""" Comprehensive unit tests for Dataset models. This test suite covers: - Dataset model validation - Document model relationships - Segment model indexing - Dataset-Document cascade deletes - Embedding storage validation """ import json import pickle from datetime import UTC, datetime from types import SimpleNamespace from unittest.mock import Mock, patch from urllib.parse import parse_qs, urlparse from uuid import uuid4 from core.rag.index_processor.constant.index_type import IndexTechniqueType from models.dataset import ( AppDatasetJoin, ChildChunk, Dataset, DatasetKeywordTable, DatasetProcessRule, Document, DocumentSegment, Embedding, ExternalKnowledgeBindings, ) from models.enums import ( DataSourceType, DocumentCreatedFrom, IndexingStatus, ProcessRuleMode, SegmentStatus, ) class TestDatasetModelValidation: """Test suite for Dataset model validation and basic operations.""" def test_dataset_creation_with_required_fields(self): """Test creating a dataset with all required fields.""" # Arrange tenant_id = str(uuid4()) created_by = str(uuid4()) # Act dataset = Dataset( tenant_id=tenant_id, name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=created_by, ) # Assert assert dataset.name == "Test Dataset" assert dataset.tenant_id == tenant_id assert dataset.data_source_type == DataSourceType.UPLOAD_FILE assert dataset.created_by == created_by # Note: Default values are set by database, not by model instantiation def test_dataset_creation_with_optional_fields(self): """Test creating a dataset with optional fields.""" # Arrange & Act dataset = Dataset( tenant_id=str(uuid4()), name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), description="Test description", indexing_technique=IndexTechniqueType.HIGH_QUALITY, embedding_model="text-embedding-ada-002", embedding_model_provider="openai", ) # Assert assert dataset.description == "Test description" assert dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY assert dataset.embedding_model == "text-embedding-ada-002" assert dataset.embedding_model_provider == "openai" def test_dataset_indexing_technique_validation(self): """Test dataset indexing technique values.""" # Arrange & Act dataset_high_quality = Dataset( tenant_id=str(uuid4()), name="High Quality Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), indexing_technique=IndexTechniqueType.HIGH_QUALITY, ) dataset_economy = Dataset( tenant_id=str(uuid4()), name="Economy Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), indexing_technique=IndexTechniqueType.ECONOMY, ) # Assert assert dataset_high_quality.indexing_technique == IndexTechniqueType.HIGH_QUALITY assert dataset_economy.indexing_technique == IndexTechniqueType.ECONOMY assert IndexTechniqueType.HIGH_QUALITY in Dataset.INDEXING_TECHNIQUE_LIST assert IndexTechniqueType.ECONOMY in Dataset.INDEXING_TECHNIQUE_LIST def test_dataset_provider_validation(self): """Test dataset provider values.""" # Arrange & Act dataset_vendor = Dataset( tenant_id=str(uuid4()), name="Vendor Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), provider="vendor", ) dataset_external = Dataset( tenant_id=str(uuid4()), name="External Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), provider="external", ) # Assert assert dataset_vendor.provider == "vendor" assert dataset_external.provider == "external" assert "vendor" in Dataset.PROVIDER_LIST assert "external" in Dataset.PROVIDER_LIST def test_dataset_index_struct_dict_property(self): """Test index_struct_dict property parsing.""" # Arrange index_struct_data = {"type": "vector", "dimension": 1536} dataset = Dataset( tenant_id=str(uuid4()), name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), index_struct=json.dumps(index_struct_data), ) # Act result = dataset.index_struct_dict # Assert assert result == index_struct_data assert result["type"] == "vector" assert result["dimension"] == 1536 def test_dataset_index_struct_dict_property_none(self): """Test index_struct_dict property when index_struct is None.""" # Arrange dataset = Dataset( tenant_id=str(uuid4()), name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), ) # Act result = dataset.index_struct_dict # Assert assert result is None def test_dataset_external_retrieval_model_property(self): """Test