feat: Add summary index for knowledge. (#31625)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Jyong <76649700+JohnJyong@users.noreply.github.com>
Co-authored-by: zxhlyh <jasonapring2015@outlook.com>
Co-authored-by: Yansong Zhang <916125788@qq.com>
Co-authored-by: hj24 <mambahj24@gmail.com>
Co-authored-by: CodingOnStar <hanxujiang@dify.ai>
Co-authored-by: CodingOnStar <hanxujiang@dify.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

api/.importlinter

@@ -227,6 +227,9 @@ ignore_imports =
     core.workflow.nodes.knowledge_index.entities -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.knowledge_index.knowledge_index_node -> models.dataset
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> services.summary_index_service
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> tasks.generate_summary_index_task
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.index_processor.processor.paragraph_index_processor
     core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.llm.node -> models.dataset
     core.workflow.nodes.agent.agent_node -> core.tools.utils.message_transformer

api/controllers/console/datasets/datasets.py

@@ -148,6 +148,7 @@ 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
@@ -288,7 +289,14 @@ class DatasetListApi(Resource):
     @enterprise_license_required
     def get(self):
         current_user, current_tenant_id = current_account_with_tenant()
-        query = ConsoleDatasetListQuery.model_validate(request.args.to_dict())
+        # Convert query parameters to dict, handling list parameters correctly
+        query_params: dict[str, str | list[str]] = dict(request.args.to_dict())
+        # Handle ids and tag_ids as lists (Flask request.args.getlist returns list even for single value)
+        if "ids" in request.args:
+            query_params["ids"] = request.args.getlist("ids")
+        if "tag_ids" in request.args:
+            query_params["tag_ids"] = request.args.getlist("tag_ids")
+        query = ConsoleDatasetListQuery.model_validate(query_params)
         # provider = request.args.get("provider", default="vendor")
         if query.ids:
             datasets, total = DatasetService.get_datasets_by_ids(query.ids, current_tenant_id)

api/controllers/console/datasets/datasets_document.py

@@ -45,6 +45,7 @@ from models.dataset import DocumentPipelineExecutionLog
 from services.dataset_service import DatasetService, DocumentService
 from services.entities.knowledge_entities.knowledge_entities import KnowledgeConfig, ProcessRule, RetrievalModel
 from services.file_service import FileService
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 from ..app.error import (
     ProviderModelCurrentlyNotSupportError,
@@ -103,6 +104,10 @@ class DocumentRenamePayload(BaseModel):
     name: str
 
 
+class GenerateSummaryPayload(BaseModel):
+    document_list: list[str]
+
+
 class DocumentBatchDownloadZipPayload(BaseModel):
     """Request payload for bulk downloading documents as a zip archive."""
 
@@ -125,6 +130,7 @@ register_schema_models(
     RetrievalModel,
     DocumentRetryPayload,
     DocumentRenamePayload,
+    GenerateSummaryPayload,
     DocumentBatchDownloadZipPayload,
 )
 
@@ -312,6 +318,13 @@ 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
+
+        DocumentService.enrich_documents_with_summary_index_status(
+            documents=documents,
+            dataset=dataset,
+            tenant_id=current_tenant_id,
+        )
+
         if fetch:
             for document in documents:
                 completed_segments = (
@@ -797,6 +810,7 @@ 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)
@@ -832,6 +846,7 @@ 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
@@ -1255,3 +1270,137 @@ 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:
+            from werkzeug.exceptions import BadRequest
+
+            raise BadRequest("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 = DocumentService.get_documents_by_ids(dataset_id, document_list)
+
+        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("Skipping summary generation for qa_model document %s", document.id)
+                continue
+
+            # Dispatch async task
+            generate_summary_index_task.delay(dataset_id, document.id)
+            logger.info(
+                "Dispatched summary generation task for document %s in dataset %s",
+                document.id,
+                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 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 summary status detail from service
+        from services.summary_index_service import SummaryIndexService
+
+        result = SummaryIndexService.get_document_summary_status_detail(
+            document_id=document_id,
+            dataset_id=dataset_id,
+        )
+
+        return result, 200

api/controllers/console/datasets/datasets_segments.py

@@ -41,6 +41,17 @@ 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."""
+    from services.summary_index_service import SummaryIndexService
+
+    segment_dict = dict(marshal(segment, segment_fields))
+    # Query summary for this segment (only enabled summaries)
+    summary = SummaryIndexService.get_segment_summary(segment_id=segment.id, dataset_id=dataset_id)
+    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 +74,7 @@ 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):
@@ -181,8 +193,25 @@ 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:
+            from services.summary_index_service import SummaryIndexService
+
+            summary_records = SummaryIndexService.get_segments_summaries(segment_ids=segment_ids, dataset_id=dataset_id)
+            # Only include enabled summaries (already filtered by service)
+            summaries = {chunk_id: summary.summary_content for chunk_id, summary in summary_records.items()}
+
+        # Add summary to each segment
+        segments_with_summary = []
+        for segment in segments.items:
+            segment_dict = 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,
@@ -328,7 +357,7 @@ 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>")
@@ -390,10 +419,12 @@ 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

@@ -1,6 +1,13 @@
-from flask_restx import Resource
+from flask_restx import Resource, fields
 
 from controllers.common.schema import register_schema_model
+from fields.hit_testing_fields import (
+    child_chunk_fields,
+    document_fields,
+    files_fields,
+    hit_testing_record_fields,
+    segment_fields,
+)
 from libs.login import login_required
 
 from .. import console_ns
@@ -14,13 +21,45 @@ from ..wraps import (
 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/service_api/dataset/dataset.py

@@ -46,6 +46,7 @@ class DatasetCreatePayload(BaseModel):
     retrieval_model: RetrievalModel | None = None
     embedding_model: str | None = None
     embedding_model_provider: str | None = None
+    summary_index_setting: dict | None = None
 
 
 class DatasetUpdatePayload(BaseModel):
@@ -217,6 +218,7 @@ class DatasetListApi(DatasetApiResource):
                 embedding_model_provider=payload.embedding_model_provider,
                 embedding_model_name=payload.embedding_model,
                 retrieval_model=payload.retrieval_model,
+                summary_index_setting=payload.summary_index_setting,
             )
         except services.errors.dataset.DatasetNameDuplicateError:
             raise DatasetNameDuplicateError()

api/controllers/service_api/dataset/document.py

@@ -45,6 +45,7 @@ from services.entities.knowledge_entities.knowledge_entities import (
     Segmentation,
 )
 from services.file_service import FileService
+from services.summary_index_service import SummaryIndexService
 
 
 class DocumentTextCreatePayload(BaseModel):
@@ -508,6 +509,12 @@ class DocumentListApi(DatasetApiResource):
         )
         documents = paginated_documents.items
 
+        DocumentService.enrich_documents_with_summary_index_status(
+            documents=documents,
+            dataset=dataset,
+            tenant_id=tenant_id,
+        )
+
         response = {
             "data": marshal(documents, document_fields),
             "has_more": len(documents) == query_params.limit,
@@ -612,6 +619,16 @@ class DocumentApi(DatasetApiResource):
         if metadata not in self.METADATA_CHOICES:
             raise InvalidMetadataError(f"Invalid metadata value: {metadata}")
 
+        # Calculate summary_index_status if needed
+        summary_index_status = None
+        has_summary_index = dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True
+        if has_summary_index and document.need_summary is True:
+            summary_index_status = SummaryIndexService.get_document_summary_index_status(
+                document_id=document_id,
+                dataset_id=dataset_id,
+                tenant_id=tenant_id,
+            )
+
         if metadata == "only":
             response = {"id": document.id, "doc_type": document.doc_type, "doc_metadata": document.doc_metadata_details}
         elif metadata == "without":
@@ -646,6 +663,8 @@ class DocumentApi(DatasetApiResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "summary_index_status": summary_index_status,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
         else:
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
@@ -681,6 +700,8 @@ class DocumentApi(DatasetApiResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "summary_index_status": summary_index_status,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
 
         return response

api/core/app/apps/base_app_generate_response_converter.py

@@ -79,6 +79,7 @@ class AppGenerateResponseConverter(ABC):
                         "document_name": resource["document_name"],
                         "score": resource["score"],
                         "content": resource["content"],
+                        "summary": resource.get("summary"),
                     }
                 )
             metadata["retriever_resources"] = updated_resources

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.get("summary_index_setting")
+        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 +434,20 @@ 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 = (
+    """Summarize the following content. Extract only the key information and main points. """
+    """Remove redundant details.
+
+Requirements:
+1. Write a concise summary in plain text
+2. Use the same language as the input content
+3. Focus on important facts, concepts, and details
+4. If images are included, describe their key information
+5. Do not use words like "好的", "ok", "I understand", "This text discusses", "The content mentions"
+6. Write directly without extra words
+
+Output only the summary text. Start summarizing now:
+
+"""
+)

api/core/rag/datasource/retrieval_service.py

@@ -24,7 +24,13 @@ from core.rag.rerank.rerank_type import RerankMode
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.signature import sign_upload_file
 from extensions.ext_database import db
-from models.dataset import ChildChunk, Dataset, DocumentSegment, SegmentAttachmentBinding
+from models.dataset import (
+    ChildChunk,
+    Dataset,
+    DocumentSegment,
+    DocumentSegmentSummary,
+    SegmentAttachmentBinding,
+)
 from models.dataset import Document as DatasetDocument
 from models.model import UploadFile
 from services.external_knowledge_service import ExternalDatasetService
@@ -389,15 +395,15 @@ class RetrievalService:
                 .all()
             }
 
