diff --git a/llama_stack/distribution/routers/inference.py b/llama_stack/distribution/routers/inference.py index a5cc8c4b5..4ac016231 100644 --- a/llama_stack/distribution/routers/inference.py +++ b/llama_stack/distribution/routers/inference.py @@ -77,13 +77,13 @@ class InferenceRouter(Inference): self.tokenizer = Tokenizer.get_instance() self.formatter = ChatFormat(self.tokenizer) + self.recording_context = None + async def initialize(self) -> None: logger.debug("InferenceRouter.initialize") - pass async def shutdown(self) -> None: logger.debug("InferenceRouter.shutdown") - pass async def register_model( self, diff --git a/llama_stack/distribution/stack.py b/llama_stack/distribution/stack.py index 40e0b9b50..63345e04a 100644 --- a/llama_stack/distribution/stack.py +++ b/llama_stack/distribution/stack.py @@ -94,6 +94,7 @@ RESOURCES = [ REGISTRY_REFRESH_INTERVAL_SECONDS = 300 REGISTRY_REFRESH_TASK = None +TEST_RECORDING_CONTEXT = None async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]): @@ -307,6 +308,15 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf async def construct_stack( run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None ) -> dict[Api, Any]: + inference_mode = os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower() + if inference_mode in ["record", "replay"]: + global TEST_RECORDING_CONTEXT + from llama_stack.testing.inference_recorder import setup_inference_recording + + TEST_RECORDING_CONTEXT = setup_inference_recording() + TEST_RECORDING_CONTEXT.__enter__() + logger.info(f"Inference recording enabled: mode={inference_mode}") + dist_registry, _ = await create_dist_registry(run_config.metadata_store, run_config.image_name) policy = run_config.server.auth.access_policy if run_config.server.auth else [] impls = await resolve_impls( @@ -352,6 +362,13 @@ async def shutdown_stack(impls: dict[Api, Any]): except (Exception, asyncio.CancelledError) as e: logger.exception(f"Failed to shutdown {impl_name}: {e}") + global TEST_RECORDING_CONTEXT + if TEST_RECORDING_CONTEXT: + try: + TEST_RECORDING_CONTEXT.__exit__(None, None, None) + except Exception as e: + logger.error(f"Error during inference recording cleanup: {e}") + global REGISTRY_REFRESH_TASK if REGISTRY_REFRESH_TASK: REGISTRY_REFRESH_TASK.cancel() diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 711ecd1fc..32a343421 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -13,13 +13,23 @@ import sqlite3 from collections.abc import Generator from contextlib import contextmanager from pathlib import Path -from typing import Any, cast +from typing import Any, Literal, cast + +from llama_stack.log import get_logger + +logger = get_logger(__name__, category="testing") # Global state for the recording system _current_mode: str | None = None _current_storage: ResponseStorage | None = None _original_methods: dict[str, Any] = {} +from openai.types.completion_choice import CompletionChoice + +# update the "finish_reason" field, since its type definition is wrong (no None is accepted) +CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None +CompletionChoice.model_rebuild() + def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict[str, Any]) -> str: """Create a normalized hash of the request for consistent matching.""" @@ -35,14 +45,14 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict def get_inference_mode() -> str: - return os.environ.get("LLAMA_STACK_INFERENCE_MODE", "live").lower() + return os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower() def setup_inference_recording(): mode = get_inference_mode() if mode not in ["live", "record", "replay"]: - raise ValueError(f"Invalid LLAMA_STACK_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'") + raise ValueError(f"Invalid LLAMA_STACK_TEST_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'") if mode == "live": # Return a no-op context manager for live mode @@ -52,23 +62,43 @@ def setup_inference_recording(): return live_mode() - if "LLAMA_STACK_RECORDING_DIR" not in os.environ: - raise ValueError("LLAMA_STACK_RECORDING_DIR must be set for recording or replaying") - storage_dir = os.environ["LLAMA_STACK_RECORDING_DIR"] + if "LLAMA_STACK_TEST_RECORDING_DIR" not in os.environ: + raise ValueError("LLAMA_STACK_TEST_RECORDING_DIR must be set for recording or replaying") + storage_dir = os.environ["LLAMA_STACK_TEST_RECORDING_DIR"] return inference_recording(mode=mode, storage_dir=storage_dir) def _serialize_response(response: Any) -> Any: if hasattr(response, "model_dump"): - return response.model_dump() + data = response.model_dump(mode="json") + return { + "__type__": f"{response.__class__.__module__}.{response.__class__.__qualname__}", + "__data__": data, + } elif hasattr(response, "__dict__"): return dict(response.