refactor: select in console datasets document controller (#34029)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

api/services/dataset_service.py

@@ -21,7 +21,7 @@ from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.helper.name_generator import generate_incremental_name
 from core.model_manager import ModelManager
 from core.rag.index_processor.constant.built_in_field import BuiltInField
-from core.rag.index_processor.constant.index_type import IndexStructureType
+from core.rag.index_processor.constant.index_type import IndexStructureType, IndexTechniqueType
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from dify_graph.file import helpers as file_helpers
 from dify_graph.model_runtime.entities.model_entities import ModelFeature, ModelType
@@ -228,7 +228,7 @@ class DatasetService:
         if db.session.query(Dataset).filter_by(name=name, tenant_id=tenant_id).first():
             raise DatasetNameDuplicateError(f"Dataset with name {name} already exists.")
         embedding_model = None
-        if indexing_technique == "high_quality":
+        if indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             model_manager = ModelManager()
             if embedding_model_provider and embedding_model_name:
                 # check if embedding model setting is valid
@@ -254,7 +254,10 @@ class DatasetService:
                         retrieval_model.reranking_model.reranking_provider_name,
                         retrieval_model.reranking_model.reranking_model_name,
                     )
-        dataset = Dataset(name=name, indexing_technique=indexing_technique)
+        dataset = Dataset(
+            name=name,
+            indexing_technique=IndexTechniqueType(indexing_technique) if indexing_technique else None,
+        )
         # dataset = Dataset(name=name, provider=provider, config=config)
         dataset.description = description
         dataset.created_by = account.id
@@ -349,7 +352,7 @@ class DatasetService:
 
     @staticmethod
     def check_dataset_model_setting(dataset):
-        if dataset.indexing_technique == "high_quality":
+        if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             try:
                 model_manager = ModelManager()
                 model_manager.get_model_instance(
@@ -717,13 +720,13 @@ class DatasetService:
         if "indexing_technique" not in data:
             return None
         if dataset.indexing_technique != data["indexing_technique"]:
-            if data["indexing_technique"] == "economy":
+            if data["indexing_technique"] == IndexTechniqueType.ECONOMY:
                 # Remove embedding model configuration for economy mode
                 filtered_data["embedding_model"] = None
                 filtered_data["embedding_model_provider"] = None
                 filtered_data["collection_binding_id"] = None
                 return "remove"
-            elif data["indexing_technique"] == "high_quality":
+            elif data["indexing_technique"] == IndexTechniqueType.HIGH_QUALITY:
                 # Configure embedding model for high quality mode
                 DatasetService._configure_embedding_model_for_high_quality(data, filtered_data)
                 return "add"
@@ -953,8 +956,8 @@ class DatasetService:
         dataset = session.merge(dataset)
         if not has_published:
             dataset.chunk_structure = knowledge_configuration.chunk_structure
-            dataset.indexing_technique = knowledge_configuration.indexing_technique
-            if knowledge_configuration.indexing_technique == "high_quality":
+            dataset.indexing_technique = IndexTechniqueType(knowledge_configuration.indexing_technique)
+            if knowledge_configuration.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                 model_manager = ModelManager()
                 embedding_model = model_manager.get_model_instance(
                     tenant_id=current_user.current_tenant_id,  # ignore type error
@@ -976,7 +979,7 @@ class DatasetService:
                     embedding_model_name,
                 )
                 dataset.collection_binding_id = dataset_collection_binding.id
-            elif knowledge_configuration.indexing_technique == "economy":
+            elif knowledge_configuration.indexing_technique == IndexTechniqueType.ECONOMY:
                 dataset.keyword_number = knowledge_configuration.keyword_number
             else:
                 raise ValueError("Invalid index method")
@@ -991,9 +994,9 @@ class DatasetService:
             action = None
             if dataset.indexing_technique != knowledge_configuration.indexing_technique:
                 # if update indexing_technique
-                if knowledge_configuration.indexing_technique == "economy":
+                if knowledge_configuration.indexing_technique == IndexTechniqueType.ECONOMY:
                     raise ValueError("Knowledge base indexing technique is not allowed to be updated to economy.")
-                elif knowledge_configuration.indexing_technique == "high_quality":
+                elif knowledge_configuration.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                     action = "add"
                     # get embedding model setting
                     try:
@@ -1018,7 +1021,7 @@ class DatasetService:
                         )
                         dataset.is_multimodal = is_multimodal
                         dataset.collection_binding_id = dataset_collection_binding.id
-                        dataset.indexing_technique = knowledge_configuration.indexing_technique
+                        dataset.indexing_technique = IndexTechniqueType(knowledge_configuration.indexing_technique)
                     except LLMBadRequestError:
                         raise ValueError(
                             "No Embedding Model available. Please configure a valid provider "
@@ -1029,7 +1032,7 @@ class DatasetService:
             else:
                 # add default plugin id to both setting sets, to make sure the plugin model provider is consistent
                 # Skip embedding model checks if not provided in the update request
-                if dataset.indexing_technique == "high_quality":
+                if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                     skip_embedding_update = False
                     try:
                         # Handle existing model provider
@@ -1089,7 +1092,7 @@ class DatasetService:
                         )
                     except ProviderTokenNotInitError as ex:
                         raise ValueError(ex.description)
-                elif dataset.indexing_technique == "economy":
+                elif dataset.indexing_technique == IndexTechniqueType.ECONOMY:
                     if dataset.keyword_number != knowledge_configuration.keyword_number:
                         dataset.keyword_number = knowledge_configuration.keyword_number
             dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
@@ -1907,8 +1910,8 @@ class DocumentService:
             if knowledge_config.indexing_technique not in Dataset.INDEXING_TECHNIQUE_LIST:
                 raise ValueError("Indexing technique is invalid")
 