-            records = []
-            include_segment_ids = set()
-            segment_child_map = {}
-
             valid_dataset_documents = {}
             image_doc_ids: list[Any] = []
             child_index_node_ids = []
             index_node_ids = []
             doc_to_document_map = {}
+            summary_segment_ids = set()  # Track segments retrieved via summary
+            summary_score_map: dict[str, float] = {}  # Map original_chunk_id to summary score
+
+            # First pass: collect all document IDs and identify summary documents
             for document in documents:
                 document_id = document.metadata.get("document_id")
                 if document_id not in dataset_documents:
@@ -408,16 +414,39 @@ class RetrievalService:
                     continue
                 valid_dataset_documents[document_id] = dataset_document
 
+                doc_id = document.metadata.get("doc_id") or ""
+                doc_to_document_map[doc_id] = document
+
+                # 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 original_chunk_id:
+                        summary_segment_ids.add(original_chunk_id)
+                        # Save summary's score for later use
+                        summary_score = document.metadata.get("score")
+                        if summary_score is not None:
+                            try:
+                                summary_score_float = float(summary_score)
+                                # If the same segment has multiple summary hits, take the highest score
+                                if original_chunk_id not in summary_score_map:
+                                    summary_score_map[original_chunk_id] = summary_score_float
+                                else:
+                                    summary_score_map[original_chunk_id] = max(
+                                        summary_score_map[original_chunk_id], summary_score_float
+                                    )
+                            except (ValueError, TypeError):
+                                # Skip invalid score values
+                                pass
+                    continue  # Skip adding to other lists for summary documents
+
                 if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
-                    doc_id = document.metadata.get("doc_id") or ""
-                    doc_to_document_map[doc_id] = document
                     if document.metadata.get("doc_type") == DocType.IMAGE:
                         image_doc_ids.append(doc_id)
                     else:
                         child_index_node_ids.append(doc_id)
                 else:
-                    doc_id = document.metadata.get("doc_id") or ""
-                    doc_to_document_map[doc_id] = document
                     if document.metadata.get("doc_type") == DocType.IMAGE:
                         image_doc_ids.append(doc_id)
                     else:
@@ -433,6 +462,7 @@ class RetrievalService:
             attachment_map: dict[str, list[dict[str, Any]]] = {}
             child_chunk_map: dict[str, list[ChildChunk]] = {}
             doc_segment_map: dict[str, list[str]] = {}
+            segment_summary_map: dict[str, str] = {}  # Map segment_id to summary content
 
             with session_factory.create_session() as session:
                 attachments = cls.get_segment_attachment_infos(image_doc_ids, session)
@@ -447,6 +477,7 @@ class RetrievalService:
                         doc_segment_map[attachment["segment_id"]].append(attachment["attachment_id"])
                     else:
                         doc_segment_map[attachment["segment_id"]] = [attachment["attachment_id"]]
+
                 child_chunk_stmt = select(ChildChunk).where(ChildChunk.index_node_id.in_(child_index_node_ids))
                 child_index_nodes = session.execute(child_chunk_stmt).scalars().all()
 
@@ -470,6 +501,7 @@ class RetrievalService:
                     index_node_segments = session.execute(document_segment_stmt).scalars().all()  # type: ignore
                     for index_node_segment in index_node_segments:
                         doc_segment_map[index_node_segment.id] = [index_node_segment.index_node_id]
+
                 if segment_ids:
                     document_segment_stmt = select(DocumentSegment).where(
                         DocumentSegment.enabled == True,
@@ -481,6 +513,40 @@ class RetrievalService:
                 if index_node_segments:
                     segments.extend(index_node_segments)
 
+                # Handle summary documents: query segments by original_chunk_id
+                if summary_segment_ids:
+                    summary_segment_ids_list = list(summary_segment_ids)
+                    summary_segment_stmt = select(DocumentSegment).where(
+                        DocumentSegment.enabled == True,
+                        DocumentSegment.status == "completed",
+                        DocumentSegment.id.in_(summary_segment_ids_list),
+                    )
+                    summary_segments = session.execute(summary_segment_stmt).scalars().all()  # type: ignore
+                    segments.extend(summary_segments)
+                    # Add summary segment IDs to segment_ids for summary query
+                    for seg in summary_segments:
+                        if seg.id not in segment_ids:
+                            segment_ids.append(seg.id)
+
+                # Batch query summaries for segments retrieved via summary (only enabled summaries)
+                if summary_segment_ids:
+                    summaries = (
+                        session.query(DocumentSegmentSummary)
+                        .filter(
+                            DocumentSegmentSummary.chunk_id.in_(list(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
+
+            include_segment_ids = set()
+            segment_child_map: dict[str, dict[str, Any]] = {}
+            records: list[dict[str, Any]] = []
+
             for segment in segments:
                 child_chunks: list[ChildChunk] = child_chunk_map.get(segment.id, [])
                 attachment_infos: list[dict[str, Any]] = attachment_map.get(segment.id, [])
@@ -489,30 +555,44 @@ class RetrievalService:
                 if ds_dataset_document and ds_dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
                     if segment.id not in include_segment_ids:
                         include_segment_ids.add(segment.id)
+                        # Check if this segment was retrieved via summary
+                        # Use summary score as base score if available, otherwise 0.0
+                        max_score = summary_score_map.get(segment.id, 0.0)
+
                         if child_chunks or attachment_infos:
                             child_chunk_details = []
-                            max_score = 0.0
                             for child_chunk in child_chunks:
-                                document = doc_to_document_map[child_chunk.index_node_id]
+                                child_document: Document | None = doc_to_document_map.get(child_chunk.index_node_id)
+                                if child_document:
+                                    child_score = child_document.metadata.get("score", 0.0)
+                                else:
+                                    child_score = 0.0
                                 child_chunk_detail = {
                                     "id": child_chunk.id,
                                     "content": child_chunk.content,
                                     "position": child_chunk.position,
-                                    "score": document.metadata.get("score", 0.0) if document else 0.0,
+                                    "score": child_score,
                                 }
                                 child_chunk_details.append(child_chunk_detail)
-                                max_score = max(max_score, document.metadata.get("score", 0.0) if document else 0.0)
+                                max_score = max(max_score, child_score)
                             for attachment_info in attachment_infos:
-                                file_document = doc_to_document_map[attachment_info["id"]]
-                                max_score = max(
-                                    max_score, file_document.metadata.get("score", 0.0) if file_document else 0.0
-                                )
+                                file_document = doc_to_document_map.get(attachment_info["id"])
+                                if file_document:
+                                    max_score = max(max_score, file_document.metadata.get("score", 0.0))
 
                             map_detail = {
                                 "max_score": max_score,
                                 "child_chunks": child_chunk_details,
                             }
                             segment_child_map[segment.id] = map_detail
+                        else:
+                            # No child chunks or attachments, use summary score if available
+                            summary_score = summary_score_map.get(segment.id)
+                            if summary_score is not None:
+                                segment_child_map[segment.id] = {
+                                    "max_score": summary_score,
+                                    "child_chunks": [],
+                                }
                         record: dict[str, Any] = {
                             "segment": segment,
                         }
@@ -520,14 +600,23 @@ class RetrievalService:
                 else:
                     if segment.id not in include_segment_ids:
                         include_segment_ids.add(segment.id)
-                        max_score = 0.0
-                        segment_document = doc_to_document_map.get(segment.index_node_id)
-                        if segment_document:
-                            max_score = max(max_score, segment_document.metadata.get("score", 0.0))
+
+                        # Check if this segment was retrieved via summary
+                        # Use summary score if available (summary retrieval takes priority)
+                        max_score = summary_score_map.get(segment.id, 0.0)
+
+                        # If not retrieved via summary, use original segment's score
+                        if segment.id not in summary_score_map:
+                            segment_document = doc_to_document_map.get(segment.index_node_id)
+                            if segment_document:
+                                max_score = max(max_score, segment_document.metadata.get("score", 0.0))
+
+                        # Also consider attachment scores
                         for attachment_info in attachment_infos:
                             file_doc = doc_to_document_map.get(attachment_info["id"])
                             if file_doc:
                                 max_score = max(max_score, file_doc.metadata.get("score", 0.0))
+
                         record = {
                             "segment": segment,
                             "score": max_score,
@@ -576,9 +665,16 @@ 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/entities/citation_metadata.py

@@ -22,3 +22,4 @@ class RetrievalSourceMetadata(BaseModel):
     doc_metadata: dict[str, Any] | None = None
     title: str | None = None
     files: list[dict[str, Any]] | None = None
+    summary: str | None = None

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 +46,17 @@ 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 +1,27 @@
 """Paragraph index processor."""
 