__dict__) else: return response -def _deserialize_response(data: dict[str, Any]) -> dict[str, Any]: +def _deserialize_response(data: dict[str, Any]) -> Any: + # Check if this is a serialized Pydantic model with type information + if isinstance(data, dict) and "__type__" in data and "__data__" in data: + try: + # Import the original class and reconstruct the object + module_path, class_name = data["__type__"].rsplit(".", 1) + module = __import__(module_path, fromlist=[class_name]) + cls = getattr(module, class_name) + + if not hasattr(cls, "model_validate"): + raise ValueError(f"Pydantic class {cls} does not support model_validate?") + + return cls.model_validate(data["__data__"]) + except (ImportError, AttributeError, TypeError, ValueError) as e: + logger.warning(f"Failed to deserialize object of type {data['__type__']}: {e}") + return data["__data__"] + return data @@ -120,6 +150,7 @@ class ResponseStorage: with open(response_path, "w") as f: json.dump({"request": request, "response": serialized_response}, f, indent=2) f.write("\n") + f.flush() # Update SQLite index with sqlite3.connect(self.db_path) as conn: @@ -214,6 +245,8 @@ async def _patched_inference_method(original_method, self, client_type, method_n endpoint = "/api/chat" elif method_name == "embed": endpoint = "/api/embeddings" + elif method_name == "list": + endpoint = "/api/tags" else: endpoint = f"/api/{method_name}" else: @@ -295,8 +328,6 @@ def patch_inference_clients(): """Install monkey patches for OpenAI client methods and Ollama AsyncClient methods.""" global _original_methods - # Import here to avoid circular imports - # Also import Ollama AsyncClient from ollama import AsyncClient as OllamaAsyncClient from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions @@ -310,6 +341,9 @@ def patch_inference_clients(): "ollama_generate": OllamaAsyncClient.generate, "ollama_chat": OllamaAsyncClient.chat, "ollama_embed": OllamaAsyncClient.embed, + "ollama_ps": OllamaAsyncClient.ps, + "ollama_pull": OllamaAsyncClient.pull, + "ollama_list": OllamaAsyncClient.list, } # Create patched methods for OpenAI client @@ -339,10 +373,24 @@ def patch_inference_clients(): async def patched_ollama_embed(self, **kwargs): return await _patched_inference_method(_original_methods["ollama_embed"], self, "ollama", "embed", **kwargs) + async def patched_ollama_ps(self, **kwargs): + logger.info("replay mode: ollama.ps() reporting success") + return [] + + async def patched_ollama_pull(self, *args, **kwargs): + logger.info("replay mode: ollama.pull() not actually pulling the model") + return None + + async def patched_ollama_list(self, **kwargs): + return await _patched_inference_method(_original_methods["ollama_list"], self, "ollama", "list", **kwargs) + # Apply Ollama patches OllamaAsyncClient.generate = patched_ollama_generate OllamaAsyncClient.chat = patched_ollama_chat OllamaAsyncClient.embed = patched_ollama_embed + OllamaAsyncClient.ps = patched_ollama_ps + OllamaAsyncClient.pull = patched_ollama_pull + OllamaAsyncClient.list = patched_ollama_list def unpatch_inference_clients(): @@ -367,6 +415,8 @@ def unpatch_inference_clients(): OllamaAsyncClient.generate = _original_methods["ollama_generate"] OllamaAsyncClient.chat = _original_methods["ollama_chat"] OllamaAsyncClient.embed = _original_methods["ollama_embed"] + OllamaAsyncClient.ps = _original_methods["ollama_ps"] + OllamaAsyncClient.list = _original_methods["ollama_list"] _original_methods.clear() diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 4cffa9333..f6b5b3026 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -185,95 +185,70 @@ def llama_stack_client(request, provider_data): if not config: raise ValueError("You must specify either --stack-config or LLAMA_STACK_CONFIG") - # Set up inference recording if enabled - inference_mode = os.environ.get("LLAMA_STACK_INFERENCE_MODE", "live").lower() - recording_context = None + # Handle server: format or server:: + if config.startswith("server:"): + parts = config.split(":") + config_name = parts[1] + port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT)) + base_url = f"http://localhost:{port}" - if inference_mode in ["record", "replay"]: - from llama_stack.testing.inference_recorder import setup_inference_recording + # Check if port is available + if is_port_available(port): + print(f"Starting llama stack server with config '{config_name}' on port {port}...") - recording_context = setup_inference_recording() - recording_context.