Streaming rag responses (#568)

* Tech spec for streaming RAG

* Support for streaming Graph/Doc RAG

tests/integration/conftest.py

@@ -382,6 +382,206 @@ def sample_kg_triples():
     ]
 
 
+# Streaming test fixtures
+
+@pytest.fixture
+def mock_streaming_llm_response():
+    """Mock streaming LLM response with realistic chunks"""
+    async def _generate_chunks():
+        """Generate realistic streaming chunks"""
+        chunks = [
+            "Machine",
+            " learning",
+            " is",
+            " a",
+            " subset",
+            " of",
+            " artificial",
+            " intelligence",
+            " that",
+            " focuses",
+            " on",
+            " algorithms",
+            " that",
+            " learn",
+            " from",
+            " data",
+            "."
+        ]
+        for chunk in chunks:
+            yield chunk
+    return _generate_chunks
+
+
+@pytest.fixture
+def sample_streaming_agent_response():
+    """Sample streaming agent response chunks"""
+    return [
+        {
+            "chunk_type": "thought",
+            "content": "I need to search",
+            "end_of_message": False,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "thought",
+            "content": " for information",
+            "end_of_message": False,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "thought",
+            "content": " about machine learning.",
+            "end_of_message": True,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "action",
+            "content": "knowledge_query",
+            "end_of_message": True,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "observation",
+            "content": "Machine learning is",
+            "end_of_message": False,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "observation",
+            "content": " a subset of AI.",
+            "end_of_message": True,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "final-answer",
+            "content": "Machine learning",
+            "end_of_message": False,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "final-answer",
+            "content": " is a subset",
+            "end_of_message": False,
+            "end_of_dialog": False
+        },
+        {
+            "chunk_type": "final-answer",
+            "content": " of artificial intelligence.",
+            "end_of_message": True,
+            "end_of_dialog": True
+        }
+    ]
+
+
+@pytest.fixture
+def streaming_chunk_collector():
+    """Helper to collect streaming chunks for assertions"""
+    class ChunkCollector:
+        def __init__(self):
+            self.chunks = []
+            self.complete = False
+
+        async def collect(self, chunk):
+            """Async callback to collect chunks"""
+            self.chunks.append(chunk)
+
+        def get_full_text(self):
+            """Concatenate all chunk content"""
+            return "".join(self.chunks)
+
+        def get_chunk_types(self):
+            """Get list of chunk types if chunks are dicts"""
+            if self.chunks and isinstance(self.chunks[0], dict):
+                return [c.get("chunk_type") for c in self.chunks]
+            return []
+
+    return ChunkCollector
+
+
+@pytest.fixture
+def mock_streaming_prompt_response():
+    """Mock streaming prompt service response"""
+    async def _generate_prompt_chunks():
+        """Generate streaming chunks for prompt responses"""
+        chunks = [
+            "Based on the",
+            " provided context,",
+            " here is",
+            " the answer:",
+            " Machine learning",
+            " enables computers",
+            " to learn",
+            " from data."
+        ]
+        for chunk in chunks:
+            yield chunk
+    return _generate_prompt_chunks
+
+
+@pytest.fixture
+def sample_rag_streaming_chunks():
+    """Sample RAG streaming response chunks"""
+    return [
+        {
+            "chunk": "Based on",
+            "end_of_stream": False
+        },
+        {
+            "chunk": " the knowledge",
+            "end_of_stream": False
+        },
+        {
+            "chunk": " graph,",
+            "end_of_stream": False
+        },
+        {
+            "chunk": " machine learning",
+            "end_of_stream": False
+        },
+        {
+            "chunk": " is a subset",
+            "end_of_stream": False
+        },
+        {
+            "chunk": " of AI.",
+            "end_of_stream": False
+        },
+        {
+            "chunk": None,
+            "end_of_stream": True,
+            "response": "Based on the knowledge graph, machine learning is a subset of AI."
+        }
+    ]
+
+
+@pytest.fixture
+def streaming_error_scenarios():
+    """Common error scenarios for streaming tests"""
+    return {
+        "connection_drop": {
+            "exception": ConnectionError,
+            "message": "Connection lost during streaming",
+            "chunks_before_error": 5
+        },
+        "timeout": {
+            "exception": TimeoutError,
+            "message": "Streaming timeout exceeded",
+            "chunks_before_error": 10
+        },
+        "rate_limit": {
+            "exception": Exception,
+            "message": "Rate limit exceeded",
+            "chunks_before_error": 3
+        },
+        "invalid_chunk": {
+            "exception": ValueError,
+            "message": "Invalid chunk format",
+            "chunks_before_error": 7
+        }
+    }
+
+
 # Test markers for integration tests
 pytestmark = pytest.mark.integration
 

