diff --git a/api/tests/unit_tests/models/test_dataset_models.py b/api/tests/unit_tests/models/test_dataset_models.py
new file mode 100644
index 0000000000..2322c556e2
--- /dev/null
+++ b/api/tests/unit_tests/models/test_dataset_models.py
@@ -0,0 +1,1341 @@
+"""
+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 unittest.mock import MagicMock, patch
+from uuid import uuid4
+
+from models.dataset import (
+    AppDatasetJoin,
+    ChildChunk,
+    Dataset,
+    DatasetKeywordTable,
+    DatasetProcessRule,
+    Document,
+    DocumentSegment,
+    Embedding,
+)
+
+
+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="upload_file",
+            created_by=created_by,
+        )
+
+        # Assert
+        assert dataset.name == "Test Dataset"
+        assert dataset.tenant_id == tenant_id
+        assert dataset.data_source_type == "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="upload_file",
+            created_by=str(uuid4()),
+            description="Test description",
+            indexing_technique="high_quality",
+            embedding_model="text-embedding-ada-002",
+            embedding_model_provider="openai",
+        )
+
+        # Assert
+        assert dataset.description == "Test description"
+        assert dataset.indexing_technique == "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="upload_file",
+            created_by=str(uuid4()),
+            indexing_technique="high_quality",
+        )
+        dataset_economy = Dataset(
+            tenant_id=str(uuid4()),
+            name="Economy Dataset",
+            data_source_type="upload_file",
+            created_by=str(uuid4()),
+            indexing_technique="economy",
+        )
+
+        # Assert
+        assert dataset_high_quality.indexing_technique == "high_quality"
+        assert dataset_economy.indexing_technique == "economy"
+        assert "high_quality" in Dataset.INDEXING_TECHNIQUE_LIST
+        assert "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="upload_file",
+            created_by=str(uuid4()),
+            provider="vendor",
+        )
+        dataset_external = Dataset(
+            tenant_id=str(uuid4()),
+            name="External Dataset",
+            data_source_type="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="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="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="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_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="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="upload_file",
+            batch="batch_001",
+            name="test_document.pdf",
+            created_from="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 == "upload_file"
+        assert document.batch == "batch_001"
+        assert document.name == "test_document.pdf"
+        assert document.created_from == "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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+            indexing_status="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+            indexing_status="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 ["parsing", "cleaning", "splitting", "indexing"]:
+            document = Document(
+                tenant_id=str(uuid4()),
+                dataset_id=str(uuid4()),
+                position=1,
+                data_source_type="upload_file",
+                batch="batch_001",
+                name="test.pdf",
+                created_from="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+            indexing_status="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+            indexing_status="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+            indexing_status="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+            indexing_status="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="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="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="completed",
+        )
+
+        # Assert
+        assert segment_waiting.status == "waiting"
+        assert segment_completed.status == "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_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="automatic",
+            created_by=created_by,
+        )
+
+        # Assert
+        assert process_rule.dataset_id == dataset_id
+        assert process_rule.mode == "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="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="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"] == "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 TestDatasetDocumentCascadeDeletes:
+    """Test suite for Dataset-Document cascade delete operations."""
+
+    def test_dataset_with_documents_relationship(self):
+        """Test dataset can track its documents."""
+        # Arrange
+        dataset_id = str(uuid4())
+        dataset = Dataset(
+            tenant_id=str(uuid4()),
+            name="Test Dataset",
+            data_source_type="upload_file",
+            created_by=str(uuid4()),
+        )
+        dataset.id = dataset_id
+
+        # Mock the database session query
+        mock_query = MagicMock()
+        mock_query.where.return_value.scalar.return_value = 3
+
+        with patch("models.dataset.db.session.query", return_value=mock_query):
+            # Act
+            total_docs = dataset.total_documents
+
+            # Assert
+            assert total_docs == 3
+
+    def test_dataset_available_documents_count(self):
+        """Test dataset can count available documents."""
+        # Arrange
+        dataset_id = str(uuid4())
+        dataset = Dataset(
+            tenant_id=str(uuid4()),
+            name="Test Dataset",
+            data_source_type="upload_file",
+            created_by=str(uuid4()),
+        )
+        dataset.id = dataset_id
+
+        # Mock the database session query
+        mock_query = MagicMock()
+        mock_query.where.return_value.scalar.return_value = 2
+
+        with patch("models.dataset.db.session.query", return_value=mock_query):
+            # Act
+            available_docs = dataset.total_available_documents
+
+            # Assert
+            assert available_docs == 2
+
+    def test_dataset_word_count_aggregation(self):
+        """Test dataset can aggregate word count from documents."""
+        # Arrange
+        dataset_id = str(uuid4())
+        dataset = Dataset(
+            tenant_id=str(uuid4()),
+            name="Test Dataset",
+            data_source_type="upload_file",
+            created_by=str(uuid4()),
+        )
+        dataset.id = dataset_id
+
+        # Mock the database session query
+        mock_query = MagicMock()
+        mock_query.with_entities.return_value.where.return_value.scalar.return_value = 5000
+
+        with patch("models.dataset.db.session.query", return_value=mock_query):
+            # Act
+            total_words = dataset.word_count
+
+            # Assert
+            assert total_words == 5000
+
+    def test_dataset_available_segment_count(self):
+        """Test dataset can count available segments."""
+        # Arrange
+        dataset_id = str(uuid4())
+        dataset = Dataset(
+            tenant_id=str(uuid4()),
+            name="Test Dataset",
+            data_source_type="upload_file",
+            created_by=str(uuid4()),
+        )
+        dataset.id = dataset_id
+
+        # Mock the database session query
+        mock_query = MagicMock()
+        mock_query.where.return_value.scalar.return_value = 15
+
+        with patch("models.dataset.db.session.query", return_value=mock_query):
+            # Act
+            segment_count = dataset.available_segment_count
+
+            # Assert
+            assert segment_count == 15
+
+    def test_document_segment_count_property(self):
+        """Test document can count its segments."""
+        # Arrange
+        document_id = str(uuid4())
+        document = Document(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            position=1,
+            data_source_type="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+        )
+        document.id = document_id
+
+        # Mock the database session query
+        mock_query = MagicMock()
+        mock_query.where.return_value.count.return_value = 10
+
+        with patch("models.dataset.db.session.query", return_value=mock_query):
+            # Act
+            segment_count = document.segment_count
+
+            # Assert
+            assert segment_count == 10
+
+    def test_document_hit_count_aggregation(self):
+        """Test document can aggregate hit count from segments."""
+        # Arrange
+        document_id = str(uuid4())
+        document = Document(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            position=1,
+            data_source_type="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+        )
+        document.id = document_id
+
+        # Mock the database session query
+        mock_query = MagicMock()
+        mock_query.with_entities.return_value.where.return_value.scalar.return_value = 25
+
+        with patch("models.dataset.db.session.query", return_value=mock_query):
+            # Act
+            hit_count = document.hit_count
+
+            # Assert
+            assert hit_count == 25
+
+
+class TestDocumentSegmentNavigation:
+    """Test suite for DocumentSegment navigation properties."""
+
+    def test_document_segment_dataset_property(self):
+        """Test segment can access its parent dataset."""
+        # Arrange
+        dataset_id = str(uuid4())
+        segment = DocumentSegment(
+            tenant_id=str(uuid4()),
+            dataset_id=dataset_id,
+            document_id=str(uuid4()),
+            position=1,
+            content="Test",
+            word_count=1,
+            tokens=2,
+            created_by=str(uuid4()),
+        )
+
+        mock_dataset = Dataset(
+            tenant_id=str(uuid4()),
+            name="Test Dataset",
+            data_source_type="upload_file",
+            created_by=str(uuid4()),
+        )
+        mock_dataset.id = dataset_id
+
+        # Mock the database session scalar
+        with patch("models.dataset.db.session.scalar", return_value=mock_dataset):
+            # Act
+            dataset = segment.dataset
+
+            # Assert
+            assert dataset is not None
+            assert dataset.id == dataset_id
+
+    def test_document_segment_document_property(self):
+        """Test segment can access its parent document."""
+        # Arrange
+        document_id = str(uuid4())
+        segment = DocumentSegment(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            document_id=document_id,
+            position=1,
+            content="Test",
+            word_count=1,
+            tokens=2,
+            created_by=str(uuid4()),
+        )
+
+        mock_document = Document(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            position=1,
+            data_source_type="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=str(uuid4()),
+        )
+        mock_document.id = document_id
+
+        # Mock the database session scalar
+        with patch("models.dataset.db.session.scalar", return_value=mock_document):
+            # Act
+            document = segment.document
+
+            # Assert
+            assert document is not None
+            assert document.id == document_id
+
+    def test_document_segment_previous_segment(self):
+        """Test segment can access previous segment."""
+        # Arrange
+        document_id = str(uuid4())
+        segment = DocumentSegment(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            document_id=document_id,
+            position=2,
+            content="Test",
+            word_count=1,
+            tokens=2,
+            created_by=str(uuid4()),
+        )
+
+        previous_segment = DocumentSegment(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            document_id=document_id,
+            position=1,
+            content="Previous",
+            word_count=1,
+            tokens=2,
+            created_by=str(uuid4()),
+        )
+
+        # Mock the database session scalar
+        with patch("models.dataset.db.session.scalar", return_value=previous_segment):
+            # Act
+            prev_seg = segment.previous_segment
+
+            # Assert
+            assert prev_seg is not None
+            assert prev_seg.position == 1
+
+    def test_document_segment_next_segment(self):
+        """Test segment can access next segment."""
+        # Arrange
+        document_id = str(uuid4())
+        segment = DocumentSegment(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            document_id=document_id,
+            position=1,
+            content="Test",
+            word_count=1,
+            tokens=2,
+            created_by=str(uuid4()),
+        )
+
+        next_segment = DocumentSegment(
+            tenant_id=str(uuid4()),
+            dataset_id=str(uuid4()),
+            document_id=document_id,
+            position=2,
+            content="Next",
+            word_count=1,
+            tokens=2,
+            created_by=str(uuid4()),
+        )
+
+        # Mock the database session scalar
+        with patch("models.dataset.db.session.scalar", return_value=next_segment):
+            # Act
+            next_seg = segment.next_segment
+
+            # Assert
+            assert next_seg is not None
+            assert next_seg.position == 2
+
+
+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="upload_file",
+            created_by=created_by,
+            indexing_technique="high_quality",
+        )
+        dataset.id = dataset_id
+
+        # Create document
+        document = Document(
+            tenant_id=tenant_id,
+            dataset_id=dataset_id,
+            position=1,
+            data_source_type="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="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="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 == "high_quality"
+        assert document.word_count == 100
+        assert segment.status == "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="upload_file",
+            batch="batch_001",
+            name="test.pdf",
+            created_from="web",
+            created_by=created_by,
+            word_count=100,
+            indexing_status="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"] == "completed"
+            assert result["segment_count"] == 5
+            assert result["hit_count"] == 10