+import logging
+import re
 import uuid
 from collections.abc import Mapping
-from typing import Any
+from typing import Any, cast
 
+logger = logging.getLogger(__name__)
+
+from core.entities.knowledge_entities import PreviewDetail
+from core.file import File, FileTransferMethod, FileType, file_manager
+from core.llm_generator.prompts import DEFAULT_GENERATOR_SUMMARY_PROMPT
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
+from core.model_runtime.entities.message_entities import (
+    ImagePromptMessageContent,
+    PromptMessage,
+    PromptMessageContentUnionTypes,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import ModelFeature, ModelType
+from core.provider_manager import ProviderManager
 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 +35,17 @@ 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 core.workflow.nodes.llm import llm_utils
+from extensions.ext_database import db
+from factories.file_factory import build_from_mapping
 from libs import helper
+from models import UploadFile
 from models.account import Account
-from models.dataset import Dataset, DatasetProcessRule
+from models.dataset import Dataset, DatasetProcessRule, DocumentSegment, SegmentAttachmentBinding
 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
 
 
 class ParagraphIndexProcessor(BaseIndexProcessor):
@@ -108,6 +131,29 @@ 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 +273,322 @@ 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.
+        In preview mode (indexing-estimate), if any summary generation fails, the method will raise an exception.
+        """
+        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."""
+            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
+
+        # 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_texts))
+        errors: list[Exception] = []
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=min(10, len(preview_texts))) as executor:
+            futures = [executor.submit(process, preview) for preview in preview_texts]
+            # 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:
+                timeout_error_msg = (
+                    f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s"
+                )
+                logger.warning("%s. Cancelling remaining tasks...", timeout_error_msg)
+                # In preview mode, timeout is also an error
+                errors.append(TimeoutError(timeout_error_msg))
+                for future in not_done:
+                    future.cancel()
+                # Wait a bit for cancellation to take effect
+                concurrent.futures.wait(not_done, timeout=5)
+
+            # Collect exceptions from completed futures
+            for future in done:
+                try:
+                    future.result()  # This will raise any exception that occurred
+                except Exception as e:
+                    logger.exception("Error in summary generation future")
+                    errors.append(e)
+
+        # In preview mode (indexing-estimate), if there are any errors, fail the request
+        if errors:
+            error_messages = [str(e) for e in errors]
+            error_summary = (
+                f"Failed to generate summaries for {len(errors)} chunk(s). "
+                f"Errors: {'; '.join(error_messages[:3])}"  # Show first 3 errors
+            )
+            if len(errors) > 3:
+                error_summary += f" (and {len(errors) - 3} more)"
+            logger.error("Summary generation failed in preview mode: %s", error_summary)
+            raise ValueError(error_summary)
+
+        return preview_texts
+
+    @staticmethod
+    def generate_summary(
+        tenant_id: str,
+        text: str,
+        summary_index_setting: dict | None = None,
+        segment_id: str | None = None,
+    ) -> tuple[str, LLMUsage]:
+        """
+        Generate summary for the given text using ModelInstance.invoke_llm and the default or custom summary prompt,
+        and supports vision models by including images from the segment attachments or text content.
+
+        Args:
+            tenant_id: Tenant ID
+            text: Text content to summarize
+            summary_index_setting: Summary index configuration
+            segment_id: Optional segment ID to fetch attachments from SegmentAttachmentBinding table
+
+        Returns:
+            Tuple of (summary_content, llm_usage) where llm_usage is LLMUsage object
+        """
+        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")
+
+        if not model_name or not model_provider_name:
+            raise ValueError("model_name and model_provider_name are required in summary_index_setting")
+
+        # Import default summary prompt
+        if not summary_prompt:
+            summary_prompt = DEFAULT_GENERATOR_SUMMARY_PROMPT
+
+        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)
+
+        # Get model schema to check if vision is supported
+        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
+        supports_vision = model_schema and model_schema.features and ModelFeature.VISION in model_schema.features
+
+        # Extract images if model supports vision
+        image_files = []
+        if supports_vision:
+            # First, try to get images from SegmentAttachmentBinding (preferred method)
+            if segment_id:
+                image_files = ParagraphIndexProcessor._extract_images_from_segment_attachments(tenant_id, segment_id)
+
+            # If no images from attachments, fall back to extracting from text
+            if not image_files:
+                image_files = ParagraphIndexProcessor._extract_images_from_text(tenant_id, text)
+
+        # Build prompt messages
+        prompt_messages = []
+
+        if image_files:
+            # If we have images, create a UserPromptMessage with both text and images
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+
+            # Add images first
+            for file in image_files:
+                try:
+                    file_content = file_manager.to_prompt_message_content(
+                        file, image_detail_config=ImagePromptMessageContent.DETAIL.LOW
+                    )
+                    prompt_message_contents.append(file_content)
+                except Exception as e:
+                    logger.warning("Failed to convert image file to prompt message content: %s", str(e))
+                    continue
+
+            # Add text content
+            if prompt_message_contents:  # Only add text if we successfully added images
+                prompt_message_contents.append(TextPromptMessageContent(data=f"{summary_prompt}\n{text}"))
+                prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+            else:
+                # If image conversion failed, fall back to text-only
+                prompt = f"{summary_prompt}\n{text}"
+                prompt_messages.append(UserPromptMessage(content=prompt))
+        else:
+            # No images, use simple text prompt
+            prompt = f"{summary_prompt}\n{text}"
+            prompt_messages.append(UserPromptMessage(content=prompt))
+
+        result = model_instance.invoke_llm(
+            prompt_messages=cast(list[PromptMessage], prompt_messages), model_parameters={}, stream=False
+        )
+
+        # Type assertion: when stream=False, invoke_llm returns LLMResult, not Generator
+        if not isinstance(result, LLMResult):
+            raise ValueError("Expected LLMResult when stream=False")
+
+        summary_content = getattr(result.message, "content", "")
+        usage = result.usage
+
+        # Deduct quota for summary generation (same as workflow nodes)
+        try:
+            llm_utils.deduct_llm_quota(tenant_id=tenant_id, model_instance=model_instance, usage=usage)
+        except Exception as e:
+            # Log but don't fail summary generation if quota deduction fails
+            logger.warning("Failed to deduct quota for summary generation: %s", str(e))
+
+        return summary_content, usage
+
+    @staticmethod
+    def _extract_images_from_text(tenant_id: str, text: str) -> list[File]:
+        """
+        Extract images from markdown text and convert them to File objects.
+
+        Args:
+            tenant_id: Tenant ID
+            text: Text content that may contain markdown image links
+
+        Returns:
+            List of File objects representing images found in the text
+        """
+        # Extract markdown images using regex pattern
+        pattern = r"!\[.*?\]\((.*?)\)"
+        images = re.findall(pattern, text)
+
+        if not images:
+            return []
+
+        upload_file_id_list = []
+
+        for image in images:
+            # For data before v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/image-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For data after v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/file-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For tools directory - direct file formats (e.g., .png, .jpg, etc.)
+            pattern = r"/files/tools/([a-f0-9\-]+)\.([a-zA-Z0-9]+)(?:\?[^\s\)\"\']*)?"
+            match = re.search(pattern, image)
+            if match:
+                # Tool files are handled differently, skip for now
+                continue
+
+        if not upload_file_id_list:
+            return []
+
+        # Get unique IDs for database query
+        unique_upload_file_ids = list(set(upload_file_id_list))
+        upload_files = (
+            db.session.query(UploadFile)
+            .where(UploadFile.id.in_(unique_upload_file_ids), UploadFile.tenant_id == tenant_id)
+            .all()
+        )
+
+        # Create File objects from UploadFile records
+        file_objects = []
+        for upload_file in upload_files:
+            # Only process image files
+            if not upload_file.mime_type or "image" not in upload_file.mime_type:
+                continue
+
+            mapping = {
+                "upload_file_id": upload_file.id,
+                "transfer_method": FileTransferMethod.LOCAL_FILE.value,
+                "type": FileType.IMAGE.value,
+            }
+
+            try:
+                file_obj = build_from_mapping(
+                    mapping=mapping,
+                    tenant_id=tenant_id,
+                )
+                file_objects.append(file_obj)
+            except Exception as e:
+                logger.warning("Failed to create File object from UploadFile %s: %s", upload_file.id, str(e))
+                continue
+
+        return file_objects
+
+    @staticmethod
+    def _extract_images_from_segment_attachments(tenant_id: str, segment_id: str) -> list[File]:
+        """
+        Extract images from SegmentAttachmentBinding table (preferred method).
+        This matches how DatasetRetrieval gets segment attachments.
+
+        Args:
+            tenant_id: Tenant ID
+            segment_id: Segment ID to fetch attachments for
+
+        Returns:
+            List of File objects representing images found in segment attachments
+        """
+        from sqlalchemy import select
+
+        # Query attachments from SegmentAttachmentBinding table
+        attachments_with_bindings = db.session.execute(
+            select(SegmentAttachmentBinding, UploadFile)
+            .join(UploadFile, UploadFile.id == SegmentAttachmentBinding.attachment_id)
+            .where(
+                SegmentAttachmentBinding.segment_id == segment_id,
+                SegmentAttachmentBinding.tenant_id == tenant_id,
+            )
+        ).all()
+
+        if not attachments_with_bindings:
+            return []
+
+        file_objects = []
+        for _, upload_file in attachments_with_bindings:
+            # Only process image files
+            if not upload_file.mime_type or "image" not in upload_file.mime_type:
+                continue
+
+            try:
+                # Create File object directly (similar to DatasetRetrieval)
+                file_obj = File(
+                    id=upload_file.id,
+                    filename=upload_file.name,
+                    extension="." + upload_file.extension,
+                    mime_type=upload_file.mime_type,
+                    tenant_id=tenant_id,
+                    type=FileType.IMAGE,
+                    transfer_method=FileTransferMethod.LOCAL_FILE,
+                    remote_url=upload_file.source_url,
+                    related_id=upload_file.id,
+                    size=upload_file.size,
+                    storage_key=upload_file.key,
+                )
+                file_objects.append(file_obj)
+            except Exception as e:
+                logger.warning("Failed to create File object from UploadFile %s: %s", upload_file.id, str(e))
+                continue
+
+        return file_objects

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

@@ -1,11 +1,14 @@
 """Paragraph index processor."""
 
 import json
+import logging
 import uuid
 from collections.abc import Mapping
 from typing import Any
 
 from configs import dify_config
+from core.db.session_factory import session_factory
+from core.entities.knowledge_entities import PreviewDetail
 from core.model_manager import ModelInstance
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -25,6 +28,9 @@ 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
+
+logger = logging.getLogger(__name__)
 
 
 class ParentChildIndexProcessor(BaseIndexProcessor):
@@ -135,6 +141,30 @@ 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
+                with session_factory.create_session() as session:
+                    segments = (
+                        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")
@@ -326,3 +356,91 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
             "preview": preview,
             "total_segments": len(parent_childs.parent_child_chunks),
         }
+
+    def generate_summary_preview(
+        self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict
+    ) -> list[PreviewDetail]:
+        """
+        For each parent chunk in preview_texts, concurrently call generate_summary to generate a summary
+        and write it to the summary attribute of PreviewDetail.
+        In preview mode (indexing-estimate), if any summary generation fails, the method will raise an exception.
+
+        Note: For parent-child structure, we only generate summaries for parent chunks.
+        """
+        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 (parent chunk)."""
+            from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+
+            if flask_app:
+                # Ensure Flask app context in worker thread
+                with flask_app.app_context():
+                    summary, _ = ParagraphIndexProcessor.generate_summary(
+                        tenant_id=tenant_id,
+                        text=preview.content,
+                        summary_index_setting=summary_index_setting,
+                    )
+                    preview.summary = summary
+            else:
+                # Fallback: try without app context (may fail)
+                summary, _ = ParagraphIndexProcessor.generate_summary(
+                    tenant_id=tenant_id,
+                    text=preview.content,
+                    summary_index_setting=summary_index_setting,
+                )
+                preview.summary = summary
+
+        # 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_texts))
+        errors: list[Exception] = []
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=min(10, len(preview_texts))) as executor:
+            futures = [executor.submit(process, preview) for preview in preview_texts]
+            # 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:
+                timeout_error_msg = (
+                    f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s"
+                )
+                logger.warning("%s. Cancelling remaining tasks...", timeout_error_msg)
+                # In preview mode, timeout is also an error
+                errors.append(TimeoutError(timeout_error_msg))
+                for future in not_done:
+                    future.cancel()
+                # Wait a bit for cancellation to take effect
+                concurrent.futures.wait(not_done, timeout=5)
+
+            # Collect exceptions from completed futures
+            for future in done:
+                try:
+                    future.result()  # This will raise any exception that occurred
+                except Exception as e:
+                    logger.exception("Error in summary generation future")
+                    errors.append(e)
+
+        # In preview mode (indexing-estimate), if there are any errors, fail the request
+        if errors:
+            error_messages = [str(e) for e in errors]
+            error_summary = (
+                f"Failed to generate summaries for {len(errors)} chunk(s). "
+                f"Errors: {'; '.join(error_messages[:3])}"  # Show first 3 errors
+            )
+            if len(errors) > 3:
+                error_summary += f" (and {len(errors) - 3} more)"
+            logger.error("Summary generation failed in preview mode: %s", error_summary)
+            raise ValueError(error_summary)
+
+        return preview_texts

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

@@ -11,6 +11,8 @@ import pandas as pd
 from flask import Flask, current_app
 from werkzeug.datastructures import FileStorage
 
+from core.db.session_factory import session_factory
+from core.entities.knowledge_entities import PreviewDetail
 from core.llm_generator.llm_generator import LLMGenerator
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -25,9 +27,10 @@ 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 +147,31 @@ 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
+                with session_factory.create_session() as session:
+                    segments = (
+                        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)
@@ -212,6 +240,17 @@ class QAIndexProcessor(BaseIndexProcessor):
             "total_segments": len(qa_chunks.qa_chunks),
         }
 
+    def generate_summary_preview(
+        self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict
+    ) -> list[PreviewDetail]:
+        """
+        QA model doesn't generate summaries, so this method returns preview_texts unchanged.
+
+        Note: QA model uses question-answer pairs, which don't require summary generation.
+        """
+        # QA model doesn't generate summaries, return as-is
+        return preview_texts
+
     def _format_qa_document(self, flask_app: Flask, tenant_id: str, document_node, all_qa_documents, document_language):
         format_documents = []
         if document_node.page_content is None or not document_node.page_content.strip():

api/core/rag/retrieval/dataset_retrieval.py

@@ -236,20 +236,24 @@ class DatasetRetrieval:
             if records:
                 for record in records:
                     segment = record.segment
+                    # Build content: if summary exists, add it before the segment content
                     if segment.answer:
-                        document_context_list.append(
-                            DocumentContext(
-                                content=f"question:{segment.get_sign_content()} answer:{segment.answer}",
-                                score=record.score,
-                            )
-                        )
+                        segment_content = f"question:{segment.get_sign_content()} answer:{segment.answer}"
                     else:
-                        document_context_list.append(
-                            DocumentContext(
-                                content=segment.get_sign_content(),
-                                score=record.score,
-                            )
+                        segment_content = segment.get_sign_content()
+
+                    # If summary exists, prepend it to the content
+                    if record.summary:
+                        final_content = f"{record.summary}\n{segment_content}"
+                    else:
+                        final_content = segment_content
+
+                    document_context_list.append(
+                        DocumentContext(
+                            content=final_content,
+                            score=record.score,
                         )
+                    )
                     if vision_enabled:
                         attachments_with_bindings = db.session.execute(
                             select(SegmentAttachmentBinding, UploadFile)
@@ -316,6 +320,9 @@ class DatasetRetrieval:
                                 source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                             else:
                                 source.content = segment.content
+                            # Add summary if this segment was retrieved via summary
+                            if hasattr(record, "summary") and record.summary:
+                                source.summary = record.summary
                             retrieval_resource_list.append(source)
         if hit_callback and retrieval_resource_list:
             retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.score or 0.0, reverse=True)

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

@@ -169,20 +169,24 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                 if records:
                     for record in records:
                         segment = record.segment
+                        # Build content: if summary exists, add it before the segment content
                         if segment.answer:
-                            document_context_list.append(
-                                DocumentContext(
-                                    content=f"question:{segment.get_sign_content()} answer:{segment.answer}",
-                                    score=record.score,
-                                )
-                            )
+                            segment_content = f"question:{segment.get_sign_content()} answer:{segment.answer}"
                         else:
-                            document_context_list.append(
-                                DocumentContext(
-                                    content=segment.get_sign_content(),
-                                    score=record.score,
-                                )
+                            segment_content = segment.get_sign_content()
+
+                        # If summary exists, prepend it to the content
+                        if record.summary:
+                            final_content = f"{record.summary}\n{segment_content}"
+                        else:
+                            final_content = segment_content
+
+                        document_context_list.append(
+                            DocumentContext(
+                                content=final_content,
+                                score=record.score,
                             )
+                        )
 
                     if self.return_resource:
                         for record in records:
@@ -216,6 +220,9 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                                     source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                                 else:
                                     source.content = segment.content
+                                # Add summary if this segment was retrieved via summary
+                                if hasattr(record, "summary") and record.summary:
+                                    source.summary = record.summary
                                 retrieval_resource_list.append(source)
 
             if self.return_resource and retrieval_resource_list:

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 +71,20 @@ 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,
@@ -148,6 +165,11 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
             )
             .scalar()
         )
+        # Update need_summary based on dataset's summary_index_setting
+        if dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True:
+            document.need_summary = True
+        else:
+            document.need_summary = False
         db.session.add(document)
         # update document segment status
         db.session.query(DocumentSegment).where(
@@ -163,6 +185,9 @@ 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 +198,304 @@ 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
+
+        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("No segments found for document %s", 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("No segments need summary generation for document %s", 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:
+                            logger.exception(
+                                "Failed to generate summary for segment %s",
+                                segment.id,
+                            )
+                            # 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)
+
+                logger.info(
+                    "Successfully generated summary index for %s segments in document %s",
+                    len(segments_to_process),
+                    document.id,
+                )
+            except Exception:
+                logger.exception("Failed to generate summary index for document %s", document.id)
+                # Don't fail the entire indexing process if summary generation fails
+        else:
+            # Production mode: asynchronous generation
+            logger.info(
+                "Queuing summary index generation task for document %s (production mode)",
+                document.id,
+            )
+            try:
+                generate_summary_index_task.delay(dataset.id, document.id, None)
+                logger.info("Summary index generation task queued for document %s", document.id)
+            except Exception:
+                logger.exception(
+                    "Failed to queue summary index generation task for document %s",
+                    document.id,
+                )
+                # 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(
+                "Generating summaries for %s chunks in preview mode (dataset: %s)",
+                chunk_count,
+                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:
+                    # 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
+
+            # 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"]))
+            errors: list[Exception] = []
+
+            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:
+                    timeout_error_msg = (
+                        f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s"
+                    )
+                    logger.warning("%s. Cancelling remaining tasks...", timeout_error_msg)
+                    # In preview mode, timeout is also an error
+                    errors.append(TimeoutError(timeout_error_msg))
+                    for future in not_done:
+                        future.cancel()
+                    # Wait a bit for cancellation to take effect
+                    concurrent.futures.wait(not_done, timeout=5)
+
+                # Collect exceptions from completed futures
+                for future in done:
+                    try:
+                        future.result()  # This will raise any exception that occurred
+                    except Exception as e:
+                        logger.exception("Error in summary generation future")
+                        errors.append(e)
+
+            # In preview mode, if there are any errors, fail the request
+            if errors:
+                error_messages = [str(e) for e in errors]
+                error_summary = (
+                    f"Failed to generate summaries for {len(errors)} chunk(s). "
+                    f"Errors: {'; '.join(error_messages[:3])}"  # Show first 3 errors
+                )
+                if len(errors) > 3:
+                    error_summary += f" (and {len(errors) - 3} more)"
+                logger.error("Summary generation failed in preview mode: %s", error_summary)
+                raise KnowledgeIndexNodeError(error_summary)
+
+            completed_count = sum(1 for item in preview_output["preview"] if item.get("summary") is not None)
+            logger.info(
+                "Completed summary generation for preview chunks: %s/%s succeeded",
+                completed_count,
+                len(preview_output["preview"]),
+            )
+
+        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(
+                                    "Enriched preview with %s existing summaries (dataset: %s, document: %s)",
+                                    matched_count,
+                                    dataset.id,
+                                    document.id,
+                                )
+
+        return preview_output
 
     @classmethod
     def version(cls) -> str:

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

@@ -419,6 +419,9 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
                             source["content"] = f"question:{segment.get_sign_content()} \nanswer:{segment.answer}"
                         else:
                             source["content"] = segment.get_sign_content()
+                        # Add summary if available
+                        if record.summary:
+                            source["summary"] = record.summary
                         retrieval_resource_list.append(source)
         if retrieval_resource_list:
             retrieval_resource_list = sorted(

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

@@ -685,6 +685,8 @@ class LLMNode(Node[LLMNodeData]):
                         if "content" not in item:
                             raise InvalidContextStructureError(f"Invalid context structure: {item}")
 
+                        if item.get("summary"):
+                            context_str += item["summary"] + "\n"
                         context_str += item["content"] + "\n"
 
                         retriever_resource = self._convert_to_original_retriever_resource(item)
@@ -746,6 +748,7 @@ class LLMNode(Node[LLMNodeData]):
                 page=metadata.get("page"),
                 doc_metadata=metadata.get("doc_metadata"),
                 files=context_dict.get("files"),
+                summary=context_dict.get("summary"),
             )
 
             return source

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/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 +33,11 @@ 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 generation status:
+    # "SUMMARIZING" (when task is queued and generating)
+    "summary_index_status": fields.String,
+    # Whether this document needs summary index generation
+    "need_summary": fields.Boolean,
 }
 
 document_with_segments_fields = {
@@ -60,6 +65,10 @@ 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 generation status:
+    # "SUMMARIZING" (when task is queued and generating)
+    "summary_index_status": fields.String,
+    "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

@@ -36,6 +36,7 @@ class RetrieverResource(ResponseModel):
     segment_position: int | None = None
     index_node_hash: str | None = None
     content: str | None = None
+    summary: str | None = None
     created_at: int | None = None
 
     @field_validator("created_at", mode="before")

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/migrations/versions/2026_01_27_1815-788d3099ae3a_add_summary_index_feature.py

@@ -0,0 +1,107 @@
+"""add summary index feature
+
+Revision ID: 788d3099ae3a
+Revises: 9d77545f524e
+Create Date: 2026-01-27 18:15:45.277928
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+
+def _is_pg(conn):
+    return conn.dialect.name == "postgresql"
+
+# revision identifiers, used by Alembic.
+revision = '788d3099ae3a'
+down_revision = '9d77545f524e'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    conn = op.get_bind()
+    if _is_pg(conn):
+        op.create_table('document_segment_summaries',
+        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('tokens', sa.Integer(), 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_summaries_pkey')
+        )
+        with op.batch_alter_table('document_segment_summaries', schema=None) as batch_op:
+            batch_op.create_index('document_segment_summaries_chunk_id_idx', ['chunk_id'], unique=False)
+            batch_op.create_index('document_segment_summaries_dataset_id_idx', ['dataset_id'], unique=False)
+            batch_op.create_index('document_segment_summaries_document_id_idx', ['document_id'], unique=False)
+            batch_op.create_index('document_segment_summaries_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))
+    else:
+        # MySQL: Use compatible syntax
+        op.create_table(
+            'document_segment_summaries',
+            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('tokens', sa.Integer(), 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_summaries_pkey'),
+        )
+        with op.batch_alter_table('document_segment_summaries', schema=None) as batch_op:
+            batch_op.create_index('document_segment_summaries_chunk_id_idx', ['chunk_id'], unique=False)
+            batch_op.create_index('document_segment_summaries_dataset_id_idx', ['dataset_id'], unique=False)
+            batch_op.create_index('document_segment_summaries_document_id_idx', ['document_id'], unique=False)
+            batch_op.create_index('document_segment_summaries_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_summaries', schema=None) as batch_op:
+        batch_op.drop_index('document_segment_summaries_status_idx')
+        batch_op.drop_index('document_segment_summaries_document_id_idx')
+        batch_op.drop_index('document_segment_summaries_dataset_id_idx')
+        batch_op.drop_index('document_segment_summaries_chunk_id_idx')
+
+    op.drop_table('document_segment_summaries')
+    # ### end Alembic commands ###

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"]
 
@@ -1575,3 +1577,36 @@ 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_summaries"
+    __table_args__ = (
+        sa.PrimaryKeyConstraint("id", name="document_segment_summaries_pkey"),
+        sa.Index("document_segment_summaries_dataset_id_idx", "dataset_id"),
+        sa.Index("document_segment_summaries_document_id_idx", "document_id"),
+        sa.Index("document_segment_summaries_chunk_id_idx", "chunk_id"),
+        sa.Index("document_segment_summaries_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)
+    tokens: Mapped[int | None] = mapped_column(sa.Integer, 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/services/dataset_service.py

@@ -89,6 +89,7 @@ from tasks.disable_segments_from_index_task import disable_segments_from_index_t
 from tasks.document_indexing_update_task import document_indexing_update_task
 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.regenerate_summary_index_task import regenerate_summary_index_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.sync_website_document_indexing_task import sync_website_document_indexing_task
@@ -211,6 +212,7 @@ class DatasetService:
         embedding_model_provider: str | None = None,
         embedding_model_name: str | None = None,
         retrieval_model: RetrievalModel | None = None,
+        summary_index_setting: dict | None = None,
     ):
         # check if dataset name already exists
         if db.session.query(Dataset).filter_by(name=name, tenant_id=tenant_id).first():
@@ -253,6 +255,8 @@ class DatasetService:
         dataset.retrieval_model = retrieval_model.model_dump() if retrieval_model else None
         dataset.permission = permission or DatasetPermissionEnum.ONLY_ME
         dataset.provider = provider
+        if summary_index_setting is not None:
+            dataset.summary_index_setting = summary_index_setting
         db.session.add(dataset)
         db.session.flush()
 
@@ -476,6 +480,11 @@ 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)
@@ -564,6 +573,9 @@ class DatasetService:
         # 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")
@@ -572,12 +584,27 @@ 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,
+                )
+
+        # Note: summary_index_setting changes do not trigger automatic regeneration of existing summaries.
+        # The new setting will only apply to:
+        # 1. New documents added after the setting change
+        # 2. Manual summary generation requests
 
         return dataset
 
@@ -616,6 +643,7 @@ 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:
@@ -854,6 +882,54 @@ 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 new setting is disabled, no need to regenerate
+        if not new_summary_setting or not new_summary_setting.get("enable"):
+            return False
+
+        # If old setting doesn't exist, no need to regenerate (no existing summaries to regenerate)
+        # Note: This task only regenerates existing summaries, not generates new ones
+        if not old_summary_setting:
+            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(
+                "Summary index setting model changed for dataset %s: old=%s/%s, new=%s/%s",
+                dataset.id,
+                old_model_provider,
+                old_model_name,
+                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
@@ -889,6 +965,9 @@ class DatasetService:
             else:
                 raise ValueError("Invalid index method")
             dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
+            # Update summary_index_setting if provided
+            if knowledge_configuration.summary_index_setting is not None:
+                dataset.summary_index_setting = knowledge_configuration.summary_index_setting
             session.add(dataset)
         else:
             if dataset.chunk_structure and dataset.chunk_structure != knowledge_configuration.chunk_structure:
@@ -994,6 +1073,9 @@ class DatasetService:
                     if dataset.keyword_number != knowledge_configuration.keyword_number:
                         dataset.keyword_number = knowledge_configuration.keyword_number
             dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
+            # Update summary_index_setting if provided
+            if knowledge_configuration.summary_index_setting is not None:
+                dataset.summary_index_setting = knowledge_configuration.summary_index_setting
             session.add(dataset)
             session.commit()
             if action:
@@ -1314,6 +1396,50 @@ class DocumentService:
         upload_file = DocumentService._get_upload_file_for_upload_file_document(document)
         return file_helpers.get_signed_file_url(upload_file_id=upload_file.id, as_attachment=True)
 
+    @staticmethod
+    def enrich_documents_with_summary_index_status(
+        documents: Sequence[Document],
+        dataset: Dataset,
+        tenant_id: str,
+    ) -> None:
+        """
+        Enrich documents with summary_index_status based on dataset summary index settings.
+
+        This method calculates and sets the summary_index_status for each document that needs summary.
+        Documents that don't need summary or when summary index is disabled will have status set to None.
+
+        Args:
+            documents: List of Document instances to enrich
+            dataset: Dataset instance containing summary_index_setting
+            tenant_id: Tenant ID for summary status lookup
+        """
+        # 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: dict[str, str | None] = {}
+        if has_summary_index and document_ids_need_summary:
+            from services.summary_index_service import SummaryIndexService
+
+            summary_status_map = SummaryIndexService.get_documents_summary_index_status(
+                document_ids=document_ids_need_summary,
+                dataset_id=dataset.id,
+                tenant_id=tenant_id,
+            )
+
+        # Add summary_index_status to each document
+        for document in documents:
+            if has_summary_index and document.need_summary is True:
+                # Get status from map, default to None (not queued yet)
+                document.summary_index_status = summary_status_map.get(str(document.id))  # type: ignore[attr-defined]
+            else:
+                # Return null if summary index is not enabled or document doesn't need summary
+                document.summary_index_status = None  # type: ignore[attr-defined]
+
     @staticmethod
     def prepare_document_batch_download_zip(
         *,
@@ -1964,6 +2090,8 @@ 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
 
@@ -2268,6 +2396,11 @@ 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,
@@ -2281,6 +2414,7 @@ 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:
@@ -2505,6 +2639,7 @@ 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,
         )
 
@@ -2686,6 +2821,14 @@ 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
+        summary_index_setting = args["process_rule"].get("summary_index_setting")
+        if summary_index_setting and summary_index_setting.get("enable"):
+            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
@@ -3154,6 +3297,35 @@ 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:
+                    # When user manually provides summary, allow saving even if summary_index_setting doesn't exist
+                    # summary_index_setting is only needed for LLM generation, not for manual summary vectorization
+                    # Vectorization uses dataset.embedding_model, which doesn't require summary_index_setting
+                    if dataset.indexing_technique == "high_quality":
+                        # 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:
+                                logger.exception("Failed to update summary for segment %s", segment.id)
+                                # Don't fail the entire update if summary update fails
             else:
                 segment_hash = helper.generate_text_hash(content)
                 tokens = 0
@@ -3228,6 +3400,73 @@ 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)
+                # Handle summary index when content changed
+                if dataset.indexing_technique == "high_quality":
+                    from models.dataset import DocumentSegmentSummary
+
+                    existing_summary = (
+                        db.session.query(DocumentSegmentSummary)
+                        .where(
+                            DocumentSegmentSummary.chunk_id == segment.id,
+                            DocumentSegmentSummary.dataset_id == dataset.id,
+                        )
+                        .first()
+                    )
+
+                    if args.summary is None:
+                        # User didn't provide summary, auto-regenerate if segment previously had summary
+                        # Auto-regeneration only happens if summary_index_setting exists and enable is True
+                        if (
+                            existing_summary
+                            and dataset.summary_index_setting
+                            and dataset.summary_index_setting.get("enable") is True
+                        ):
+                            # Segment previously had summary, regenerate it with new content
+                            from services.summary_index_service import SummaryIndexService
+
+                            try:
+                                SummaryIndexService.generate_and_vectorize_summary(
+                                    segment, dataset, dataset.summary_index_setting
+                                )
+                                logger.info("Auto-regenerated summary for segment %s after content change", segment.id)
+                            except Exception:
+                                logger.exception("Failed to auto-regenerate summary for segment %s", segment.id)
+                                # Don't fail the entire update if summary regeneration fails
+                    else:
+                        # User provided summary, check if it has changed
+                        # Manual summary updates are allowed even if summary_index_setting doesn't exist
+                        existing_summary_content = existing_summary.summary_content if existing_summary else None
+                        if existing_summary_content != args.summary:
+                            # Summary has changed, use user-provided summary
+                            from services.summary_index_service import SummaryIndexService
+
+                            try:
+                                SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
+                                logger.info("Updated summary for segment %s with user-provided content", segment.id)
+                            except Exception:
+                                logger.exception("Failed to update summary for segment %s", segment.id)
+                                # Don't fail the entire update if summary update fails
+                        else:
+                            # Summary hasn't changed, regenerate based on new content
+                            # Auto-regeneration only happens if summary_index_setting exists and enable is True
+                            if (
+                                existing_summary
+                                and dataset.summary_index_setting
+                                and dataset.summary_index_setting.get("enable") is True
+                            ):
+                                from services.summary_index_service import SummaryIndexService
+
+                                try:
+                                    SummaryIndexService.generate_and_vectorize_summary(
+                                        segment, dataset, dataset.summary_index_setting
+                                    )
+                                    logger.info(
+                                        "Regenerated summary for segment %s after content change (summary unchanged)",
+                                        segment.id,
+                                    )
+                                except Exception:
+                                    logger.exception("Failed to regenerate summary for segment %s", segment.id)
+                                    # Don't fail the entire update if summary regeneration fails
             # update multimodel vector index
             VectorService.update_multimodel_vector(segment, args.attachment_ids or [], dataset)
         except Exception as e:
@@ -3616,6 +3855,39 @@ class SegmentService:
         )
         return result if isinstance(result, DocumentSegment) else None
 
+    @classmethod
+    def get_segments_by_document_and_dataset(
+        cls,
+        document_id: str,
+        dataset_id: str,
+        status: str | None = None,
+        enabled: bool | None = None,
+    ) -> Sequence[DocumentSegment]:
+        """
+        Get segments for a document in a dataset with optional filtering.
+
+        Args:
+            document_id: Document ID
+            dataset_id: Dataset ID
+            status: Optional status filter (e.g., "completed")
+            enabled: Optional enabled filter (True/False)
+
+        Returns:
+            Sequence of DocumentSegment instances
+        """
+        query = select(DocumentSegment).where(
+            DocumentSegment.document_id == document_id,
+            DocumentSegment.dataset_id == dataset_id,
+        )
+
+        if status is not None:
+            query = query.where(DocumentSegment.status == status)
+
+        if enabled is not None:
+            query = query.where(DocumentSegment.enabled == enabled)
+
+        return db.session.scalars(query).all()
+
 
 class DatasetCollectionBindingService:
     @classmethod

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/entities/knowledge_entities/rag_pipeline_entities.py

@@ -116,6 +116,8 @@ class KnowledgeConfiguration(BaseModel):
     embedding_model: str = ""
     keyword_number: int | None = 10
     retrieval_model: RetrievalSetting
+    # add summary index setting
+    summary_index_setting: dict | None = None
 
     @field_validator("embedding_model_provider", mode="before")
     @classmethod

api/services/rag_pipeline/rag_pipeline_dsl_service.py

@@ -343,6 +343,9 @@ class RagPipelineDslService:
                         dataset.embedding_model_provider = knowledge_configuration.embedding_model_provider
                     elif knowledge_configuration.indexing_technique == "economy":
                         dataset.keyword_number = knowledge_configuration.keyword_number
+                    # Update summary_index_setting if provided
+                    if knowledge_configuration.summary_index_setting is not None:
+                        dataset.summary_index_setting = knowledge_configuration.summary_index_setting
                     dataset.pipeline_id = pipeline.id
                     self._session.add(dataset)
                     self._session.commit()
@@ -477,6 +480,9 @@ class RagPipelineDslService:
                         dataset.embedding_model_provider = knowledge_configuration.embedding_model_provider
                     elif knowledge_configuration.indexing_technique == "economy":
                         dataset.keyword_number = knowledge_configuration.keyword_number
+                    # Update summary_index_setting if provided
+                    if knowledge_configuration.summary_index_setting is not None:
+                        dataset.summary_index_setting = knowledge_configuration.summary_index_setting
                     dataset.pipeline_id = pipeline.id
                     self._session.add(dataset)
                     self._session.commit()

api/tasks/add_document_to_index_task.py

@@ -118,6 +118,19 @@ def add_document_to_index_task(dataset_document_id: str):
             )
             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("Failed to enable summaries for document %s: %s", 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/batch_clean_document_task.py

@@ -50,7 +50,9 @@ def batch_clean_document_task(document_ids: list[str], dataset_id: str, doc_form
             if segments:
                 index_node_ids = [segment.index_node_id for segment in segments]
                 index_processor = IndexProcessorFactory(doc_form).init_index_processor()
-                index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
+                index_processor.clean(
+                    dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
+                )
 
                 for segment in segments:
                     image_upload_file_ids = get_image_upload_file_ids(segment.content)

api/tasks/clean_document_task.py

@@ -51,7 +51,9 @@ def clean_document_task(document_id: str, dataset_id: str, doc_form: str, file_i
             if segments:
                 index_node_ids = [segment.index_node_id for segment in segments]
                 index_processor = IndexProcessorFactory(doc_form).init_index_processor()
-                index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
+                index_processor.clean(
+                    dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
+                )
 
                 for segment in segments:
                     image_upload_file_ids = get_image_upload_file_ids(segment.content)

api/tasks/clean_notion_document_task.py

@@ -42,7 +42,9 @@ def clean_notion_document_task(document_ids: list[str], dataset_id: str):
                 ).all()
                 index_node_ids = [segment.index_node_id for segment in segments]
 
-                index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
+                index_processor.clean(
+                    dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
+                )
                 segment_ids = [segment.id for segment in segments]
                 segment_delete_stmt = delete(DocumentSegment).where(DocumentSegment.id.in_(segment_ids))
                 session.execute(segment_delete_stmt)

api/tasks/delete_segment_from_index_task.py

@@ -47,6 +47,7 @@ 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,
@@ -54,6 +55,7 @@ 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

@@ -60,6 +60,18 @@ def disable_segment_from_index_task(segment_id: str):
             index_processor = IndexProcessorFactory(index_type).init_index_processor()
             index_processor.clean(dataset, [segment.index_node_id])
 
+            # 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("Failed to disable summary for segment %s: %s", segment.id, str(e))
+
             end_at = time.perf_counter()
             logger.info(
                 click.style(

api/tasks/disable_segments_from_index_task.py

@@ -68,6 +68,21 @@ def disable_segments_from_index_task(segment_ids: list, dataset_id: str, documen
                     index_node_ids.extend(attachment_ids)
             index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=False)
 
+            # 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("Failed to disable summaries for segments: %s", str(e))
+
             end_at = time.perf_counter()
             logger.info(click.style(f"Segments removed from index latency: {end_at - start_at}", fg="green"))
         except Exception:

api/tasks/document_indexing_task.py

@@ -14,6 +14,7 @@ from enums.cloud_plan import CloudPlan
 from libs.datetime_utils import naive_utc_now
 from models.dataset import Dataset, Document
 from services.feature_service import FeatureService
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 logger = logging.getLogger(__name__)
 