tests/integration/test_agent_streaming_integration.py

@@ -0,0 +1,360 @@
+"""
+Integration tests for Agent Manager Streaming Functionality
+
+These tests verify the streaming behavior of the Agent service, testing
+chunk-by-chunk delivery of thoughts, actions, observations, and final answers.
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+from trustgraph.agent.react.agent_manager import AgentManager
+from trustgraph.agent.react.tools import KnowledgeQueryImpl
+from trustgraph.agent.react.types import Tool, Argument
+from tests.utils.streaming_assertions import (
+    assert_agent_streaming_chunks,
+    assert_streaming_chunks_valid,
+    assert_callback_invoked,
+    assert_chunk_types_valid,
+)
+
+
+@pytest.mark.integration
+class TestAgentStreaming:
+    """Integration tests for Agent streaming functionality"""
+
+    @pytest.fixture
+    def mock_prompt_client_streaming(self):
+        """Mock prompt client with streaming support"""
+        client = AsyncMock()
+
+        async def agent_react_streaming(variables, timeout=600, streaming=False, chunk_callback=None):
+            # Both modes return the same text for equivalence
+            full_text = """Thought: I need to search for information about machine learning.
+Action: knowledge_query
+Args: {
+    "question": "What is machine learning?"
+}"""
+
+            if streaming and chunk_callback:
+                # Send realistic line-by-line chunks
+                # This tests that the parser properly handles "Args:" starting a new chunk
+                # (which previously caused a bug where action_buffer was overwritten)
+                chunks = [
+                    "Thought: I need to search for information about machine learning.\n",
+                    "Action: knowledge_query\n",
+                    "Args: {\n",  # This used to trigger bug - Args: at start of chunk
+                    '    "question": "What is machine learning?"\n',
+                    "}"
+                ]
+
+                for chunk in chunks:
+                    await chunk_callback(chunk)
+
+                return full_text
+            else:
+                # Non-streaming response - same text
+                return full_text
+
+        client.agent_react.side_effect = agent_react_streaming
+        return client
+
+    @pytest.fixture
+    def mock_flow_context(self, mock_prompt_client_streaming):
+        """Mock flow context with streaming prompt client"""
+        context = MagicMock()
+
+        # Mock graph RAG client
+        graph_rag_client = AsyncMock()
+        graph_rag_client.rag.return_value = "Machine learning is a subset of AI."
+
+        def context_router(service_name):
+            if service_name == "prompt-request":
+                return mock_prompt_client_streaming
+            elif service_name == "graph-rag-request":
+                return graph_rag_client
+            else:
+                return AsyncMock()
+
+        context.side_effect = context_router
+        return context
+
+    @pytest.fixture
+    def sample_tools(self):
+        """Sample tool configuration"""
+        return {
+            "knowledge_query": Tool(
+                name="knowledge_query",
+                description="Query the knowledge graph",
+                arguments=[
+                    Argument(
+                        name="question",
+                        type="string",
+                        description="The question to ask"
+                    )
+                ],
+                implementation=KnowledgeQueryImpl,
+                config={}
+            )
+        }
+
+    @pytest.fixture
+    def agent_manager(self, sample_tools):
+        """Create AgentManager instance with streaming support"""
+        return AgentManager(
+            tools=sample_tools,
+            additional_context="You are a helpful AI assistant."
+        )
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_thought_chunks(self, agent_manager, mock_flow_context):
+        """Test that thought chunks are streamed correctly"""
+        # Arrange
+        thought_chunks = []
+
+        async def think(chunk):
+            thought_chunks.append(chunk)
+
+        # Act
+        await agent_manager.react(
+            question="What is machine learning?",
+            history=[],
+            think=think,
+            observe=AsyncMock(),
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert
+        assert len(thought_chunks) > 0
+        assert_streaming_chunks_valid(thought_chunks, min_chunks=1)
+
+        # Verify thought content makes sense
+        full_thought = "".join(thought_chunks)
+        assert "search" in full_thought.lower() or "information" in full_thought.lower()
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_observation_chunks(self, agent_manager, mock_flow_context):
+        """Test that observation chunks are streamed correctly"""
+        # Arrange
+        observation_chunks = []
+
+        async def observe(chunk):
+            observation_chunks.append(chunk)
+
+        # Act
+        await agent_manager.react(
+            question="What is machine learning?",
+            history=[],
+            think=AsyncMock(),
+            observe=observe,
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert
+        # Note: Observations come from tool execution, which may or may not be streamed
+        # depending on the tool implementation
+        # For now, verify callback was set up
+        assert observe is not None
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_vs_non_streaming(self, agent_manager, mock_flow_context):
+        """Test that streaming and non-streaming produce equivalent results"""
+        # Arrange
+        question = "What is machine learning?"
+        history = []
+
+        # Act - Non-streaming
+        non_streaming_result = await agent_manager.react(
+            question=question,
+            history=history,
+            think=AsyncMock(),
+            observe=AsyncMock(),
+            context=mock_flow_context,
+            streaming=False
+        )
+
+        # Act - Streaming
+        thought_chunks = []
+        observation_chunks = []
+
+        async def think(chunk):
+            thought_chunks.append(chunk)
+
+        async def observe(chunk):
+            observation_chunks.append(chunk)
+
+        streaming_result = await agent_manager.react(
+            question=question,
+            history=history,
+            think=think,
+            observe=observe,
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert - Results should be equivalent (or both valid)
+        assert non_streaming_result is not None
+        assert streaming_result is not None
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_callback_invocation(self, agent_manager, mock_flow_context):
+        """Test that callbacks are invoked with correct parameters"""
+        # Arrange
+        think = AsyncMock()
+        observe = AsyncMock()
+
+        # Act
+        await agent_manager.react(
+            question="What is machine learning?",
+            history=[],
+            think=think,
+            observe=observe,
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert - Think callback should be invoked
+        assert think.call_count > 0
+
+        # Verify all callback invocations had string arguments
+        for call in think.call_args_list:
+            assert len(call.args) > 0
+            assert isinstance(call.args[0], str)
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_without_callbacks(self, agent_manager, mock_flow_context):
+        """Test streaming parameter without callbacks (should work gracefully)"""
+        # Arrange & Act
+        result = await agent_manager.react(
+            question="What is machine learning?",
+            history=[],
+            think=AsyncMock(),
+            observe=AsyncMock(),
+            context=mock_flow_context,
+            streaming=True  # Streaming enabled with mock callbacks
+        )
+
+        # Assert - Should complete without error
+        assert result is not None
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_with_conversation_history(self, agent_manager, mock_flow_context):
+        """Test streaming with existing conversation history"""
+        # Arrange
+        # History should be a list of Action objects
+        from trustgraph.agent.react.types import Action
+        history = [
+            Action(
+                thought="I need to search for information about machine learning",
+                name="knowledge_query",
+                arguments={"question": "What is machine learning?"},
+                observation="Machine learning is a subset of AI that enables computers to learn from data."
+            )
+        ]
+        think = AsyncMock()
+
+        # Act
+        result = await agent_manager.react(
+            question="Tell me more about neural networks",
+            history=history,
+            think=think,
+            observe=AsyncMock(),
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert
+        assert result is not None
+        assert think.call_count > 0
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_error_propagation(self, agent_manager, mock_flow_context):
+        """Test that errors during streaming are properly propagated"""
+        # Arrange
+        mock_prompt_client = mock_flow_context("prompt-request")
+        mock_prompt_client.agent_react.side_effect = Exception("Prompt service error")
+
+        think = AsyncMock()
+        observe = AsyncMock()
+
+        # Act & Assert
+        with pytest.raises(Exception) as exc_info:
+            await agent_manager.react(
+                question="test question",
+                history=[],
+                think=think,
+                observe=observe,
+                context=mock_flow_context,
+                streaming=True
+            )
+
+        assert "Prompt service error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_multi_step_reasoning(self, agent_manager, mock_flow_context,
+                                                         mock_prompt_client_streaming):
+        """Test streaming through multi-step reasoning process"""
+        # Arrange - Mock a multi-step response
+        step_responses = [
+            """Thought: I need to search for basic information.
+Action: knowledge_query
+Args: {"question": "What is AI?"}""",
+            """Thought: Now I can answer the question.
+Final Answer: AI is the simulation of human intelligence in machines."""
+        ]
+
+        call_count = 0
+
+        async def multi_step_agent_react(variables, timeout=600, streaming=False, chunk_callback=None):
+            nonlocal call_count
+            response = step_responses[min(call_count, len(step_responses) - 1)]
+            call_count += 1
+
+            if streaming and chunk_callback:
+                for chunk in response.split():
+                    await chunk_callback(chunk + " ")
+                return response
+            return response
+
+        mock_prompt_client_streaming.agent_react.side_effect = multi_step_agent_react
+
+        think = AsyncMock()
+        observe = AsyncMock()
+
+        # Act
+        result = await agent_manager.react(
+            question="What is artificial intelligence?",
+            history=[],
+            think=think,
+            observe=observe,
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert
+        assert result is not None
+        assert think.call_count > 0
+
+    @pytest.mark.asyncio
+    async def test_agent_streaming_preserves_tool_config(self, agent_manager, mock_flow_context):
+        """Test that streaming preserves tool configuration and context"""
+        # Arrange
+        think = AsyncMock()
+        observe = AsyncMock()
+
+        # Act
+        await agent_manager.react(
+            question="What is machine learning?",
+            history=[],
+            think=think,
+            observe=observe,
+            context=mock_flow_context,
+            streaming=True
+        )
+
+        # Assert - Verify prompt client was called with streaming
+        mock_prompt_client = mock_flow_context("prompt-request")
+        call_args = mock_prompt_client.agent_react.call_args
+        assert call_args.kwargs['streaming'] is True
+        assert call_args.kwargs['chunk_callback'] is not None

