', sa.String(length=255), server_default=sa.text("'en-US'::character varying"), nullable=False),
+    sa.PrimaryKeyConstraint('id', name='exporler_banner_pkey')
+    )
+    op.create_table('trial_apps',
+    sa.Column('id', models.types.StringUUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
+    sa.Column('app_id', models.types.StringUUID(), nullable=False),
+    sa.Column('tenant_id', models.types.StringUUID(), nullable=False),
+    sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.Column('trial_limit', sa.Integer(), nullable=False),
+    sa.PrimaryKeyConstraint('id', name='trial_app_pkey'),
+    sa.UniqueConstraint('app_id', name='unique_trail_app_id')
+    )
+    with op.batch_alter_table('trial_apps', schema=None) as batch_op:
+        batch_op.create_index('trial_app_app_id_idx', ['app_id'], unique=False)
+        batch_op.create_index('trial_app_tenant_id_idx', ['tenant_id'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('trial_apps', schema=None) as batch_op:
+        batch_op.drop_index('trial_app_tenant_id_idx')
+        batch_op.drop_index('trial_app_app_id_idx')
+
+    op.drop_table('trial_apps')
+    op.drop_table('exporle_banners')
+    with op.batch_alter_table('account_trial_app_records', schema=None) as batch_op:
+        batch_op.drop_index('account_trial_app_record_app_id_idx')
+        batch_op.drop_index('account_trial_app_record_account_id_idx')
+
+    op.drop_table('account_trial_app_records')
+    # ### end Alembic commands ###

api/migrations/versions/2026_01_12_1358-562dcce7d77c_add_summaryindex_feature.py

@@ -0,0 +1,69 @@
+"""add SummaryIndex feature
+
+Revision ID: 562dcce7d77c
+Revises: 03ea244985ce
+Create Date: 2026-01-12 13:58:40.584802
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = '562dcce7d77c'
+down_revision = '03ea244985ce'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('document_segment_summary',
+    sa.Column('id', models.types.StringUUID(), nullable=False),
+    sa.Column('dataset_id', models.types.StringUUID(), nullable=False),
+    sa.Column('document_id', models.types.StringUUID(), nullable=False),
+    sa.Column('chunk_id', models.types.StringUUID(), nullable=False),
+    sa.Column('summary_content', models.types.LongText(), nullable=True),
+    sa.Column('summary_index_node_id', sa.String(length=255), nullable=True),
+    sa.Column('summary_index_node_hash', sa.String(length=255), nullable=True),
+    sa.Column('status', sa.String(length=32), server_default=sa.text("'generating'"), nullable=False),
+    sa.Column('error', models.types.LongText(), nullable=True),
+    sa.Column('enabled', sa.Boolean(), server_default=sa.text('true'), nullable=False),
+    sa.Column('disabled_at', sa.DateTime(), nullable=True),
+    sa.Column('disabled_by', models.types.StringUUID(), nullable=True),
+    sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
+    sa.PrimaryKeyConstraint('id', name='document_segment_summary_pkey')
+    )
+    with op.batch_alter_table('document_segment_summary', schema=None) as batch_op:
+        batch_op.create_index('document_segment_summary_chunk_id_idx', ['chunk_id'], unique=False)
+        batch_op.create_index('document_segment_summary_dataset_id_idx', ['dataset_id'], unique=False)
+        batch_op.create_index('document_segment_summary_document_id_idx', ['document_id'], unique=False)
+        batch_op.create_index('document_segment_summary_status_idx', ['status'], unique=False)
+
+    with op.batch_alter_table('datasets', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('summary_index_setting', models.types.AdjustedJSON(), nullable=True))
+
+    with op.batch_alter_table('documents', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('need_summary', sa.Boolean(), server_default=sa.text('false'), nullable=True))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('documents', schema=None) as batch_op:
+        batch_op.drop_column('need_summary')
+
+    with op.batch_alter_table('datasets', schema=None) as batch_op:
+        batch_op.drop_column('summary_index_setting')
+
+    with op.batch_alter_table('document_segment_summary', schema=None) as batch_op:
+        batch_op.drop_index('document_segment_summary_status_idx')
+        batch_op.drop_index('document_segment_summary_document_id_idx')
+        batch_op.drop_index('document_segment_summary_dataset_id_idx')
+        batch_op.drop_index('document_segment_summary_chunk_id_idx')
+
+    op.drop_table('document_segment_summary')
+    # ### end Alembic commands ###

api/models/__init__.py

@@ -35,6 +35,7 @@ from .enums import (
     WorkflowTriggerStatus,
 )
 from .model import (
+    AccountTrialAppRecord,
     ApiRequest,
     ApiToken,
     App,
@@ -47,8 +48,10 @@ from .model import (
     DatasetRetrieverResource,
     DifySetup,
     EndUser,
+    ExporleBanner,
     IconType,
     InstalledApp,
+    LLMGenerationDetail,
     Message,
     MessageAgentThought,
     MessageAnnotation,
@@ -62,6 +65,7 @@ from .model import (
     TagBinding,
     TenantCreditPool,
     TraceAppConfig,
+    TrialApp,
     UploadFile,
 )
 from .oauth import DatasourceOauthParamConfig, DatasourceProvider
@@ -114,6 +118,7 @@ __all__ = [
     "Account",
     "AccountIntegrate",
     "AccountStatus",
+    "AccountTrialAppRecord",
     "ApiRequest",
     "ApiToken",
     "ApiToolProvider",
@@ -150,11 +155,13 @@ __all__ = [
     "DocumentSegment",
     "Embedding",
     "EndUser",
+    "ExporleBanner",
     "ExternalKnowledgeApis",
     "ExternalKnowledgeBindings",
     "IconType",
     "InstalledApp",
     "InvitationCode",
+    "LLMGenerationDetail",
     "LoadBalancingModelConfig",
     "Message",
     "MessageAgentThought",
@@ -188,6 +195,7 @@ __all__ = [
     "ToolLabelBinding",
     "ToolModelInvoke",
     "TraceAppConfig",
+    "TrialApp",
     "TriggerOAuthSystemClient",
     "TriggerOAuthTenantClient",
     "TriggerSubscription",

api/models/dataset.py

@@ -72,6 +72,7 @@ class Dataset(Base):
     keyword_number = mapped_column(sa.Integer, nullable=True, server_default=sa.text("10"))
     collection_binding_id = mapped_column(StringUUID, nullable=True)
     retrieval_model = mapped_column(AdjustedJSON, nullable=True)
+    summary_index_setting = mapped_column(AdjustedJSON, nullable=True)
     built_in_field_enabled = mapped_column(sa.Boolean, nullable=False, server_default=sa.text("false"))
     icon_info = mapped_column(AdjustedJSON, nullable=True)
     runtime_mode = mapped_column(sa.String(255), nullable=True, server_default=sa.text("'general'"))
@@ -419,6 +420,7 @@ class Document(Base):
     doc_metadata = mapped_column(AdjustedJSON, nullable=True)
     doc_form = mapped_column(String(255), nullable=False, server_default=sa.text("'text_model'"))
     doc_language = mapped_column(String(255), nullable=True)
+    need_summary: Mapped[bool | None] = mapped_column(sa.Boolean, nullable=True, server_default=sa.text("false"))
 
     DATA_SOURCES = ["upload_file", "notion_import", "website_crawl"]
 
@@ -1567,3 +1569,34 @@ class SegmentAttachmentBinding(Base):
     segment_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
     attachment_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
     created_at: Mapped[datetime] = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+
+
+class DocumentSegmentSummary(Base):
+    __tablename__ = "document_segment_summary"
+    __table_args__ = (
+        sa.PrimaryKeyConstraint("id", name="document_segment_summary_pkey"),
+        sa.Index("document_segment_summary_dataset_id_idx", "dataset_id"),
+        sa.Index("document_segment_summary_document_id_idx", "document_id"),
+        sa.Index("document_segment_summary_chunk_id_idx", "chunk_id"),
+        sa.Index("document_segment_summary_status_idx", "status"),
+    )
+
+    id: Mapped[str] = mapped_column(StringUUID, nullable=False, default=lambda: str(uuid4()))
+    dataset_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
+    document_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
+    # corresponds to DocumentSegment.id or parent chunk id
+    chunk_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
+    summary_content: Mapped[str] = mapped_column(LongText, nullable=True)
+    summary_index_node_id: Mapped[str] = mapped_column(String(255), nullable=True)
+    summary_index_node_hash: Mapped[str] = mapped_column(String(255), nullable=True)
+    status: Mapped[str] = mapped_column(String(32), nullable=False, server_default=sa.text("'generating'"))
+    error: Mapped[str] = mapped_column(LongText, nullable=True)
+    enabled: Mapped[bool] = mapped_column(sa.Boolean, nullable=False, server_default=sa.text("true"))
+    disabled_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
+    disabled_by = mapped_column(StringUUID, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.current_timestamp())
+    updated_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.current_timestamp(), onupdate=func.current_timestamp())
+
+    def __repr__(self):
+        return f"<DocumentSegmentSummary id={self.id} chunk_id={self.chunk_id} status={self.status}>"
+

api/models/model.py

@@ -33,6 +33,8 @@ from .provider_ids import GenericProviderID
 from .types import LongText, StringUUID
 
 if TYPE_CHECKING:
+    from core.app.entities.llm_generation_entities import LLMGenerationDetailData
+
     from .workflow import Workflow
 
