diff --git a/llama_stack/distribution/routers/inference.py b/llama_stack/distribution/routers/inference.py index a5cc8c4b5..c864b0eb0 100644 --- a/llama_stack/distribution/routers/inference.py +++ b/llama_stack/distribution/routers/inference.py @@ -79,11 +79,9 @@ class InferenceRouter(Inference): 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..1dbcbb7fa 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]: + if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ: + from llama_stack.testing.inference_recorder import setup_inference_recording + + global TEST_RECORDING_CONTEXT + TEST_RECORDING_CONTEXT = setup_inference_recording() + if TEST_RECORDING_CONTEXT: + TEST_RECORDING_CONTEXT.__enter__() + logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_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/__init__.py b/llama_stack/testing/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/testing/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py new file mode 100644 index 000000000..abfefa0ce --- /dev/null +++ b/llama_stack/testing/inference_recorder.py @@ -0,0 +1,480 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from __future__ import annotations # for forward references + +import hashlib +import json +import os +import sqlite3 +from collections.abc import Generator +from contextlib import contextmanager +from enum import StrEnum +from pathlib import Path +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() + + +class InferenceMode(StrEnum): + LIVE = "live" + RECORD = "record" + REPLAY = "replay" + + +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.""" + # Extract just the endpoint path + from urllib.parse import urlparse + + parsed = urlparse(url) + normalized = {"method": method.upper(), "endpoint": parsed.path, "body": body} + + # Create hash - sort_keys=True ensures deterministic ordering + normalized_json = json.dumps(normalized, sort_keys=True) + return hashlib.sha256(normalized_json.encode()).hexdigest() + + +def get_inference_mode() -> InferenceMode: + return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower()) + + +def setup_inference_recording(): + """ + Returns a context manager that can be used to record or replay inference requests. This is to be used in tests + to increase their reliability and reduce reliance on expensive, external services. + + Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases. + Calls to the /models endpoint are not currently trapped. We probably need to add support for this. + + Two environment variables are required: + - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. + - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. + + The recordings are stored in a SQLite database and a JSON file for each request. The SQLite database is used to + quickly find the correct recording for a given request. The JSON files are used to store the request and response + bodies. + """ + mode = get_inference_mode() + + if mode not in InferenceMode: + raise ValueError(f"Invalid LLAMA_STACK_TEST_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'") + + if mode == InferenceMode.LIVE: + return None + + 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"): + 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]) -> 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 + + +class ResponseStorage: + """Handles SQLite index + JSON file storage/retrieval for inference recordings.""" + + def __init__(self, test_dir: Path): + self.test_dir = test_dir + self.responses_dir = self.test_dir / "responses" + self.db_path = self.test_dir / "index.sqlite" + + self._ensure_directories() + self._init_database() + + def _ensure_directories(self): + self.test_dir.mkdir(parents=True, exist_ok=True) + self.responses_dir.mkdir(exist_ok=True) + + def _init_database(self): + with sqlite3.connect(self.db_path) as conn: + conn.execute(""" + CREATE TABLE IF NOT EXISTS recordings ( + request_hash TEXT PRIMARY KEY, + response_file TEXT, + endpoint TEXT, + model TEXT, + timestamp TEXT, + is_streaming BOOLEAN + ) + """) + + def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): + """Store a request/response pair.""" + # Generate unique response filename + response_file = f"{request_hash[:12]}.json" + response_path = self.responses_dir / response_file + + # Serialize response body if needed + serialized_response = dict(response) + if "body" in serialized_response: + if isinstance(serialized_response["body"], list): + # Handle streaming responses (list of chunks) + serialized_response["body"] = [_serialize_response(chunk) for chunk in serialized_response["body"]] + else: + # Handle single response + serialized_response["body"] = _serialize_response(serialized_response["body"]) + + # Save