@@ -99,6 +100,78 @@ 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 = session.query(Dataset).where(Dataset.id == dataset_id).first()
+            if not dataset:
+                logger.warning("Dataset %s not found after indexing", dataset_id)
+                return
+
+            if dataset.indexing_technique == "high_quality":
+                summary_index_setting = dataset.summary_index_setting
+                if summary_index_setting and summary_index_setting.get("enable"):
+                    # expire all session to get latest document's indexing status
+                    session.expire_all()
+                    # 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 = (
+                            session.query(Document)
+                            .where(Document.id == document_id, Document.dataset_id == dataset_id)
+                            .first()
+                        )
+                        if document:
+                            logger.info(
+                                "Checking document %s for summary generation: status=%s, doc_form=%s, need_summary=%s",
+                                document_id,
+                                document.indexing_status,
+                                document.doc_form,
+                                document.need_summary,
+                            )
+                            if (
+                                document.indexing_status == "completed"
+                                and document.doc_form != "qa_model"
+                                and document.need_summary is True
+                            ):
+                                try:
+                                    generate_summary_index_task.delay(dataset.id, document_id, None)
+                                    logger.info(
+                                        "Queued summary index generation task for document %s in dataset %s "
+                                        "after indexing completed",
+                                        document_id,
+                                        dataset.id,
+                                    )
+                                except Exception:
+                                    logger.exception(
+                                        "Failed to queue summary index generation task for document %s",
+                                        document_id,
+                                    )
+                                    # Don't fail the entire indexing process if summary task queuing fails
+                            else:
+                                logger.info(
+                                    "Skipping summary generation for document %s: "
+                                    "status=%s, doc_form=%s, need_summary=%s",
+                                    document_id,
+                                    document.indexing_status,
+                                    document.doc_form,
+                                    document.need_summary,
+                                )
+                        else:
+                            logger.warning("Document %s not found after indexing", document_id)
+                else:
+                    logger.info(
+                        "Summary index generation skipped for dataset %s: summary_index_setting.enable=%s",
+                        dataset.id,
+                        summary_index_setting.get("enable") if summary_index_setting else None,
+                    )
+            else:
+                logger.info(
+                    "Summary index generation skipped for dataset %s: indexing_technique=%s (not 'high_quality')",
+                    dataset.id,
+                    dataset.indexing_technique,
+                )
         except DocumentIsPausedError as ex:
             logger.info(click.style(str(ex), fg="yellow"))
         except Exception:

api/tasks/enable_segment_to_index_task.py

@@ -106,6 +106,17 @@ def enable_segment_to_index_task(segment_id: str):
             # save vector index
             index_processor.load(dataset, [document], multimodal_documents=multimodel_documents)
 
+            # Enable summary index for this segment
+            from services.summary_index_service import SummaryIndexService
+
+            try:
+                SummaryIndexService.enable_summaries_for_segments(
+                    dataset=dataset,
+                    segment_ids=[segment.id],
+                )
+            except Exception as e:
+                logger.warning("Failed to enable summary for segment %s: %s", segment.id, str(e))
+
             end_at = time.perf_counter()
             logger.info(click.style(f"Segment enabled to index: {segment.id} latency: {end_at - start_at}", fg="green"))
         except Exception as e:

api/tasks/enable_segments_to_index_task.py

@@ -106,6 +106,18 @@ def enable_segments_to_index_task(segment_ids: list, dataset_id: str, document_i
             # save vector index
             index_processor.load(dataset, documents, multimodal_documents=multimodal_documents)
 
+            # Enable summary indexes for these segments
+            from services.summary_index_service import SummaryIndexService
+
+            segment_ids_list = [segment.id for segment in segments]
+            try:
+                SummaryIndexService.enable_summaries_for_segments(
+                    dataset=dataset,
+                    segment_ids=segment_ids_list,
+                )
+            except Exception as e:
+                logger.warning("Failed to enable summaries for segments: %s", str(e))
+
             end_at = time.perf_counter()
             logger.info(click.style(f"Segments enabled to index latency: {end_at - start_at}", fg="green"))
         except Exception as e:

api/tasks/generate_summary_index_task.py

@@ -0,0 +1,119 @@
+"""Async task for generating summary indexes."""
+
+import logging
+import time
+
+import click
+from celery import shared_task
+
+from core.db.session_factory import session_factory
+from models.dataset import Dataset, DocumentSegment
+from models.dataset import Document as DatasetDocument
+from services.summary_index_service import SummaryIndexService
+
+logger = logging.getLogger(__name__)
+
+
+@shared_task(queue="dataset")
+def generate_summary_index_task(dataset_id: str, document_id: str, segment_ids: list[str] | None = None):
+    """
+    Async generate summary index for document segments.
+
+    Args:
+        dataset_id: Dataset ID
+        document_id: Document ID
+        segment_ids: Optional list of specific segment IDs to process. If None, process all segments.
+
+    Usage:
+        generate_summary_index_task.delay(dataset_id, document_id)
+        generate_summary_index_task.delay(dataset_id, document_id, segment_ids)
+    """
+    logger.info(
+        click.style(
+            f"Start generating summary index for document {document_id} in dataset {dataset_id}",
+            fg="green",
+        )
+    )
+    start_at = time.perf_counter()
+
+    try:
+        with session_factory.create_session() as session:
+            dataset = session.query(Dataset).where(Dataset.id == dataset_id).first()
+            if not dataset:
+                logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
+                return
+
+            document = session.query(DatasetDocument).where(DatasetDocument.id == document_id).first()
+            if not document:
+                logger.error(click.style(f"Document not found: {document_id}", fg="red"))
+                return
+
+            # Check if document needs summary
+            if not document.need_summary:
+                logger.info(
+                    click.style(
+                        f"Skipping summary generation for document {document_id}: need_summary is False",
+                        fg="cyan",
+                    )
+                )
+                return
+
+            # Only generate summary index for high_quality indexing technique
+            if dataset.indexing_technique != "high_quality":
+                logger.info(
+                    click.style(
+                        f"Skipping summary generation for dataset {dataset_id}: "
+                        f"indexing_technique is {dataset.indexing_technique}, not 'high_quality'",
+                        fg="cyan",
+                    )
+                )
+                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"):
+                logger.info(
+                    click.style(
+                        f"Summary index is disabled for dataset {dataset_id}",
+                        fg="cyan",
+                    )
+                )
+                return
+
+            # Determine if only parent chunks should be processed
+            only_parent_chunks = dataset.chunk_structure == "parent_child_index"
+
+            # Generate summaries
+            summary_records = SummaryIndexService.generate_summaries_for_document(
+                dataset=dataset,
+                document=document,
+                summary_index_setting=summary_index_setting,
+                segment_ids=segment_ids,
+                only_parent_chunks=only_parent_chunks,
+            )
+
+            end_at = time.perf_counter()
+            logger.info(
+                click.style(
+                    f"Summary index generation completed for document {document_id}: "
+                    f"{len(summary_records)} summaries generated, latency: {end_at - start_at}",
+                    fg="green",
+                )
+            )
+
+    except Exception as e:
+        logger.exception("Failed to generate summary index for document %s", document_id)
+        # Update document segments with error status if needed
+        if segment_ids:
+            error_message = f"Summary generation failed: {str(e)}"
+            with session_factory.create_session() as session:
+                session.query(DocumentSegment).filter(
+                    DocumentSegment.id.in_(segment_ids),
+                    DocumentSegment.dataset_id == dataset_id,
+                ).update(
+                    {
+                        DocumentSegment.error: error_message,
+                    },
+                    synchronize_session=False,
+                )
+                session.commit()

api/tasks/regenerate_summary_index_task.py

@@ -0,0 +1,315 @@
+"""Task for regenerating summary indexes when dataset settings change."""
+
+import logging
+import time
+from collections import defaultdict
+
+import click
+from celery import shared_task
+from sqlalchemy import or_, select
+
+from core.db.session_factory import session_factory
+from models.dataset import Dataset, DocumentSegment, DocumentSegmentSummary
+from models.dataset import Document as DatasetDocument
+from services.summary_index_service import SummaryIndexService
+
+logger = logging.getLogger(__name__)
+
+
+@shared_task(queue="dataset")
+def regenerate_summary_index_task(
+    dataset_id: str,
+    regenerate_reason: str = "summary_model_changed",
+    regenerate_vectors_only: bool = False,
+):
+    """
+    Regenerate summary indexes for all documents in a dataset.
+
+    This task is triggered when:
+    1. summary_index_setting model changes (regenerate_reason="summary_model_changed")
+       - Regenerates summary content and vectors for all existing summaries
+    2. embedding_model changes (regenerate_reason="embedding_model_changed")
+       - Only regenerates vectors for existing summaries (keeps summary content)
+
+    Args:
+        dataset_id: Dataset ID
+        regenerate_reason: Reason for regeneration ("summary_model_changed" or "embedding_model_changed")
+        regenerate_vectors_only: If True, only regenerate vectors without regenerating summary content
+    """
+    logger.info(
+        click.style(
+            f"Start regenerate summary index for dataset {dataset_id}, reason: {regenerate_reason}",
+            fg="green",
+        )
+    )
+    start_at = time.perf_counter()
+
+    try:
+        with session_factory.create_session() as session:
+            dataset = session.query(Dataset).filter_by(id=dataset_id).first()
+            if not dataset:
+                logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
+                return
+
+            # Only regenerate summary index for high_quality indexing technique
+            if dataset.indexing_technique != "high_quality":
+                logger.info(
+                    click.style(
+                        f"Skipping summary regeneration for dataset {dataset_id}: "
+                        f"indexing_technique is {dataset.indexing_technique}, not 'high_quality'",
+                        fg="cyan",
+                    )
+                )
+                return
+
+            # Check if summary index is enabled (only for summary_model change)
+            # For embedding_model change, we still re-vectorize existing summaries even if setting is disabled
+            summary_index_setting = dataset.summary_index_setting
+            if not regenerate_vectors_only:
+                # For summary_model change, require summary_index_setting to be enabled
+                if not summary_index_setting or not summary_index_setting.get("enable"):
+                    logger.info(
+                        click.style(
+                            f"Summary index is disabled for dataset {dataset_id}",
+                            fg="cyan",
+                        )
+                    )
+                    return
+
+            total_segments_processed = 0
+            total_segments_failed = 0
+
+            if regenerate_vectors_only:
+                # For embedding_model change: directly query all segments with existing summaries
+                # Don't require document indexing_status == "completed"
+                # Include summaries with status "completed" or "error" (if they have content)
+                segments_with_summaries = (
+                    session.query(DocumentSegment, DocumentSegmentSummary)
+                    .join(
+                        DocumentSegmentSummary,
+                        DocumentSegment.id == DocumentSegmentSummary.chunk_id,
+                    )
+                    .join(
+                        DatasetDocument,
+                        DocumentSegment.document_id == DatasetDocument.id,
+                    )
+                    .where(
+                        DocumentSegment.dataset_id == dataset_id,
+                        DocumentSegment.status == "completed",  # Segment must be completed
+                        DocumentSegment.enabled == True,
+                        DocumentSegmentSummary.dataset_id == dataset_id,
+                        DocumentSegmentSummary.summary_content.isnot(None),  # Must have summary content
+                        # Include completed summaries or error summaries (with content)
+                        or_(
+                            DocumentSegmentSummary.status == "completed",
+                            DocumentSegmentSummary.status == "error",
+                        ),
+                        DatasetDocument.enabled == True,  # Document must be enabled
+                        DatasetDocument.archived == False,  # Document must not be archived
+                        DatasetDocument.doc_form != "qa_model",  # Skip qa_model documents
+                    )
+                    .order_by(DocumentSegment.document_id.asc(), DocumentSegment.position.asc())
+                    .all()
+                )
+
+                if not segments_with_summaries:
+                    logger.info(
+                        click.style(
+                            f"No segments with summaries found for re-vectorization in dataset {dataset_id}",
+                            fg="cyan",
+                        )
+                    )
+                    return
+
+                logger.info(
+                    "Found %s segments with summaries for re-vectorization in dataset %s",
+                    len(segments_with_summaries),
+                    dataset_id,
+                )
+
+                # Group by document for logging
+                segments_by_document = defaultdict(list)
+                for segment, summary_record in segments_with_summaries:
+                    segments_by_document[segment.document_id].append((segment, summary_record))
+
+                logger.info(
+                    "Segments grouped into %s documents for re-vectorization",
+                    len(segments_by_document),
+                )
+
+                for document_id, segment_summary_pairs in segments_by_document.items():
+                    logger.info(
+                        "Re-vectorizing summaries for %s segments in document %s",
+                        len(segment_summary_pairs),
+                        document_id,
+                    )
+
+                    for segment, summary_record in segment_summary_pairs:
+                        try:
+                            # Delete old vector
+                            if summary_record.summary_index_node_id:
+                                try:
+                                    from core.rag.datasource.vdb.vector_factory import Vector
+
+                                    vector = Vector(dataset)
+                                    vector.delete_by_ids([summary_record.summary_index_node_id])
+                                except Exception as e:
+                                    logger.warning(
+                                        "Failed to delete old summary vector for segment %s: %s",
+                                        segment.id,
+                                        str(e),
+                                    )
+
+                            # Re-vectorize with new embedding model
+                            SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
+                            session.commit()
+                            total_segments_processed += 1
+
+                        except Exception as e:
+                            logger.error(
+                                "Failed to re-vectorize summary for segment %s: %s",
+                                segment.id,
+                                str(e),
+                                exc_info=True,
+                            )
+                            total_segments_failed += 1
+                            # Update summary record with error status
+                            summary_record.status = "error"
+                            summary_record.error = f"Re-vectorization failed: {str(e)}"
+                            session.add(summary_record)
+                            session.commit()
+                            continue
+
+            else:
+                # For summary_model change: require document indexing_status == "completed"
+                # Get all documents with completed indexing status
+                dataset_documents = session.scalars(
+                    select(DatasetDocument).where(
+                        DatasetDocument.dataset_id == dataset_id,
+                        DatasetDocument.indexing_status == "completed",
+                        DatasetDocument.enabled == True,
+                        DatasetDocument.archived == False,
+                    )
+                ).all()
+
+                if not dataset_documents:
+                    logger.info(
+                        click.style(
+                            f"No documents found for summary regeneration in dataset {dataset_id}",
+                            fg="cyan",
+                        )
+                    )
+                    return
+
+                logger.info(
+                    "Found %s documents for summary regeneration in dataset %s",
+                    len(dataset_documents),
+                    dataset_id,
+                )
+
+                for dataset_document in dataset_documents:
+                    # Skip qa_model documents
+                    if dataset_document.doc_form == "qa_model":
+                        continue
+
+                    try:
+                        # Get all segments with existing summaries
+                        segments = (
+                            session.query(DocumentSegment)
+                            .join(
+                                DocumentSegmentSummary,
+                                DocumentSegment.id == DocumentSegmentSummary.chunk_id,
+                            )
+                            .where(
+                                DocumentSegment.document_id == dataset_document.id,
+                                DocumentSegment.dataset_id == dataset_id,
+                                DocumentSegment.status == "completed",
+                                DocumentSegment.enabled == True,
+                                DocumentSegmentSummary.dataset_id == dataset_id,
+                            )
+                            .order_by(DocumentSegment.position.asc())
+                            .all()
+                        )
+
+                        if not segments:
+                            continue
+
+                        logger.info(
+                            "Regenerating summaries for %s segments in document %s",
+                            len(segments),
+                            dataset_document.id,
+                        )
+
+                        for segment in segments:
+                            summary_record = None
+                            try:
+                                # Get existing summary record
+                                summary_record = (
+                                    session.query(DocumentSegmentSummary)
+                                    .filter_by(
+                                        chunk_id=segment.id,
+                                        dataset_id=dataset_id,
+                                    )
+                                    .first()
+                                )
+
+                                if not summary_record:
+                                    logger.warning("Summary record not found for segment %s, skipping", segment.id)
+                                    continue
+
+                                # Regenerate both summary content and vectors (for summary_model change)
+                                SummaryIndexService.generate_and_vectorize_summary(
+                                    segment, dataset, summary_index_setting
+                                )
+                                session.commit()
+                                total_segments_processed += 1
+
+                            except Exception as e:
+                                logger.error(
+                                    "Failed to regenerate summary for segment %s: %s",
+                                    segment.id,
+                                    str(e),
+                                    exc_info=True,
+                                )
+                                total_segments_failed += 1
+                                # Update summary record with error status
+                                if summary_record:
+                                    summary_record.status = "error"
+                                    summary_record.error = f"Regeneration failed: {str(e)}"
+                                    session.add(summary_record)
+                                    session.commit()
+                                continue
+
+                    except Exception as e:
+                        logger.error(
+                            "Failed to process document %s for summary regeneration: %s",
+                            dataset_document.id,
+                            str(e),
+                            exc_info=True,
+                        )
+                        continue
+
+            end_at = time.perf_counter()
+            if regenerate_vectors_only:
+                logger.info(
+                    click.style(
+                        f"Summary re-vectorization completed for dataset {dataset_id}: "
+                        f"{total_segments_processed} segments processed successfully, "
+                        f"{total_segments_failed} segments failed, "
+                        f"latency: {end_at - start_at:.2f}s",
+                        fg="green",
+                    )
+                )
+            else:
+                logger.info(
+                    click.style(
+                        f"Summary index regeneration completed for dataset {dataset_id}: "
+                        f"{total_segments_processed} segments processed successfully, "
+                        f"{total_segments_failed} segments failed, "
+                        f"latency: {end_at - start_at:.2f}s",
+                        fg="green",
+                    )
+                )
+
+    except Exception:
+        logger.exception("Regenerate summary index failed for dataset %s", dataset_id)

api/tasks/remove_document_from_index_task.py

@@ -46,6 +46,21 @@ def remove_document_from_index_task(document_id: str):
             index_processor = IndexProcessorFactory(document.doc_form).init_index_processor()
 
             segments = session.scalars(select(DocumentSegment).where(DocumentSegment.document_id == document.id)).all()
+
+            # Disable 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.disable_summaries_for_segments(
+                        dataset=dataset,
+                        segment_ids=segment_ids_list,
+                        disabled_by=document.disabled_by,
+                    )
+                except Exception as e:
+                    logger.warning("Failed to disable summaries for document %s: %s", document.id, str(e))
+
             index_node_ids = [segment.index_node_id for segment in segments]
             if index_node_ids:
                 try:

api/tests/unit_tests/services/test_dataset_service_update_dataset.py

@@ -138,6 +138,7 @@ class TestDatasetServiceUpdateDataset:
                 "services.dataset_service.DatasetCollectionBindingService.get_dataset_collection_binding"
             ) as mock_get_binding,
             patch("services.dataset_service.deal_dataset_vector_index_task") as mock_task,
+            patch("services.dataset_service.regenerate_summary_index_task") as mock_regenerate_task,
             patch(
                 "services.dataset_service.current_user", create_autospec(Account, instance=True)
             ) as mock_current_user,
@@ -147,6 +148,7 @@ class TestDatasetServiceUpdateDataset:
                 "model_manager": mock_model_manager,
                 "get_binding": mock_get_binding,
                 "task": mock_task,
+                "regenerate_task": mock_regenerate_task,
                 "current_user": mock_current_user,
             }
 
@@ -549,6 +551,13 @@ class TestDatasetServiceUpdateDataset:
         # Verify vector index task was triggered
         mock_internal_provider_dependencies["task"].delay.assert_called_once_with("dataset-123", "update")
 
+        # Verify regenerate summary index task was triggered (when embedding_model changes)
+        mock_internal_provider_dependencies["regenerate_task"].delay.assert_called_once_with(
+            "dataset-123",
+            regenerate_reason="embedding_model_changed",
+            regenerate_vectors_only=True,
+        )
+
         # Verify return value
         assert result == dataset