tests/integration/test_document_rag_streaming_integration.py

@@ -0,0 +1,274 @@
+"""
+Integration tests for DocumentRAG streaming functionality
+
+These tests verify the streaming behavior of DocumentRAG, testing token-by-token
+response delivery through the complete pipeline.
+"""
+
+import pytest
+from unittest.mock import AsyncMock
+from trustgraph.retrieval.document_rag.document_rag import DocumentRag
+from tests.utils.streaming_assertions import (
+    assert_streaming_chunks_valid,
+    assert_callback_invoked,
+)
+
+
+@pytest.mark.integration
+class TestDocumentRagStreaming:
+    """Integration tests for DocumentRAG streaming"""
+
+    @pytest.fixture
+    def mock_embeddings_client(self):
+        """Mock embeddings client"""
+        client = AsyncMock()
+        client.embed.return_value = [[0.1, 0.2, 0.3, 0.4, 0.5]]
+        return client
+
+    @pytest.fixture
+    def mock_doc_embeddings_client(self):
+        """Mock document embeddings client"""
+        client = AsyncMock()
+        client.query.return_value = [
+            "Machine learning is a subset of AI.",
+            "Deep learning uses neural networks.",
+            "Supervised learning needs labeled data."
+        ]
+        return client
+
+    @pytest.fixture
+    def mock_streaming_prompt_client(self, mock_streaming_llm_response):
+        """Mock prompt client with streaming support"""
+        client = AsyncMock()
+
+        async def document_prompt_side_effect(query, documents, timeout=600, streaming=False, chunk_callback=None):
+            # Both modes return the same text
+            full_text = "Machine learning is a subset of artificial intelligence that focuses on algorithms that learn from data."
+
+            if streaming and chunk_callback:
+                # Simulate streaming chunks
+                async for chunk in mock_streaming_llm_response():
+                    await chunk_callback(chunk)
+                return full_text
+            else:
+                # Non-streaming response - same text
+                return full_text
+
+        client.document_prompt.side_effect = document_prompt_side_effect
+        return client
+
+    @pytest.fixture
+    def document_rag_streaming(self, mock_embeddings_client, mock_doc_embeddings_client,
+                                mock_streaming_prompt_client):
+        """Create DocumentRag instance with streaming support"""
+        return DocumentRag(
+            embeddings_client=mock_embeddings_client,
+            doc_embeddings_client=mock_doc_embeddings_client,
+            prompt_client=mock_streaming_prompt_client,
+            verbose=True
+        )
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_basic(self, document_rag_streaming, streaming_chunk_collector):
+        """Test basic DocumentRAG streaming functionality"""
+        # Arrange
+        query = "What is machine learning?"
+        collector = streaming_chunk_collector()
+
+        # Act
+        result = await document_rag_streaming.query(
+            query=query,
+            user="test_user",
+            collection="test_collection",
+            doc_limit=10,
+            streaming=True,
+            chunk_callback=collector.collect
+        )
+
+        # Assert
+        assert_streaming_chunks_valid(collector.chunks, min_chunks=1)
+        assert_callback_invoked(AsyncMock(call_count=len(collector.chunks)), min_calls=1)
+
+        # Verify full response matches concatenated chunks
+        full_from_chunks = collector.get_full_text()
+        assert result == full_from_chunks
+
+        # Verify content is reasonable
+        assert len(result) > 0
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_vs_non_streaming(self, document_rag_streaming):
+        """Test that streaming and non-streaming produce equivalent results"""
+        # Arrange
+        query = "What is machine learning?"
+        user = "test_user"
+        collection = "test_collection"
+        doc_limit = 10
+
+        # Act - Non-streaming
+        non_streaming_result = await document_rag_streaming.query(
+            query=query,
+            user=user,
+            collection=collection,
+            doc_limit=doc_limit,
+            streaming=False
+        )
+
+        # Act - Streaming
+        streaming_chunks = []
+
+        async def collect(chunk):
+            streaming_chunks.append(chunk)
+
+        streaming_result = await document_rag_streaming.query(
+            query=query,
+            user=user,
+            collection=collection,
+            doc_limit=doc_limit,
+            streaming=True,
+            chunk_callback=collect
+        )
+
+        # Assert - Results should be equivalent
+        assert streaming_result == non_streaming_result
+        assert len(streaming_chunks) > 0
+        assert "".join(streaming_chunks) == streaming_result
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_callback_invocation(self, document_rag_streaming):
+        """Test that chunk callback is invoked correctly"""
+        # Arrange
+        callback = AsyncMock()
+
+        # Act
+        result = await document_rag_streaming.query(
+            query="test query",
+            user="test_user",
+            collection="test_collection",
+            doc_limit=5,
+            streaming=True,
+            chunk_callback=callback
+        )
+
+        # Assert
+        assert callback.call_count > 0
+        assert result is not None
+
+        # Verify all callback invocations had string arguments
+        for call in callback.call_args_list:
+            assert isinstance(call.args[0], str)
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_without_callback(self, document_rag_streaming):
+        """Test streaming parameter without callback (should fall back to non-streaming)"""
+        # Arrange & Act
+        result = await document_rag_streaming.query(
+            query="test query",
+            user="test_user",
+            collection="test_collection",
+            doc_limit=5,
+            streaming=True,
+            chunk_callback=None  # No callback provided
+        )
+
+        # Assert - Should complete without error
+        assert result is not None
+        assert isinstance(result, str)
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_with_no_documents(self, document_rag_streaming,
+                                                             mock_doc_embeddings_client):
+        """Test streaming with no documents found"""
+        # Arrange
+        mock_doc_embeddings_client.query.return_value = []  # No documents
+        callback = AsyncMock()
+
+        # Act
+        result = await document_rag_streaming.query(
+            query="unknown topic",
+            user="test_user",
+            collection="test_collection",
+            doc_limit=10,
+            streaming=True,
+            chunk_callback=callback
+        )
+
+        # Assert - Should still produce streamed response
+        assert result is not None
+        assert callback.call_count > 0
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_error_propagation(self, document_rag_streaming,
+                                                              mock_embeddings_client):
+        """Test that errors during streaming are properly propagated"""
+        # Arrange
+        mock_embeddings_client.embed.side_effect = Exception("Embeddings error")
+        callback = AsyncMock()
+
+        # Act & Assert
+        with pytest.raises(Exception) as exc_info:
+            await document_rag_streaming.query(
+                query="test query",
+                user="test_user",
+                collection="test_collection",
+                doc_limit=5,
+                streaming=True,
+                chunk_callback=callback
+            )
+
+        assert "Embeddings error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_with_different_doc_limits(self, document_rag_streaming,
+                                                                      mock_doc_embeddings_client):
+        """Test streaming with various document limits"""
+        # Arrange
+        callback = AsyncMock()
+        doc_limits = [1, 5, 10, 20]
+
+        for limit in doc_limits:
+            # Reset mocks
+            mock_doc_embeddings_client.reset_mock()
+            callback.reset_mock()
+
+            # Act
+            result = await document_rag_streaming.query(
+                query="test query",
+                user="test_user",
+                collection="test_collection",
+                doc_limit=limit,
+                streaming=True,
+                chunk_callback=callback
+            )
+
+            # Assert
+            assert result is not None
+            assert callback.call_count > 0
+
+            # Verify doc_limit was passed correctly
+            call_args = mock_doc_embeddings_client.query.call_args
+            assert call_args.kwargs['limit'] == limit
+
+    @pytest.mark.asyncio
+    async def test_document_rag_streaming_preserves_user_collection(self, document_rag_streaming,
+                                                                      mock_doc_embeddings_client):
+        """Test that streaming preserves user/collection isolation"""
+        # Arrange
+        callback = AsyncMock()
+        user = "test_user_123"
+        collection = "test_collection_456"
+
+        # Act
+        await document_rag_streaming.query(
+            query="test query",
+            user=user,
+            collection=collection,
+            doc_limit=10,
+            streaming=True,
+            chunk_callback=callback
+        )
+
+        # Assert - Verify user/collection were passed to document embeddings client
+        call_args = mock_doc_embeddings_client.query.call_args
+        assert call_args.kwargs['user'] == user
+        assert call_args.kwargs['collection'] == collection

tests/integration/test_graph_rag_integration.py

@@ -0,0 +1,269 @@
+"""
+Integration tests for GraphRAG retrieval system
+
+These tests verify the end-to-end functionality of the GraphRAG system,
+testing the coordination between embeddings, graph retrieval, triple querying, and prompt services.
+Following the TEST_STRATEGY.md approach for integration testing.
+
+NOTE: This is the first integration test file for GraphRAG (previously had only unit tests).
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+from trustgraph.retrieval.graph_rag.graph_rag import GraphRag
+
+
+@pytest.mark.integration
+class TestGraphRagIntegration:
+    """Integration tests for GraphRAG system coordination"""
+
+    @pytest.fixture
+    def mock_embeddings_client(self):
+        """Mock embeddings client that returns realistic vector embeddings"""
+        client = AsyncMock()
+        client.embed.return_value = [
+            [0.1, 0.2, 0.3, 0.4, 0.5],  # Realistic 5-dimensional embedding
+        ]
+        return client
+
+    @pytest.fixture
+    def mock_graph_embeddings_client(self):
+        """Mock graph embeddings client that returns realistic entities"""
+        client = AsyncMock()
+        client.query.return_value = [
+            "http://trustgraph.ai/e/machine-learning",
+            "http://trustgraph.ai/e/artificial-intelligence",
+            "http://trustgraph.ai/e/neural-networks"
+        ]
+        return client
+
+    @pytest.fixture
+    def mock_triples_client(self):
+        """Mock triples client that returns realistic knowledge graph triples"""
+        client = AsyncMock()
+
+        # Mock different queries return different triples
+        async def query_side_effect(s=None, p=None, o=None, limit=None, user=None, collection=None):
+            # Mock label queries
+            if p == "http://www.w3.org/2000/01/rdf-schema#label":
+                if s == "http://trustgraph.ai/e/machine-learning":
+                    return [MagicMock(s=s, p=p, o="Machine Learning")]
+                elif s == "http://trustgraph.ai/e/artificial-intelligence":
+                    return [MagicMock(s=s, p=p, o="Artificial Intelligence")]
+                elif s == "http://trustgraph.ai/e/neural-networks":
+                    return [MagicMock(s=s, p=p, o="Neural Networks")]
+                return []
+
+            # Mock relationship queries
+            if s == "http://trustgraph.ai/e/machine-learning":
+                return [
+                    MagicMock(
+                        s="http://trustgraph.ai/e/machine-learning",
+                        p="http://trustgraph.ai/is_subset_of",
+                        o="http://trustgraph.ai/e/artificial-intelligence"
+                    ),
+                    MagicMock(
+                        s="http://trustgraph.ai/e/machine-learning",
+                        p="http://www.w3.org/2000/01/rdf-schema#label",
+                        o="Machine Learning"
+                    )
+                ]
+
+            return []
+
+        client.query.side_effect = query_side_effect
+        return client
+
+    @pytest.fixture
+    def mock_prompt_client(self):
+        """Mock prompt client that generates realistic responses"""
+        client = AsyncMock()
+        client.kg_prompt.return_value = (
+            "Machine learning is a subset of artificial intelligence that enables computers "
+            "to learn from data without being explicitly programmed. It uses algorithms "
+            "and statistical models to find patterns in data."
+        )
+        return client
+
+    @pytest.fixture
+    def graph_rag(self, mock_embeddings_client, mock_graph_embeddings_client,
+                  mock_triples_client, mock_prompt_client):
+        """Create GraphRag instance with mocked dependencies"""
+        return GraphRag(
+            embeddings_client=mock_embeddings_client,
+            graph_embeddings_client=mock_graph_embeddings_client,
+            triples_client=mock_triples_client,
+            prompt_client=mock_prompt_client,
+            verbose=True
+        )
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_end_to_end_flow(self, graph_rag, mock_embeddings_client,
+                                             mock_graph_embeddings_client, mock_triples_client,
+                                             mock_prompt_client):
+        """Test complete GraphRAG pipeline from query to response"""
+        # Arrange
+        query = "What is machine learning?"
+        user = "test_user"
+        collection = "ml_knowledge"
+        entity_limit = 50
+        triple_limit = 30
+
+        # Act
+        result = await graph_rag.query(
+            query=query,
+            user=user,
+            collection=collection,
+            entity_limit=entity_limit,
+            triple_limit=triple_limit
+        )
+
+        # Assert - Verify service coordination
+
+        # 1. Should compute embeddings for query
+        mock_embeddings_client.embed.assert_called_once_with(query)
+
+        # 2. Should query graph embeddings to find relevant entities
+        mock_graph_embeddings_client.query.assert_called_once()
+        call_args = mock_graph_embeddings_client.query.call_args
+        assert call_args.kwargs['vectors'] == [[0.1, 0.2, 0.3, 0.4, 0.5]]
+        assert call_args.kwargs['limit'] == entity_limit
+        assert call_args.kwargs['user'] == user
+        assert call_args.kwargs['collection'] == collection
+
+        # 3. Should query triples to build knowledge subgraph
+        assert mock_triples_client.query.call_count > 0
+
+        # 4. Should call prompt with knowledge graph
+        mock_prompt_client.kg_prompt.assert_called_once()
+        call_args = mock_prompt_client.kg_prompt.call_args
+        assert call_args.args[0] == query  # First arg is query
+        assert isinstance(call_args.args[1], list)  # Second arg is kg (list of triples)
+
+        # Verify final response
+        assert result is not None
+        assert isinstance(result, str)
+        assert "machine learning" in result.lower()
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_with_different_limits(self, graph_rag, mock_embeddings_client,
+                                                    mock_graph_embeddings_client):
+        """Test GraphRAG with various entity and triple limits"""
+        # Arrange
+        query = "Explain neural networks"
+        test_configs = [
+            {"entity_limit": 10, "triple_limit": 10},
+            {"entity_limit": 50, "triple_limit": 30},
+            {"entity_limit": 100, "triple_limit": 100},
+        ]
+
+        for config in test_configs:
+            # Reset mocks
+            mock_embeddings_client.reset_mock()
+            mock_graph_embeddings_client.reset_mock()
+
+            # Act
+            await graph_rag.query(
+                query=query,
+                user="test_user",
+                collection="test_collection",
+                entity_limit=config["entity_limit"],
+                triple_limit=config["triple_limit"]
+            )
+
+            # Assert
+            call_args = mock_graph_embeddings_client.query.call_args
+            assert call_args.kwargs['limit'] == config["entity_limit"]
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_error_propagation(self, graph_rag, mock_embeddings_client):
+        """Test that errors from underlying services are properly propagated"""
+        # Arrange
+        mock_embeddings_client.embed.side_effect = Exception("Embeddings service error")
+
+        # Act & Assert
+        with pytest.raises(Exception) as exc_info:
+            await graph_rag.query(
+                query="test query",
+                user="test_user",
+                collection="test_collection"
+            )
+
+        assert "Embeddings service error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_with_empty_knowledge_graph(self, graph_rag, mock_graph_embeddings_client,
+                                                         mock_triples_client, mock_prompt_client):
+        """Test GraphRAG handles empty knowledge graph gracefully"""
+        # Arrange
+        mock_graph_embeddings_client.query.return_value = []  # No entities found
+        mock_triples_client.query.return_value = []  # No triples found
+
+        # Act
+        result = await graph_rag.query(
+            query="unknown topic",
+            user="test_user",
+            collection="test_collection"
+        )
+
+        # Assert
+        # Should still call prompt client with empty knowledge graph
+        mock_prompt_client.kg_prompt.assert_called_once()
+        call_args = mock_prompt_client.kg_prompt.call_args
+        assert isinstance(call_args.args[1], list)  # kg should be a list
+        assert result is not None
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_label_caching(self, graph_rag, mock_triples_client):
+        """Test that label lookups are cached to reduce redundant queries"""
+        # Arrange
+        query = "What is machine learning?"
+
+        # First query
+        await graph_rag.query(
+            query=query,
+            user="test_user",
+            collection="test_collection"
+        )
+
+        first_call_count = mock_triples_client.query.call_count
+        mock_triples_client.reset_mock()
+
+        # Second identical query
+        await graph_rag.query(
+            query=query,
+            user="test_user",
+            collection="test_collection"
+        )
+
+        second_call_count = mock_triples_client.query.call_count
+
+        # Assert - Second query should make fewer triple queries due to caching
+        # Note: This is a weak assertion because caching behavior depends on
+        # implementation details, but it verifies the concept
+        assert second_call_count >= 0  # Should complete without errors
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_multi_user_isolation(self, graph_rag, mock_graph_embeddings_client):
+        """Test that different users/collections are properly isolated"""
+        # Arrange
+        query = "test query"
+        user1, collection1 = "user1", "collection1"
+        user2, collection2 = "user2", "collection2"
+
+        # Act
+        await graph_rag.query(query=query, user=user1, collection=collection1)
+        await graph_rag.query(query=query, user=user2, collection=collection2)
+
+        # Assert - Both users should have separate queries
+        assert mock_graph_embeddings_client.query.call_count == 2
+
+        # Verify first call
+        first_call = mock_graph_embeddings_client.query.call_args_list[0]
+        assert first_call.kwargs['user'] == user1
+        assert first_call.kwargs['collection'] == collection1
+
+        # Verify second call
+        second_call = mock_graph_embeddings_client.query.call_args_list[1]
+        assert second_call.kwargs['user'] == user2
+        assert second_call.kwargs['collection'] == collection2

tests/integration/test_graph_rag_streaming_integration.py

@@ -0,0 +1,249 @@
+"""
+Integration tests for GraphRAG streaming functionality
+
+These tests verify the streaming behavior of GraphRAG, testing token-by-token
+response delivery through the complete pipeline.
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+from trustgraph.retrieval.graph_rag.graph_rag import GraphRag
+from tests.utils.streaming_assertions import (
+    assert_streaming_chunks_valid,
+    assert_rag_streaming_chunks,
+    assert_streaming_content_matches,
+    assert_callback_invoked,
+)
+
+
+@pytest.mark.integration
+class TestGraphRagStreaming:
+    """Integration tests for GraphRAG streaming"""
+
+    @pytest.fixture
+    def mock_embeddings_client(self):
+        """Mock embeddings client"""
+        client = AsyncMock()
+        client.embed.return_value = [[0.1, 0.2, 0.3, 0.4, 0.5]]
+        return client
+
+    @pytest.fixture
+    def mock_graph_embeddings_client(self):
+        """Mock graph embeddings client"""
+        client = AsyncMock()
+        client.query.return_value = [
+            "http://trustgraph.ai/e/machine-learning",
+        ]
+        return client
+
+    @pytest.fixture
+    def mock_triples_client(self):
+        """Mock triples client with minimal responses"""
+        client = AsyncMock()
+
+        async def query_side_effect(s=None, p=None, o=None, limit=None, user=None, collection=None):
+            if p == "http://www.w3.org/2000/01/rdf-schema#label":
+                return [MagicMock(s=s, p=p, o="Machine Learning")]
+            return []
+
+        client.query.side_effect = query_side_effect
+        return client
+
+    @pytest.fixture
+    def mock_streaming_prompt_client(self, mock_streaming_llm_response):
+        """Mock prompt client with streaming support"""
+        client = AsyncMock()
+
+        async def kg_prompt_side_effect(query, kg, timeout=600, streaming=False, chunk_callback=None):
+            # Both modes return the same text
+            full_text = "Machine learning is a subset of artificial intelligence that focuses on algorithms that learn from data."
+
+            if streaming and chunk_callback:
+                # Simulate streaming chunks
+                async for chunk in mock_streaming_llm_response():
+                    await chunk_callback(chunk)
+                return full_text
+            else:
+                # Non-streaming response - same text
+                return full_text
+
+        client.kg_prompt.side_effect = kg_prompt_side_effect
+        return client
+
+    @pytest.fixture
+    def graph_rag_streaming(self, mock_embeddings_client, mock_graph_embeddings_client,
+                            mock_triples_client, mock_streaming_prompt_client):
+        """Create GraphRag instance with streaming support"""
+        return GraphRag(
+            embeddings_client=mock_embeddings_client,
+            graph_embeddings_client=mock_graph_embeddings_client,
+            triples_client=mock_triples_client,
+            prompt_client=mock_streaming_prompt_client,
+            verbose=True
+        )
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_basic(self, graph_rag_streaming, streaming_chunk_collector):
+        """Test basic GraphRAG streaming functionality"""
+        # Arrange
+        query = "What is machine learning?"
+        collector = streaming_chunk_collector()
+
+        # Act
+        result = await graph_rag_streaming.query(
+            query=query,
+            user="test_user",
+            collection="test_collection",
+            streaming=True,
+            chunk_callback=collector.collect
+        )
+
+        # Assert
+        assert_streaming_chunks_valid(collector.chunks, min_chunks=1)
+        assert_callback_invoked(AsyncMock(call_count=len(collector.chunks)), min_calls=1)
+
+        # Verify full response matches concatenated chunks
+        full_from_chunks = collector.get_full_text()
+        assert result == full_from_chunks
+
+        # Verify content is reasonable
+        assert "machine" in result.lower() or "learning" in result.lower()
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_vs_non_streaming(self, graph_rag_streaming):
+        """Test that streaming and non-streaming produce equivalent results"""
+        # Arrange
+        query = "What is machine learning?"
+        user = "test_user"
+        collection = "test_collection"
+
+        # Act - Non-streaming
+        non_streaming_result = await graph_rag_streaming.query(
+            query=query,
+            user=user,
+            collection=collection,
+            streaming=False
+        )
+
+        # Act - Streaming
+        streaming_chunks = []
+
+        async def collect(chunk):
+            streaming_chunks.append(chunk)
+
+        streaming_result = await graph_rag_streaming.query(
+            query=query,
+            user=user,
+            collection=collection,
+            streaming=True,
+            chunk_callback=collect
+        )
+
+        # Assert - Results should be equivalent
+        assert streaming_result == non_streaming_result
+        assert len(streaming_chunks) > 0
+        assert "".join(streaming_chunks) == streaming_result
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_callback_invocation(self, graph_rag_streaming):
+        """Test that chunk callback is invoked correctly"""
+        # Arrange
+        callback = AsyncMock()
+
+        # Act
+        result = await graph_rag_streaming.query(
+            query="test query",
+            user="test_user",
+            collection="test_collection",
+            streaming=True,
+            chunk_callback=callback
+        )
+
+        # Assert
+        assert callback.call_count > 0
+        assert result is not None
+
+        # Verify all callback invocations had string arguments
+        for call in callback.call_args_list:
+            assert isinstance(call.args[0], str)
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_without_callback(self, graph_rag_streaming):
+        """Test streaming parameter without callback (should fall back to non-streaming)"""
+        # Arrange & Act
+        result = await graph_rag_streaming.query(
+            query="test query",
+            user="test_user",
+            collection="test_collection",
+            streaming=True,
+            chunk_callback=None  # No callback provided
+        )
+
+        # Assert - Should complete without error
+        assert result is not None
+        assert isinstance(result, str)
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_with_empty_kg(self, graph_rag_streaming,
+                                                      mock_graph_embeddings_client):
+        """Test streaming with empty knowledge graph"""
+        # Arrange
+        mock_graph_embeddings_client.query.return_value = []  # No entities
+        callback = AsyncMock()
+
+        # Act
+        result = await graph_rag_streaming.query(
+            query="unknown topic",
+            user="test_user",
+            collection="test_collection",
+            streaming=True,
+            chunk_callback=callback
+        )
+
+        # Assert - Should still produce streamed response
+        assert result is not None
+        assert callback.call_count > 0
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_error_propagation(self, graph_rag_streaming,
+                                                          mock_embeddings_client):
+        """Test that errors during streaming are properly propagated"""
+        # Arrange
+        mock_embeddings_client.embed.side_effect = Exception("Embeddings error")
+        callback = AsyncMock()
+
+        # Act & Assert
+        with pytest.raises(Exception) as exc_info:
+            await graph_rag_streaming.query(
+                query="test query",
+                user="test_user",
+                collection="test_collection",
+                streaming=True,
+                chunk_callback=callback
+            )
+
+        assert "Embeddings error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_graph_rag_streaming_preserves_parameters(self, graph_rag_streaming,
+                                                             mock_graph_embeddings_client):
+        """Test that streaming preserves all query parameters"""
+        # Arrange
+        callback = AsyncMock()
+        entity_limit = 25
+        triple_limit = 15
+
+        # Act
+        await graph_rag_streaming.query(
+            query="test query",
+            user="test_user",
+            collection="test_collection",
+            entity_limit=entity_limit,
+            triple_limit=triple_limit,
+            streaming=True,
+            chunk_callback=callback
+        )
+
+        # Assert - Verify parameters were passed to underlying services
+        call_args = mock_graph_embeddings_client.query.call_args
+        assert call_args.kwargs['limit'] == entity_limit