response to JSON file + 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: + conn.execute( + """ + INSERT OR REPLACE INTO recordings + (request_hash, response_file, endpoint, model, timestamp, is_streaming) + VALUES (?, ?, ?, ?, datetime('now'), ?) + """, + ( + request_hash, + response_file, + request.get("endpoint", ""), + request.get("model", ""), + response.get("is_streaming", False), + ), + ) + + def find_recording(self, request_hash: str) -> dict[str, Any] | None: + """Find a recorded response by request hash.""" + with sqlite3.connect(self.db_path) as conn: + result = conn.execute( + "SELECT response_file FROM recordings WHERE request_hash = ?", (request_hash,) + ).fetchone() + + if not result: + return None + + response_file = result[0] + response_path = self.responses_dir / response_file + + if not response_path.exists(): + return None + + with open(response_path) as f: + data = json.load(f) + + # Deserialize response body if needed + if "response" in data and "body" in data["response"]: + if isinstance(data["response"]["body"], list): + # Handle streaming responses + data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] + else: + # Handle single response + data["response"]["body"] = _deserialize_response(data["response"]["body"]) + + return cast(dict[str, Any], data) + + +async def _patched_inference_method(original_method, self, client_type, method_name=None, *args, **kwargs): + global _current_mode, _current_storage + + if _current_mode == InferenceMode.LIVE or _current_storage is None: + # Normal operation + return await original_method(self, *args, **kwargs) + + # Get base URL and endpoint based on client type + if client_type == "openai": + base_url = str(self._client.base_url) + + # Determine endpoint based on the method's module/class path + method_str = str(original_method) + if "chat.completions" in method_str: + endpoint = "/v1/chat/completions" + elif "embeddings" in method_str: + endpoint = "/v1/embeddings" + elif "completions" in method_str: + endpoint = "/v1/completions" + else: + # Fallback - try to guess from the self object + if hasattr(self, "_resource") and hasattr(self._resource, "_resource"): + resource_name = getattr(self._resource._resource, "_resource", "unknown") + if "chat" in str(resource_name): + endpoint = "/v1/chat/completions" + elif "embeddings" in str(resource_name): + endpoint = "/v1/embeddings" + else: + endpoint = "/v1/completions" + else: + endpoint = "/v1/completions" + + elif client_type == "ollama": + # Get base URL from the client (Ollama client uses host attribute) + base_url = getattr(self, "host", "http://localhost:11434") + if not base_url.startswith("http"): + base_url = f"http://{base_url}" + + # Determine endpoint based on method name + if method_name == "generate": + endpoint = "/api/generate" + elif method_name == "chat": + endpoint = "/api/chat" + elif method_name == "embed": + endpoint = "/api/embeddings" + elif method_name == "list": + endpoint = "/api/tags" + else: + endpoint = f"/api/{method_name}" + else: + raise ValueError(f"Unknown client type: {client_type}") + + url = base_url.rstrip("/") + endpoint + + # Normalize request for matching + method = "POST" + headers = {} + body = kwargs + + request_hash = normalize_request(method, url, headers, body) + + if _current_mode == InferenceMode.REPLAY: + recording = _current_storage.find_recording(request_hash) + if recording: + response_body = recording["response"]["body"] + + if recording["response"].get("is_streaming", False): + + async def replay_stream(): + for chunk in response_body: + yield chunk + + return replay_stream() + else: + return response_body + else: + raise RuntimeError( + f"No recorded response found for request hash: {request_hash}\n" + f"Endpoint: {endpoint}\n" + f"Model: {body.get('model', 'unknown')}\n" + f"To record this response, run with LLAMA_STACK_INFERENCE_MODE=record" + ) + + elif _current_mode == InferenceMode.RECORD: + response = await original_method(self, *args, **kwargs) + + request_data = { + "method": method, + "url": url, + "headers": headers, + "body": body, + "endpoint": endpoint, + "model": body.get("model", ""), + } + + # Determine if this is a streaming request based on request parameters + is_streaming = body.get("stream", False) + + if is_streaming: + # For streaming responses, we need to collect all chunks immediately before yielding + # This ensures the recording is saved even if the generator isn't fully consumed + chunks = [] + async for chunk in response: + chunks.append(chunk) + + # Store the recording immediately + response_data = {"body": chunks, "is_streaming": True} + _current_storage.store_recording(request_hash, request_data, response_data) + + # Return