-            dataset.indexing_technique = knowledge_config.indexing_technique
-            if knowledge_config.indexing_technique == "high_quality":
+            dataset.indexing_technique = IndexTechniqueType(knowledge_config.indexing_technique)
+            if knowledge_config.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                 model_manager = ModelManager()
                 if knowledge_config.embedding_model and knowledge_config.embedding_model_provider:
                     dataset_embedding_model = knowledge_config.embedding_model
@@ -2689,7 +2692,7 @@ class DocumentService:
 
         dataset_collection_binding_id = None
         retrieval_model = None
-        if knowledge_config.indexing_technique == "high_quality":
+        if knowledge_config.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             assert knowledge_config.embedding_model_provider
             assert knowledge_config.embedding_model
             dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
@@ -2712,7 +2715,7 @@ class DocumentService:
             tenant_id=tenant_id,
             name="",
             data_source_type=knowledge_config.data_source.info_list.data_source_type,
-            indexing_technique=knowledge_config.indexing_technique,
+            indexing_technique=IndexTechniqueType(knowledge_config.indexing_technique),
             created_by=account.id,
             embedding_model=knowledge_config.embedding_model,
             embedding_model_provider=knowledge_config.embedding_model_provider,
@@ -3125,7 +3128,7 @@ class SegmentService:
         doc_id = str(uuid.uuid4())
         segment_hash = helper.generate_text_hash(content)
         tokens = 0
-        if dataset.indexing_technique == "high_quality":
+        if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             model_manager = ModelManager()
             embedding_model = model_manager.get_model_instance(
                 tenant_id=current_user.current_tenant_id,
@@ -3208,7 +3211,7 @@ class SegmentService:
         try:
             with redis_client.lock(lock_name, timeout=600):
                 embedding_model = None
-                if dataset.indexing_technique == "high_quality":
+                if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                     model_manager = ModelManager()
                     embedding_model = model_manager.get_model_instance(
                         tenant_id=current_user.current_tenant_id,
@@ -3230,7 +3233,7 @@ class SegmentService:
                     doc_id = str(uuid.uuid4())
                     segment_hash = helper.generate_text_hash(content)
                     tokens = 0
-                    if dataset.indexing_technique == "high_quality" and embedding_model:
+                    if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY and embedding_model:
                         # calc embedding use tokens
                         if document.doc_form == IndexStructureType.QA_INDEX:
                             tokens = embedding_model.get_text_embedding_num_tokens(
@@ -3345,7 +3348,7 @@ class SegmentService:
                 if document.doc_form == IndexStructureType.PARENT_CHILD_INDEX and args.regenerate_child_chunks:
                     # regenerate child chunks
                     # get embedding model instance
-                    if dataset.indexing_technique == "high_quality":
+                    if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         # check embedding model setting
                         model_manager = ModelManager()
 
@@ -3382,7 +3385,7 @@ class SegmentService:
                     # 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":
+                    if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         # Query existing summary from database
                         from models.dataset import DocumentSegmentSummary
 