tests/integration/test_prompt_streaming_integration.py

@@ -0,0 +1,404 @@
+"""
+Integration tests for Prompt Service Streaming Functionality
+
+These tests verify the streaming behavior of the Prompt service,
+testing how it coordinates between templates and text completion streaming.
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+from trustgraph.prompt.template.service import Processor
+from trustgraph.schema import PromptRequest, PromptResponse, TextCompletionResponse
+from tests.utils.streaming_assertions import (
+    assert_streaming_chunks_valid,
+    assert_callback_invoked,
+)
+
+
+@pytest.mark.integration
+class TestPromptStreaming:
+    """Integration tests for Prompt service streaming"""
+
+    @pytest.fixture
+    def mock_flow_context_streaming(self):
+        """Mock flow context with streaming text completion support"""
+        context = MagicMock()
+
+        # Mock text completion client with streaming
+        text_completion_client = AsyncMock()
+
+        async def streaming_request(request, recipient=None, timeout=600):
+            """Simulate streaming text completion"""
+            if request.streaming and recipient:
+                # Simulate streaming chunks
+                chunks = [
+                    "Machine", " learning", " is", " a", " field",
+                    " of", " artificial", " intelligence", "."
+                ]
+
+                for i, chunk_text in enumerate(chunks):
+                    is_final = (i == len(chunks) - 1)
+                    response = TextCompletionResponse(
+                        response=chunk_text,
+                        error=None,
+                        end_of_stream=is_final
+                    )
+                    final = await recipient(response)
+                    if final:
+                        break
+
+                # Final empty chunk
+                await recipient(TextCompletionResponse(
+                    response="",
+                    error=None,
+                    end_of_stream=True
+                ))
+
+        text_completion_client.request = streaming_request
+
+        # Mock response producer
+        response_producer = AsyncMock()
+
+        def context_router(service_name):
+            if service_name == "text-completion-request":
+                return text_completion_client
+            elif service_name == "response":
+                return response_producer
+            else:
+                return AsyncMock()
+
+        context.side_effect = context_router
+        return context
+
+    @pytest.fixture
+    def mock_prompt_manager(self):
+        """Mock PromptManager with simple template"""
+        manager = MagicMock()
+
+        async def invoke_template(kind, input_vars, llm_function):
+            """Simulate template invocation"""
+            # Call the LLM function with simple prompts
+            system = "You are a helpful assistant."
+            prompt = f"Question: {input_vars.get('question', 'test')}"
+            result = await llm_function(system, prompt)
+            return result
+
+        manager.invoke = invoke_template
+        return manager
+
+    @pytest.fixture
+    def prompt_processor_streaming(self, mock_prompt_manager):
+        """Create Prompt processor with streaming support"""
+        processor = MagicMock()
+        processor.manager = mock_prompt_manager
+        processor.config_key = "prompt"
+
+        # Bind the actual on_request method
+        processor.on_request = Processor.on_request.__get__(processor, Processor)
+
+        return processor
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_basic(self, prompt_processor_streaming, mock_flow_context_streaming):
+        """Test basic prompt streaming functionality"""
+        # Arrange
+        request = PromptRequest(
+            id="kg_prompt",
+            terms={"question": '"What is machine learning?"'},
+            streaming=True
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": "test-123"}
+
+        consumer = MagicMock()
+
+        # Act
+        await prompt_processor_streaming.on_request(
+            message, consumer, mock_flow_context_streaming
+        )
+
+        # Assert
+        # Verify response producer was called multiple times (for streaming chunks)
+        response_producer = mock_flow_context_streaming("response")
+        assert response_producer.send.call_count > 0
+
+        # Verify streaming chunks were sent
+        calls = response_producer.send.call_args_list
+        assert len(calls) > 1  # Should have multiple chunks
+
+        # Check that responses have end_of_stream flag
+        for call in calls:
+            response = call.args[0]
+            assert isinstance(response, PromptResponse)
+            assert hasattr(response, 'end_of_stream')
+
+        # Last response should have end_of_stream=True
+        last_call = calls[-1]
+        last_response = last_call.args[0]
+        assert last_response.end_of_stream is True
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_non_streaming_mode(self, prompt_processor_streaming,
+                                                         mock_flow_context_streaming):
+        """Test prompt service in non-streaming mode"""
+        # Arrange
+        request = PromptRequest(
+            id="kg_prompt",
+            terms={"question": '"What is AI?"'},
+            streaming=False  # Non-streaming
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": "test-456"}
+
+        consumer = MagicMock()
+
+        # Mock non-streaming text completion
+        text_completion_client = mock_flow_context_streaming("text-completion-request")
+
+        async def non_streaming_text_completion(system, prompt, streaming=False):
+            return "AI is the simulation of human intelligence in machines."
+
+        text_completion_client.text_completion = non_streaming_text_completion
+
+        # Act
+        await prompt_processor_streaming.on_request(
+            message, consumer, mock_flow_context_streaming
+        )
+
+        # Assert
+        # Verify response producer was called once (non-streaming)
+        response_producer = mock_flow_context_streaming("response")
+        # Note: In non-streaming mode, the service sends a single response
+        assert response_producer.send.call_count >= 1
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_chunk_forwarding(self, prompt_processor_streaming,
+                                                       mock_flow_context_streaming):
+        """Test that prompt service forwards chunks immediately"""
+        # Arrange
+        request = PromptRequest(
+            id="test_prompt",
+            terms={"question": '"Test query"'},
+            streaming=True
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": "test-789"}
+
+        consumer = MagicMock()
+
+        # Act
+        await prompt_processor_streaming.on_request(
+            message, consumer, mock_flow_context_streaming
+        )
+
+        # Assert
+        # Verify chunks were forwarded with proper structure
+        response_producer = mock_flow_context_streaming("response")
+        calls = response_producer.send.call_args_list
+
+        for call in calls:
+            response = call.args[0]
+            # Each response should have text and end_of_stream fields
+            assert hasattr(response, 'text')
+            assert hasattr(response, 'end_of_stream')
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_error_handling(self, prompt_processor_streaming):
+        """Test error handling during streaming"""
+        # Arrange
+        from trustgraph.schema import Error
+        context = MagicMock()
+
+        # Mock text completion client that raises an error
+        text_completion_client = AsyncMock()
+
+        async def failing_request(request, recipient=None, timeout=600):
+            if recipient:
+                # Send error response with proper Error schema
+                error_response = TextCompletionResponse(
+                    response="",
+                    error=Error(message="Text completion error", type="processing_error"),
+                    end_of_stream=True
+                )
+                await recipient(error_response)
+
+        text_completion_client.request = failing_request
+
+        # Mock response producer to capture error response
+        response_producer = AsyncMock()
+
+        def context_router(service_name):
+            if service_name == "text-completion-request":
+                return text_completion_client
+            elif service_name == "response":
+                return response_producer
+            else:
+                return AsyncMock()
+
+        context.side_effect = context_router
+
+        request = PromptRequest(
+            id="test_prompt",
+            terms={"question": '"Test"'},
+            streaming=True
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": "test-error"}
+
+        consumer = MagicMock()
+
+        # Act - The service catches errors and sends error responses, doesn't raise
+        await prompt_processor_streaming.on_request(message, consumer, context)
+
+        # Assert - Verify error response was sent
+        assert response_producer.send.call_count > 0
+
+        # Check that at least one response contains an error
+        error_sent = False
+        for call in response_producer.send.call_args_list:
+            response = call.args[0]
+            if hasattr(response, 'error') and response.error:
+                error_sent = True
+                assert "Text completion error" in response.error.message
+                break
+
+        assert error_sent, "Expected error response to be sent"
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_preserves_message_id(self, prompt_processor_streaming,
+                                                           mock_flow_context_streaming):
+        """Test that message IDs are preserved through streaming"""
+        # Arrange
+        message_id = "unique-test-id-12345"
+
+        request = PromptRequest(
+            id="test_prompt",
+            terms={"question": '"Test"'},
+            streaming=True
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": message_id}
+
+        consumer = MagicMock()
+
+        # Act
+        await prompt_processor_streaming.on_request(
+            message, consumer, mock_flow_context_streaming
+        )
+
+        # Assert
+        # Verify all responses were sent with the correct message ID
+        response_producer = mock_flow_context_streaming("response")
+        calls = response_producer.send.call_args_list
+
+        for call in calls:
+            properties = call.kwargs.get('properties')
+            assert properties is not None
+            assert properties['id'] == message_id
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_empty_response_handling(self, prompt_processor_streaming):
+        """Test handling of empty responses during streaming"""
+        # Arrange
+        context = MagicMock()
+
+        # Mock text completion that sends empty chunks
+        text_completion_client = AsyncMock()
+
+        async def empty_streaming_request(request, recipient=None, timeout=600):
+            if request.streaming and recipient:
+                # Send empty chunk followed by final marker
+                await recipient(TextCompletionResponse(
+                    response="",
+                    error=None,
+                    end_of_stream=False
+                ))
+                await recipient(TextCompletionResponse(
+                    response="",
+                    error=None,
+                    end_of_stream=True
+                ))
+
+        text_completion_client.request = empty_streaming_request
+        response_producer = AsyncMock()
+
+        def context_router(service_name):
+            if service_name == "text-completion-request":
+                return text_completion_client
+            elif service_name == "response":
+                return response_producer
+            else:
+                return AsyncMock()
+
+        context.side_effect = context_router
+
+        request = PromptRequest(
+            id="test_prompt",
+            terms={"question": '"Test"'},
+            streaming=True
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": "test-empty"}
+
+        consumer = MagicMock()
+
+        # Act
+        await prompt_processor_streaming.on_request(message, consumer, context)
+
+        # Assert
+        # Should still send responses even if empty (including final marker)
+        assert response_producer.send.call_count > 0
+
+        # Last response should have end_of_stream=True
+        last_call = response_producer.send.call_args_list[-1]
+        last_response = last_call.args[0]
+        assert last_response.end_of_stream is True
+
+    @pytest.mark.asyncio
+    async def test_prompt_streaming_concatenation_matches_complete(self, prompt_processor_streaming,
+                                                                     mock_flow_context_streaming):
+        """Test that streaming chunks concatenate to form complete response"""
+        # Arrange
+        request = PromptRequest(
+            id="test_prompt",
+            terms={"question": '"What is ML?"'},
+            streaming=True
+        )
+
+        message = MagicMock()
+        message.value.return_value = request
+        message.properties.return_value = {"id": "test-concat"}
+
+        consumer = MagicMock()
+
+        # Act
+        await prompt_processor_streaming.on_request(
+            message, consumer, mock_flow_context_streaming
+        )
+
+        # Assert
+        # Collect all response texts
+        response_producer = mock_flow_context_streaming("response")
+        calls = response_producer.send.call_args_list
+
+        chunk_texts = []
+        for call in calls:
+            response = call.args[0]
+            if response.text and not response.end_of_stream:
+                chunk_texts.append(response.text)
+
+        # Verify chunks concatenate to expected result
+        full_text = "".join(chunk_texts)
+        assert full_text == "Machine learning is a field of artificial intelligence"