a generator that replays the stored chunks + async def replay_recorded_stream(): + for chunk in chunks: + yield chunk + + return replay_recorded_stream() + else: + response_data = {"body": response, "is_streaming": False} + _current_storage.store_recording(request_hash, request_data, response_data) + return response + + else: + raise AssertionError(f"Invalid mode: {_current_mode}") + + +def patch_inference_clients(): + """Install monkey patches for OpenAI client methods and Ollama AsyncClient methods.""" + global _original_methods + + from ollama import AsyncClient as OllamaAsyncClient + from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions + from openai.resources.completions import AsyncCompletions + from openai.resources.embeddings import AsyncEmbeddings + + # Store original methods for both OpenAI and Ollama clients + _original_methods = { + "chat_completions_create": AsyncChatCompletions.create, + "completions_create": AsyncCompletions.create, + "embeddings_create": AsyncEmbeddings.create, + "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 + async def patched_chat_completions_create(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["chat_completions_create"], self, "openai", *args, **kwargs + ) + + async def patched_completions_create(self, *args, **kwargs): + return await _patched_inference_method(_original_methods["completions_create"], self, "openai", *args, **kwargs) + + async def patched_embeddings_create(self, *args, **kwargs): + return await _patched_inference_method(_original_methods["embeddings_create"], self, "openai", *args, **kwargs) + + # Apply OpenAI patches + AsyncChatCompletions.create = patched_chat_completions_create + AsyncCompletions.create = patched_completions_create + AsyncEmbeddings.create = patched_embeddings_create + + # Create patched methods for Ollama client + async def patched_ollama_generate(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["ollama_generate"], self, "ollama", "generate", *args, **kwargs + ) + + async def patched_ollama_chat(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["ollama_chat"], self, "ollama", "chat", *args, **kwargs + ) + + async def patched_ollama_embed(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["ollama_embed"], self, "ollama", "embed", *args, **kwargs + ) + + async def patched_ollama_ps(self, *args, **kwargs): + return await _patched_inference_method(_original_methods["ollama_ps"], self, "ollama", "ps", *args, **kwargs) + + async def patched_ollama_pull(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["ollama_pull"], self, "ollama", "pull", *args, **kwargs + ) + + async def patched_ollama_list(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["ollama_list"], self, "ollama", "list", *args, **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(): + """Remove monkey patches and restore original OpenAI and Ollama client methods.""" + global _original_methods + + if not _original_methods: + return + + # Import here to avoid circular imports + from ollama import AsyncClient as OllamaAsyncClient + from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions + from openai.resources.completions import AsyncCompletions + from openai.resources.embeddings import AsyncEmbeddings + + # Restore OpenAI client methods + AsyncChatCompletions.create = _original_methods["chat_completions_create"] + AsyncCompletions.create = _original_methods["completions_create"] + AsyncEmbeddings.create = _original_methods["embeddings_create"] + + # Restore Ollama client methods if they were patched + 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.pull = _original_methods["ollama_pull"] + OllamaAsyncClient.list = _original_methods["ollama_list"] + + _original_methods.clear() + + +@contextmanager +def inference_recording(mode: str = "live", storage_dir: str | Path | None = None) -> Generator[None, None, None]: + """Context manager for inference recording/replaying.""" + global _current_mode, _current_storage + + # Set defaults + if storage_dir is None: + storage_dir_path = Path.home() / ".llama" / "recordings" + else: + storage_dir_path = Path(storage_dir) + + # Store previous state + prev_mode = _current_mode + prev_storage = _current_storage + + try: + _current_mode = mode + + if mode in ["record", "replay"]: + _current_storage = ResponseStorage(storage_dir_path) + patch_inference_clients() + + yield + + finally: + # Restore previous state + if mode in ["record", "replay"]: + unpatch_inference_clients() + + _current_mode = prev_mode + _current_storage = prev_storage diff --git a/tests/integration/inference/recordings/index.sqlite b/tests/integration/inference/recordings/index.sqlite