__enter__() - print(f"Inference recording enabled: mode={inference_mode}") + # Start server + server_process = start_llama_stack_server(config_name) - try: - # Handle server: format or server:: - if config.startswith("server:"): - parts = config.split(":") - config_name = parts[1] - port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT)) - base_url = f"http://localhost:{port}" - - # Check if port is available - if is_port_available(port): - print(f"Starting llama stack server with config '{config_name}' on port {port}...") - - # Start server - server_process = start_llama_stack_server(config_name) - - # Wait for server to be ready - if not wait_for_server_ready(base_url, timeout=120, process=server_process): - print("Server failed to start within timeout") - server_process.terminate() - raise RuntimeError( - f"Server failed to start within timeout. Check that config '{config_name}' exists and is valid. " - f"See server.log for details." - ) - - print(f"Server is ready at {base_url}") - - # Store process for potential cleanup (pytest will handle termination at session end) - request.session._llama_stack_server_process = server_process - else: - print(f"Port {port} is already in use, assuming server is already running...") - - client = LlamaStackClient( - base_url=base_url, - provider_data=provider_data, - timeout=int(os.environ.get("LLAMA_STACK_CLIENT_TIMEOUT", "30")), - ) - else: - # check if this looks like a URL using proper URL parsing - try: - parsed_url = urlparse(config) - if parsed_url.scheme and parsed_url.netloc: - client = LlamaStackClient( - base_url=config, - provider_data=provider_data, - ) - else: - raise ValueError("Not a URL") - except Exception as e: - # If URL parsing fails, treat as library config - if "=" in config: - run_config = run_config_from_adhoc_config_spec(config) - run_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".yaml") - with open(run_config_file.name, "w") as f: - yaml.dump(run_config.model_dump(), f) - config = run_config_file.name - - client = LlamaStackAsLibraryClient( - config, - provider_data=provider_data, - skip_logger_removal=True, + # Wait for server to be ready + if not wait_for_server_ready(base_url, timeout=120, process=server_process): + print("Server failed to start within timeout") + server_process.terminate() + raise RuntimeError( + f"Server failed to start within timeout. Check that config '{config_name}' exists and is valid. " + f"See server.log for details." ) - if not client.initialize(): - raise RuntimeError("Initialization failed") from e - # Store recording context for cleanup - if recording_context: - request.session._inference_recording_context = recording_context + print(f"Server is ready at {base_url}") - return client + # Store process for potential cleanup (pytest will handle termination at session end) + request.session._llama_stack_server_process = server_process + else: + print(f"Port {port} is already in use, assuming server is already running...") + return LlamaStackClient( + base_url=base_url, + provider_data=provider_data, + timeout=int(os.environ.get("LLAMA_STACK_CLIENT_TIMEOUT", "30")), + ) + + # check if this looks like a URL using proper URL parsing + try: + parsed_url = urlparse(config) + if parsed_url.scheme and parsed_url.netloc: + return LlamaStackClient( + base_url=config, + provider_data=provider_data, + ) except Exception: - # Clean up recording context on error - if recording_context: - try: - recording_context.__exit__(None, None, None) - except Exception as cleanup_error: - print(f"Warning: Error cleaning up recording context: {cleanup_error}") - raise + # If URL parsing fails, treat as non-URL config + pass + + if "=" in config: + run_config = run_config_from_adhoc_config_spec(config) + run_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".yaml") + with open(run_config_file.name, "w") as f: + yaml.dump(run_config.model_dump(), f) + config = run_config_file.name + + client = LlamaStackAsLibraryClient( + config, + provider_data=provider_data, + skip_logger_removal=True, + ) + if not client.initialize(): + raise RuntimeError("Initialization failed") + + return client @pytest.fixture(scope="session") @@ -289,20 +264,9 @@ def compat_client(request): @pytest.fixture(scope="session", autouse=True) def cleanup_server_process(request): - """Cleanup server process and inference recording at the end of the test session.""" + """Cleanup server process at the end of the test session.""" yield # Run tests - # Clean up inference recording context - if hasattr(request.session, "_inference_recording_context"): - recording_context = request.session._inference_recording_context - if recording_context: - try: - print("Cleaning up inference recording context...") - recording_context.__exit__(None, None, None) - except Exception as e: - print(f"Error during inference recording cleanup: {e}") - - # Clean up server process if hasattr(request.session, "_llama_stack_server_process"): server_process = request.session._llama_stack_server_process if server_process: diff --git a/tests/integration/inference/recordings/index.sqlite b/tests/integration/inference/recordings/index.sqlite index 92a07773f..cd8636177 100644 Binary files a/tests/integration/inference/recordings/index.sqlite and b/tests/integration/inference/recordings/index.sqlite differ diff --git a/tests/integration/inference/recordings/responses/1b8394f90636.json b/tests/integration/inference/recordings/responses/1b8394f90636.json index 5faef2bc4..a6658727d 100644 --- a/tests/integration/inference/recordings/responses/1b8394f90636.json +++ b/tests/integration/inference/recordings/responses/1b8394f90636.json @@ -19,19 +19,22 @@ }, "response": { "body": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:53.555099Z", - "done": true, - "done_reason": "stop", - "total_duration": 2124168875, - "load_duration": 58506875, - "prompt_eval_count": 18, - "prompt_eval_duration": 70072583, - "eval_count": 43, - "eval_duration": 1994446917, - "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)", - "thinking": null, - "context": null + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T17:37:01.657977Z", + "done": true, + "done_reason": "stop", + "total_duration": 1860746958, + "load_duration": 59632791, + "prompt_eval_count": 18, + "prompt_eval_duration": 66333291, + "eval_count": 43, + "eval_duration": 1734228875, + "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)", + "thinking": null, + "context": null + } }, "is_streaming": false } diff --git a/tests/integration/inference/recordings/responses/211b1562d4e6.json b/tests/integration/inference/recordings/responses/211b1562d4e6.json index 509027aba..8a501796b 100644 --- a/tests/integration/inference/recordings/responses/211b1562d4e6.json +++ b/tests/integration/inference/recordings/responses/211b1562d4e6.json @@ -17,19 +17,22 @@ }, "response": { "body": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:57.535525Z", - "done": true, - "done_reason": "stop", - "total_duration": 358691334, - "load_duration": 76787334, - "prompt_eval_count": 23, - "prompt_eval_duration": 72235375, - "eval_count": 6, - "eval_duration": 208986666, - "response": "Humans live on Earth.", - "thinking": null, - "context": null + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T17:37:05.658757Z", + "done": true, + "done_reason": "stop", + "total_duration": 327636667, + "load_duration": 55751292, + "prompt_eval_count": 23, + "prompt_eval_duration": 65865625, + "eval_count": 6, + "eval_duration": 205366667, + "response": "Humans live on Earth.", + "thinking": null, + "context": null + } }, "is_streaming": false } diff --git a/tests/integration/inference/recordings/responses/3c3f13cb7794.json b/tests/integration/inference/recordings/responses/3c3f13cb7794.json index a377e815d..fe9430bff 100644 --- a/tests/integration/inference/recordings/responses/3c3f13cb7794.json +++ b/tests/integration/inference/recordings/responses/3c3f13cb7794.json @@ -18,169 +18,202 @@ "response": { "body": [ { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:57.691771Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "The", - "thinking": null, - "context": null + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T17:37:05.833193Z", + "done": false, + "done_reason": null, + "total_duration": null, + "load_duration": null, + "prompt_eval_count": null, + "prompt_eval_duration": null, + "eval_count": null, + "eval_duration": null, + "response": "The", + "thinking": null, + "context": null + } }, { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:57.732262Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " Latin", - "thinking": null, - "context": null + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T17:37:05.874274Z", + "done": false, + "done_reason": null, + "total_duration": null, + "load_duration": null, + "prompt_eval_count": null, + "prompt_eval_duration": null, + "eval_count": null, + "eval_duration": null, + "response": " Latin", + "thinking": null, + "context": null + } }, { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:57.77294Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " word", - "thinking": null, - "context": null + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T17:37:05.915195Z", + "done": false, + "done_reason": null, + "total_duration": null, + "load_duration": null, + "prompt_eval_count": null, + "prompt_eval_duration": null, + "eval_count": null, + "eval_duration": null, + "response": " word", + "thinking": null, + "context": null + } }, { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:57.814484Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " for", - "thinking": null, - "context": null + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T17:37:05.955964Z", + "done": false, + "done_reason": null, + "total_duration": null, + "load_duration": null, + "prompt_eval_count": null, + "prompt_eval_duration": null, + "eval_count": null, + "eval_duration": null, + "response": " for", + "thinking": null, + "context": null + } }, { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T04:16:57.854875Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - 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import ( ResponseStorage, inference_recording, @@ -27,44 +36,36 @@ def temp_storage_dir(): @pytest.fixture -def mock_openai_response(): - """Mock OpenAI response object.""" - mock_response = Mock() - mock_response.choices = [Mock()] - mock_response.choices[0].message.content = "Hello! I'm doing well, thank you for asking." - mock_response.model_dump.return_value = { - "id": "chatcmpl-test123", - "object": "chat.completion", - "choices": [ - { - "index": 0, - "message": {"role": "assistant", "content": "Hello! I'm doing well, thank you for asking."}, - "finish_reason": "stop", - } +def real_openai_chat_response(): + """Real OpenAI chat completion response using proper Pydantic objects.""" + return OpenAIChatCompletion( + id="chatcmpl-test123", + choices=[ + OpenAIChoice( + index=0, + message=OpenAIAssistantMessageParam( + role="assistant", content="Hello! I'm doing well, thank you for asking." + ), + finish_reason="stop", + ) ], - "model": "llama3.2:3b", - "usage": {"prompt_tokens": 10, "completion_tokens": 15, "total_tokens": 25}, - } - - return mock_response + created=1234567890, + model="llama3.2:3b", + ) @pytest.fixture -def mock_embeddings_response(): - """Mock OpenAI embeddings response object.""" - mock_response = Mock() - mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3]), Mock(embedding=[0.4, 0.5, 0.6])] - mock_response.model_dump.return_value = { - "object": "list", - "data": [ - {"object": "embedding", "embedding": [0.1, 0.2, 0.3], "index": 0}, - {"object": "embedding", "embedding": [0.4, 0.5, 0.6], "index": 1}, +def real_embeddings_response(): + """Real OpenAI embeddings response using proper Pydantic objects.""" + return OpenAIEmbeddingsResponse( + object="list", + data=[ + OpenAIEmbeddingData(object="embedding", embedding=[0.1, 0.2, 0.3], index=0), + OpenAIEmbeddingData(object="embedding", embedding=[0.4, 0.5, 0.6], index=1), ], - "model": "nomic-embed-text", - "usage": {"prompt_tokens": 6, "total_tokens": 6}, - } - - return mock_response + model="nomic-embed-text", + usage=OpenAIEmbeddingUsage(prompt_tokens=6, total_tokens=6), + ) class TestInferenceRecording: @@ -160,11 +161,11 @@ class TestInferenceRecording: assert retrieved["request"]["model"] == "llama3.2:3b" assert retrieved["response"]["body"]["content"] == "test response" - async def test_recording_mode(self, temp_storage_dir, mock_openai_response): + async def test_recording_mode(self, temp_storage_dir, real_openai_chat_response): """Test that recording mode captures and stores responses.""" async def mock_create(*args, **kwargs): - return mock_openai_response + return real_openai_chat_response temp_storage_dir = temp_storage_dir / "test_recording_mode" with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): @@ -188,11 +189,11 @@ class TestInferenceRecording: assert recordings == 1 - async def test_replay_mode(self, temp_storage_dir, mock_openai_response): + async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response): """Test that replay mode returns stored responses without making real calls.""" async def mock_create(*args, **kwargs): - return mock_openai_response + return real_openai_chat_response temp_storage_dir = temp_storage_dir / "test_replay_mode" # First, record a response @@ -200,7 +201,7 @@ class TestInferenceRecording: with inference_recording(mode="record", storage_dir=str(temp_storage_dir)): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - await client.chat.completions.create( + response = await client.chat.completions.create( model="llama3.2:3b", messages=[{"role": "user", "content": "Hello, how are you?"}], temperature=0.7, @@ -220,7 +221,7 @@ class TestInferenceRecording: ) # Verify we got the recorded response - assert response["choices"][0]["message"]["content"] == "Hello! I'm doing well, thank you for asking." + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." # Verify the original method was NOT called mock_create_patch.assert_not_called() @@ -237,11 +238,11 @@ class TestInferenceRecording: model="llama3.2:3b", messages=[{"role": "user", "content": "This was never recorded"}] ) - async def test_embeddings_recording(self, temp_storage_dir, mock_embeddings_response): + async def test_embeddings_recording(self, temp_storage_dir, real_embeddings_response): """Test recording and replay of embeddings calls.""" async def mock_create(*args, **kwargs): - return mock_embeddings_response + return real_embeddings_response temp_storage_dir = temp_storage_dir / "test_embeddings_recording" # Record @@ -265,17 +266,17 @@ class TestInferenceRecording: ) # Verify we got the recorded response - assert len(response["data"]) == 2 - assert response["data"][0]["embedding"] == [0.1, 0.2, 0.3] + assert len(response.data) == 2 + assert response.data[0].embedding == [0.1, 0.2, 0.3] # Verify original method was not called mock_create_patch.assert_not_called() - async def test_live_mode(self, mock_openai_response): + async def test_live_mode(self, real_openai_chat_response): """Test that live mode passes through to original methods.""" async def mock_create(*args, **kwargs): - return mock_openai_response + return real_openai_chat_response with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): with inference_recording(mode="live"):