tests/integration/test_text_completion_streaming_integration.py

@@ -0,0 +1,366 @@
+"""
+Integration tests for Text Completion Streaming Functionality
+
+These tests verify the streaming behavior of the Text Completion service,
+testing token-by-token response delivery through the complete pipeline.
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+from openai.types.chat import ChatCompletionChunk
+from openai.types.chat.chat_completion_chunk import Choice as StreamChoice, ChoiceDelta
+
+from trustgraph.model.text_completion.openai.llm import Processor
+from trustgraph.base import LlmChunk
+from tests.utils.streaming_assertions import (
+    assert_streaming_chunks_valid,
+    assert_callback_invoked,
+)
+
+
+@pytest.mark.integration
+class TestTextCompletionStreaming:
+    """Integration tests for Text Completion streaming"""
+
+    @pytest.fixture
+    def mock_streaming_openai_client(self, mock_streaming_llm_response):
+        """Mock OpenAI client with streaming support"""
+        client = MagicMock()
+
+        def create_streaming_completion(**kwargs):
+            """Generator that yields streaming chunks"""
+            # Check if streaming is enabled
+            if not kwargs.get('stream', False):
+                raise ValueError("Expected streaming mode")
+
+            # Simulate OpenAI streaming response
+            chunks_text = [
+                "Machine", " learning", " is", " a", " subset",
+                " of", " AI", " that", " enables", " computers",
+                " to", " learn", " from", " data", "."
+            ]
+
+            for text in chunks_text:
+                delta = ChoiceDelta(content=text, role=None)
+                choice = StreamChoice(index=0, delta=delta, finish_reason=None)
+                chunk = ChatCompletionChunk(
+                    id="chatcmpl-streaming",
+                    choices=[choice],
+                    created=1234567890,
+                    model="gpt-3.5-turbo",
+                    object="chat.completion.chunk"
+                )
+                yield chunk
+
+        # Return a new generator each time create is called
+        client.chat.completions.create.side_effect = lambda **kwargs: create_streaming_completion(**kwargs)
+        return client
+
+    @pytest.fixture
+    def text_completion_processor_streaming(self, mock_streaming_openai_client):
+        """Create text completion processor with streaming support"""
+        processor = MagicMock()
+        processor.default_model = "gpt-3.5-turbo"
+        processor.temperature = 0.7
+        processor.max_output = 1024
+        processor.openai = mock_streaming_openai_client
+
+        # Bind the actual streaming method
+        processor.generate_content_stream = Processor.generate_content_stream.__get__(
+            processor, Processor
+        )
+
+        return processor
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_basic(self, text_completion_processor_streaming,
+                                                     streaming_chunk_collector):
+        """Test basic text completion streaming functionality"""
+        # Arrange
+        system_prompt = "You are a helpful assistant."
+        user_prompt = "What is machine learning?"
+        collector = streaming_chunk_collector()
+
+        # Act - Collect all chunks
+        chunks = []
+        async for chunk in text_completion_processor_streaming.generate_content_stream(
+            system_prompt, user_prompt
+        ):
+            chunks.append(chunk)
+            if chunk.text:
+                await collector.collect(chunk.text)
+
+        # Assert
+        assert len(chunks) > 1  # Should have multiple chunks
+
+        # Verify all chunks are LlmChunk objects
+        for chunk in chunks:
+            assert isinstance(chunk, LlmChunk)
+            assert chunk.model == "gpt-3.5-turbo"
+
+        # Verify last chunk has is_final=True
+        assert chunks[-1].is_final is True
+
+        # Verify we got meaningful content
+        full_text = collector.get_full_text()
+        assert "machine" in full_text.lower() or "learning" in full_text.lower()
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_chunk_structure(self, text_completion_processor_streaming):
+        """Test that streaming chunks have correct structure"""
+        # Arrange
+        system_prompt = "You are a helpful assistant."
+        user_prompt = "Explain AI."
+
+        # Act
+        chunks = []
+        async for chunk in text_completion_processor_streaming.generate_content_stream(
+            system_prompt, user_prompt
+        ):
+            chunks.append(chunk)
+
+        # Assert - Verify chunk structure
+        for i, chunk in enumerate(chunks[:-1]):  # All except last
+            assert isinstance(chunk, LlmChunk)
+            assert chunk.text is not None
+            assert chunk.model == "gpt-3.5-turbo"
+            assert chunk.is_final is False
+
+        # Last chunk should be final marker
+        final_chunk = chunks[-1]
+        assert final_chunk.is_final is True
+        assert final_chunk.model == "gpt-3.5-turbo"
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_concatenation(self, text_completion_processor_streaming):
+        """Test that chunks concatenate to form complete response"""
+        # Arrange
+        system_prompt = "You are a helpful assistant."
+        user_prompt = "What is AI?"
+
+        # Act - Collect all chunk texts
+        chunk_texts = []
+        async for chunk in text_completion_processor_streaming.generate_content_stream(
+            system_prompt, user_prompt
+        ):
+            if chunk.text and not chunk.is_final:
+                chunk_texts.append(chunk.text)
+
+        # Assert
+        full_text = "".join(chunk_texts)
+        assert len(full_text) > 0
+        assert len(chunk_texts) > 1  # Should have multiple chunks
+
+        # Verify completeness - should be a coherent sentence
+        assert full_text == "Machine learning is a subset of AI that enables computers to learn from data."
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_final_marker(self, text_completion_processor_streaming):
+        """Test that final chunk properly marks end of stream"""
+        # Arrange
+        system_prompt = "You are a helpful assistant."
+        user_prompt = "Test query"
+
+        # Act
+        chunks = []
+        async for chunk in text_completion_processor_streaming.generate_content_stream(
+            system_prompt, user_prompt
+        ):
+            chunks.append(chunk)
+
+        # Assert
+        # Should have at least content chunks + final marker
+        assert len(chunks) >= 2
+
+        # Only the last chunk should have is_final=True
+        for chunk in chunks[:-1]:
+            assert chunk.is_final is False
+
+        assert chunks[-1].is_final is True
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_model_parameter(self, mock_streaming_openai_client):
+        """Test that model parameter is preserved in streaming"""
+        # Arrange
+        processor = MagicMock()
+        processor.default_model = "gpt-4"
+        processor.temperature = 0.5
+        processor.max_output = 2048
+        processor.openai = mock_streaming_openai_client
+        processor.generate_content_stream = Processor.generate_content_stream.__get__(
+            processor, Processor
+        )
+
+        # Act
+        chunks = []
+        async for chunk in processor.generate_content_stream("System", "Prompt"):
+            chunks.append(chunk)
+
+        # Assert
+        # Verify OpenAI was called with correct model
+        call_args = mock_streaming_openai_client.chat.completions.create.call_args
+        assert call_args.kwargs['model'] == "gpt-4"
+        assert call_args.kwargs['temperature'] == 0.5
+        assert call_args.kwargs['max_tokens'] == 2048
+        assert call_args.kwargs['stream'] is True
+
+        # Verify chunks have correct model
+        for chunk in chunks:
+            assert chunk.model == "gpt-4"
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_temperature_parameter(self, mock_streaming_openai_client):
+        """Test that temperature parameter is applied in streaming"""
+        # Arrange
+        temperatures = [0.0, 0.5, 1.0, 1.5]
+
+        for temp in temperatures:
+            processor = MagicMock()
+            processor.default_model = "gpt-3.5-turbo"
+            processor.temperature = temp
+            processor.max_output = 1024
+            processor.openai = mock_streaming_openai_client
+            processor.generate_content_stream = Processor.generate_content_stream.__get__(
+                processor, Processor
+            )
+
+            # Act
+            chunks = []
+            async for chunk in processor.generate_content_stream("System", "Prompt"):
+                chunks.append(chunk)
+                if chunk.is_final:
+                    break
+
+            # Assert
+            call_args = mock_streaming_openai_client.chat.completions.create.call_args
+            assert call_args.kwargs['temperature'] == temp
+
+            # Reset mock for next iteration
+            mock_streaming_openai_client.reset_mock()
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_error_propagation(self):
+        """Test that errors during streaming are properly propagated"""
+        # Arrange
+        mock_client = MagicMock()
+
+        def failing_stream(**kwargs):
+            yield from []
+            raise Exception("Streaming error")
+
+        mock_client.chat.completions.create.return_value = failing_stream()
+
+        processor = MagicMock()
+        processor.default_model = "gpt-3.5-turbo"
+        processor.temperature = 0.7
+        processor.max_output = 1024
+        processor.openai = mock_client
+        processor.generate_content_stream = Processor.generate_content_stream.__get__(
+            processor, Processor
+        )
+
+        # Act & Assert
+        with pytest.raises(Exception) as exc_info:
+            async for chunk in processor.generate_content_stream("System", "Prompt"):
+                pass
+
+        assert "Streaming error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_empty_chunks_filtered(self, mock_streaming_openai_client):
+        """Test that empty chunks are handled correctly"""
+        # Arrange - Mock that returns some empty chunks
+        def create_streaming_with_empties(**kwargs):
+            chunks_text = ["Hello", "", " world", "", "!"]
+
+            for text in chunks_text:
+                delta = ChoiceDelta(content=text if text else None, role=None)
+                choice = StreamChoice(index=0, delta=delta, finish_reason=None)
+                chunk = ChatCompletionChunk(
+                    id="chatcmpl-streaming",
+                    choices=[choice],
+                    created=1234567890,
+                    model="gpt-3.5-turbo",
+                    object="chat.completion.chunk"
+                )
+                yield chunk
+
+        mock_streaming_openai_client.chat.completions.create.side_effect = lambda **kwargs: create_streaming_with_empties(**kwargs)
+
+        processor = MagicMock()
+        processor.default_model = "gpt-3.5-turbo"
+        processor.temperature = 0.7
+        processor.max_output = 1024
+        processor.openai = mock_streaming_openai_client
+        processor.generate_content_stream = Processor.generate_content_stream.__get__(
+            processor, Processor
+        )
+
+        # Act
+        chunks = []
+        async for chunk in processor.generate_content_stream("System", "Prompt"):
+            chunks.append(chunk)
+
+        # Assert - Only non-empty chunks should be yielded (plus final marker)
+        text_chunks = [c for c in chunks if not c.is_final]
+        assert len(text_chunks) == 3  # "Hello", " world", "!"
+        assert "".join(c.text for c in text_chunks) == "Hello world!"
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_prompt_construction(self, mock_streaming_openai_client):
+        """Test that system and user prompts are correctly combined for streaming"""
+        # Arrange
+        processor = MagicMock()
+        processor.default_model = "gpt-3.5-turbo"
+        processor.temperature = 0.7
+        processor.max_output = 1024
+        processor.openai = mock_streaming_openai_client
+        processor.generate_content_stream = Processor.generate_content_stream.__get__(
+            processor, Processor
+        )
+
+        system_prompt = "You are an expert."
+        user_prompt = "Explain quantum physics."
+
+        # Act
+        chunks = []
+        async for chunk in processor.generate_content_stream(system_prompt, user_prompt):
+            chunks.append(chunk)
+            if chunk.is_final:
+                break
+
+        # Assert - Verify prompts were combined correctly
+        call_args = mock_streaming_openai_client.chat.completions.create.call_args
+        messages = call_args.kwargs['messages']
+        assert len(messages) == 1
+
+        message_content = messages[0]['content'][0]['text']
+        assert system_prompt in message_content
+        assert user_prompt in message_content
+        assert message_content.startswith(system_prompt)
+
+    @pytest.mark.asyncio
+    async def test_text_completion_streaming_chunk_count(self, text_completion_processor_streaming):
+        """Test that streaming produces expected number of chunks"""
+        # Arrange
+        system_prompt = "You are a helpful assistant."
+        user_prompt = "Test"
+
+        # Act
+        chunks = []
+        async for chunk in text_completion_processor_streaming.generate_content_stream(
+            system_prompt, user_prompt
+        ):
+            chunks.append(chunk)
+
+        # Assert
+        # Should have 15 content chunks + 1 final marker = 16 total
+        assert len(chunks) == 16
+
+        # 15 content chunks
+        content_chunks = [c for c in chunks if not c.is_final]
+        assert len(content_chunks) == 15
+
+        # 1 final marker
+        final_chunks = [c for c in chunks if c.is_final]
+        assert len(final_chunks) == 1