new file mode 100644 index 000000000..90827470c Binary files /dev/null and b/tests/integration/inference/recordings/index.sqlite differ diff --git a/tests/integration/inference/recordings/responses/12913f20f6ac.json b/tests/integration/inference/recordings/responses/12913f20f6ac.json new file mode 100644 index 000000000..9934da93c --- /dev/null +++ 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You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"get_object_namespace_list\",\n \"description\": \"Get the list of objects in a namespace\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"kind\", \"namespace\"],\n \"properties\": {\n \"kind\": {\n \"type\": \"string\",\n \"description\": \"the type of object\"\n },\n \"namespace\": {\n \"type\": \"string\",\n \"description\": \"the name of the namespace\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat pods are in the namespace openshift-lightspeed?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[get_object_namespace_list(kind=\"pod\", namespace=\"openshift-lightspeed\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nthe objects are pod1, pod2, pod3<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", + "options": { + "temperature": 0.0 + }, + "stream": true + }, + "endpoint": "/api/generate", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": [ + { + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T18:47:48.95435Z", + "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": "[]", + "thinking": null, + "context": null + } + }, + { + "__type__": "ollama._types.GenerateResponse", + "__data__": { + "model": "llama3.2:3b-instruct-fp16", + "created_at": "2025-07-29T18:47:48.996247Z", + "done": true, + "done_reason": "stop", + "total_duration": 667274458, + "load_duration": 80712750, + "prompt_eval_count": 386, + "prompt_eval_duration": 543388792, + "eval_count": 2, + "eval_duration": 42471125, + "response": "", + "thinking": null, + "context": null + } + } + ], + "is_streaming": true + } +} diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py new file mode 100644 index 000000000..1dbd14540 --- /dev/null +++ b/tests/unit/distribution/test_inference_recordings.py @@ -0,0 +1,291 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import sqlite3 +import tempfile +from pathlib import Path +from unittest.mock import patch + +import pytest +from openai import AsyncOpenAI + +# Import the real Pydantic response types instead of using Mocks +from llama_stack.apis.inference import ( + OpenAIAssistantMessageParam, + OpenAIChatCompletion, + OpenAIChoice, + OpenAIEmbeddingData, + OpenAIEmbeddingsResponse, + OpenAIEmbeddingUsage, +) +from llama_stack.testing.inference_recorder import ( + InferenceMode, + ResponseStorage, + inference_recording, + normalize_request, +) + + +@pytest.fixture +def temp_storage_dir(): + """Create a temporary directory for test recordings.""" + with tempfile.TemporaryDirectory() as temp_dir: + yield Path(temp_dir) + + +@pytest.fixture +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", + ) + ], + created=1234567890, + model="llama3.2:3b", + ) + + +@pytest.fixture +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=OpenAIEmbeddingUsage(prompt_tokens=6, total_tokens=6), + ) + + +class TestInferenceRecording: + """Test the inference recording system.""" + + def test_request_normalization(self): + """Test that request normalization produces consistent hashes.""" + # Test basic normalization + hash1 = normalize_request( + "POST", + "http://localhost:11434/v1/chat/completions", + {}, + {"model": "llama3.2:3b", "messages": [{"role": "user", "content": "Hello world"}], "temperature": 0.7}, + ) + + # Same request should produce same hash + hash2 = normalize_request( + "POST", + "http://localhost:11434/v1/chat/completions", + {}, + {"model": "llama3.2:3b", "messages": [{"role": "user", "content": "Hello world"}], "temperature": 0.7}, + ) + + assert hash1 == hash2 + + # Different content should produce different hash + hash3 = normalize_request( + "POST", + "http://localhost:11434/v1/chat/completions", + {}, + { + "model": "llama3.2:3b", + "messages": [{"role": "user", "content": "Different message"}], + "temperature": 0.7, + }, + ) + + assert hash1 != hash3 + + def test_request_normalization_edge_cases(self): + """Test request normalization is precise about request content.""" + # Test that different whitespace produces different hashes (no normalization) + hash1 = normalize_request( + "POST", + "http://test/v1/chat/completions", + {}, + {"messages": [{"role": "user", "content": "Hello world\n\n"}]}, + ) + hash2 = normalize_request( + "POST", "http://test/v1/chat/completions", {}, {"messages": [{"role": "user", "content": "Hello world"}]} + ) + assert hash1 != hash2 # Different whitespace should produce different hashes + + # Test that different float precision produces different hashes (no rounding) + hash3 = normalize_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7000001}) + hash4 = normalize_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7}) + assert hash3 != hash4 # Different precision should produce different hashes + + def test_response_storage(self, temp_storage_dir): + """Test the ResponseStorage class.""" + temp_storage_dir = temp_storage_dir / "test_response_storage" + storage = ResponseStorage(temp_storage_dir) + + # Test directory creation + assert storage.test_dir.exists() + assert storage.responses_dir.exists() + assert storage.db_path.exists() + + # Test storing and retrieving a recording + request_hash = "test_hash_123" + request_data = { + "method": "POST", + "url": "http://localhost:11434/v1/chat/completions", + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b", + } + response_data = {"body": {"content": "test response"}, "is_streaming": False} + + storage.store_recording(request_hash, request_data, response_data) + + # Verify SQLite record + with sqlite3.connect(storage.db_path) as conn: + result = conn.execute("SELECT * FROM recordings WHERE request_hash = ?", (request_hash,)).fetchone() + + assert result is not None + assert result[0] == request_hash # request_hash + assert result[2] == "/v1/chat/completions" # endpoint + assert result[3] == "llama3.2:3b" # model + + # Verify file storage and retrieval + retrieved = storage.find_recording(request_hash) + assert retrieved is not None + assert retrieved["request"]["model"] == "llama3.2:3b" + assert retrieved["response"]["body"]["content"] == "test 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 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): + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + ) + + # Verify the response was returned correctly + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + + # Verify recording was stored + storage = ResponseStorage(temp_storage_dir) + with sqlite3.connect(storage.db_path) as conn: + recordings = conn.execute("SELECT COUNT(*) FROM recordings").fetchone()[0] + + assert recordings == 1 + + 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 real_openai_chat_response + + temp_storage_dir = temp_storage_dir / "test_replay_mode" + # First, record a response + with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + ) + + # Now test replay mode - should not call the original method + with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create_patch: + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + ) + + # Verify we got the recorded response + 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() + + async def test_replay_missing_recording(self, temp_storage_dir): + """Test that replay mode fails when no recording is found.""" + temp_storage_dir = temp_storage_dir / "test_replay_missing_recording" + with patch("openai.resources.chat.completions.AsyncCompletions.create"): + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + with pytest.raises(RuntimeError, match="No recorded response found"): + await client.chat.completions.create( + model="llama3.2:3b", messages=[{"role": "user", "content": "This was never recorded"}] + ) + + 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 real_embeddings_response + + temp_storage_dir = temp_storage_dir / "test_embeddings_recording" + # Record + with patch("openai.resources.embeddings.AsyncEmbeddings.create", side_effect=mock_create): + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.embeddings.create( + model="nomic-embed-text", input=["Hello world", "Test embedding"] + ) + + assert len(response.data) == 2 + + # Replay + with patch("openai.resources.embeddings.AsyncEmbeddings.create") as mock_create_patch: + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.embeddings.create( + model="nomic-embed-text", input=["Hello world", "Test embedding"] + ) + + # Verify we got the recorded response + 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, real_openai_chat_response): + """Test that live mode passes through to original methods.""" + + async def mock_create(*args, **kwargs): + return real_openai_chat_response + + with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): + with inference_recording(mode=InferenceMode.LIVE): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + + response = await client.chat.completions.create( + model="llama3.2:3b", messages=[{"role": "user", "content": "Hello"}] + ) + + # Verify the response was returned + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking."