diff --git a/tests/unit/test_retrieval/test_document_rag.py b/tests/unit/test_retrieval/test_document_rag.py
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+++ b/tests/unit/test_retrieval/test_document_rag.py
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+"""
+Tests for DocumentRAG retrieval implementation
+"""
+
+import pytest
+from unittest.mock import MagicMock, AsyncMock
+
+from trustgraph.retrieval.document_rag.document_rag import DocumentRag, Query
+
+
+class TestDocumentRag:
+    """Test cases for DocumentRag class"""
+
+    def test_document_rag_initialization_with_defaults(self):
+        """Test DocumentRag initialization with default verbose setting"""
+        # Create mock clients
+        mock_prompt_client = MagicMock()
+        mock_embeddings_client = MagicMock()
+        mock_doc_embeddings_client = MagicMock()
+        
+        # Initialize DocumentRag
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client
+        )
+        
+        # Verify initialization
+        assert document_rag.prompt_client == mock_prompt_client
+        assert document_rag.embeddings_client == mock_embeddings_client
+        assert document_rag.doc_embeddings_client == mock_doc_embeddings_client
+        assert document_rag.verbose is False  # Default value
+
+    def test_document_rag_initialization_with_verbose(self):
+        """Test DocumentRag initialization with verbose enabled"""
+        # Create mock clients
+        mock_prompt_client = MagicMock()
+        mock_embeddings_client = MagicMock()
+        mock_doc_embeddings_client = MagicMock()
+        
+        # Initialize DocumentRag with verbose=True
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client,
+            verbose=True
+        )
+        
+        # Verify initialization
+        assert document_rag.prompt_client == mock_prompt_client
+        assert document_rag.embeddings_client == mock_embeddings_client
+        assert document_rag.doc_embeddings_client == mock_doc_embeddings_client
+        assert document_rag.verbose is True
+
+
+class TestQuery:
+    """Test cases for Query class"""
+
+    def test_query_initialization_with_defaults(self):
+        """Test Query initialization with default parameters"""
+        # Create mock DocumentRag
+        mock_rag = MagicMock()
+        
+        # Initialize Query with defaults
+        query = Query(
+            rag=mock_rag,
+            user="test_user",
+            collection="test_collection",
+            verbose=False
+        )
+        
+        # Verify initialization
+        assert query.rag == mock_rag
+        assert query.user == "test_user"
+        assert query.collection == "test_collection"
+        assert query.verbose is False
+        assert query.doc_limit == 20  # Default value
+
+    def test_query_initialization_with_custom_doc_limit(self):
+        """Test Query initialization with custom doc_limit"""
+        # Create mock DocumentRag
+        mock_rag = MagicMock()
+        
+        # Initialize Query with custom doc_limit
+        query = Query(
+            rag=mock_rag,
+            user="custom_user",
+            collection="custom_collection",
+            verbose=True,
+            doc_limit=50
+        )
+        
+        # Verify initialization
+        assert query.rag == mock_rag
+        assert query.user == "custom_user"
+        assert query.collection == "custom_collection"
+        assert query.verbose is True
+        assert query.doc_limit == 50
+
+    @pytest.mark.asyncio
+    async def test_get_vector_method(self):
+        """Test Query.get_vector method calls embeddings client correctly"""
+        # Create mock DocumentRag with embeddings client
+        mock_rag = MagicMock()
+        mock_embeddings_client = AsyncMock()
+        mock_rag.embeddings_client = mock_embeddings_client
+        
+        # Mock the embed method to return test vectors
+        expected_vectors = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+        mock_embeddings_client.embed.return_value = expected_vectors
+        
+        # Initialize Query
+        query = Query(
+            rag=mock_rag,
+            user="test_user",
+            collection="test_collection",
+            verbose=False
+        )
+        
+        # Call get_vector
+        test_query = "What documents are relevant?"
+        result = await query.get_vector(test_query)
+        
+        # Verify embeddings client was called correctly
+        mock_embeddings_client.embed.assert_called_once_with(test_query)
+        
+        # Verify result matches expected vectors
+        assert result == expected_vectors
+
+    @pytest.mark.asyncio
+    async def test_get_docs_method(self):
+        """Test Query.get_docs method retrieves documents correctly"""
+        # Create mock DocumentRag with clients
+        mock_rag = MagicMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        mock_rag.embeddings_client = mock_embeddings_client
+        mock_rag.doc_embeddings_client = mock_doc_embeddings_client
+        
+        # Mock the embedding and document query responses
+        test_vectors = [[0.1, 0.2, 0.3]]
+        mock_embeddings_client.embed.return_value = test_vectors
+        
+        # Mock document results
+        test_docs = ["Document 1 content", "Document 2 content"]
+        mock_doc_embeddings_client.query.return_value = test_docs
+        
+        # Initialize Query
+        query = Query(
+            rag=mock_rag,
+            user="test_user",
+            collection="test_collection",
+            verbose=False,
+            doc_limit=15
+        )
+        
+        # Call get_docs
+        test_query = "Find relevant documents"
+        result = await query.get_docs(test_query)
+        
+        # Verify embeddings client was called
+        mock_embeddings_client.embed.assert_called_once_with(test_query)
+        
+        # Verify doc embeddings client was called correctly
+        mock_doc_embeddings_client.query.assert_called_once_with(
+            test_vectors,
+            limit=15,
+            user="test_user",
+            collection="test_collection"
+        )
+        
+        # Verify result is list of documents
+        assert result == test_docs
+
+    @pytest.mark.asyncio
+    async def test_document_rag_query_method(self):
+        """Test DocumentRag.query method orchestrates full document RAG pipeline"""
+        # Create mock clients
+        mock_prompt_client = AsyncMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        
+        # Mock embeddings and document responses
+        test_vectors = [[0.1, 0.2, 0.3]]
+        test_docs = ["Relevant document content", "Another document"]
+        expected_response = "This is the document RAG response"
+        
+        mock_embeddings_client.embed.return_value = test_vectors
+        mock_doc_embeddings_client.query.return_value = test_docs
+        mock_prompt_client.document_prompt.return_value = expected_response
+        
+        # Initialize DocumentRag
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client,
+            verbose=False
+        )
+        
+        # Call DocumentRag.query
+        result = await document_rag.query(
+            query="test query",
+            user="test_user",
+            collection="test_collection",
+            doc_limit=10
+        )
+        
+        # Verify embeddings client was called
+        mock_embeddings_client.embed.assert_called_once_with("test query")
+        
+        # Verify doc embeddings client was called
+        mock_doc_embeddings_client.query.assert_called_once_with(
+            test_vectors,
+            limit=10,
+            user="test_user",
+            collection="test_collection"
+        )
+        
+        # Verify prompt client was called with documents and query
+        mock_prompt_client.document_prompt.assert_called_once_with(
+            query="test query",
+            documents=test_docs
+        )
+        
+        # Verify result
+        assert result == expected_response
+
+    @pytest.mark.asyncio
+    async def test_document_rag_query_with_defaults(self):
+        """Test DocumentRag.query method with default parameters"""
+        # Create mock clients
+        mock_prompt_client = AsyncMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        
+        # Mock responses
+        mock_embeddings_client.embed.return_value = [[0.1, 0.2]]
+        mock_doc_embeddings_client.query.return_value = ["Default doc"]
+        mock_prompt_client.document_prompt.return_value = "Default response"
+        
+        # Initialize DocumentRag
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client
+        )
+        
+        # Call DocumentRag.query with minimal parameters
+        result = await document_rag.query("simple query")
+        
+        # Verify default parameters were used
+        mock_doc_embeddings_client.query.assert_called_once_with(
+            [[0.1, 0.2]],
+            limit=20,  # Default doc_limit
+            user="trustgraph",  # Default user
+            collection="default"  # Default collection
+        )
+        
+        assert result == "Default response"
+
+    @pytest.mark.asyncio
+    async def test_get_docs_with_verbose_output(self):
+        """Test Query.get_docs method with verbose logging"""
+        # Create mock DocumentRag with clients
+        mock_rag = MagicMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        mock_rag.embeddings_client = mock_embeddings_client
+        mock_rag.doc_embeddings_client = mock_doc_embeddings_client
+        
+        # Mock responses
+        mock_embeddings_client.embed.return_value = [[0.7, 0.8]]
+        mock_doc_embeddings_client.query.return_value = ["Verbose test doc"]
+        
+        # Initialize Query with verbose=True
+        query = Query(
+            rag=mock_rag,
+            user="test_user",
+            collection="test_collection",
+            verbose=True,
+            doc_limit=5
+        )
+        
+        # Call get_docs
+        result = await query.get_docs("verbose test")
+        
+        # Verify calls were made
+        mock_embeddings_client.embed.assert_called_once_with("verbose test")
+        mock_doc_embeddings_client.query.assert_called_once()
+        
+        # Verify result
+        assert result == ["Verbose test doc"]
+
+    @pytest.mark.asyncio
+    async def test_document_rag_query_with_verbose(self):
+        """Test DocumentRag.query method with verbose logging enabled"""
+        # Create mock clients
+        mock_prompt_client = AsyncMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        
+        # Mock responses
+        mock_embeddings_client.embed.return_value = [[0.3, 0.4]]
+        mock_doc_embeddings_client.query.return_value = ["Verbose doc content"]
+        mock_prompt_client.document_prompt.return_value = "Verbose RAG response"
+        
+        # Initialize DocumentRag with verbose=True
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client,
+            verbose=True
+        )
+        
+        # Call DocumentRag.query
+        result = await document_rag.query("verbose query test")
+        
+        # Verify all clients were called
+        mock_embeddings_client.embed.assert_called_once_with("verbose query test")
+        mock_doc_embeddings_client.query.assert_called_once()
+        mock_prompt_client.document_prompt.assert_called_once_with(
+            query="verbose query test",
+            documents=["Verbose doc content"]
+        )
+        
+        assert result == "Verbose RAG response"
+
+    @pytest.mark.asyncio
+    async def test_get_docs_with_empty_results(self):
+        """Test Query.get_docs method when no documents are found"""
+        # Create mock DocumentRag with clients
+        mock_rag = MagicMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        mock_rag.embeddings_client = mock_embeddings_client
+        mock_rag.doc_embeddings_client = mock_doc_embeddings_client
+        
+        # Mock responses - empty document list
+        mock_embeddings_client.embed.return_value = [[0.1, 0.2]]
+        mock_doc_embeddings_client.query.return_value = []  # No documents found
+        
+        # Initialize Query
+        query = Query(
+            rag=mock_rag,
+            user="test_user",
+            collection="test_collection",
+            verbose=False
+        )
+        
+        # Call get_docs
+        result = await query.get_docs("query with no results")
+        
+        # Verify calls were made
+        mock_embeddings_client.embed.assert_called_once_with("query with no results")
+        mock_doc_embeddings_client.query.assert_called_once()
+        
+        # Verify empty result is returned
+        assert result == []
+
+    @pytest.mark.asyncio
+    async def test_document_rag_query_with_empty_documents(self):
+        """Test DocumentRag.query method when no documents are retrieved"""
+        # Create mock clients
+        mock_prompt_client = AsyncMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        
+        # Mock responses - no documents found
+        mock_embeddings_client.embed.return_value = [[0.5, 0.6]]
+        mock_doc_embeddings_client.query.return_value = []  # Empty document list
+        mock_prompt_client.document_prompt.return_value = "No documents found response"
+        
+        # Initialize DocumentRag
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client,
+            verbose=False
+        )
+        
+        # Call DocumentRag.query
+        result = await document_rag.query("query with no matching docs")
+        
+        # Verify prompt client was called with empty document list
+        mock_prompt_client.document_prompt.assert_called_once_with(
+            query="query with no matching docs",
+            documents=[]
+        )
+        
+        assert result == "No documents found response"
+
+    @pytest.mark.asyncio
+    async def test_get_vector_with_verbose(self):
+        """Test Query.get_vector method with verbose logging"""
+        # Create mock DocumentRag with embeddings client
+        mock_rag = MagicMock()
+        mock_embeddings_client = AsyncMock()
+        mock_rag.embeddings_client = mock_embeddings_client
+        
+        # Mock the embed method
+        expected_vectors = [[0.9, 1.0, 1.1]]
+        mock_embeddings_client.embed.return_value = expected_vectors
+        
+        # Initialize Query with verbose=True
+        query = Query(
+            rag=mock_rag,
+            user="test_user",
+            collection="test_collection",
+            verbose=True
+        )
+        
+        # Call get_vector
+        result = await query.get_vector("verbose vector test")
+        
+        # Verify embeddings client was called
+        mock_embeddings_client.embed.assert_called_once_with("verbose vector test")
+        
+        # Verify result
+        assert result == expected_vectors
+
+    @pytest.mark.asyncio
+    async def test_document_rag_integration_flow(self):
+        """Test complete DocumentRag integration with realistic data flow"""
+        # Create mock clients
+        mock_prompt_client = AsyncMock()
+        mock_embeddings_client = AsyncMock()
+        mock_doc_embeddings_client = AsyncMock()
+        
+        # Mock realistic responses
+        query_text = "What is machine learning?"
+        query_vectors = [[0.1, 0.2, 0.3, 0.4, 0.5]]
+        retrieved_docs = [
+            "Machine learning is a subset of artificial intelligence...",
+            "ML algorithms learn patterns from data to make predictions...",
+            "Common ML techniques include supervised and unsupervised learning..."
+        ]
+        final_response = "Machine learning is a field of AI that enables computers to learn and improve from experience without being explicitly programmed."
+        
+        mock_embeddings_client.embed.return_value = query_vectors
+        mock_doc_embeddings_client.query.return_value = retrieved_docs
+        mock_prompt_client.document_prompt.return_value = final_response
+        
+        # Initialize DocumentRag
+        document_rag = DocumentRag(
+            prompt_client=mock_prompt_client,
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client,
+            verbose=False
+        )
+        
+        # Execute full pipeline
+        result = await document_rag.query(
+            query=query_text,
+            user="research_user", 
+            collection="ml_knowledge",
+            doc_limit=25
+        )
+        
+        # Verify complete pipeline execution
+        mock_embeddings_client.embed.assert_called_once_with(query_text)
+        
+        mock_doc_embeddings_client.query.assert_called_once_with(
+            query_vectors,
+            limit=25,
+            user="research_user",
+            collection="ml_knowledge"
+        )
+        
+        mock_prompt_client.document_prompt.assert_called_once_with(
+            query=query_text,
+            documents=retrieved_docs
+        )
+        
+        # Verify final result
+        assert result == final_response
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