external_retrieval_model property with default values.""" # Arrange dataset = Dataset( tenant_id=str(uuid4()), name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), ) # Act result = dataset.external_retrieval_model # Assert assert result["top_k"] == 2 assert result["score_threshold"] == 0.0 def test_dataset_external_knowledge_info_returns_none_for_cross_tenant_template(self): """Test external datasets fail closed when the bound template is outside the tenant.""" dataset = Dataset( tenant_id=str(uuid4()), name="External Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), provider="external", ) binding = Mock(spec=ExternalKnowledgeBindings) binding.external_knowledge_id = "knowledge-1" binding.external_knowledge_api_id = str(uuid4()) with patch("models.dataset.db") as mock_db: mock_db.session.scalar.side_effect = [binding, None] assert dataset.external_knowledge_info is None def test_dataset_retrieval_model_dict_property(self): """Test retrieval_model_dict property with default values.""" # Arrange dataset = Dataset( tenant_id=str(uuid4()), name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=str(uuid4()), ) # Act result = dataset.retrieval_model_dict # Assert assert result["top_k"] == 2 assert result["reranking_enable"] is False assert result["score_threshold_enabled"] is False def test_dataset_gen_collection_name_by_id(self): """Test static method for generating collection name.""" # Arrange dataset_id = "12345678-1234-1234-1234-123456789abc" # Act collection_name = Dataset.gen_collection_name_by_id(dataset_id) # Assert assert "12345678_1234_1234_1234_123456789abc" in collection_name assert "-" not in collection_name.split("_")[-1] class TestDocumentModelRelationships: """Test suite for Document model relationships and properties.""" def test_document_creation_with_required_fields(self): """Test creating a document with all required fields.""" # Arrange tenant_id = str(uuid4()) dataset_id = str(uuid4()) created_by = str(uuid4()) # Act document = Document( tenant_id=tenant_id, dataset_id=dataset_id, position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test_document.pdf", created_from=DocumentCreatedFrom.WEB, created_by=created_by, ) # Assert assert document.tenant_id == tenant_id assert document.dataset_id == dataset_id assert document.position == 1 assert document.data_source_type == DataSourceType.UPLOAD_FILE assert document.batch == "batch_001" assert document.name == "test_document.pdf" assert document.created_from == DocumentCreatedFrom.WEB assert document.created_by == created_by # Note: Default values are set by database, not by model instantiation def test_document_data_source_types(self): """Test document data source type validation.""" # Assert assert "upload_file" in Document.DATA_SOURCES assert "notion_import" in Document.DATA_SOURCES assert "website_crawl" in Document.DATA_SOURCES def test_document_display_status_queuing(self): """Test document display_status property for queuing state.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=IndexingStatus.WAITING, ) # Act status = document.display_status # Assert assert status == "queuing" def test_document_display_status_paused(self): """Test document display_status property for paused state.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=IndexingStatus.PARSING, is_paused=True, ) # Act status = document.display_status # Assert assert status == "paused" def test_document_display_status_indexing(self): """Test document display_status property for indexing state.""" # Arrange for indexing_status in [ IndexingStatus.PARSING, IndexingStatus.CLEANING, IndexingStatus.SPLITTING, IndexingStatus.INDEXING, ]: document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=indexing_status, ) # Act status = document.display_status # Assert assert status == "indexing" def test_document_display_status_error(self): """Test document display_status property for error state.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=IndexingStatus.ERROR, ) # Act status = document.display_status # Assert assert status == "error" def test_document_display_status_available(self): """Test document display_status property for available state.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=IndexingStatus.COMPLETED, enabled=True, archived=False, ) # Act status = document.display_status # Assert assert status == "available" def test_document_display_status_disabled(self): """Test document display_status property for disabled state.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=IndexingStatus.COMPLETED, enabled=False, archived=False, ) # Act status = document.display_status # Assert assert status == "disabled" def test_document_display_status_archived(self): """Test document display_status property for archived state.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), indexing_status=IndexingStatus.COMPLETED, archived=True, ) # Act status = document.display_status # Assert assert status == "archived" def test_document_data_source_info_dict_property(self): """Test data_source_info_dict property parsing.""" # Arrange data_source_info = {"upload_file_id": str(uuid4()), "file_name": "test.pdf"} document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), data_source_info=json.dumps(data_source_info), ) # Act result = document.data_source_info_dict # Assert assert result == data_source_info assert "upload_file_id" in result assert "file_name" in result def test_document_data_source_info_dict_property_empty(self): """Test data_source_info_dict property when data_source_info is None.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), ) # Act result = document.data_source_info_dict # Assert assert result == {} def test_document_average_segment_length(self): """Test average_segment_length property calculation.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), word_count=1000, ) # Mock segment_count property with patch.object(Document, "segment_count", new_callable=lambda: property(lambda self: 10)): # Act result = document.average_segment_length # Assert assert result == 100 def test_document_average_segment_length_zero(self): """Test average_segment_length property when word_count is zero.""" # Arrange document = Document( tenant_id=str(uuid4()), dataset_id=str(uuid4()), position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=str(uuid4()), word_count=0, ) # Act result = document.average_segment_length # Assert assert result == 0 class TestDocumentSegmentIndexing: """Test suite for DocumentSegment model indexing and operations.""" def test_document_segment_creation_with_required_fields(self): """Test creating a document segment with all required fields.""" # Arrange tenant_id = str(uuid4()) dataset_id = str(uuid4()) document_id = str(uuid4()) created_by = str(uuid4()) # Act segment = DocumentSegment( tenant_id=tenant_id, dataset_id=dataset_id, document_id=document_id, position=1, content="This is a test segment content.", word_count=6, tokens=10, created_by=created_by, ) # Assert assert segment.tenant_id == tenant_id assert segment.dataset_id == dataset_id assert segment.document_id == document_id assert segment.position == 1 assert segment.content == "This is a test segment content." assert segment.word_count == 6 assert segment.tokens == 10 assert segment.created_by == created_by # Note: Default values are set by database, not by model instantiation def test_document_segment_with_indexing_fields(self): """Test creating a document segment with indexing fields.""" # Arrange index_node_id = str(uuid4()) index_node_hash = "abc123hash" keywords = ["test", "segment", "indexing"] # Act segment = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content="Test content", word_count=2, tokens=5, created_by=str(uuid4()), index_node_id=index_node_id, index_node_hash=index_node_hash, keywords=keywords, ) # Assert assert segment.index_node_id == index_node_id assert segment.index_node_hash == index_node_hash assert segment.keywords == keywords def test_document_segment_with_answer_field(self): """Test creating a document segment with answer field for QA model.""" # Arrange content = "What is AI?" answer = "AI stands for Artificial Intelligence." # Act segment = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content=content, answer=answer, word_count=3, tokens=8, created_by=str(uuid4()), ) # Assert assert segment.content == content assert segment.answer == answer def test_document_segment_status_transitions(self): """Test document segment status field values.""" # Arrange & Act segment_waiting = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content="Test", word_count=1, tokens=2, created_by=str(uuid4()), status=SegmentStatus.WAITING, ) segment_completed = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content="Test", word_count=1, tokens=2, created_by=str(uuid4()), status=SegmentStatus.COMPLETED, ) # Assert assert segment_waiting.status == SegmentStatus.WAITING assert segment_completed.status == SegmentStatus.COMPLETED def test_document_segment_enabled_disabled_tracking(self): """Test document segment enabled/disabled state tracking.""" # Arrange disabled_by = str(uuid4()) disabled_at = datetime.now(UTC) # Act segment = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content="Test", word_count=1, tokens=2, created_by=str(uuid4()), enabled=False, disabled_by=disabled_by, disabled_at=disabled_at, ) # Assert assert segment.enabled is False assert segment.disabled_by == disabled_by assert segment.disabled_at == disabled_at def test_document_segment_hit_count_tracking(self): """Test document segment hit count tracking.""" # Arrange & Act segment = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content="Test", word_count=1, tokens=2, created_by=str(uuid4()), hit_count=5, ) # Assert assert segment.hit_count == 5 def test_document_segment_attachments_prefers_files_url_for_source_url(self, monkeypatch): """Test attachment source URLs use FILES_URL before falling back to CONSOLE_API_URL.""" # Arrange segment = DocumentSegment( tenant_id="tenant-1", dataset_id="dataset-1", document_id="document-1", position=1, content="Test", word_count=1, tokens=2, created_by="user-1", ) segment.id = "segment-1" attachment = SimpleNamespace( id="upload-1", name="image.png", size=128, extension="png", mime_type="image/png", ) monkeypatch.setattr("models.dataset.time.time", lambda: 1700000000) monkeypatch.setattr("models.dataset.os.urandom", lambda _: b"\x01" * 16) monkeypatch.setattr("models.dataset.dify_config.SECRET_KEY", "unit-secret") monkeypatch.setattr("models.dataset.dify_config.FILES_URL", "https://files.example.com") monkeypatch.setattr("models.dataset.dify_config.CONSOLE_API_URL", "https://console.example.com") with patch("models.dataset.db") as mock_db: mock_db.session.execute.return_value.all.return_value = [(Mock(), attachment)] # Act attachments = segment.attachments # Assert assert len(attachments) == 1 source_url = attachments[0]["source_url"] parsed = urlparse(source_url) query = parse_qs(parsed.query) assert parsed.netloc == "files.example.com" assert parsed.path == "/files/upload-1/image-preview" assert query["timestamp"] == ["1700000000"] assert query["nonce"] == ["01010101010101010101010101010101"] assert query["sign"][0] def test_document_segment_error_tracking(self): """Test document segment error tracking.""" # Arrange error_message = "Indexing failed due to timeout" stopped_at = datetime.now(UTC) # Act segment = DocumentSegment( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), position=1, content="Test", word_count=1, tokens=2, created_by=str(uuid4()), error=error_message, stopped_at=stopped_at, ) # Assert assert segment.error == error_message assert segment.stopped_at == stopped_at class TestEmbeddingStorage: """Test suite for Embedding model storage and retrieval.""" def test_embedding_creation_with_required_fields(self): """Test creating an embedding with required fields.""" # Arrange model_name = "text-embedding-ada-002" hash_value = "abc123hash" provider_name = "openai" # Act embedding = Embedding( model_name=model_name, hash=hash_value, provider_name=provider_name, embedding=b"binary_data", ) # Assert assert embedding.model_name == model_name assert embedding.hash == hash_value assert embedding.provider_name == provider_name assert embedding.embedding == b"binary_data" def test_embedding_set_and_get_embedding(self): """Test setting and getting embedding data.""" # Arrange embedding_data = [0.1, 0.2, 0.3, 0.4, 0.5] embedding = Embedding( model_name="text-embedding-ada-002", hash="test_hash", provider_name="openai", embedding=b"", ) # Act embedding.set_embedding(embedding_data) retrieved_data = embedding.get_embedding() # Assert assert retrieved_data == embedding_data assert len(retrieved_data) == 5 assert retrieved_data[0] == 0.1 assert retrieved_data[4] == 0.5 def test_embedding_pickle_serialization(self): """Test embedding data is properly pickled.""" # Arrange embedding_data = [0.1, 0.2, 0.3] embedding = Embedding( model_name="text-embedding-ada-002", hash="test_hash", provider_name="openai", embedding=b"", ) # Act embedding.set_embedding(embedding_data) # Assert # Verify the embedding is stored as pickled binary data assert isinstance(embedding.embedding, bytes) # Verify we can unpickle it unpickled_data = pickle.loads(embedding.embedding) # noqa: S301 assert unpickled_data == embedding_data def test_embedding_with_large_vector(self): """Test embedding with large dimension vector.""" # Arrange # Simulate a 1536-dimension vector (OpenAI ada-002 size) large_embedding_data = [0.001 * i for i in range(1536)] embedding = Embedding( model_name="text-embedding-ada-002", hash="large_vector_hash", provider_name="openai", embedding=b"", ) # Act embedding.set_embedding(large_embedding_data) retrieved_data = embedding.get_embedding() # Assert assert len(retrieved_data) == 1536 assert retrieved_data[0] == 0.0 assert abs(retrieved_data[1535] - 1.535) < 0.0001 # Float comparison with tolerance class TestDatasetProcessRule: """Test suite for DatasetProcessRule model.""" def test_dataset_process_rule_creation(self): """Test creating a dataset process rule.""" # Arrange dataset_id = str(uuid4()) created_by = str(uuid4()) # Act process_rule = DatasetProcessRule( dataset_id=dataset_id, mode=ProcessRuleMode.AUTOMATIC, created_by=created_by, rules=None ) # Assert assert process_rule.dataset_id == dataset_id assert process_rule.mode == ProcessRuleMode.AUTOMATIC assert process_rule.created_by == created_by def test_dataset_process_rule_modes(self): """Test dataset process rule mode validation.""" # Assert assert "automatic" in DatasetProcessRule.MODES assert "custom" in DatasetProcessRule.MODES assert "hierarchical" in DatasetProcessRule.MODES def test_dataset_process_rule_with_rules_dict(self): """Test dataset process rule with rules dictionary.""" # Arrange rules_data = { "pre_processing_rules": [ {"id": "remove_extra_spaces", "enabled": True}, {"id": "remove_urls_emails", "enabled": False}, ], "segmentation": {"delimiter": "\n", "max_tokens": 500, "chunk_overlap": 50}, } process_rule = DatasetProcessRule( dataset_id=str(uuid4()), mode=ProcessRuleMode.CUSTOM, created_by=str(uuid4()), rules=json.dumps(rules_data), ) # Act result = process_rule.rules_dict # Assert assert result == rules_data assert "pre_processing_rules" in result assert "segmentation" in result def test_dataset_process_rule_to_dict(self): """Test dataset process rule to_dict method.""" # Arrange dataset_id = str(uuid4()) rules_data = {"test": "data"} process_rule = DatasetProcessRule( dataset_id=dataset_id, mode=ProcessRuleMode.AUTOMATIC, created_by=str(uuid4()), rules=json.dumps(rules_data), ) # Act result = process_rule.to_dict() # Assert assert result["dataset_id"] == dataset_id assert result["mode"] == ProcessRuleMode.AUTOMATIC assert result["rules"] == rules_data def test_dataset_process_rule_automatic_rules(self): """Test dataset process rule automatic rules constant.""" # Act automatic_rules = DatasetProcessRule.AUTOMATIC_RULES # Assert assert "pre_processing_rules" in automatic_rules assert "segmentation" in automatic_rules assert automatic_rules["segmentation"]["max_tokens"] == 500 class TestDatasetKeywordTable: """Test suite for DatasetKeywordTable model.""" def test_dataset_keyword_table_creation(self): """Test creating a dataset keyword table.""" # Arrange dataset_id = str(uuid4()) keyword_data = {"test": ["node1", "node2"], "keyword": ["node3"]} # Act keyword_table = DatasetKeywordTable( dataset_id=dataset_id, keyword_table=json.dumps(keyword_data), ) # Assert assert keyword_table.dataset_id == dataset_id assert keyword_table.data_source_type == "database" # Default value def test_dataset_keyword_table_data_source_type(self): """Test dataset keyword table data source type.""" # Arrange & Act keyword_table = DatasetKeywordTable( dataset_id=str(uuid4()), keyword_table="{}", data_source_type="file", ) # Assert assert keyword_table.data_source_type == "file" class TestAppDatasetJoin: """Test suite for AppDatasetJoin model.""" def test_app_dataset_join_creation(self): """Test creating an app-dataset join relationship.""" # Arrange app_id = str(uuid4()) dataset_id = str(uuid4()) # Act join = AppDatasetJoin( app_id=app_id, dataset_id=dataset_id, ) # Assert assert join.app_id == app_id assert join.dataset_id == dataset_id # Note: ID is auto-generated when saved to database class TestChildChunk: """Test suite for ChildChunk model.""" def test_child_chunk_creation(self): """Test creating a child chunk.""" # Arrange tenant_id = str(uuid4()) dataset_id = str(uuid4()) document_id = str(uuid4()) segment_id = str(uuid4()) created_by = str(uuid4()) # Act child_chunk = ChildChunk( tenant_id=tenant_id, dataset_id=dataset_id, document_id=document_id, segment_id=segment_id, position=1, content="Child chunk content", word_count=3, created_by=created_by, ) # Assert assert child_chunk.tenant_id == tenant_id assert child_chunk.dataset_id == dataset_id assert child_chunk.document_id == document_id assert child_chunk.segment_id == segment_id assert child_chunk.position == 1 assert child_chunk.content == "Child chunk content" assert child_chunk.word_count == 3 assert child_chunk.created_by == created_by # Note: Default values are set by database, not by model instantiation def test_child_chunk_with_indexing_fields(self): """Test creating a child chunk with indexing fields.""" # Arrange index_node_id = str(uuid4()) index_node_hash = "child_hash_123" # Act child_chunk = ChildChunk( tenant_id=str(uuid4()), dataset_id=str(uuid4()), document_id=str(uuid4()), segment_id=str(uuid4()), position=1, content="Test content", word_count=2, created_by=str(uuid4()), index_node_id=index_node_id, index_node_hash=index_node_hash, ) # Assert assert child_chunk.index_node_id == index_node_id assert child_chunk.index_node_hash == index_node_hash class TestModelIntegration: """Test suite for model integration scenarios.""" def test_complete_dataset_document_segment_hierarchy(self): """Test complete hierarchy from dataset to segment.""" # Arrange tenant_id = str(uuid4()) dataset_id = str(uuid4()) document_id = str(uuid4()) created_by = str(uuid4()) # Create dataset dataset = Dataset( tenant_id=tenant_id, name="Test Dataset", data_source_type=DataSourceType.UPLOAD_FILE, created_by=created_by, indexing_technique=IndexTechniqueType.HIGH_QUALITY, ) dataset.id = dataset_id # Create document document = Document( tenant_id=tenant_id, dataset_id=dataset_id, position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=created_by, word_count=100, ) document.id = document_id # Create segment segment = DocumentSegment( tenant_id=tenant_id, dataset_id=dataset_id, document_id=document_id, position=1, content="Test segment content", word_count=3, tokens=5, created_by=created_by, status=SegmentStatus.COMPLETED, ) # Assert assert dataset.id == dataset_id assert document.dataset_id == dataset_id assert segment.dataset_id == dataset_id assert segment.document_id == document_id assert dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY assert document.word_count == 100 assert segment.status == SegmentStatus.COMPLETED def test_document_to_dict_serialization(self): """Test document to_dict method for serialization.""" # Arrange tenant_id = str(uuid4()) dataset_id = str(uuid4()) created_by = str(uuid4()) document = Document( tenant_id=tenant_id, dataset_id=dataset_id, position=1, data_source_type=DataSourceType.UPLOAD_FILE, batch="batch_001", name="test.pdf", created_from=DocumentCreatedFrom.WEB, created_by=created_by, word_count=100, indexing_status=IndexingStatus.COMPLETED, ) # Mock segment_count and hit_count with ( patch.object(Document, "segment_count", new_callable=lambda: property(lambda self: 5)), patch.object(Document, "hit_count", new_callable=lambda: property(lambda self: 10)), ): # Act result = document.to_dict() # Assert assert result["tenant_id"] == tenant_id assert result["dataset_id"] == dataset_id assert result["name"] == "test.pdf" assert result["word_count"] == 100 assert result["indexing_status"] == IndexingStatus.COMPLETED assert result["segment_count"] == 5 assert result["hit_count"] == 10