@@ -3409,7 +3412,7 @@ class SegmentService:
             else:
                 segment_hash = helper.generate_text_hash(content)
                 tokens = 0
-                if dataset.indexing_technique == "high_quality":
+                if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                     model_manager = ModelManager()
                     embedding_model = model_manager.get_model_instance(
                         tenant_id=current_user.current_tenant_id,
@@ -3449,7 +3452,7 @@ class SegmentService:
                 db.session.commit()
                 if document.doc_form == IndexStructureType.PARENT_CHILD_INDEX and args.regenerate_child_chunks:
                     # get embedding model instance
-                    if dataset.indexing_technique == "high_quality":
+                    if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         # check embedding model setting
                         model_manager = ModelManager()
 
@@ -3481,7 +3484,7 @@ class SegmentService:
                     # update segment vector index
                     VectorService.update_segment_vector(args.keywords, segment, dataset)
                 # Handle summary index when content changed
-                if dataset.indexing_technique == "high_quality":
+                if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                     from models.dataset import DocumentSegmentSummary
 
                     existing_summary = (

api/services/rag_pipeline/rag_pipeline_dsl_service.py

@@ -22,6 +22,7 @@ from sqlalchemy.orm import Session
 from core.helper import ssrf_proxy
 from core.helper.name_generator import generate_incremental_name
 from core.plugin.entities.plugin import PluginDependency
+from core.rag.index_processor.constant.index_type import IndexTechniqueType
 from core.workflow.nodes.datasource.entities import DatasourceNodeData
 from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from core.workflow.nodes.knowledge_retrieval.entities import KnowledgeRetrievalNodeData
@@ -311,13 +312,13 @@ class RagPipelineDslService:
                                 "icon_background": icon_background,
                                 "icon_url": icon_url,
                             },
-                            indexing_technique=knowledge_configuration.indexing_technique,
+                            indexing_technique=IndexTechniqueType(knowledge_configuration.indexing_technique),
                             created_by=account.id,
                             retrieval_model=knowledge_configuration.retrieval_model.model_dump(),
                             runtime_mode=DatasetRuntimeMode.RAG_PIPELINE,
                             chunk_structure=knowledge_configuration.chunk_structure,
                         )
-                    if knowledge_configuration.indexing_technique == "high_quality":
+                    if knowledge_configuration.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         dataset_collection_binding = (
                             self._session.query(DatasetCollectionBinding)
                             .where(
@@ -343,7 +344,7 @@ class RagPipelineDslService:
                         dataset.collection_binding_id = dataset_collection_binding_id
                         dataset.embedding_model = knowledge_configuration.embedding_model
                         dataset.embedding_model_provider = knowledge_configuration.embedding_model_provider
-                    elif knowledge_configuration.indexing_technique == "economy":
+                    elif knowledge_configuration.indexing_technique == IndexTechniqueType.ECONOMY:
                         dataset.keyword_number = knowledge_configuration.keyword_number
                     # Update summary_index_setting if provided
                     if knowledge_configuration.summary_index_setting is not None:
@@ -443,18 +444,18 @@ class RagPipelineDslService:
                                 "icon_background": icon_background,
                                 "icon_url": icon_url,
                             },
-                            indexing_technique=knowledge_configuration.indexing_technique,
+                            indexing_technique=IndexTechniqueType(knowledge_configuration.indexing_technique),
                             created_by=account.id,
                             retrieval_model=knowledge_configuration.retrieval_model.model_dump(),
                             runtime_mode=DatasetRuntimeMode.RAG_PIPELINE,
                             chunk_structure=knowledge_configuration.chunk_structure,
                         )
                     else:
-                        dataset.indexing_technique = knowledge_configuration.indexing_technique
+                        dataset.indexing_technique = IndexTechniqueType(knowledge_configuration.indexing_technique)
                         dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
                         dataset.runtime_mode = DatasetRuntimeMode.RAG_PIPELINE
                         dataset.chunk_structure = knowledge_configuration.chunk_structure
-                    if knowledge_configuration.indexing_technique == "high_quality":
+                    if knowledge_configuration.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         dataset_collection_binding = (
                             self._session.query(DatasetCollectionBinding)
                             .where(
@@ -480,7 +481,7 @@ class RagPipelineDslService:
                         dataset.collection_binding_id = dataset_collection_binding_id
                         dataset.embedding_model = knowledge_configuration.embedding_model
                         dataset.embedding_model_provider = knowledge_configuration.embedding_model_provider
-                    elif knowledge_configuration.indexing_technique == "economy":
+                    elif knowledge_configuration.indexing_technique == IndexTechniqueType.ECONOMY:
                         dataset.keyword_number = knowledge_configuration.keyword_number
                     # Update summary_index_setting if provided
                     if knowledge_configuration.summary_index_setting is not None:
@@ -772,7 +773,7 @@ class RagPipelineDslService:
                         )
                     case _ if typ == KNOWLEDGE_INDEX_NODE_TYPE:
                         knowledge_index_entity = KnowledgeConfiguration.model_validate(node["data"])
-                        if knowledge_index_entity.indexing_technique == "high_quality":
+                        if knowledge_index_entity.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                             if knowledge_index_entity.embedding_model_provider:
                                 dependencies.append(
                                     DependenciesAnalysisService.analyze_model_provider_dependency(

api/services/rag_pipeline/rag_pipeline_transform_service.py

@@ -9,7 +9,7 @@ from flask_login import current_user
 
 from constants import DOCUMENT_EXTENSIONS
 from core.plugin.impl.plugin import PluginInstaller
-from core.rag.index_processor.constant.index_type import IndexStructureType
+from core.rag.index_processor.constant.index_type import IndexStructureType, IndexTechniqueType
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
 from factories import variable_factory
@@ -105,29 +105,29 @@ class RagPipelineTransformService:
         if doc_form == IndexStructureType.PARAGRAPH_INDEX:
             match datasource_type:
                 case DataSourceType.UPLOAD_FILE:
-                    if indexing_technique == "high_quality":
+                    if indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         # get graph from transform.file-general-high-quality.yml
                         with open(f"{Path(__file__).parent}/transform/file-general-high-quality.yml") as f:
                             pipeline_yaml = yaml.safe_load(f)
-                    if indexing_technique == "economy":
+                    if indexing_technique == IndexTechniqueType.ECONOMY:
                         # get graph from transform.file-general-economy.yml
                         with open(f"{Path(__file__).parent}/transform/file-general-economy.yml") as f:
                             pipeline_yaml = yaml.safe_load(f)
                 case DataSourceType.NOTION_IMPORT:
-                    if indexing_technique == "high_quality":
+                    if indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         # get graph from transform.notion-general-high-quality.yml
                         with open(f"{Path(__file__).parent}/transform/notion-general-high-quality.yml") as f:
                             pipeline_yaml = yaml.safe_load(f)
-                    if indexing_technique == "economy":
+                    if indexing_technique == IndexTechniqueType.ECONOMY:
                         # get graph from transform.notion-general-economy.yml
                         with open(f"{Path(__file__).parent}/transform/notion-general-economy.yml") as f:
                             pipeline_yaml = yaml.safe_load(f)
                 case DataSourceType.WEBSITE_CRAWL:
-                    if indexing_technique == "high_quality":
+                    if indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                         # get graph from transform.website-crawl-general-high-quality.yml
                         with open(f"{Path(__file__).parent}/transform/website-crawl-general-high-quality.yml") as f:
                             pipeline_yaml = yaml.safe_load(f)
-                    if indexing_technique == "economy":
+                    if indexing_technique == IndexTechniqueType.ECONOMY:
                         # get graph from transform.website-crawl-general-economy.yml
                         with open(f"{Path(__file__).parent}/transform/website-crawl-general-economy.yml") as f:
                             pipeline_yaml = yaml.safe_load(f)
@@ -170,11 +170,11 @@ class RagPipelineTransformService:
     ):
         knowledge_configuration_dict = node.get("data", {})
 
-        if indexing_technique == "high_quality":
+        if indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             knowledge_configuration.embedding_model = dataset.embedding_model
             knowledge_configuration.embedding_model_provider = dataset.embedding_model_provider
         if retrieval_model:
-            if indexing_technique == "economy":
+            if indexing_technique == IndexTechniqueType.ECONOMY:
                 retrieval_model.search_method = RetrievalMethod.KEYWORD_SEARCH
             knowledge_configuration.retrieval_model = retrieval_model
         else:

api/services/summary_index_service.py

@@ -12,6 +12,7 @@ from core.db.session_factory import session_factory
 from core.model_manager import ModelManager
 from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.index_processor.constant.doc_type import DocType
+from core.rag.index_processor.constant.index_type import IndexTechniqueType
 from core.rag.index_processor.index_processor_base import SummaryIndexSettingDict
 from core.rag.models.document import Document
 from dify_graph.model_runtime.entities.llm_entities import LLMUsage
@@ -140,7 +141,7 @@ class SummaryIndexService:
             session: Optional SQLAlchemy session. If provided, uses this session instead of creating a new one.
                     If not provided, creates a new session and commits automatically.
         """
-        if dataset.indexing_technique != "high_quality":
+        if dataset.indexing_technique != IndexTechniqueType.HIGH_QUALITY:
             logger.warning(
                 "Summary vectorization skipped for dataset %s: indexing_technique is not high_quality",
                 dataset.id,
@@ -724,7 +725,7 @@ class SummaryIndexService:
             List of created DocumentSegmentSummary instances
         """
         # Only generate summary index for high_quality indexing technique
-        if dataset.indexing_technique != "high_quality":
+        if dataset.indexing_technique != IndexTechniqueType.HIGH_QUALITY:
             logger.info(
                 "Skipping summary generation for dataset %s: indexing_technique is %s, not 'high_quality'",
                 dataset.id,
@@ -851,7 +852,7 @@ class SummaryIndexService:
             )
 
             # Remove from vector database (but keep records)
-            if dataset.indexing_technique == "high_quality":
+            if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                 summary_node_ids = [s.summary_index_node_id for s in summaries if s.summary_index_node_id]
                 if summary_node_ids:
                     try:
@@ -889,7 +890,7 @@ class SummaryIndexService:
             segment_ids: List of segment IDs to enable summaries for. If None, enable all.
         """
         # Only enable summary index for high_quality indexing technique
-        if dataset.indexing_technique != "high_quality":
+        if dataset.indexing_technique != IndexTechniqueType.HIGH_QUALITY:
             return
 
         with session_factory.create_session() as session:
@@ -981,7 +982,7 @@ class SummaryIndexService:
                 return
 
             # Delete from vector database
-            if dataset.indexing_technique == "high_quality":
+            if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                 summary_node_ids = [s.summary_index_node_id for s in summaries if s.summary_index_node_id]
                 if summary_node_ids:
                     vector = Vector(dataset)
@@ -1012,7 +1013,7 @@ class SummaryIndexService:
             Updated DocumentSegmentSummary instance, or None if indexing technique is not high_quality
         """
         # Only update summary index for high_quality indexing technique
-        if dataset.indexing_technique != "high_quality":
+        if dataset.indexing_technique != IndexTechniqueType.HIGH_QUALITY:
             return None
 
         # When user manually provides summary, allow saving even if summary_index_setting doesn't exist

api/services/vector_service.py

@@ -4,7 +4,7 @@ from core.model_manager import ModelInstance, ModelManager
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.index_processor.constant.doc_type import DocType
-from core.rag.index_processor.constant.index_type import IndexStructureType
+from core.rag.index_processor.constant.index_type import IndexStructureType, IndexTechniqueType
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
 from core.rag.models.document import AttachmentDocument, Document
@@ -45,7 +45,7 @@ class VectorService:
                 if not processing_rule:
                     raise ValueError("No processing rule found.")
                 # get embedding model instance
-                if dataset.indexing_technique == "high_quality":
+                if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                     # check embedding model setting
                     model_manager = ModelManager()
 
@@ -112,7 +112,7 @@ class VectorService:
                 "dataset_id": segment.dataset_id,
             },
         )
-        if dataset.indexing_technique == "high_quality":
+        if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             # update vector index
             vector = Vector(dataset=dataset)
             vector.delete_by_ids([segment.index_node_id])
@@ -197,7 +197,7 @@ class VectorService:
                 "dataset_id": child_segment.dataset_id,
             },
         )
-        if dataset.indexing_technique == "high_quality":
+        if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             # save vector index
             vector = Vector(dataset=dataset)
             vector.add_texts([child_document], duplicate_check=True)
@@ -237,7 +237,7 @@ class VectorService:
             delete_node_ids.append(update_child_chunk.index_node_id)
         for delete_child_chunk in delete_child_chunks:
             delete_node_ids.append(delete_child_chunk.index_node_id)
-        if dataset.indexing_technique == "high_quality":
+        if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
             # update vector index
             vector = Vector(dataset=dataset)
             if delete_node_ids:
@@ -252,7 +252,7 @@ class VectorService:
 
     @classmethod
     def update_multimodel_vector(cls, segment: DocumentSegment, attachment_ids: list[str], dataset: Dataset):
-        if dataset.indexing_technique != "high_quality":
+        if dataset.indexing_technique != IndexTechniqueType.HIGH_QUALITY:
             return
 
         attachments = segment.attachments