add tests

tests/utilities/test_events.py

@@ -38,6 +38,7 @@ from crewai.utilities.events.llm_events import (
     LLMCallFailedEvent,
     LLMCallStartedEvent,
     LLMCallType,
+    LLMStreamChunkEvent,
 )
 from crewai.utilities.events.task_events import (
     TaskCompletedEvent,
@@ -615,3 +616,233 @@ def test_llm_emits_call_failed_event():
         assert len(received_events) == 1
         assert received_events[0].type == "llm_call_failed"
         assert received_events[0].error == error_message
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_emits_stream_chunk_events():
+    """Test that LLM emits stream chunk events when streaming is enabled."""
+    received_chunks = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_stream_chunk(source, event):
+            received_chunks.append(event.chunk)
+
+        # Create an LLM with streaming enabled
+        llm = LLM(model="gpt-3.5-turbo", stream=True)
+
+        # Call the LLM with a simple message
+        response = llm.call("Tell me a short joke")
+
+        # Verify that we received chunks
+        assert len(received_chunks) > 0
+
+        # Verify that concatenating all chunks equals the final response
+        assert "".join(received_chunks) == response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_no_stream_chunks_when_streaming_disabled():
+    """Test that LLM doesn't emit stream chunk events when streaming is disabled."""
+    received_chunks = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_stream_chunk(source, event):
+            received_chunks.append(event.chunk)
+
+        # Create an LLM with streaming disabled
+        llm = LLM(model="gpt-3.5-turbo", stream=False)
+
+        # Call the LLM with a simple message
+        response = llm.call("Tell me a short joke")
+
+        # Verify that we didn't receive any chunks
+        assert len(received_chunks) == 0
+
+        # Verify we got a response
+        assert response and isinstance(response, str)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_tool_calling_with_streaming():
+    """Test that tool calling works correctly with streaming enabled."""
+    received_chunks = []
+    tool_called = False
+
+    def sample_tool(text: str) -> str:
+        nonlocal tool_called
+        tool_called = True
+        return f"Tool processed: {text}"
+
+    available_functions = {"sample_tool": sample_tool}
+
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "sample_tool",
+                "description": "A sample tool that processes text",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "text": {"type": "string", "description": "The text to process"}
+                    },
+                    "required": ["text"],
+                },
+            },
+        }
+    ]
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_stream_chunk(source, event):
+            received_chunks.append(event.chunk)
+
+        # Create an LLM with streaming enabled
+        llm = LLM(model="gpt-4", stream=True)
+
+        # Store original methods
+        original_call = llm.call
+        original_handle_tool_call = llm._handle_tool_call
+
+        # Create a mock call method that simulates streaming and tool calling
+        def mock_call(messages, tools=None, callbacks=None, available_functions=None):
+            # Emit some chunks first
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="I'll process "))
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="that text "))
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="for you."))
+
+            # Call the tool
+            if available_functions and "sample_tool" in available_functions:
+                result = available_functions["sample_tool"]("Hello, world!")
+                return result
+
+            return "No tool was called"
+
+        # Replace the methods with our mocks
+        llm.call = mock_call
+
+        try:
+            # Call the LLM with a message that should trigger tool use
+            response = llm.call(
+                "Process this text with the sample tool: 'Hello, world!'",
+                tools=tools,
+                available_functions=available_functions,
+            )
+
+            # Verify that we received chunks
+            assert len(received_chunks) == 3
+            assert "".join(received_chunks) == "I'll process that text for you."
+
+            # Verify that the tool was called
+            assert tool_called
+
+            # Verify the response contains the tool's output
+            assert response == "Tool processed: Hello, world!"
+
+        finally:
+            # Restore the original methods
+            llm.call = original_call
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_streaming_fallback_to_non_streaming():
+    """Test that streaming falls back to non-streaming when there's an error."""
+    received_chunks = []
+    fallback_called = False
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_stream_chunk(source, event):
+            received_chunks.append(event.chunk)
+
+        # Create an LLM with streaming enabled
+        llm = LLM(model="gpt-3.5-turbo", stream=True)
+
+        # Store original methods
+        original_call = llm.call
+
+        # Create a mock call method that handles the streaming error
+        def mock_call(messages, tools=None, callbacks=None, available_functions=None):
+            nonlocal fallback_called
+            # Emit a couple of chunks to simulate partial streaming
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1"))
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2"))
+
+            # Mark that fallback would be called
+            fallback_called = True
+
+            # Return a response as if fallback succeeded
+            return "Fallback response after streaming error"
+
+        # Replace the call method with our mock
+        llm.call = mock_call
+
+        try:
+            # Call the LLM
+            response = llm.call("Tell me a short joke")
+
+            # Verify that we received some chunks
+            assert len(received_chunks) == 2
+            assert received_chunks[0] == "Test chunk 1"
+            assert received_chunks[1] == "Test chunk 2"
+
+            # Verify fallback was triggered
+            assert fallback_called
+
+            # Verify we got the fallback response
+            assert response == "Fallback response after streaming error"
+
+        finally:
+            # Restore the original method
+            llm.call = original_call
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_streaming_empty_response_handling():
+    """Test that streaming handles empty responses correctly."""
+    received_chunks = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_stream_chunk(source, event):
+            received_chunks.append(event.chunk)
+
+        # Create an LLM with streaming enabled
+        llm = LLM(model="gpt-3.5-turbo", stream=True)
+
+        # Store original methods
+        original_call = llm.call
+
+        # Create a mock call method that simulates empty chunks
+        def mock_call(messages, tools=None, callbacks=None, available_functions=None):
+            # Emit a few empty chunks
+            for _ in range(3):
+                crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk=""))
+
+            # Return the default message for empty responses
+            return "I apologize, but I couldn't generate a proper response. Please try again or rephrase your request."
+
+        # Replace the call method with our mock
+        llm.call = mock_call
+
+        try:
+            # Call the LLM - this should handle empty response
+            response = llm.call("Tell me a short joke")
+
+            # Verify that we received empty chunks
+            assert len(received_chunks) == 3
+            assert all(chunk == "" for chunk in received_chunks)
+
+            # Verify the response is the default message for empty responses
+            assert "I apologize" in response and "couldn't generate" in response
+
+        finally:
+            # Restore the original method
+            llm.call = original_call