 
@@ -603,6 +605,64 @@ class InstalledApp(TypeBase):
         return tenant
 
 
+class TrialApp(Base):
+    __tablename__ = "trial_apps"
+    __table_args__ = (
+        sa.PrimaryKeyConstraint("id", name="trial_app_pkey"),
+        sa.Index("trial_app_app_id_idx", "app_id"),
+        sa.Index("trial_app_tenant_id_idx", "tenant_id"),
+        sa.UniqueConstraint("app_id", name="unique_trail_app_id"),
+    )
+
+    id = mapped_column(StringUUID, server_default=sa.text("uuid_generate_v4()"))
+    app_id = mapped_column(StringUUID, nullable=False)
+    tenant_id = mapped_column(StringUUID, nullable=False)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    trial_limit = mapped_column(sa.Integer, nullable=False, default=3)
+
+    @property
+    def app(self) -> App | None:
+        app = db.session.query(App).where(App.id == self.app_id).first()
+        return app
+
+
+class AccountTrialAppRecord(Base):
+    __tablename__ = "account_trial_app_records"
+    __table_args__ = (
+        sa.PrimaryKeyConstraint("id", name="user_trial_app_pkey"),
+        sa.Index("account_trial_app_record_account_id_idx", "account_id"),
+        sa.Index("account_trial_app_record_app_id_idx", "app_id"),
+        sa.UniqueConstraint("account_id", "app_id", name="unique_account_trial_app_record"),
+    )
+    id = mapped_column(StringUUID, server_default=sa.text("uuid_generate_v4()"))
+    account_id = mapped_column(StringUUID, nullable=False)
+    app_id = mapped_column(StringUUID, nullable=False)
+    count = mapped_column(sa.Integer, nullable=False, default=0)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+
+    @property
+    def app(self) -> App | None:
+        app = db.session.query(App).where(App.id == self.app_id).first()
+        return app
+
+    @property
+    def user(self) -> Account | None:
+        user = db.session.query(Account).where(Account.id == self.account_id).first()
+        return user
+
+
+class ExporleBanner(Base):
+    __tablename__ = "exporle_banners"
+    __table_args__ = (sa.PrimaryKeyConstraint("id", name="exporler_banner_pkey"),)
+    id = mapped_column(StringUUID, server_default=sa.text("uuid_generate_v4()"))
+    content = mapped_column(sa.JSON, nullable=False)
+    link = mapped_column(String(255), nullable=False)
+    sort = mapped_column(sa.Integer, nullable=False)
+    status = mapped_column(sa.String(255), nullable=False, server_default=sa.text("'enabled'::character varying"))
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    language = mapped_column(String(255), nullable=False, server_default=sa.text("'en-US'::character varying"))
+
+
 class OAuthProviderApp(TypeBase):
     """
     Globally shared OAuth provider app information.
@@ -1204,6 +1264,18 @@ class Message(Base):
             .all()
         )
 
+    # FIXME (Novice) -- It's easy to cause N+1 query problem here.
+    @property
+    def generation_detail(self) -> dict[str, Any] | None:
+        """
+        Get LLM generation detail for this message.
+        Returns the detail as a dictionary or None if not found.
+        """
+        detail = db.session.query(LLMGenerationDetail).filter_by(message_id=self.id).first()
+        if detail:
+            return detail.to_dict()
+        return None
+
     @property
     def retriever_resources(self) -> Any:
         return self.message_metadata_dict.get("retriever_resources") if self.message_metadata else []
@@ -2107,3 +2179,87 @@ class TenantCreditPool(Base):
 
     def has_sufficient_credits(self, required_credits: int) -> bool:
         return self.remaining_credits >= required_credits
+
+
+class LLMGenerationDetail(Base):
+    """
+    Store LLM generation details including reasoning process and tool calls.
+
+    Association (choose one):
+    - For apps with Message: use message_id (one-to-one)
+    - For Workflow: use workflow_run_id + node_id (one run may have multiple LLM nodes)
+    """
+
+    __tablename__ = "llm_generation_details"
+    __table_args__ = (
+        sa.PrimaryKeyConstraint("id", name="llm_generation_detail_pkey"),
+        sa.Index("idx_llm_generation_detail_message", "message_id"),
+        sa.Index("idx_llm_generation_detail_workflow", "workflow_run_id", "node_id"),
+        sa.CheckConstraint(
+            "(message_id IS NOT NULL AND workflow_run_id IS NULL AND node_id IS NULL)"
+            " OR "
+            "(message_id IS NULL AND workflow_run_id IS NOT NULL AND node_id IS NOT NULL)",
+            name="ck_llm_generation_detail_assoc_mode",
+        ),
+    )
+
+    id: Mapped[str] = mapped_column(StringUUID, default=lambda: str(uuid4()))
+    tenant_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
+    app_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
+
+    # Association fields (choose one)
+    message_id: Mapped[str | None] = mapped_column(StringUUID, nullable=True, unique=True)
+    workflow_run_id: Mapped[str | None] = mapped_column(StringUUID, nullable=True)
+    node_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
+
+    # Core data as JSON strings
+    reasoning_content: Mapped[str | None] = mapped_column(LongText)
+    tool_calls: Mapped[str | None] = mapped_column(LongText)
+    sequence: Mapped[str | None] = mapped_column(LongText)
+
+    created_at: Mapped[datetime] = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+
+    def to_domain_model(self) -> LLMGenerationDetailData:
+        """Convert to Pydantic domain model with proper validation."""
+        from core.app.entities.llm_generation_entities import LLMGenerationDetailData
+
+        return LLMGenerationDetailData(
+            reasoning_content=json.loads(self.reasoning_content) if self.reasoning_content else [],
+            tool_calls=json.loads(self.tool_calls) if self.tool_calls else [],
+            sequence=json.loads(self.sequence) if self.sequence else [],
+        )
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for API response."""
+        return self.to_domain_model().to_response_dict()
+
+    @classmethod
+    def from_domain_model(
+        cls,
+        data: LLMGenerationDetailData,
+        *,
+        tenant_id: str,
+        app_id: str,
+        message_id: str | None = None,
+        workflow_run_id: str | None = None,
+        node_id: str | None = None,
+    ) -> LLMGenerationDetail:
+        """Create from Pydantic domain model."""
+        # Enforce association mode at object creation time as well.
+        message_mode = message_id is not None
+        workflow_mode = workflow_run_id is not None or node_id is not None
+        if message_mode and workflow_mode:
+            raise ValueError("LLMGenerationDetail cannot set both message_id and workflow_run_id/node_id.")
+        if not message_mode and not (workflow_run_id and node_id):
+            raise ValueError("LLMGenerationDetail requires either message_id or workflow_run_id+node_id.")
+
+        return cls(
+            tenant_id=tenant_id,
+            app_id=app_id,
+            message_id=message_id,
+            workflow_run_id=workflow_run_id,
+            node_id=node_id,
+            reasoning_content=json.dumps(data.reasoning_content) if data.reasoning_content else None,
+            tool_calls=json.dumps([tc.model_dump() for tc in data.tool_calls]) if data.tool_calls else None,
+            sequence=json.dumps([seg.model_dump() for seg in data.sequence]) if data.sequence else None,
+        )

api/models/workflow.py

@@ -59,6 +59,37 @@ from .types import EnumText, LongText, StringUUID
 logger = logging.getLogger(__name__)
 
 
+def is_generation_outputs(outputs: Mapping[str, Any]) -> bool:
+    if not outputs:
+        return False
+
+    allowed_sequence_types = {"reasoning", "content", "tool_call"}
+
+    def valid_sequence_item(item: Mapping[str, Any]) -> bool:
+        return isinstance(item, Mapping) and item.get("type") in allowed_sequence_types
+
+    def valid_value(value: Any) -> bool:
+        if not isinstance(value, Mapping):
+            return False
+
+        content = value.get("content")
+        reasoning_content = value.get("reasoning_content")
+        tool_calls = value.get("tool_calls")
+        sequence = value.get("sequence")
+
+        return (
+            isinstance(content, str)
+            and isinstance(reasoning_content, list)
+            and all(isinstance(item, str) for item in reasoning_content)
+            and isinstance(tool_calls, list)
+            and all(isinstance(item, Mapping) for item in tool_calls)
+            and isinstance(sequence, list)
+            and all(valid_sequence_item(item) for item in sequence)
+        )
+
+    return all(valid_value(value) for value in outputs.values())
+
+
 class WorkflowType(StrEnum):
     """
     Workflow Type Enum
@@ -667,6 +698,10 @@ class WorkflowRun(Base):
     def workflow(self):
         return db.session.query(Workflow).where(Workflow.id == self.workflow_id).first()
 
+    @property
+    def outputs_as_generation(self):
+        return is_generation_outputs(self.outputs_dict)
+
     def to_dict(self):
         return {
             "id": self.id,
@@ -680,6 +715,7 @@ class WorkflowRun(Base):
             "inputs": self.inputs_dict,
             "status": self.status,
             "outputs": self.outputs_dict,
+            "outputs_as_generation": self.outputs_as_generation,
             "error": self.error,
             "elapsed_time": self.elapsed_time,
             "total_tokens": self.total_tokens,

api/services/annotation_service.py

@@ -355,7 +355,6 @@ class AppAnnotationService:
     def batch_import_app_annotations(cls, app_id, file: FileStorage):
         """
         Batch import annotations from CSV file with enhanced security checks.
-
         Security features:
         - File size validation
         - Row count limits (min/max)
@@ -364,7 +363,6 @@ class AppAnnotationService:
         - Concurrency tracking
         """
         from configs import dify_config
-
         # get app info
         current_user, current_tenant_id = current_account_with_tenant()
         app = (
@@ -448,27 +446,31 @@ class AppAnnotationService:
                     f"The CSV file must contain at least {min_records} valid annotation record(s). "
                     f"Found {len(result)} valid record(s)."
                 )
-
             # Check annotation quota limit
             features = FeatureService.get_features(current_tenant_id)
             if features.billing.enabled:
                 annotation_quota_limit = features.annotation_quota_limit
                 if annotation_quota_limit.limit < len(result) + annotation_quota_limit.size:
-                    raise ValueError("The number of annotations exceeds the limit of your subscription.")
+                    raise ValueError(
-            # async job
+                        f"The number of annotations ({len(result)}) would exceed your subscription limit. "
+                        f"Current usage: {annotation_quota_limit.size}/{annotation_quota_limit.limit}. "
+                        f"Available: {annotation_quota_limit.limit - annotation_quota_limit.size}."
+                    )
+
+            # Create async job
             job_id = str(uuid.uuid4())
             indexing_cache_key = f"app_annotation_batch_import_{str(job_id)}"
-
             # Register job in active tasks list for concurrency tracking
             current_time = int(naive_utc_now().timestamp() * 1000)
             active_jobs_key = f"annotation_import_active:{current_tenant_id}"
             redis_client.zadd(active_jobs_key, {job_id: current_time})
             redis_client.expire(active_jobs_key, 7200)  # 2 hours TTL
-
             # Set job status
             redis_client.setnx(indexing_cache_key, "waiting")
-            batch_import_annotations_task.delay(str(job_id), result, app_id, current_tenant_id, current_user.id)
+            redis_client.expire(indexing_cache_key, 3600)  # 1 hour TTL
 
+            # Send batch import task
+            batch_import_annotations_task.delay(str(job_id), result, app_id, current_tenant_id, current_user.id)
         except ValueError as e:
             return {"error_msg": str(e)}
         except Exception as e:

api/services/billing_service.py

@@ -44,6 +44,33 @@ class BillingService:
         billing_info = cls._send_request("GET", "/subscription/info", params=params)
         return billing_info
 
+    @classmethod
+    def get_info_bulk(cls, tenant_ids: Sequence[str]) -> dict[str, str]:
+        """
+        Bulk billing info fetch via billing API.
+
+        Payload: {"tenant_ids": ["t1", "t2", ...]} (max 200 per request)
+
+        Returns:
+            Mapping of tenant_id -> plan
+        """
+        results: dict[str, str] = {}
+
+        chunk_size = 200
+        for i in range(0, len(tenant_ids), chunk_size):
+            chunk = tenant_ids[i : i + chunk_size]
+            try:
+                resp = cls._send_request("POST", "/subscription/plan/batch", json={"tenant_ids": chunk})
+                data = resp.get("data", {})
+                for tenant_id, plan in data.items():
+                    if isinstance(plan, str):
+                        results[tenant_id] = plan
+            except Exception:
+                logger.exception("Failed to fetch billing info batch for tenants: %s", chunk)
+                continue
+
+        return results
+
     @classmethod
     def get_tenant_feature_plan_usage_info(cls, tenant_id: str):
         params = {"tenant_id": tenant_id}

api/services/clear_free_plan_expired_workflow_run_logs.py

@@ -0,0 +1,171 @@
+import datetime
+import logging
+from collections.abc import Iterable, Sequence
+
+import click
+from sqlalchemy.orm import Session, sessionmaker
+
+from configs import dify_config
+from enums.cloud_plan import CloudPlan
+from extensions.ext_database import db
+from models.workflow import WorkflowRun
+from repositories.api_workflow_run_repository import APIWorkflowRunRepository
+from repositories.sqlalchemy_api_workflow_node_execution_repository import (
+    DifyAPISQLAlchemyWorkflowNodeExecutionRepository,
+)
+from repositories.sqlalchemy_workflow_trigger_log_repository import SQLAlchemyWorkflowTriggerLogRepository
+from services.billing_service import BillingService
+
+logger = logging.getLogger(__name__)
+
+
+class WorkflowRunCleanup:
+    def __init__(
+        self,
+        days: int,
+        batch_size: int,
+        start_after: datetime.datetime | None = None,
+        end_before: datetime.datetime | None = None,
+        workflow_run_repo: APIWorkflowRunRepository | None = None,
+    ):
+        if (start_after is None) ^ (end_before is None):
+            raise ValueError("start_after and end_before must be both set or both omitted.")
+
+        computed_cutoff = datetime.datetime.now() - datetime.timedelta(days=days)
+        self.window_start = start_after
+        self.window_end = end_before or computed_cutoff
+
+        if self.window_start and self.window_end <= self.window_start:
+            raise ValueError("end_before must be greater than start_after.")
+
+        self.batch_size = batch_size
+        self.billing_cache: dict[str, CloudPlan | None] = {}
+        self.workflow_run_repo: APIWorkflowRunRepository
+        if workflow_run_repo:
+            self.workflow_run_repo = workflow_run_repo
+        else:
+            # Lazy import to avoid circular dependencies during module import
+            from repositories.factory import DifyAPIRepositoryFactory
+
+            session_maker = sessionmaker(bind=db.engine, expire_on_commit=False)
+            self.workflow_run_repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
+
+    def run(self) -> None:
+        click.echo(
+            click.style(
+                f"Cleaning workflow runs "
+                f"{'between ' + self.window_start.isoformat() + ' and ' if self.window_start else 'before '}"
+                f"{self.window_end.isoformat()} (batch={self.batch_size})",
+                fg="white",
+            )
+        )
+
+        total_runs_deleted = 0
+        batch_index = 0
+        last_seen: tuple[datetime.datetime, str] | None = None
+
+        while True:
+            run_rows = self.workflow_run_repo.get_runs_batch_by_time_range(
+                start_after=self.window_start,
+                end_before=self.window_end,
+                last_seen=last_seen,
+                batch_size=self.batch_size,
+            )
+            if not run_rows:
+                break
+
+            batch_index += 1
+            last_seen = (run_rows[-1].created_at, run_rows[-1].id)
+            tenant_ids = {row.tenant_id for row in run_rows}
+            free_tenants = self._filter_free_tenants(tenant_ids)
+            free_runs = [row for row in run_rows if row.tenant_id in free_tenants]
+            paid_or_skipped = len(run_rows) - len(free_runs)
+
+            if not free_runs:
+                click.echo(
+                    click.style(
+                        f"[batch #{batch_index}] skipped (no sandbox runs in batch, {paid_or_skipped} paid/unknown)",
+                        fg="yellow",
+                    )
+                )
+                continue
+
+            try:
+                counts = self.workflow_run_repo.delete_runs_with_related(
+                    free_runs,
+                    delete_node_executions=self._delete_node_executions,
+                    delete_trigger_logs=self._delete_trigger_logs,
+                )
+            except Exception:
+                logger.exception("Failed to delete workflow runs batch ending at %s", last_seen[0])
+                raise
+
+            total_runs_deleted += counts["runs"]
+            click.echo(
+                click.style(
+                    f"[batch #{batch_index}] deleted runs: {counts['runs']} "
+                    f"(nodes {counts['node_executions']}, offloads {counts['offloads']}, "
+                    f"app_logs {counts['app_logs']}, trigger_logs {counts['trigger_logs']}, "
+                    f"pauses {counts['pauses']}, pause_reasons {counts['pause_reasons']}); "
+                    f"skipped {paid_or_skipped} paid/unknown",
+                    fg="green",
+                )
+            )
+
+        if self.window_start:
+            summary_message = (
+                f"Cleanup complete. Deleted {total_runs_deleted} workflow runs "
+                f"between {self.window_start.isoformat()} and {self.window_end.isoformat()}"
+            )
+        else:
+            summary_message = (
+                f"Cleanup complete. Deleted {total_runs_deleted} workflow runs before {self.window_end.isoformat()}"
+            )
+
+        click.echo(click.style(summary_message, fg="white"))
+
+    def _filter_free_tenants(self, tenant_ids: Iterable[str]) -> set[str]:
+        if not dify_config.BILLING_ENABLED:
+            return set(tenant_ids)
+
+        tenant_id_list = list(tenant_ids)
+        uncached_tenants = [tenant_id for tenant_id in tenant_id_list if tenant_id not in self.billing_cache]
+
+        if uncached_tenants:
+            try:
+                bulk_info = BillingService.get_info_bulk(uncached_tenants)
+            except Exception:
+                bulk_info = {}
+                logger.exception("Failed to fetch billing plans in bulk for tenants: %s", uncached_tenants)
+
+            for tenant_id in uncached_tenants:
+                plan: CloudPlan | None = None
+                info = bulk_info.get(tenant_id)
+                if info:
+                    try:
+                        plan = CloudPlan(info)
+                    except Exception:
+                        logger.exception("Failed to parse billing plan for tenant %s", tenant_id)
+                else:
+                    logger.warning("Missing billing info for tenant %s in bulk resp; treating as non-free", tenant_id)
+
+                self.billing_cache[tenant_id] = plan
+
+        return {tenant_id for tenant_id in tenant_id_list if self.billing_cache.get(tenant_id) == CloudPlan.SANDBOX}
+
+    def _delete_trigger_logs(self, session: Session, run_ids: Sequence[str]) -> int:
+        trigger_repo = SQLAlchemyWorkflowTriggerLogRepository(session)
+        return trigger_repo.delete_by_run_ids(run_ids)
+
+    def _delete_node_executions(self, session: Session, runs: Sequence[WorkflowRun]) -> tuple[int, int]:
+        run_contexts: list[DifyAPISQLAlchemyWorkflowNodeExecutionRepository.RunContext] = [
+            {
+                "run_id": run.id,
+                "tenant_id": run.tenant_id,
+                "app_id": run.app_id,
+                "workflow_id": run.workflow_id,
+                "triggered_from": run.triggered_from,
+            }
+            for run in runs
+        ]
+        return DifyAPISQLAlchemyWorkflowNodeExecutionRepository.delete_by_runs(session, run_contexts)

api/services/dataset_service.py

@@ -89,6 +89,7 @@ from tasks.enable_segments_to_index_task import enable_segments_to_index_task
 from tasks.recover_document_indexing_task import recover_document_indexing_task
 from tasks.remove_document_from_index_task import remove_document_from_index_task
 from tasks.retry_document_indexing_task import retry_document_indexing_task
+from tasks.regenerate_summary_index_task import regenerate_summary_index_task
 from tasks.sync_website_document_indexing_task import sync_website_document_indexing_task
 
 logger = logging.getLogger(__name__)
@@ -474,6 +475,11 @@ class DatasetService:
         if external_retrieval_model:
             dataset.retrieval_model = external_retrieval_model
 
+        # Update summary index setting if provided
+        summary_index_setting = data.get("summary_index_setting", None)
+        if summary_index_setting is not None:
+            dataset.summary_index_setting = summary_index_setting
+
         # Update basic dataset properties
         dataset.name = data.get("name", dataset.name)
         dataset.description = data.get("description", dataset.description)
@@ -556,12 +562,20 @@ class DatasetService:
         # Handle indexing technique changes and embedding model updates
         action = DatasetService._handle_indexing_technique_change(dataset, data, filtered_data)
 
+        # Check if summary_index_setting model changed (before updating database)
+        summary_model_changed = DatasetService._check_summary_index_setting_model_changed(
+            dataset, data
+        )
+
         # Add metadata fields
         filtered_data["updated_by"] = user.id
         filtered_data["updated_at"] = naive_utc_now()
         # update Retrieval model
         if data.get("retrieval_model"):
             filtered_data["retrieval_model"] = data["retrieval_model"]
+        # update summary index setting
+        if data.get("summary_index_setting"):
+            filtered_data["summary_index_setting"] = data.get("summary_index_setting")
         # update icon info
         if data.get("icon_info"):
             filtered_data["icon_info"] = data.get("icon_info")
@@ -570,12 +584,30 @@ class DatasetService:
         db.session.query(Dataset).filter_by(id=dataset.id).update(filtered_data)
         db.session.commit()
 
+        # Reload dataset to get updated values
+        db.session.refresh(dataset)
+
         # update pipeline knowledge base node data
         DatasetService._update_pipeline_knowledge_base_node_data(dataset, user.id)
 
         # Trigger vector index task if indexing technique changed
         if action:
             deal_dataset_vector_index_task.delay(dataset.id, action)
+            # If embedding_model changed, also regenerate summary vectors
+            if action == "update":
+                regenerate_summary_index_task.delay(
+                    dataset.id,
+                    regenerate_reason="embedding_model_changed",
+                    regenerate_vectors_only=True,
+                )
+
+        # Trigger summary index regeneration if summary model changed
+        if summary_model_changed:
+            regenerate_summary_index_task.delay(
+                dataset.id,
+                regenerate_reason="summary_model_changed",
+                regenerate_vectors_only=False,
+            )
 
         return dataset
 
@@ -614,6 +646,7 @@ class DatasetService:
                             knowledge_index_node_data["chunk_structure"] = dataset.chunk_structure
                             knowledge_index_node_data["indexing_technique"] = dataset.indexing_technique  # pyright: ignore[reportAttributeAccessIssue]
                             knowledge_index_node_data["keyword_number"] = dataset.keyword_number
+                            knowledge_index_node_data["summary_index_setting"] = dataset.summary_index_setting
                             node["data"] = knowledge_index_node_data
                             updated = True
                         except Exception:
@@ -852,6 +885,49 @@ class DatasetService:
         )
         filtered_data["collection_binding_id"] = dataset_collection_binding.id
 
+    @staticmethod
+    def _check_summary_index_setting_model_changed(dataset: Dataset, data: dict[str, Any]) -> bool:
+        """
+        Check if summary_index_setting model (model_name or model_provider_name) has changed.
+
+        Args:
+            dataset: Current dataset object
+            data: Update data dictionary
+
+        Returns:
+            bool: True if summary model changed, False otherwise
+        """
+        # Check if summary_index_setting is being updated
+        if "summary_index_setting" not in data or data.get("summary_index_setting") is None:
+            return False
+
+        new_summary_setting = data.get("summary_index_setting")
+        old_summary_setting = dataset.summary_index_setting
+
+        # If old setting doesn't exist or is disabled, no need to regenerate
+        if not old_summary_setting or not old_summary_setting.get("enable"):
+            return False
+
+        # If new setting is disabled, no need to regenerate
+        if not new_summary_setting or not new_summary_setting.get("enable"):
+            return False
+
+        # Compare model_name and model_provider_name
+        old_model_name = old_summary_setting.get("model_name")
+        old_model_provider = old_summary_setting.get("model_provider_name")
+        new_model_name = new_summary_setting.get("model_name")
+        new_model_provider = new_summary_setting.get("model_provider_name")
+
+        # Check if model changed
+        if old_model_name != new_model_name or old_model_provider != new_model_provider:
+            logger.info(
+                f"Summary index setting model changed for dataset {dataset.id}: "
+                f"old={old_model_provider}/{old_model_name}, new={new_model_provider}/{new_model_name}"
+            )
+            return True
+
+        return False
+
     @staticmethod
     def update_rag_pipeline_dataset_settings(
         session: Session, dataset: Dataset, knowledge_configuration: KnowledgeConfiguration, has_published: bool = False
@@ -1824,6 +1900,8 @@ class DocumentService:
                         DuplicateDocumentIndexingTaskProxy(
                             dataset.tenant_id, dataset.id, duplicate_document_ids
                         ).delay()
+                    # Note: Summary index generation is triggered in document_indexing_task after indexing completes
+                    # to ensure segments are available. See tasks/document_indexing_task.py
             except LockNotOwnedError:
                 pass
 
@@ -2128,6 +2206,14 @@ class DocumentService:
         name: str,
         batch: str,
     ):
+        # Set need_summary based on dataset's summary_index_setting
+        need_summary = False
+        if (
+            dataset.summary_index_setting
+            and dataset.summary_index_setting.get("enable") is True
+        ):
+            need_summary = True
+        
         document = Document(
             tenant_id=dataset.tenant_id,
             dataset_id=dataset.id,
@@ -2141,6 +2227,7 @@ class DocumentService:
             created_by=account.id,
             doc_form=document_form,
             doc_language=document_language,
+            need_summary=need_summary,
         )
         doc_metadata = {}
         if dataset.built_in_field_enabled:
@@ -2365,6 +2452,7 @@ class DocumentService:
             embedding_model_provider=knowledge_config.embedding_model_provider,
             collection_binding_id=dataset_collection_binding_id,
             retrieval_model=retrieval_model.model_dump() if retrieval_model else None,
+            summary_index_setting=knowledge_config.summary_index_setting,
             is_multimodal=knowledge_config.is_multimodal,
         )
 
@@ -2546,6 +2634,14 @@ class DocumentService:
             if not isinstance(args["process_rule"]["rules"]["segmentation"]["max_tokens"], int):
                 raise ValueError("Process rule segmentation max_tokens is invalid")
 
+        # valid summary index setting
+        if args["process_rule"]["summary_index_setting"] and args["process_rule"]["summary_index_setting"]["enable"]:
+            summary_index_setting = args["process_rule"]["summary_index_setting"]
+            if "model_name" not in summary_index_setting or not summary_index_setting["model_name"]:
+                raise ValueError("Summary index model name is required")
+            if "model_provider_name" not in summary_index_setting or not summary_index_setting["model_provider_name"]:
+                raise ValueError("Summary index model provider name is required")
+
     @staticmethod
     def batch_update_document_status(
         dataset: Dataset, document_ids: list[str], action: Literal["enable", "disable", "archive", "un_archive"], user
@@ -3014,6 +3110,37 @@ class SegmentService:
                     if args.enabled or keyword_changed:
                         # update segment vector index
                         VectorService.update_segment_vector(args.keywords, segment, dataset)
+                # update summary index if summary is provided and has changed
+                if args.summary is not None:
+                    # Check if summary index is enabled
+                    has_summary_index = (
+                        dataset.indexing_technique == "high_quality"
+                        and dataset.summary_index_setting
+                        and dataset.summary_index_setting.get("enable") is True
+                    )
+                    
+                    if has_summary_index:
+                        # Query existing summary from database
+                        from models.dataset import DocumentSegmentSummary
+                        existing_summary = (
+                            db.session.query(DocumentSegmentSummary)
+                            .where(
+                                DocumentSegmentSummary.chunk_id == segment.id,
+                                DocumentSegmentSummary.dataset_id == dataset.id,
+                            )
+                            .first()
+                        )
+                        
+                        # Check if summary has changed
+                        existing_summary_content = existing_summary.summary_content if existing_summary else None
+                        if existing_summary_content != args.summary:
+                            # Summary has changed, update it
+                            from services.summary_index_service import SummaryIndexService
+                            try:
+                                SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
+                            except Exception as e:
+                                logger.exception(f"Failed to update summary for segment {segment.id}: {str(e)}")
+                                # Don't fail the entire update if summary update fails
             else:
                 segment_hash = helper.generate_text_hash(content)
                 tokens = 0
@@ -3088,6 +3215,15 @@ class SegmentService:
                 elif document.doc_form in (IndexStructureType.PARAGRAPH_INDEX, IndexStructureType.QA_INDEX):
                     # update segment vector index
                     VectorService.update_segment_vector(args.keywords, segment, dataset)
+            # update summary index if summary is provided
+            if args.summary is not None:
+                from services.summary_index_service import SummaryIndexService
+
+                try:
+                    SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
+                except Exception as e:
+                    logger.exception(f"Failed to update summary for segment {segment.id}: {str(e)}")
+                    # Don't fail the entire update if summary update fails
             # update multimodel vector index
             VectorService.update_multimodel_vector(segment, args.attachment_ids or [], dataset)
         except Exception as e:

api/services/entities/knowledge_entities/knowledge_entities.py

@@ -119,6 +119,7 @@ class KnowledgeConfig(BaseModel):
     data_source: DataSource | None = None
     process_rule: ProcessRule | None = None
     retrieval_model: RetrievalModel | None = None
+    summary_index_setting: dict | None = None
     doc_form: str = "text_model"
     doc_language: str = "English"
     embedding_model: str | None = None
@@ -141,6 +142,7 @@ class SegmentUpdateArgs(BaseModel):
     regenerate_child_chunks: bool = False
     enabled: bool | None = None
     attachment_ids: list[str] | None = None
+    summary: str | None = None  # Summary content for summary index
 
 
 class ChildChunkUpdateArgs(BaseModel):

api/services/feature_service.py

@@ -170,6 +170,8 @@ class SystemFeatureModel(BaseModel):
     plugin_installation_permission: PluginInstallationPermissionModel = PluginInstallationPermissionModel()
     enable_change_email: bool = True
     plugin_manager: PluginManagerModel = PluginManagerModel()
+    enable_trial_app: bool = False
+    enable_explore_banner: bool = False
 
 
 class FeatureService:
@@ -225,6 +227,8 @@ class FeatureService:
         system_features.is_allow_register = dify_config.ALLOW_REGISTER
         system_features.is_allow_create_workspace = dify_config.ALLOW_CREATE_WORKSPACE
         system_features.is_email_setup = dify_config.MAIL_TYPE is not None and dify_config.MAIL_TYPE != ""
+        system_features.enable_trial_app = dify_config.ENABLE_TRIAL_APP
+        system_features.enable_explore_banner = dify_config.ENABLE_EXPLORE_BANNER
 
     @classmethod
     def _fulfill_params_from_env(cls, features: FeatureModel):

api/services/llm_generation_service.py

@@ -0,0 +1,37 @@
+"""
+LLM Generation Detail Service.
+
+Provides methods to query and attach generation details to workflow node executions
+and messages, avoiding N+1 query problems.
+"""
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from core.app.entities.llm_generation_entities import LLMGenerationDetailData
+from models import LLMGenerationDetail
+
+
+class LLMGenerationService:
+    """Service for handling LLM generation details."""
+
+    def __init__(self, session: Session):
+        self._session = session
+
+    def get_generation_detail_for_message(self, message_id: str) -> LLMGenerationDetailData | None:
+        """Query generation detail for a specific message."""
+        stmt = select(LLMGenerationDetail).where(LLMGenerationDetail.message_id == message_id)
+        detail = self._session.scalars(stmt).first()
+        return detail.to_domain_model() if detail else None
+
+    def get_generation_details_for_messages(
+        self,
+        message_ids: list[str],
+    ) -> dict[str, LLMGenerationDetailData]:
+        """Batch query generation details for multiple messages."""
+        if not message_ids:
+            return {}
+
+        stmt = select(LLMGenerationDetail).where(LLMGenerationDetail.message_id.in_(message_ids))
+        details = self._session.scalars(stmt).all()
+        return {detail.message_id: detail.to_domain_model() for detail in details if detail.message_id}

api/services/recommended_app_service.py

@@ -1,4 +1,7 @@
 from configs import dify_config
+from extensions.ext_database import db
+from models.model import AccountTrialAppRecord, TrialApp
+from services.feature_service import FeatureService
 from services.recommend_app.recommend_app_factory import RecommendAppRetrievalFactory
 
 
@@ -20,6 +23,15 @@ class RecommendedAppService:
                 )
             )
 
+        if FeatureService.get_system_features().enable_trial_app:
+            apps = result["recommended_apps"]
+            for app in apps:
+                app_id = app["app_id"]
+                trial_app_model = db.session.query(TrialApp).where(TrialApp.app_id == app_id).first()
+                if trial_app_model:
+                    app["can_trial"] = True
+                else:
+                    app["can_trial"] = False
         return result
 
     @classmethod
@@ -32,4 +44,30 @@ class RecommendedAppService:
         mode = dify_config.HOSTED_FETCH_APP_TEMPLATES_MODE
         retrieval_instance = RecommendAppRetrievalFactory.get_recommend_app_factory(mode)()
         result: dict = retrieval_instance.get_recommend_app_detail(app_id)
+        if FeatureService.get_system_features().enable_trial_app:
+            app_id = result["id"]
+            trial_app_model = db.session.query(TrialApp).where(TrialApp.app_id == app_id).first()
+            if trial_app_model:
+                result["can_trial"] = True
+            else:
+                result["can_trial"] = False
         return result
+
+    @classmethod
+    def add_trial_app_record(cls, app_id: str, account_id: str):
+        """
+        Add trial app record.
+        :param app_id: app id
+        :return:
+        """
+        account_trial_app_record = (
+            db.session.query(AccountTrialAppRecord)
+            .where(AccountTrialAppRecord.app_id == app_id, AccountTrialAppRecord.account_id == account_id)
+            .first()
+        )
+        if account_trial_app_record:
+            account_trial_app_record.count += 1
+            db.session.commit()
+        else:
+            db.session.add(AccountTrialAppRecord(app_id=app_id, count=1, account_id=account_id))
+            db.session.commit()

api/services/summary_index_service.py

@@ -0,0 +1,612 @@
+"""Summary index service for generating and managing document segment summaries."""
+
+import logging
+import time
+import uuid
+from typing import Any
+
+from core.rag.datasource.vdb.vector_factory import Vector
+from core.rag.index_processor.constant.doc_type import DocType
+from core.rag.models.document import Document
+from extensions.ext_database import db
+from libs import helper
+from models.dataset import Dataset, DocumentSegment, DocumentSegmentSummary
+from models.dataset import Document as DatasetDocument
+
+logger = logging.getLogger(__name__)
+
+
+class SummaryIndexService:
+    """Service for generating and managing summary indexes."""
+
+    @staticmethod
+    def generate_summary_for_segment(
+        segment: DocumentSegment,
+        dataset: Dataset,
+        summary_index_setting: dict,
+    ) -> str:
+        """
+        Generate summary for a single segment.
+
+        Args:
+            segment: DocumentSegment to generate summary for
+            dataset: Dataset containing the segment
+            summary_index_setting: Summary index configuration
+
+        Returns:
+            Generated summary text
+
+        Raises:
+            ValueError: If summary_index_setting is invalid or generation fails
+        """
+        # Reuse the existing generate_summary method from ParagraphIndexProcessor
+        # Use lazy import to avoid circular import
+        from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+
+        summary_content = ParagraphIndexProcessor.generate_summary(
+            tenant_id=dataset.tenant_id,
+            text=segment.content,
+            summary_index_setting=summary_index_setting,
+        )
+
+        if not summary_content:
+            raise ValueError("Generated summary is empty")
+
+        return summary_content
+
+    @staticmethod
+    def create_summary_record(
+        segment: DocumentSegment,
+        dataset: Dataset,
+        summary_content: str,
+        status: str = "generating",
+    ) -> DocumentSegmentSummary:
+        """
+        Create or update a DocumentSegmentSummary record.
+        If a summary record already exists for this segment, it will be updated instead of creating a new one.
+
+        Args:
+            segment: DocumentSegment to create summary for
+            dataset: Dataset containing the segment
+            summary_content: Generated summary content
+            status: Summary status (default: "generating")
+
+        Returns:
+            Created or updated DocumentSegmentSummary instance
+        """
+        # Check if summary record already exists
+        existing_summary = (
+            db.session.query(DocumentSegmentSummary)
+            .filter_by(chunk_id=segment.id, dataset_id=dataset.id)
+            .first()
+        )
+        
+        if existing_summary:
+            # Update existing record
+            existing_summary.summary_content = summary_content
+            existing_summary.status = status
+            existing_summary.error = None  # Clear any previous errors
+            # Re-enable if it was disabled
+            if not existing_summary.enabled:
+                existing_summary.enabled = True
+                existing_summary.disabled_at = None
+                existing_summary.disabled_by = None
+            db.session.add(existing_summary)
+            db.session.flush()
+            return existing_summary
+        else:
+            # Create new record (enabled by default)
+            summary_record = DocumentSegmentSummary(
+                dataset_id=dataset.id,
+                document_id=segment.document_id,
+                chunk_id=segment.id,
+                summary_content=summary_content,
+                status=status,
+                enabled=True,  # Explicitly set enabled to True
+            )
+            db.session.add(summary_record)
+            db.session.flush()
+            return summary_record
+
+    @staticmethod
+    def vectorize_summary(
+        summary_record: DocumentSegmentSummary,
+        segment: DocumentSegment,
+        dataset: Dataset,
+    ) -> None:
+        """
+        Vectorize summary and store in vector database.
+
+        Args:
+            summary_record: DocumentSegmentSummary record
+            segment: Original DocumentSegment
+            dataset: Dataset containing the segment
+        """
+        if dataset.indexing_technique != "high_quality":
+            logger.warning(
+                f"Summary vectorization skipped for dataset {dataset.id}: "
+                "indexing_technique is not high_quality"
+            )
+            return
+
+        # Reuse existing index_node_id if available (like segment does), otherwise generate new one
+        old_summary_node_id = summary_record.summary_index_node_id
+        if old_summary_node_id:
+            # Reuse existing index_node_id (like segment behavior)
+            summary_index_node_id = old_summary_node_id
+        else:
+            # Generate new index node ID only for new summaries
+            summary_index_node_id = str(uuid.uuid4())
+        
+        # Always regenerate hash (in case summary content changed)
+        summary_hash = helper.generate_text_hash(summary_record.summary_content)
+        
+        # Delete old vector only if we're reusing the same index_node_id (to overwrite)
+        # If index_node_id changed, the old vector should have been deleted elsewhere
+        if old_summary_node_id and old_summary_node_id == summary_index_node_id:
+            try:
+                vector = Vector(dataset)
+                vector.delete_by_ids([old_summary_node_id])
+            except Exception as e:
+                logger.warning(
+                    f"Failed to delete old summary vector for segment {segment.id}: {str(e)}. "
+                    "Continuing with new vectorization."
+                )
+
+        # Create document with summary content and metadata
+        summary_document = Document(
+            page_content=summary_record.summary_content,
+            metadata={
+                "doc_id": summary_index_node_id,
+                "doc_hash": summary_hash,
+                "dataset_id": dataset.id,
+                "document_id": segment.document_id,
+                "original_chunk_id": segment.id,  # Key: link to original chunk
+                "doc_type": DocType.TEXT,
+                "is_summary": True,  # Identifier for summary documents
+            },
+        )
+
+        # Vectorize and store with retry mechanism for connection errors
+        max_retries = 3
+        retry_delay = 2.0
+        
+        for attempt in range(max_retries):
+            try:
+                vector = Vector(dataset)
+                vector.add_texts([summary_document], duplicate_check=True)
+                
+                # Success - update summary record with index node info
+                summary_record.summary_index_node_id = summary_index_node_id
+                summary_record.summary_index_node_hash = summary_hash
+                summary_record.status = "completed"
+                db.session.add(summary_record)
+                db.session.flush()
+                return  # Success, exit function
+                
+            except (ConnectionError, Exception) as e:
+                error_str = str(e).lower()
+                # Check if it's a connection-related error that might be transient
+                is_connection_error = any(keyword in error_str for keyword in [
+                    "connection", "disconnected", "timeout", "network", 
+                    "could not connect", "server disconnected", "weaviate"
+                ])
+                
+                if is_connection_error and attempt < max_retries - 1:
+                    # Retry for connection errors
+                    wait_time = retry_delay * (2 ** attempt)  # Exponential backoff
+                    logger.warning(
+                        f"Vectorization attempt {attempt + 1}/{max_retries} failed for segment {segment.id}: {str(e)}. "
+                        f"Retrying in {wait_time:.1f} seconds..."
+                    )
+                    time.sleep(wait_time)
+                    continue
+                else:
+                    # Final attempt failed or non-connection error - log and update status
+                    logger.error(
+                        f"Failed to vectorize summary for segment {segment.id} after {attempt + 1} attempts: {str(e)}",
+                        exc_info=True
+                    )
+                    summary_record.status = "error"
+                    summary_record.error = f"Vectorization failed: {str(e)}"
+                    db.session.add(summary_record)
+                    db.session.flush()
+                    raise
+
+    @staticmethod
+    def generate_and_vectorize_summary(
+        segment: DocumentSegment,
+        dataset: Dataset,
+        summary_index_setting: dict,
+    ) -> DocumentSegmentSummary:
+        """
+        Generate summary for a segment and vectorize it.
+
+        Args:
+            segment: DocumentSegment to generate summary for
+            dataset: Dataset containing the segment
+            summary_index_setting: Summary index configuration
+
+        Returns:
+            Created DocumentSegmentSummary instance
+
+        Raises:
+            ValueError: If summary generation fails
+        """
+        try:
+            # Generate summary
+            summary_content = SummaryIndexService.generate_summary_for_segment(
+                segment, dataset, summary_index_setting
+            )
+
+            # Create or update summary record (will handle overwrite internally)
+            summary_record = SummaryIndexService.create_summary_record(
+                segment, dataset, summary_content, status="generating"
+            )
+
+            # Vectorize summary (will delete old vector if exists before creating new one)
+            SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
+
+            db.session.commit()
+            logger.info(f"Successfully generated and vectorized summary for segment {segment.id}")
+            return summary_record
+
+        except Exception as e:
+            logger.exception(f"Failed to generate summary for segment {segment.id}: {str(e)}")
+            # Update summary record with error status if it exists
+            summary_record = (
+                db.session.query(DocumentSegmentSummary)
+                .filter_by(chunk_id=segment.id, dataset_id=dataset.id)
+                .first()
+            )
+            if summary_record:
+                summary_record.status = "error"
+                summary_record.error = str(e)
+                db.session.add(summary_record)
+                db.session.commit()
+            raise
+
+    @staticmethod
+    def generate_summaries_for_document(
+        dataset: Dataset,
+        document: DatasetDocument,
+        summary_index_setting: dict,
+        segment_ids: list[str] | None = None,
+        only_parent_chunks: bool = False,
+    ) -> list[DocumentSegmentSummary]:
+        """
+        Generate summaries for all segments in a document including vectorization.
+
+        Args:
+            dataset: Dataset containing the document
+            document: DatasetDocument to generate summaries for
+            summary_index_setting: Summary index configuration
+            segment_ids: Optional list of specific segment IDs to process
+            only_parent_chunks: If True, only process parent chunks (for parent-child mode)
+
+        Returns:
+            List of created DocumentSegmentSummary instances
+        """
+        # Only generate summary index for high_quality indexing technique
+        if dataset.indexing_technique != "high_quality":
+            logger.info(
+                f"Skipping summary generation for dataset {dataset.id}: "
+                f"indexing_technique is {dataset.indexing_technique}, not 'high_quality'"
+            )
+            return []
+
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            logger.info(f"Summary index is disabled for dataset {dataset.id}")
+            return []
+
+        # Skip qa_model documents
+        if document.doc_form == "qa_model":
+            logger.info(f"Skipping summary generation for qa_model document {document.id}")
+            return []
+
+        logger.info(
+            f"Starting summary generation for document {document.id} in dataset {dataset.id}, "
+            f"segment_ids: {len(segment_ids) if segment_ids else 'all'}, "
+            f"only_parent_chunks: {only_parent_chunks}"
+        )
+
+        # Query segments (only enabled segments)
+        query = db.session.query(DocumentSegment).filter_by(
+            dataset_id=dataset.id,
+            document_id=document.id,
+            status="completed",
+            enabled=True,  # Only generate summaries for enabled segments
+        )
+
+        if segment_ids:
+            query = query.filter(DocumentSegment.id.in_(segment_ids))
+
+        segments = query.all()
+
+        if not segments:
+            logger.info(f"No segments found for document {document.id}")
+            return []
+
+        summary_records = []
+
+        for segment in segments:
+            # For parent-child mode, only process parent chunks
+            # In parent-child mode, all DocumentSegments are parent chunks,
+            # so we process all of them. Child chunks are stored in ChildChunk table
+            # and are not DocumentSegments, so they won't be in the segments list.
+            # This check is mainly for clarity and future-proofing.
+            if only_parent_chunks:
+                # In parent-child mode, all segments in the query are parent chunks
+                # Child chunks are not DocumentSegments, so they won't appear here
+                # We can process all segments
+                pass
+
+            try:
+                summary_record = SummaryIndexService.generate_and_vectorize_summary(
+                    segment, dataset, summary_index_setting
+                )
+                summary_records.append(summary_record)
+            except Exception as e:
+                logger.error(f"Failed to generate summary for segment {segment.id}: {str(e)}")
+                # Continue with other segments
+                continue
+
+        logger.info(
+            f"Completed summary generation for document {document.id}: "
+            f"{len(summary_records)} summaries generated and vectorized"
+        )
+        return summary_records
+
+    @staticmethod
+    def disable_summaries_for_segments(
+        dataset: Dataset,
+        segment_ids: list[str] | None = None,
+        disabled_by: str | None = None,
+    ) -> None:
+        """
+        Disable summary records and remove vectors from vector database for segments.
+        Unlike delete, this preserves the summary records but marks them as disabled.
+
+        Args:
+            dataset: Dataset containing the segments
+            segment_ids: List of segment IDs to disable summaries for. If None, disable all.
+            disabled_by: User ID who disabled the summaries
+        """
+        from libs.datetime_utils import naive_utc_now
+        
+        query = db.session.query(DocumentSegmentSummary).filter_by(
+            dataset_id=dataset.id,
+            enabled=True,  # Only disable enabled summaries
+        )
+
+        if segment_ids:
+            query = query.filter(DocumentSegmentSummary.chunk_id.in_(segment_ids))
+
+        summaries = query.all()
+
+        if not summaries:
+            return
+
+        logger.info(
+            f"Disabling {len(summaries)} summary records for dataset {dataset.id}, "
+            f"segment_ids: {len(segment_ids) if segment_ids else 'all'}"
+        )
+
+        # Remove from vector database (but keep records)
+        if dataset.indexing_technique == "high_quality":
+            summary_node_ids = [
+                s.summary_index_node_id for s in summaries if s.summary_index_node_id
+            ]
+            if summary_node_ids:
+                try:
+                    vector = Vector(dataset)
+                    vector.delete_by_ids(summary_node_ids)
+                except Exception as e:
+                    logger.warning(f"Failed to remove summary vectors: {str(e)}")
+
+        # Disable summary records (don't delete)
+        now = naive_utc_now()
+        for summary in summaries:
+            summary.enabled = False
+            summary.disabled_at = now
+            summary.disabled_by = disabled_by
+            db.session.add(summary)
+
+        db.session.commit()
+        logger.info(f"Disabled {len(summaries)} summary records for dataset {dataset.id}")
+
+    @staticmethod
+    def enable_summaries_for_segments(
+        dataset: Dataset,
+        segment_ids: list[str] | None = None,
+    ) -> None:
+        """
+        Enable summary records and re-add vectors to vector database for segments.
+
+        Args:
+            dataset: Dataset containing the segments
+            segment_ids: List of segment IDs to enable summaries for. If None, enable all.
+        """
+        # Only enable summary index for high_quality indexing technique
+        if dataset.indexing_technique != "high_quality":
+            return
+
+        # Check if summary index is enabled
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            return
+
+        query = db.session.query(DocumentSegmentSummary).filter_by(
+            dataset_id=dataset.id,
+            enabled=False,  # Only enable disabled summaries
+        )
+
+        if segment_ids:
+            query = query.filter(DocumentSegmentSummary.chunk_id.in_(segment_ids))
+
+        summaries = query.all()
+
+        if not summaries:
+            return
+
+        logger.info(
+            f"Enabling {len(summaries)} summary records for dataset {dataset.id}, "
+            f"segment_ids: {len(segment_ids) if segment_ids else 'all'}"
+        )
+
+        # Re-vectorize and re-add to vector database
+        enabled_count = 0
+        for summary in summaries:
+            # Get the original segment
+            segment = db.session.query(DocumentSegment).filter_by(
+                id=summary.chunk_id,
+                dataset_id=dataset.id,
+            ).first()
+            
+            if not segment or not segment.enabled or segment.status != "completed":
+                continue
+
+            if not summary.summary_content:
+                continue
+
+            try:
+                # Re-vectorize summary
+                SummaryIndexService.vectorize_summary(summary, segment, dataset)
+                
+                # Enable summary record
+                summary.enabled = True
+                summary.disabled_at = None
+                summary.disabled_by = None
+                db.session.add(summary)
+                enabled_count += 1
+            except Exception as e:
+                logger.error(f"Failed to re-vectorize summary {summary.id}: {str(e)}")
+                # Keep it disabled if vectorization fails
+                continue
+
+        db.session.commit()
+        logger.info(f"Enabled {enabled_count} summary records for dataset {dataset.id}")
+
+    @staticmethod
+    def delete_summaries_for_segments(
+        dataset: Dataset,
+        segment_ids: list[str] | None = None,
+    ) -> None:
+        """
+        Delete summary records and vectors for segments (used only for actual deletion scenarios).
+        For disable/enable operations, use disable_summaries_for_segments/enable_summaries_for_segments.
+
+        Args:
+            dataset: Dataset containing the segments
+            segment_ids: List of segment IDs to delete summaries for. If None, delete all.
+        """
+        query = db.session.query(DocumentSegmentSummary).filter_by(dataset_id=dataset.id)
+
+        if segment_ids:
+            query = query.filter(DocumentSegmentSummary.chunk_id.in_(segment_ids))
+
+        summaries = query.all()
+
+        if not summaries:
+            return
+
+        # Delete from vector database
+        if dataset.indexing_technique == "high_quality":
+            summary_node_ids = [
+                s.summary_index_node_id for s in summaries if s.summary_index_node_id
+            ]
+            if summary_node_ids:
+                vector = Vector(dataset)
+                vector.delete_by_ids(summary_node_ids)
+
+        # Delete summary records
+        for summary in summaries:
+            db.session.delete(summary)
+
+        db.session.commit()
+        logger.info(f"Deleted {len(summaries)} summary records for dataset {dataset.id}")
+
+    @staticmethod
+    def update_summary_for_segment(
+        segment: DocumentSegment,
+        dataset: Dataset,
+        summary_content: str,
+    ) -> DocumentSegmentSummary | None:
+        """
+        Update summary for a segment and re-vectorize it.
+
+        Args:
+            segment: DocumentSegment to update summary for
+            dataset: Dataset containing the segment
+            summary_content: New summary content
+
+        Returns:
+            Updated DocumentSegmentSummary instance, or None if summary index is not enabled
+        """
+        # Only update summary index for high_quality indexing technique
+        if dataset.indexing_technique != "high_quality":
+            return None
+
+        # Check if summary index is enabled
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            return None
+
+        # Skip qa_model documents
+        if segment.document and segment.document.doc_form == "qa_model":
+            return None
+
+        try:
+            # Find existing summary record
+            summary_record = (
+                db.session.query(DocumentSegmentSummary)
+                .filter_by(chunk_id=segment.id, dataset_id=dataset.id)
+                .first()
+            )
+
+            if summary_record:
+                # Update existing summary
+                old_summary_node_id = summary_record.summary_index_node_id
+
+                # Update summary content
+                summary_record.summary_content = summary_content
+                summary_record.status = "generating"
+                db.session.add(summary_record)
+                db.session.flush()
+
+                # Delete old vector if exists
+                if old_summary_node_id:
+                    vector = Vector(dataset)
+                    vector.delete_by_ids([old_summary_node_id])
+
+                # Re-vectorize summary
+                SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
+
+                db.session.commit()
+                logger.info(f"Successfully updated and re-vectorized summary for segment {segment.id}")
+                return summary_record
+            else:
+                # Create new summary record if doesn't exist
+                summary_record = SummaryIndexService.create_summary_record(
+                    segment, dataset, summary_content, status="generating"
+                )
+                SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
+                db.session.commit()
+                logger.info(f"Successfully created and vectorized summary for segment {segment.id}")
+                return summary_record
+
+        except Exception as e:
+            logger.exception(f"Failed to update summary for segment {segment.id}: {str(e)}")
+            # Update summary record with error status if it exists
+            summary_record = (
+                db.session.query(DocumentSegmentSummary)
+                .filter_by(chunk_id=segment.id, dataset_id=dataset.id)
+                .first()
+            )
+            if summary_record:
+                summary_record.status = "error"
+                summary_record.error = str(e)
+                db.session.add(summary_record)
+                db.session.commit()
+            raise
+

api/tasks/add_document_to_index_task.py

@@ -117,6 +117,18 @@ def add_document_to_index_task(dataset_document_id: str):
         )
         db.session.commit()
 
+        # Enable summary indexes for all segments in this document
+        from services.summary_index_service import SummaryIndexService
+        segment_ids_list = [segment.id for segment in segments]
+        if segment_ids_list:
+            try:
+                SummaryIndexService.enable_summaries_for_segments(
+                    dataset=dataset,
+                    segment_ids=segment_ids_list,
+                )
+            except Exception as e:
+                logger.warning(f"Failed to enable summaries for document {dataset_document.id}: {str(e)}")
+
         end_at = time.perf_counter()
         logger.info(
             click.style(f"Document added to index: {dataset_document.id} latency: {end_at - start_at}", fg="green")

api/tasks/annotation/batch_import_annotations_task.py

@@ -31,7 +31,6 @@ def batch_import_annotations_task(job_id: str, content_list: list[dict], app_id:
     start_at = time.perf_counter()
     indexing_cache_key = f"app_annotation_batch_import_{str(job_id)}"
     active_jobs_key = f"annotation_import_active:{tenant_id}"
-
     # get app info
     app = db.session.query(App).where(App.id == app_id, App.tenant_id == tenant_id, App.status == "normal").first()
 
@@ -100,6 +99,5 @@ def batch_import_annotations_task(job_id: str, content_list: list[dict], app_id:
             except Exception as cleanup_error:
                 # Log but don't fail if cleanup fails - the job will be auto-expired
                 logger.warning("Failed to clean up active job tracking for %s: %s", job_id, cleanup_error)
-
             # Close database session
             db.session.close()

api/tasks/delete_segment_from_index_task.py

@@ -42,6 +42,7 @@ def delete_segment_from_index_task(
         doc_form = dataset_document.doc_form
 
         # Proceed with index cleanup using the index_node_ids directly
+        # For actual deletion, we should delete summaries (not just disable them)
         index_processor = IndexProcessorFactory(doc_form).init_index_processor()
         index_processor.clean(
             dataset,
@@ -49,6 +50,7 @@ def delete_segment_from_index_task(
             with_keywords=True,
             delete_child_chunks=True,
             precomputed_child_node_ids=child_node_ids,
+            delete_summaries=True,  # Actually delete summaries when segment is deleted
         )
         if dataset.is_multimodal:
             # delete segment attachment binding

api/tasks/disable_segment_from_index_task.py

@@ -53,6 +53,17 @@ def disable_segment_from_index_task(segment_id: str):
             logger.info(click.style(f"Segment {segment.id} document status is invalid, pass.", fg="cyan"))
             return
 
+        # Disable summary index for this segment
+        from services.summary_index_service import SummaryIndexService
+        try:
+            SummaryIndexService.disable_summaries_for_segments(
+                dataset=dataset,
+                segment_ids=[segment.id],
+                disabled_by=segment.disabled_by,
+            )
+        except Exception as e:
+            logger.warning(f"Failed to disable summary for segment {segment.id}: {str(e)}")
+
         index_type = dataset_document.doc_form
         index_processor = IndexProcessorFactory(index_type).init_index_processor()
         index_processor.clean(dataset, [segment.index_node_id])

api/tasks/disable_segments_from_index_task.py

@@ -58,12 +58,25 @@ def disable_segments_from_index_task(segment_ids: list, dataset_id: str, documen
         return
 
     try:
+        # Disable summary indexes for these segments
+        from services.summary_index_service import SummaryIndexService
+        segment_ids_list = [segment.id for segment in segments]
+        try:
+            # Get disabled_by from first segment (they should all have the same disabled_by)
+            disabled_by = segments[0].disabled_by if segments else None
+            SummaryIndexService.disable_summaries_for_segments(
+                dataset=dataset,
+                segment_ids=segment_ids_list,
+                disabled_by=disabled_by,
+            )
+        except Exception as e:
+            logger.warning(f"Failed to disable summaries for segments: {str(e)}")
+
         index_node_ids = [segment.index_node_id for segment in segments]
         if dataset.is_multimodal:
-            segment_ids = [segment.id for segment in segments]
             segment_attachment_bindings = (
                 db.session.query(SegmentAttachmentBinding)
-                .where(SegmentAttachmentBinding.segment_id.in_(segment_ids))
+                .where(SegmentAttachmentBinding.segment_id.in_(segment_ids_list))
                 .all()
             )
             if segment_attachment_bindings:

api/tasks/document_indexing_task.py

@@ -8,6 +8,7 @@ from celery import shared_task
 from configs import dify_config
 from core.entities.document_task import DocumentTask
 from core.indexing_runner import DocumentIsPausedError, IndexingRunner
+from tasks.generate_summary_index_task import generate_summary_index_task
 from core.rag.pipeline.queue import TenantIsolatedTaskQueue
 from enums.cloud_plan import CloudPlan
 from extensions.ext_database import db
@@ -100,6 +101,60 @@ def _document_indexing(dataset_id: str, document_ids: Sequence[str]):
         indexing_runner.run(documents)
         end_at = time.perf_counter()
         logger.info(click.style(f"Processed dataset: {dataset_id} latency: {end_at - start_at}", fg="green"))
+        
+        # Trigger summary index generation for completed documents if enabled
+        # Only generate for high_quality indexing technique and when summary_index_setting is enabled
+        # Re-query dataset to get latest summary_index_setting (in case it was updated)
+        dataset = db.session.query(Dataset).where(Dataset.id == dataset_id).first()
+        if not dataset:
+            logger.warning(f"Dataset {dataset_id} not found after indexing")
+            return
+        
+        if dataset.indexing_technique == "high_quality":
+            summary_index_setting = dataset.summary_index_setting
+            if summary_index_setting and summary_index_setting.get("enable"):
+                # Check each document's indexing status and trigger summary generation if completed
+                for document_id in document_ids:
+                    # Re-query document to get latest status (IndexingRunner may have updated it)
+                    document = (
+                        db.session.query(Document)
+                        .where(Document.id == document_id, Document.dataset_id == dataset_id)
+                        .first()
+                    )
+                    if document:
+                        logger.info(
+                            f"Checking document {document_id} for summary generation: "
+                            f"status={document.indexing_status}, doc_form={document.doc_form}"
+                        )
+                        if document.indexing_status == "completed" and document.doc_form != "qa_model":
+                            try:
+                                generate_summary_index_task.delay(dataset.id, document_id, None)
+                                logger.info(
+                                    f"Queued summary index generation task for document {document_id} "
+                                    f"in dataset {dataset.id} after indexing completed"
+                                )
+                            except Exception as e:
+                                logger.exception(
+                                    f"Failed to queue summary index generation task for document {document_id}: {str(e)}"
+                                )
+                                # Don't fail the entire indexing process if summary task queuing fails
+                        else:
+                            logger.info(
+                                f"Skipping summary generation for document {document_id}: "
+                                f"status={document.indexing_status}, doc_form={document.doc_form}"
+                            )
+                    else:
+                        logger.warning(f"Document {document_id} not found after indexing")
+            else:
+                logger.info(
+                    f"Summary index generation skipped for dataset {dataset.id}: "
+                    f"summary_index_setting.enable={summary_index_setting.get('enable') if summary_index_setting else None}"
+                )
+        else:
+            logger.info(
+                f"Summary index generation skipped for dataset {dataset.id}: "
+                f"indexing_technique={dataset.indexing_technique} (not 'high_quality')"
+            )
     except DocumentIsPausedError as ex:
         logger.info(click.style(str(ex), fg="yellow"))
     except Exception: