From f50ce11a3b106ffb5faec08223140eb6678b77bf Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 9 Oct 2025 14:27:51 -0700 Subject: [PATCH] feat(tests): make inference_recorder into api_recorder (include tool_invoke) (#3403) Renames `inference_recorder.py` to `api_recorder.py` and extends it to support recording/replaying tool invocations in addition to inference calls. This allows us to record web-search, etc. tool calls and thereafter apply recordings for `tests/integration/responses` ## Test Plan ``` export OPENAI_API_KEY=... export TAVILY_SEARCH_API_KEY=... ./scripts/integration-tests.sh --stack-config ci-tests \ --suite responses --inference-mode record-if-missing ``` --- .github/workflows/integration-tests.yml | 6 +- .../workflows/record-integration-tests.yml | 3 + .../contributing/testing/record-replay.mdx | 8 +- llama_stack/core/id_generation.py | 42 + llama_stack/core/server/server.py | 16 +- llama_stack/core/stack.py | 8 +- llama_stack/core/testing_context.py | 44 + .../responses/openai_responses.py | 2 +- .../agents/meta_reference/responses/utils.py | 47 +- .../providers/inline/files/localfs/files.py | 7 +- .../providers/remote/files/s3/files.py | 3 +- .../utils/memory/openai_vector_store_mixin.py | 5 +- ...{inference_recorder.py => api_recorder.py} | 456 +- scripts/integration-tests.sh | 2 +- tests/common/mcp.py | 34 +- 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tests/integration/responses/recordings/fbc3663df932c59c014cfcc0673ff0747040227532b0896782a36c10aa78803a.json create mode 100644 tests/integration/responses/recordings/fcda6586a6ca4e21a0620a4b5e90b6b5dded8d869048d1b21bcd30d580d67c7e.json create mode 100644 tests/integration/responses/recordings/fcf8b46b3df5c6bf9f47c2506a3ab8aeea95e27f8d517e4983b2e901f8f394ad.json create mode 100644 tests/integration/responses/recordings/ff6e00eb8aec8fdcc47b31f5c3a8b443e41b265a0841e512de443fb0f365c132.json create mode 100644 tests/integration/responses/recordings/ff97fb5600b418714696253193b5d07fcca0c6beae86daf887542859a55bee67.json create mode 100644 tests/unit/distribution/test_api_recordings.py delete mode 100644 tests/unit/distribution/test_inference_recordings.py diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index ace1f4edc..d8e8ec91c 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -54,14 +54,14 @@ jobs: # Define (setup, suite) pairs - they are always matched and cannot be independent # Weekly schedule (Sun 1 AM): vllm+base # Input test-setup=ollama-vision: ollama-vision+vision - # Default (including test-setup=ollama): both ollama+base and ollama-vision+vision + # Default (including test-setup=ollama): ollama+base, ollama-vision+vision, gpt+responses config: >- ${{ github.event.schedule == '1 0 * * 0' && fromJSON('[{"setup": "vllm", "suite": "base"}]') || github.event.inputs.test-setup == 'ollama-vision' && fromJSON('[{"setup": "ollama-vision", "suite": "vision"}]') - || fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}]') + || fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}, {"setup": "gpt", "suite": "responses"}]') }} steps: @@ -79,6 +79,8 @@ jobs: - name: Run tests uses: ./.github/actions/run-and-record-tests + env: + OPENAI_API_KEY: dummy with: stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }} setup: ${{ matrix.config.setup }} diff --git a/.github/workflows/record-integration-tests.yml b/.github/workflows/record-integration-tests.yml index 65a04f125..57f95580e 100644 --- a/.github/workflows/record-integration-tests.yml +++ b/.github/workflows/record-integration-tests.yml @@ -61,6 +61,9 @@ jobs: - name: Run and record tests uses: ./.github/actions/run-and-record-tests + env: + # Set OPENAI_API_KEY if using gpt setup + OPENAI_API_KEY: ${{ inputs.test-setup == 'gpt' && secrets.OPENAI_API_KEY || '' }} with: stack-config: 'server:ci-tests' # recording must be done with server since more tests are run setup: ${{ inputs.test-setup || 'ollama' }} diff --git a/docs/docs/contributing/testing/record-replay.mdx b/docs/docs/contributing/testing/record-replay.mdx index 47803c150..cc3eb2b9d 100644 --- a/docs/docs/contributing/testing/record-replay.mdx +++ b/docs/docs/contributing/testing/record-replay.mdx @@ -68,7 +68,9 @@ recordings/ Direct API calls with no recording or replay: ```python -with inference_recording(mode=InferenceMode.LIVE): +from llama_stack.testing.api_recorder import api_recording, APIRecordingMode + +with api_recording(mode=APIRecordingMode.LIVE): response = await client.chat.completions.create(...) ``` @@ -79,7 +81,7 @@ Use for initial development and debugging against real APIs. Captures API interactions while passing through real responses: ```python -with inference_recording(mode=InferenceMode.RECORD, storage_dir="./recordings"): +with api_recording(mode=APIRecordingMode.RECORD, storage_dir="./recordings"): response = await client.chat.completions.create(...) # Real API call made, response captured AND returned ``` @@ -96,7 +98,7 @@ The recording process: Returns stored responses instead of making API calls: ```python -with inference_recording(mode=InferenceMode.REPLAY, storage_dir="./recordings"): +with api_recording(mode=APIRecordingMode.REPLAY, storage_dir="./recordings"): response = await client.chat.completions.create(...) # No API call made, cached response returned instantly ``` diff --git a/llama_stack/core/id_generation.py b/llama_stack/core/id_generation.py new file mode 100644 index 000000000..c60a7bb49 --- /dev/null +++ b/llama_stack/core/id_generation.py @@ -0,0 +1,42 @@ +# 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 collections.abc import Callable + +IdFactory = Callable[[], str] +IdOverride = Callable[[str, IdFactory], str] + +_id_override: IdOverride | None = None + + +def generate_object_id(kind: str, factory: IdFactory) -> str: + """Generate an identifier for the given kind using the provided factory. + + Allows tests to override ID generation deterministically by installing an + override callback via :func:`set_id_override`. + """ + + override = _id_override + if override is not None: + return override(kind, factory) + return factory() + + +def set_id_override(override: IdOverride) -> IdOverride | None: + """Install an override used to generate deterministic identifiers.""" + + global _id_override + + previous = _id_override + _id_override = override + return previous + + +def reset_id_override(previous: IdOverride | None) -> None: + """Restore the previous override returned by :func:`set_id_override`.""" + + global _id_override + _id_override = previous diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index 6b38e1ac6..e19092816 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -232,14 +232,25 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable: await log_request_pre_validation(request) + test_context_token = None + # Use context manager with both provider data and auth attributes with request_provider_data_context(request.headers, user): + if os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE"): + from llama_stack.core.testing_context import ( + TEST_CONTEXT, + reset_test_context, + sync_test_context_from_provider_data, + ) + + test_context_token = sync_test_context_from_provider_data() + is_streaming = is_streaming_request(func.__name__, request, **kwargs) try: if is_streaming: gen = preserve_contexts_async_generator( - sse_generator(func(**kwargs)), [CURRENT_TRACE_CONTEXT, PROVIDER_DATA_VAR] + sse_generator(func(**kwargs)), [CURRENT_TRACE_CONTEXT, PROVIDER_DATA_VAR, TEST_CONTEXT] ) return StreamingResponse(gen, media_type="text/event-stream") else: @@ -258,6 +269,9 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable: else: logger.error(f"Error executing endpoint {route=} {method=}: {str(e)}") raise translate_exception(e) from e + finally: + if test_context_token is not None: + reset_test_context(test_context_token) sig = inspect.signature(func) diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index acc02eeff..49f6b9cc9 100644 --- a/llama_stack/core/stack.py +++ b/llama_stack/core/stack.py @@ -316,13 +316,13 @@ class Stack: # asked for in the run config. async def initialize(self): if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ: - from llama_stack.testing.inference_recorder import setup_inference_recording + from llama_stack.testing.api_recorder import setup_api_recording global TEST_RECORDING_CONTEXT - TEST_RECORDING_CONTEXT = setup_inference_recording() + TEST_RECORDING_CONTEXT = setup_api_recording() if TEST_RECORDING_CONTEXT: TEST_RECORDING_CONTEXT.__enter__() - logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}") + logger.info(f"API recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}") dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name) policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else [] @@ -381,7 +381,7 @@ class Stack: try: TEST_RECORDING_CONTEXT.__exit__(None, None, None) except Exception as e: - logger.error(f"Error during inference recording cleanup: {e}") + logger.error(f"Error during API recording cleanup: {e}") global REGISTRY_REFRESH_TASK if REGISTRY_REFRESH_TASK: diff --git a/llama_stack/core/testing_context.py b/llama_stack/core/testing_context.py new file mode 100644 index 000000000..23cef751b --- /dev/null +++ b/llama_stack/core/testing_context.py @@ -0,0 +1,44 @@ +# 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 os +from contextvars import ContextVar + +from llama_stack.core.request_headers import PROVIDER_DATA_VAR + +TEST_CONTEXT: ContextVar[str | None] = ContextVar("llama_stack_test_context", default=None) + + +def get_test_context() -> str | None: + return TEST_CONTEXT.get() + + +def set_test_context(value: str | None): + return TEST_CONTEXT.set(value) + + +def reset_test_context(token) -> None: + TEST_CONTEXT.reset(token) + + +def sync_test_context_from_provider_data(): + """Sync test context from provider data when running in server test mode.""" + if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ: + return None + + stack_config_type = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client") + if stack_config_type != "server": + return None + + try: + provider_data = PROVIDER_DATA_VAR.get() + except LookupError: + provider_data = None + + if provider_data and "__test_id" in provider_data: + return TEST_CONTEXT.set(provider_data["__test_id"]) + + return None diff --git a/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py b/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py index 245203f10..da8b01f40 100644 --- a/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py +++ b/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py @@ -108,7 +108,7 @@ class OpenAIResponsesImpl: # Use stored messages directly and convert only new input message_adapter = TypeAdapter(list[OpenAIMessageParam]) messages = message_adapter.validate_python(previous_response.messages) - new_messages = await convert_response_input_to_chat_messages(input) + new_messages = await convert_response_input_to_chat_messages(input, previous_messages=messages) messages.extend(new_messages) else: # Backward compatibility: reconstruct from inputs diff --git a/llama_stack/providers/inline/agents/meta_reference/responses/utils.py b/llama_stack/providers/inline/agents/meta_reference/responses/utils.py index 5b013b9c4..a3316a635 100644 --- a/llama_stack/providers/inline/agents/meta_reference/responses/utils.py +++ b/llama_stack/providers/inline/agents/meta_reference/responses/utils.py @@ -103,9 +103,13 @@ async def convert_response_content_to_chat_content( async def convert_response_input_to_chat_messages( input: str | list[OpenAIResponseInput], + previous_messages: list[OpenAIMessageParam] | None = None, ) -> list[OpenAIMessageParam]: """ Convert the input from an OpenAI Response API request into OpenAI Chat Completion messages. + + :param input: The input to convert + :param previous_messages: Optional previous messages to check for function_call references """ messages: list[OpenAIMessageParam] = [] if isinstance(input, list): @@ -169,16 +173,53 @@ async def convert_response_input_to_chat_messages( raise ValueError( f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context" ) + # Skip user messages that duplicate the last user message in previous_messages + # This handles cases where input includes context for function_call_outputs + if previous_messages and input_item.role == "user": + last_user_msg = None + for msg in reversed(previous_messages): + if isinstance(msg, OpenAIUserMessageParam): + last_user_msg = msg + break + if last_user_msg: + last_user_content = getattr(last_user_msg, "content", None) + if last_user_content == content: + continue # Skip duplicate user message messages.append(message_type(content=content)) if len(tool_call_results): - raise ValueError( - f"Received function_call_output(s) with call_id(s) {tool_call_results.keys()}, but no corresponding function_call" - ) + # Check if unpaired function_call_outputs reference function_calls from previous messages + if previous_messages: + previous_call_ids = _extract_tool_call_ids(previous_messages) + for call_id in list(tool_call_results.keys()): + if call_id in previous_call_ids: + # Valid: this output references a call from previous messages + # Add the tool message + messages.append(tool_call_results[call_id]) + del tool_call_results[call_id] + + # If still have unpaired outputs, error + if len(tool_call_results): + raise ValueError( + f"Received function_call_output(s) with call_id(s) {tool_call_results.keys()}, but no corresponding function_call" + ) else: messages.append(OpenAIUserMessageParam(content=input)) return messages +def _extract_tool_call_ids(messages: list[OpenAIMessageParam]) -> set[str]: + """Extract all tool_call IDs from messages.""" + call_ids = set() + for msg in messages: + if isinstance(msg, OpenAIAssistantMessageParam): + tool_calls = getattr(msg, "tool_calls", None) + if tool_calls: + for tool_call in tool_calls: + # tool_call is a Pydantic model, use attribute access + call_ids.add(tool_call.id) + return call_ids + + async def convert_response_text_to_chat_response_format( text: OpenAIResponseText, ) -> OpenAIResponseFormatParam: diff --git a/llama_stack/providers/inline/files/localfs/files.py b/llama_stack/providers/inline/files/localfs/files.py index be1da291a..a76b982ce 100644 --- a/llama_stack/providers/inline/files/localfs/files.py +++ b/llama_stack/providers/inline/files/localfs/files.py @@ -22,6 +22,7 @@ from llama_stack.apis.files import ( OpenAIFilePurpose, ) from llama_stack.core.datatypes import AccessRule +from llama_stack.core.id_generation import generate_object_id from llama_stack.log import get_logger from llama_stack.providers.utils.files.form_data import parse_expires_after from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType @@ -65,7 +66,7 @@ class LocalfsFilesImpl(Files): def _generate_file_id(self) -> str: """Generate a unique file ID for OpenAI API.""" - return f"file-{uuid.uuid4().hex}" + return generate_object_id("file", lambda: f"file-{uuid.uuid4().hex}") def _get_file_path(self, file_id: str) -> Path: """Get the filesystem path for a file ID.""" @@ -95,7 +96,9 @@ class LocalfsFilesImpl(Files): raise RuntimeError("Files provider not initialized") if expires_after is not None: - raise NotImplementedError("File expiration is not supported by this provider") + logger.warning( + f"File expiration is not supported by this provider, ignoring expires_after: {expires_after}" + ) file_id = self._generate_file_id() file_path = self._get_file_path(file_id) diff --git a/llama_stack/providers/remote/files/s3/files.py b/llama_stack/providers/remote/files/s3/files.py index eb339b31e..c0e9f81d6 100644 --- a/llama_stack/providers/remote/files/s3/files.py +++ b/llama_stack/providers/remote/files/s3/files.py @@ -23,6 +23,7 @@ from llama_stack.apis.files import ( OpenAIFilePurpose, ) from llama_stack.core.datatypes import AccessRule +from llama_stack.core.id_generation import generate_object_id from llama_stack.providers.utils.files.form_data import parse_expires_after from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore @@ -198,7 +199,7 @@ class S3FilesImpl(Files): purpose: Annotated[OpenAIFilePurpose, Form()], expires_after: Annotated[ExpiresAfter | None, Depends(parse_expires_after)] = None, ) -> OpenAIFileObject: - file_id = f"file-{uuid.uuid4().hex}" + file_id = generate_object_id("file", lambda: f"file-{uuid.uuid4().hex}") filename = getattr(file, "filename", None) or "uploaded_file" diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index c179eba6c..ddfef9ba2 100644 --- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -40,6 +40,7 @@ from llama_stack.apis.vector_io import ( VectorStoreSearchResponse, VectorStoreSearchResponsePage, ) +from llama_stack.core.id_generation import generate_object_id from llama_stack.log import get_logger from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.vector_store import ( @@ -352,7 +353,7 @@ class OpenAIVectorStoreMixin(ABC): """Creates a vector store.""" created_at = int(time.time()) # Derive the canonical vector_db_id (allow override, else generate) - vector_db_id = provider_vector_db_id or f"vs_{uuid.uuid4()}" + vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}") if provider_id is None: raise ValueError("Provider ID is required") @@ -986,7 +987,7 @@ class OpenAIVectorStoreMixin(ABC): chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto() created_at = int(time.time()) - batch_id = f"batch_{uuid.uuid4()}" + batch_id = generate_object_id("vector_store_file_batch", lambda: f"batch_{uuid.uuid4()}") # File batches expire after 7 days expires_at = created_at + (7 * 24 * 60 * 60) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/api_recorder.py similarity index 64% rename from llama_stack/testing/inference_recorder.py rename to llama_stack/testing/api_recorder.py index 16071f80f..8964a28d2 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/api_recorder.py @@ -9,7 +9,8 @@ from __future__ import annotations # for forward references import hashlib import json import os -from collections.abc import Generator +import re +from collections.abc import Callable, Generator from contextlib import contextmanager from enum import StrEnum from pathlib import Path @@ -17,6 +18,7 @@ from typing import Any, Literal, cast from openai import NOT_GIVEN, OpenAI +from llama_stack.core.id_generation import reset_id_override, set_id_override from llama_stack.log import get_logger logger = get_logger(__name__, category="testing") @@ -29,13 +31,14 @@ _current_mode: str | None = None _current_storage: ResponseStorage | None = None _original_methods: dict[str, Any] = {} +# Per-test deterministic ID counters (test_id -> id_kind -> counter) +_id_counters: dict[str, dict[str, int]] = {} + # Test context uses ContextVar since it changes per-test and needs async isolation -from contextvars import ContextVar - -_test_context: ContextVar[str | None] = ContextVar("_test_context", default=None) - from openai.types.completion_choice import CompletionChoice +from llama_stack.core.testing_context import get_test_context + # 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() @@ -44,14 +47,89 @@ REPO_ROOT = Path(__file__).parent.parent.parent DEFAULT_STORAGE_DIR = REPO_ROOT / "tests/integration/common" -class InferenceMode(StrEnum): +class APIRecordingMode(StrEnum): LIVE = "live" RECORD = "record" REPLAY = "replay" RECORD_IF_MISSING = "record-if-missing" -def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict[str, Any]) -> str: +_ID_KIND_PREFIXES: dict[str, str] = { + "file": "file-", + "vector_store": "vs_", + "vector_store_file_batch": "batch_", + "tool_call": "call_", +} + + +_FLOAT_IN_STRING_PATTERN = re.compile(r"(-?\d+\.\d{4,})") + + +def _normalize_numeric_literal_strings(value: str) -> str: + """Round any long decimal literals embedded in strings for stable hashing.""" + + def _replace(match: re.Match[str]) -> str: + number = float(match.group(0)) + return f"{number:.5f}" + + return _FLOAT_IN_STRING_PATTERN.sub(_replace, value) + + +def _normalize_body_for_hash(value: Any) -> Any: + """Recursively normalize a JSON-like value to improve hash stability.""" + + if isinstance(value, dict): + return {key: _normalize_body_for_hash(item) for key, item in value.items()} + if isinstance(value, list): + return [_normalize_body_for_hash(item) for item in value] + if isinstance(value, tuple): + return tuple(_normalize_body_for_hash(item) for item in value) + if isinstance(value, float): + return round(value, 5) + if isinstance(value, str): + return _normalize_numeric_literal_strings(value) + return value + + +def _allocate_test_scoped_id(kind: str) -> str | None: + """Return the next deterministic ID for the given kind within the current test.""" + + global _id_counters + + test_id = get_test_context() + prefix = _ID_KIND_PREFIXES.get(kind) + + if prefix is None: + return None + + if not test_id: + raise ValueError(f"Test ID is required for {kind} ID allocation") + + key = test_id + if key not in _id_counters: + _id_counters[key] = {} + + # each test should get a contiguous block of IDs otherwise we will get + # collisions between tests inside other systems (like file storage) which + # expect IDs to be unique + test_hash = hashlib.sha256(test_id.encode()).hexdigest() + test_hash_int = int(test_hash, 16) + counter = test_hash_int % 1000000000000 + + counter = _id_counters[key].get(kind, counter) + 1 + _id_counters[key][kind] = counter + + return f"{prefix}{counter}" + + +def _deterministic_id_override(kind: str, factory: Callable[[], str]) -> str: + deterministic_id = _allocate_test_scoped_id(kind) + if deterministic_id is not None: + return deterministic_id + return factory() + + +def normalize_inference_request(method: str, url: str, headers: dict[str, Any], body: dict[str, Any]) -> str: """Create a normalized hash of the request for consistent matching. Includes test_id from context to ensure test isolation - identical requests @@ -60,50 +138,39 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict Exception: Model list endpoints (/v1/models, /api/tags) exclude test_id since they are infrastructure/shared and need to work across session setup and tests. """ + # Extract just the endpoint path from urllib.parse import urlparse parsed = urlparse(url) + + body_for_hash = _normalize_body_for_hash(body) + normalized: dict[str, Any] = { "method": method.upper(), "endpoint": parsed.path, - "body": body, + "body": body_for_hash, } # Include test_id for isolation, except for shared infrastructure endpoints if parsed.path not in ("/api/tags", "/v1/models"): - normalized["test_id"] = _test_context.get() + normalized["test_id"] = get_test_context() - # Create hash - sort_keys=True ensures deterministic ordering normalized_json = json.dumps(normalized, sort_keys=True) return hashlib.sha256(normalized_json.encode()).hexdigest() -def _sync_test_context_from_provider_data(): - """In server mode, sync test ID from provider_data to _test_context. +def normalize_tool_request(provider_name: str, tool_name: str, kwargs: dict[str, Any]) -> str: + """Create a normalized hash of the tool request for consistent matching.""" + normalized = { + "provider": provider_name, + "tool_name": tool_name, + "kwargs": kwargs, + } - This ensures that storage operations (which read from _test_context) work correctly - in server mode where the test ID arrives via HTTP header → provider_data. - - Returns a token to reset _test_context, or None if no sync was needed. - """ - stack_config_type = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client") - - if stack_config_type != "server": - return None - - try: - from llama_stack.core.request_headers import PROVIDER_DATA_VAR - - provider_data = PROVIDER_DATA_VAR.get() - - if provider_data and "__test_id" in provider_data: - test_id = provider_data["__test_id"] - return _test_context.set(test_id) - except ImportError: - pass - - return None + # Create hash - sort_keys=True ensures deterministic ordering + normalized_json = json.dumps(normalized, sort_keys=True) + return hashlib.sha256(normalized_json.encode()).hexdigest() def patch_httpx_for_test_id(): @@ -127,13 +194,12 @@ def patch_httpx_for_test_id(): def patched_prepare_request(self, request): # Call original first (it's a sync method that returns None) # Determine which original to call based on client type - if "llama_stack_client" in self.__class__.__module__: - _original_methods["llama_stack_client_prepare_request"](self, request) - _original_methods["openai_prepare_request"](self, request) + _original_methods["llama_stack_client_prepare_request"](self, request) + _original_methods["openai_prepare_request"](self, request) # Only inject test ID in server mode stack_config_type = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client") - test_id = _test_context.get() + test_id = get_test_context() if stack_config_type == "server" and test_id: provider_data_header = request.headers.get("X-LlamaStack-Provider-Data") @@ -162,23 +228,22 @@ def unpatch_httpx_for_test_id(): LlamaStackClient._prepare_request = _original_methods["llama_stack_client_prepare_request"] del _original_methods["llama_stack_client_prepare_request"] - - # Also restore OpenAI client if it was patched - if "openai_prepare_request" in _original_methods: - OpenAI._prepare_request = _original_methods["openai_prepare_request"] - del _original_methods["openai_prepare_request"] + OpenAI._prepare_request = _original_methods["openai_prepare_request"] + del _original_methods["openai_prepare_request"] -def get_inference_mode() -> InferenceMode: - return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower()) +def get_api_recording_mode() -> APIRecordingMode: + return APIRecordingMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower()) -def setup_inference_recording(): +def setup_api_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. + Returns a context manager that can be used to record or replay API requests (inference and tools). + 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. + Currently supports: + - Inference: OpenAI and Ollama clients + - Tools: Search providers (Tavily) Two environment variables are supported: - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', 'replay', or 'record-if-missing'. Default is 'replay'. @@ -190,15 +255,15 @@ def setup_inference_recording(): The recordings are stored as JSON files. """ - mode = get_inference_mode() - if mode == InferenceMode.LIVE: + mode = get_api_recording_mode() + if mode == APIRecordingMode.LIVE: return None storage_dir = os.environ.get("LLAMA_STACK_TEST_RECORDING_DIR", DEFAULT_STORAGE_DIR) - return inference_recording(mode=mode, storage_dir=storage_dir) + return api_recording(mode=mode, storage_dir=storage_dir) -def _normalize_response_data(data: dict[str, Any], request_hash: str) -> dict[str, Any]: +def _normalize_response(data: dict[str, Any], request_hash: str) -> dict[str, Any]: """Normalize fields that change between recordings but don't affect functionality. This reduces noise in git diffs by making IDs deterministic and timestamps constant. @@ -234,7 +299,7 @@ def _serialize_response(response: Any, request_hash: str = "") -> Any: if hasattr(response, "model_dump"): data = response.model_dump(mode="json") # Normalize fields to reduce noise - data = _normalize_response_data(data, request_hash) + data = _normalize_response(data, request_hash) return { "__type__": f"{response.__class__.__module__}.{response.__class__.__qualname__}", "__data__": data, @@ -282,7 +347,7 @@ class ResponseStorage: For test at "tests/integration/inference/test_foo.py::test_bar", returns "tests/integration/inference/recordings/". """ - test_id = _test_context.get() + test_id = get_test_context() if test_id: # Extract the directory path from the test nodeid # e.g., "tests/integration/inference/test_basic.py::test_foo[params]" @@ -297,7 +362,7 @@ class ResponseStorage: # Fallback for non-test contexts return self.base_dir / "recordings" - def _ensure_directories(self): + def _ensure_directory(self): """Ensure test-specific directories exist.""" test_dir = self._get_test_dir() test_dir.mkdir(parents=True, exist_ok=True) @@ -305,7 +370,7 @@ class ResponseStorage: def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): """Store a request/response pair.""" - responses_dir = self._ensure_directories() + responses_dir = self._ensure_directory() # Use FULL hash (not truncated) response_file = f"{request_hash}.json" @@ -334,9 +399,10 @@ class ResponseStorage: with open(response_path, "w") as f: json.dump( { - "test_id": _test_context.get(), + "test_id": get_test_context(), "request": request, "response": serialized_response, + "id_normalization_mapping": {}, }, f, indent=2, @@ -394,6 +460,14 @@ def _recording_from_file(response_path) -> dict[str, Any]: with open(response_path) as f: data = json.load(f) + mapping = data.get("id_normalization_mapping") or {} + if mapping: + serialized = json.dumps(data) + for normalized, original in mapping.items(): + serialized = serialized.replace(original, normalized) + data = json.loads(serialized) + data["id_normalization_mapping"] = {} + # Deserialize response body if needed if "response" in data and "body" in data["response"]: if isinstance(data["response"]["body"], list): @@ -464,131 +538,168 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) return {"request": canonical_req, "response": {"body": body, "is_streaming": False}} +async def _patched_tool_invoke_method( + original_method, provider_name: str, self, tool_name: str, kwargs: dict[str, Any] +): + """Patched version of tool runtime invoke_tool method for recording/replay.""" + global _current_mode, _current_storage + + if _current_mode == APIRecordingMode.LIVE or _current_storage is None: + # Normal operation + return await original_method(self, tool_name, kwargs) + + request_hash = normalize_tool_request(provider_name, tool_name, kwargs) + + if _current_mode in (APIRecordingMode.REPLAY, APIRecordingMode.RECORD_IF_MISSING): + recording = _current_storage.find_recording(request_hash) + if recording: + return recording["response"]["body"] + elif _current_mode == APIRecordingMode.REPLAY: + raise RuntimeError( + f"No recorded tool result found for {provider_name}.{tool_name}\n" + f"Request: {kwargs}\n" + f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record" + ) + # If RECORD_IF_MISSING and no recording found, fall through to record + + if _current_mode in (APIRecordingMode.RECORD, APIRecordingMode.RECORD_IF_MISSING): + # Make the tool call and record it + result = await original_method(self, tool_name, kwargs) + + request_data = { + "test_id": get_test_context(), + "provider": provider_name, + "tool_name": tool_name, + "kwargs": kwargs, + } + response_data = {"body": result, "is_streaming": False} + + # Store the recording + _current_storage.store_recording(request_hash, request_data, response_data) + return result + + else: + raise AssertionError(f"Invalid mode: {_current_mode}") + + async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs): global _current_mode, _current_storage mode = _current_mode storage = _current_storage - if mode == InferenceMode.LIVE or storage is None: + if mode == APIRecordingMode.LIVE or storage is None: if endpoint == "/v1/models": return original_method(self, *args, **kwargs) else: return await original_method(self, *args, **kwargs) - # In server mode, sync test ID from provider_data to _test_context for storage operations - test_context_token = _sync_test_context_from_provider_data() + # Get base URL based on client type + if client_type == "openai": + base_url = str(self._client.base_url) - try: - # Get base URL based on client type - if client_type == "openai": - base_url = str(self._client.base_url) + # the OpenAI client methods may pass NOT_GIVEN for unset parameters; filter these out + kwargs = {k: v for k, v in kwargs.items() if v is not NOT_GIVEN} + 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}" + else: + raise ValueError(f"Unknown client type: {client_type}") - # the OpenAI client methods may pass NOT_GIVEN for unset parameters; filter these out - kwargs = {k: v for k, v in kwargs.items() if v is not NOT_GIVEN} - 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}" + url = base_url.rstrip("/") + endpoint + # Special handling for Databricks URLs to avoid leaking workspace info + # e.g. https://adb-1234567890123456.7.cloud.databricks.com -> https://...cloud.databricks.com + if "cloud.databricks.com" in url: + url = "__databricks__" + url.split("cloud.databricks.com")[-1] + method = "POST" + headers = {} + body = kwargs + + request_hash = normalize_inference_request(method, url, headers, body) + + # Try to find existing recording for REPLAY or RECORD_IF_MISSING modes + recording = None + if mode == APIRecordingMode.REPLAY or mode == APIRecordingMode.RECORD_IF_MISSING: + # Special handling for model-list endpoints: merge all recordings with this hash + if endpoint in ("/api/tags", "/v1/models"): + records = storage._model_list_responses(request_hash) + recording = _combine_model_list_responses(endpoint, records) else: - raise ValueError(f"Unknown client type: {client_type}") + recording = storage.find_recording(request_hash) - url = base_url.rstrip("/") + endpoint - # Special handling for Databricks URLs to avoid leaking workspace info - # e.g. https://adb-1234567890123456.7.cloud.databricks.com -> https://...cloud.databricks.com - if "cloud.databricks.com" in url: - url = "__databricks__" + url.split("cloud.databricks.com")[-1] - method = "POST" - headers = {} - body = kwargs + if recording: + response_body = recording["response"]["body"] - request_hash = normalize_request(method, url, headers, body) + if recording["response"].get("is_streaming", False): - # Try to find existing recording for REPLAY or RECORD_IF_MISSING modes - recording = None - if mode == InferenceMode.REPLAY or mode == InferenceMode.RECORD_IF_MISSING: - # Special handling for model-list endpoints: merge all recordings with this hash - if endpoint in ("/api/tags", "/v1/models"): - records = storage._model_list_responses(request_hash) - recording = _combine_model_list_responses(endpoint, records) - else: - recording = 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 - elif mode == InferenceMode.REPLAY: - # REPLAY mode requires recording to exist - raise RuntimeError( - f"No recorded response found for request hash: {request_hash}\n" - f"Request: {method} {url} {body}\n" - f"Model: {body.get('model', 'unknown')}\n" - f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record" - ) - - if mode == InferenceMode.RECORD or (mode == InferenceMode.RECORD_IF_MISSING and not recording): - if endpoint == "/v1/models": - response = original_method(self, *args, **kwargs) - else: - response = await original_method(self, *args, **kwargs) - - # we want to store the result of the iterator, not the iterator itself - if endpoint == "/v1/models": - response = [m async for m in response] - - 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} - 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: + async def replay_stream(): + for chunk in response_body: yield chunk - return replay_recorded_stream() + return replay_stream() else: - response_data = {"body": response, "is_streaming": False} - storage.store_recording(request_hash, request_data, response_data) - return response + return response_body + elif mode == APIRecordingMode.REPLAY: + # REPLAY mode requires recording to exist + raise RuntimeError( + f"No recorded response found for request hash: {request_hash}\n" + f"Request: {method} {url} {body}\n" + f"Model: {body.get('model', 'unknown')}\n" + f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record" + ) + if mode == APIRecordingMode.RECORD or (mode == APIRecordingMode.RECORD_IF_MISSING and not recording): + if endpoint == "/v1/models": + response = original_method(self, *args, **kwargs) else: - raise AssertionError(f"Invalid mode: {mode}") - finally: - if test_context_token: - _test_context.reset(test_context_token) + response = await original_method(self, *args, **kwargs) + + # we want to store the result of the iterator, not the iterator itself + if endpoint == "/v1/models": + response = [m async for m in response] + + 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: list[Any] = [] + async for chunk in response: + chunks.append(chunk) + + # Store the recording immediately + response_data = {"body": chunks, "is_streaming": True} + 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} + storage.store_recording(request_hash, request_data, response_data) + return response + + else: + raise AssertionError(f"Invalid mode: {mode}") def patch_inference_clients(): - """Install monkey patches for OpenAI client methods and Ollama AsyncClient methods.""" + """Install monkey patches for OpenAI client methods, Ollama AsyncClient methods, and tool runtime methods.""" global _original_methods from ollama import AsyncClient as OllamaAsyncClient @@ -597,7 +708,9 @@ def patch_inference_clients(): from openai.resources.embeddings import AsyncEmbeddings from openai.resources.models import AsyncModels - # Store original methods for both OpenAI and Ollama clients + from llama_stack.providers.remote.tool_runtime.tavily_search.tavily_search import TavilySearchToolRuntimeImpl + + # Store original methods for OpenAI, Ollama clients, and tool runtimes _original_methods = { "chat_completions_create": AsyncChatCompletions.create, "completions_create": AsyncCompletions.create, @@ -609,6 +722,7 @@ def patch_inference_clients(): "ollama_ps": OllamaAsyncClient.ps, "ollama_pull": OllamaAsyncClient.pull, "ollama_list": OllamaAsyncClient.list, + "tavily_invoke_tool": TavilySearchToolRuntimeImpl.invoke_tool, } # Create patched methods for OpenAI client @@ -681,9 +795,18 @@ def patch_inference_clients(): OllamaAsyncClient.pull = patched_ollama_pull OllamaAsyncClient.list = patched_ollama_list + # Create patched methods for tool runtimes + async def patched_tavily_invoke_tool(self, tool_name: str, kwargs: dict[str, Any]): + return await _patched_tool_invoke_method( + _original_methods["tavily_invoke_tool"], "tavily", self, tool_name, kwargs + ) + + # Apply tool runtime patches + TavilySearchToolRuntimeImpl.invoke_tool = patched_tavily_invoke_tool + def unpatch_inference_clients(): - """Remove monkey patches and restore original OpenAI and Ollama client methods.""" + """Remove monkey patches and restore original OpenAI, Ollama client, and tool runtime methods.""" global _original_methods if not _original_methods: @@ -696,6 +819,8 @@ def unpatch_inference_clients(): from openai.resources.embeddings import AsyncEmbeddings from openai.resources.models import AsyncModels + from llama_stack.providers.remote.tool_runtime.tavily_search.tavily_search import TavilySearchToolRuntimeImpl + # Restore OpenAI client methods AsyncChatCompletions.create = _original_methods["chat_completions_create"] AsyncCompletions.create = _original_methods["completions_create"] @@ -710,17 +835,21 @@ def unpatch_inference_clients(): OllamaAsyncClient.pull = _original_methods["ollama_pull"] OllamaAsyncClient.list = _original_methods["ollama_list"] + # Restore tool runtime methods + TavilySearchToolRuntimeImpl.invoke_tool = _original_methods["tavily_invoke_tool"] + _original_methods.clear() @contextmanager -def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]: - """Context manager for inference recording/replaying.""" +def api_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]: + """Context manager for API recording/replaying (inference and tools).""" global _current_mode, _current_storage # Store previous state prev_mode = _current_mode prev_storage = _current_storage + previous_override = None try: _current_mode = mode @@ -729,7 +858,9 @@ def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Gen if storage_dir is None: raise ValueError("storage_dir is required for record, replay, and record-if-missing modes") _current_storage = ResponseStorage(Path(storage_dir)) + _id_counters.clear() patch_inference_clients() + previous_override = set_id_override(_deterministic_id_override) yield @@ -737,6 +868,7 @@ def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Gen # Restore previous state if mode in ["record", "replay", "record-if-missing"]: unpatch_inference_clients() + reset_id_override(previous_override) _current_mode = prev_mode _current_storage = prev_storage diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index 0f4ae2bb4..a0342af7d 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -30,7 +30,7 @@ Options: --stack-config STRING Stack configuration to use (required) --suite STRING Test suite to run (default: 'base') --setup STRING Test setup (models, env) to use (e.g., 'ollama', 'ollama-vision', 'gpt', 'vllm') - --inference-mode STRING Inference mode: record or replay (default: replay) + --inference-mode STRING Inference mode: replay, record-if-missing or record (default: replay) --subdirs STRING Comma-separated list of test subdirectories to run (overrides suite) --pattern STRING Regex pattern to pass to pytest -k --collect-only Collect tests only without running them (skips server startup) diff --git a/tests/common/mcp.py b/tests/common/mcp.py index 357ea4d41..644becd2d 100644 --- a/tests/common/mcp.py +++ b/tests/common/mcp.py @@ -159,7 +159,6 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal import threading import time - import httpx import uvicorn from mcp.server.fastmcp import FastMCP from mcp.server.sse import SseServerTransport @@ -171,6 +170,11 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal server = FastMCP("FastMCP Test Server", log_level="WARNING") + # Silence verbose MCP server logs + import logging # allow-direct-logging + + logging.getLogger("mcp.server.lowlevel.server").setLevel(logging.WARNING) + tools = tools or default_tools() # Register all tools with the server @@ -234,29 +238,25 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal logger.debug(f"Starting MCP server thread on port {port}") server_thread.start() - # Polling until the server is ready - timeout = 10 + # Wait for the server thread to be running + # Note: We can't use a simple HTTP GET health check on /sse because it's an SSE endpoint + # that expects a long-lived connection, not a simple request/response + timeout = 2 start_time = time.time() server_url = f"http://localhost:{port}/sse" - logger.debug(f"Waiting for MCP server to be ready at {server_url}") + logger.debug(f"Waiting for MCP server thread to start on port {port}") while time.time() - start_time < timeout: - try: - response = httpx.get(server_url) - if response.status_code in [200, 401]: - logger.debug(f"MCP server is ready on port {port} (status: {response.status_code})") - break - except httpx.RequestError as e: - logger.debug(f"Server not ready yet, retrying... 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"replay" + if "SQLITE_STORE_DIR" not in os.environ: + os.environ["SQLITE_STORE_DIR"] = tempfile.mkdtemp() + + # Set test stack config type for api_recorder test isolation stack_config = session.config.getoption("--stack-config", default=None) if stack_config and stack_config.startswith("server:"): os.environ["LLAMA_STACK_TEST_STACK_CONFIG_TYPE"] = "server" @@ -43,8 +49,6 @@ def pytest_sessionstart(session): os.environ["LLAMA_STACK_TEST_STACK_CONFIG_TYPE"] = "library_client" logger.info(f"Test stack config type: library_client (stack_config={stack_config})") - from llama_stack.testing.inference_recorder import patch_httpx_for_test_id - patch_httpx_for_test_id() @@ -55,15 +59,13 @@ def _track_test_context(request): This fixture runs for every test and stores the test's nodeid in a contextvar that the recording system can access to determine which subdirectory to use. """ - from llama_stack.testing.inference_recorder import _test_context + from llama_stack.core.testing_context import reset_test_context, set_test_context - # Store the test nodeid (e.g., "tests/integration/responses/test_basic.py::test_foo[params]") - token = _test_context.set(request.node.nodeid) + token = set_test_context(request.node.nodeid) yield - # Cleanup - _test_context.reset(token) + reset_test_context(token) def pytest_runtest_teardown(item): @@ -121,9 +123,13 @@ def pytest_configure(config): # Apply defaults if not provided explicitly for dest, value in setup_obj.defaults.items(): current = getattr(config.option, dest, None) - if not current: + if current is None: setattr(config.option, dest, value) + # Apply global fallback for embedding_dimension if still not set + if getattr(config.option, "embedding_dimension", None) is None: + config.option.embedding_dimension = 384 + def pytest_addoption(parser): parser.addoption( @@ -161,8 +167,8 @@ def pytest_addoption(parser): parser.addoption( "--embedding-dimension", type=int, - default=384, - help="Output dimensionality of the embedding model to use for testing. Default: 384", + default=None, + help="Output dimensionality of the embedding model to use for testing. Default: 384 (or setup-specific)", ) parser.addoption( @@ -236,7 +242,9 @@ def pytest_generate_tests(metafunc): continue params.append(fixture_name) - val = metafunc.config.getoption(option) + # Use getattr on config.option to see values set by pytest_configure fallbacks + dest = option.lstrip("-").replace("-", "_") + val = getattr(metafunc.config.option, dest, None) values = [v.strip() for v in str(val).split(",")] if val else [None] param_values[fixture_name] = values diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 68aa2b60b..554d9f7bd 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -129,6 +129,8 @@ def client_with_models( model_ids = {m.identifier for m in client.models.list()} model_ids.update(m.provider_resource_id for m in client.models.list()) + # TODO: fix this crap where we use the first provider randomly + # that cannot be right. I think the test should just specify the provider_id if text_model_id and text_model_id not in model_ids: client.models.register(model_id=text_model_id, provider_id=inference_providers[0]) if vision_model_id and vision_model_id not in model_ids: @@ -183,6 +185,12 @@ def llama_stack_client(request): # would be forced to use llama_stack_client, which is not what we want. print("\ninstantiating llama_stack_client") start_time = time.time() + + # Patch httpx to inject test ID for server-mode test isolation + from llama_stack.testing.api_recorder import patch_httpx_for_test_id + + patch_httpx_for_test_id() + client = instantiate_llama_stack_client(request.session) print(f"llama_stack_client instantiated in {time.time() - start_time:.3f}s") return client diff --git a/tests/integration/responses/fixtures/test_cases.py b/tests/integration/responses/fixtures/test_cases.py index bdd1a5d81..0e72bbcae 100644 --- a/tests/integration/responses/fixtures/test_cases.py +++ b/tests/integration/responses/fixtures/test_cases.py @@ -41,32 +41,33 @@ basic_test_cases = [ ), id="saturn", ), - pytest.param( - ResponsesTestCase( - input=[ - { - "role": "user", - "content": [ - { - "type": "input_text", - "text": "what teams are playing in this image?", - } - ], - }, - { - "role": "user", - "content": [ - { - "type": "input_image", - "image_url": "https://upload.wikimedia.org/wikipedia/commons/3/3b/LeBron_James_Layup_%28Cleveland_vs_Brooklyn_2018%29.jpg", - } - ], - }, - ], - expected="brooklyn nets", - ), - id="image_input", - ), + # TODO: Add image input test case, since this test case got Nerfed by OpenAI with a refusal + # pytest.param( + # ResponsesTestCase( + # input=[ + # { + # "role": "user", + # "content": [ + # { + # "type": "input_text", + # "text": "what teams are playing in this image?", + # } + # ], + # }, + # { + # "role": "user", + # "content": [ + # { + # "type": "input_image", + # "image_url": "https://upload.wikimedia.org/wikipedia/commons/3/3b/LeBron_James_Layup_%28Cleveland_vs_Brooklyn_2018%29.jpg", + # } + # ], + # }, + # ], + # expected="brooklyn nets", + # ), + # id="image_input", + # ), ] # Multi-turn test cases diff --git a/tests/integration/responses/helpers.py b/tests/integration/responses/helpers.py index 7c988402f..605b64b3c 100644 --- a/tests/integration/responses/helpers.py +++ b/tests/integration/responses/helpers.py @@ -7,7 +7,7 @@ import time -def new_vector_store(openai_client, name): +def new_vector_store(openai_client, name, embedding_model, embedding_dimension): """Create a new vector store, cleaning up any existing one with the same name.""" # Ensure we don't reuse an existing vector store vector_stores = openai_client.vector_stores.list() @@ -16,7 +16,21 @@ def new_vector_store(openai_client, name): openai_client.vector_stores.delete(vector_store_id=vector_store.id) # Create a new vector store - vector_store = 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"tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]", + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "How many experts does the Llama 4 Maverick model have?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_aPe1vS1v5bIwPgl789D5bfmW", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_aPe1vS1v5bIwPgl789D5bfmW", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-690481987689, score: 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"tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536-llama_experts_pdf]", + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "text-embedding-3-small", + "input": [ + "Llama Stack \nLlama Stack Overview \nLlama Stack standardizes the core building blocks that simplify AI application development. It codifies best \npractices\n \nacross\n \nthe\n \nLlama\n \necosystem.\n \nMore\n \nspecifically,\n \nit\n \nprovides\n \u25cf Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. \u25cf Plugin architecture to support the rich ecosystem of different API implementations in various \nenvironments,\n \nincluding\n \nlocal\n \ndevelopment,\n \non-premises,\n \ncloud,\n \nand\n \nmobile.\n \u25cf Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly \nand\n \nreliably\n \nin\n \nany\n \nenvironment.\n \u25cf Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android. \u25cf Standalone applications as examples for how to build production-grade AI applications with Llama \nStack.\n \nLlama Stack Benefits \n\u25cf Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy \nflexible\n \ndeployment\n \nchoices.\n \u25cf Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI \napplications\n \nwith\n \nconsistent\n \napplication\n \nbehavior.\n \u25cf Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. ", + ", \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. 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"https://api.openai.com/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "What are the engineering updates from the US?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_toDXiP4WpUNQvvvLkTGEbQAr", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"latest engineering updates US 2023\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_toDXiP4WpUNQvvvLkTGEbQAr", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-710505118847, score: 1.4208534597848683, attributes: {'region': 'us', 'category': 'engineering', 'date': 1680307200.0, 'filename': 'us_engineering_q2.txt', 'document_id': 'file-710505118847', 'token_count': 18.0, 'metadata_token_count': 32.0} (cite as 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New features deployed in the US region.\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "The above results were retrieved to help answer the user's query: \"latest engineering updates US 2023\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n" + } + ] + } + ], + "stream": true, + "tools": [ + { + "type": "function", + "function": { + "name": "knowledge_search", + "description": "Search for information in a database.", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The query to search for. 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b/tests/integration/responses/recordings/34a1b84c2a8546684f4c4d596662df62d17b1259684141dab22de4ec174e4c5c.json new file mode 100644 index 000000000..471b5f5fd --- /dev/null +++ b/tests/integration/responses/recordings/34a1b84c2a8546684f4c4d596662df62d17b1259684141dab22de4ec174e4c5c.json @@ -0,0 +1,2463 @@ +{ + "test_id": "tests/integration/responses/test_file_search.py::test_response_file_search_filter_by_date_range[openai_client-txt=openai/gpt-4o]", + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "What happened in Q1 2023?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_PfY7bcXP30PsqQxRzUa1peOc", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Q1 2023 events\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_PfY7bcXP30PsqQxRzUa1peOc", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 2 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-9a997160dd604f7cb6fef981cd0f9f22, score: 1.028104385476394, attributes: {'region': 'us', 'category': 'marketing', 'date': 1672531200.0, 'filename': 'us_marketing_q1.txt', 'document_id': 'file-9a997160dd604f7cb6fef981cd0f9f22', 'token_count': 21.0, 'metadata_token_count': 32.0} (cite as <|file-9a997160dd604f7cb6fef981cd0f9f22|>)\nUS promotional campaigns for Q1 2023. Revenue increased by 15% in the US region.\n" + }, + { + "type": "text", + "text": "[2] document_id: file-6a78d72beefc4b369ba49ff1fc24d55b, score: 0.9312513654699082, attributes: {'region': 'eu', 'category': 'marketing', 'date': 1672531200.0, 'filename': 'eu_marketing_q1.txt', 'document_id': 'file-6a78d72beefc4b369ba49ff1fc24d55b', 'token_count': 17.0, 'metadata_token_count': 32.0} (cite as <|file-6a78d72beefc4b369ba49ff1fc24d55b|>)\nEuropean advertising campaign results for Q1 2023. Strong growth in EU markets.\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "The above results were retrieved to help answer the user's query: \"Q1 2023 events\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. 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First, get the experiment ID for 'boiling_point', then use that ID to get the experiment results. Tell me the boiling point in Celsius." + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_kQzwGe637M0VpMEBFvXlAXTR", + "type": "function", + "function": { + "name": "get_experiment_id", + "arguments": "{\"experiment_name\":\"boiling_point\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_kQzwGe637M0VpMEBFvXlAXTR", + "content": "unhandled errors in a TaskGroup (1 sub-exception)" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_KInRNLIriUKirjfBCuJqJTWT", + "type": "function", + "function": { + "name": "get_experiment_id", + "arguments": "{\"experiment_name\":\"boiling_point\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_KInRNLIriUKirjfBCuJqJTWT", + "content": [ + { + "type": "text", + "text": "exp_004" + } + ] + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_biQv6t2r31f5CPlharTSDRdH", + "type": "function", + "function": { + "name": "get_experiment_results", + "arguments": "{\"experiment_id\":\"exp_004\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_biQv6t2r31f5CPlharTSDRdH", + "content": [ + { + "type": "text", + "text": "Boiling Point: 100\u00b0C, Status: Verified" + } + ] + } + ], + "stream": true, + "tools": [ + { + "type": "function", + "function": { + "name": "get_user_id", + "description": "\n Get the user ID for a given username. 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Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n" + } + ] + } + ], + "stream": true, + "tools": [ + { + "type": "function", + "function": { + "name": "knowledge_search", + "description": "Search for information in a database.", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The query to search for. 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"tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536-llama_experts]", + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "How many experts does the Llama 4 Maverick model have?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_9Ofp0Uepi3uOnEmuFtm9yvCU", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_9Ofp0Uepi3uOnEmuFtm9yvCU", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-78420035045, score: 2.933222791810999, attributes: {'filename': 'test_response_non_streaming_file_search.txt', 'document_id': 'file-78420035045', 'token_count': 10.0, 'metadata_token_count': 13.0} (cite as <|file-78420035045|>)\nLlama 4 Maverick has 128 experts\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". 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It codifies best \npractices\n \nacross\n \nthe\n \nLlama\n \necosystem.\n \nMore\n \nspecifically,\n \nit\n \nprovides\n \u25cf Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. \u25cf Plugin architecture to support the rich ecosystem of different API implementations in various \nenvironments,\n \nincluding\n \nlocal\n \ndevelopment,\n \non-premises,\n \ncloud,\n \nand\n \nmobile.\n \u25cf Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly \nand\n \nreliably\n \nin\n \nany\n \nenvironment.\n \u25cf Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android. \u25cf Standalone applications as examples for how to build production-grade AI applications with Llama \nStack.\n \nLlama Stack Benefits \n\u25cf Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy \nflexible\n \ndeployment\n \nchoices.\n \u25cf Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI \napplications\n \nwith\n \nconsistent\n \napplication\n \nbehavior.\n \u25cf Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. ", + ", \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. 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With a 17 billion parameter mixture-of-experts architecture (128 experts), this model offers industry-leading performance for multimodal tasks like natural assistant-like chat, image recognition, and coding tasks. Llama 4 Maverick features an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 17B activated parameters (400B total) and incorporates early fusion for native multimodality. The model uses 128 experts to efficiently handle both text and image inputs while maintaining high performance across chat, knowledge, and code generation tasks, with a knowledge cutoff of August 2024. * For multimodal applications, this model supports up to 5 image inputs create( model =\\\"meta-llama/llama-4-maverick-17b-128e-instruct\\\", messages =[ { \\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"Explain why fast inference is critical for reasoning models\\\" } ] ) print(completion.\", \"score\": 0.9170729, \"raw_content\": null}, {\"url\": \"https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E\", \"title\": \"meta-llama/Llama-4-Maverick-17B-128E - Hugging Face\", \"content\": \"Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate\", \"score\": 0.8021998, \"raw_content\": null}, {\"url\": \"https://www.ibm.com/new/announcements/meta-llama-4-maverick-and-llama-4-scout-now-available-in-watsonx-ai\", \"title\": \"Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx ...\", \"content\": \"# Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx.ai **IBM is excited to announce the addition of Meta\\u2019s latest generation of open models, Llama 4, to** **watsonx.ai****.** Llama 4 Scout and Llama 4 Maverick, the first mixture of experts (MoE) models released by Meta, provide frontier multimodal performance, high speeds, low cost, and industry leading context length. 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With a 17 billion parameter mixture-of-experts architecture (128 experts), this model offers industry-leading performance for multimodal tasks like natural assistant-like chat, image recognition, and coding tasks. Llama 4 Maverick features an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 17B activated parameters (400B total) and incorporates early fusion for native multimodality. The model uses 128 experts to efficiently handle both text and image inputs while maintaining high performance across chat, knowledge, and code generation tasks, with a knowledge cutoff of August 2024. * For multimodal applications, this model supports up to 5 image inputs create( model =\\\"meta-llama/llama-4-maverick-17b-128e-instruct\\\", messages =[ { \\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"Explain why fast inference is critical for reasoning models\\\" } ] ) print(completion.\", \"score\": 0.9170729, \"raw_content\": null}, {\"url\": \"https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E\", \"title\": \"meta-llama/Llama-4-Maverick-17B-128E - Hugging Face\", \"content\": \"Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate\", \"score\": 0.8021998, \"raw_content\": null}, {\"url\": \"https://www.ibm.com/new/announcements/meta-llama-4-maverick-and-llama-4-scout-now-available-in-watsonx-ai\", \"title\": \"Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx ...\", \"content\": \"# Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx.ai **IBM is excited to announce the addition of Meta\\u2019s latest generation of open models, Llama 4, to** **watsonx.ai****.** Llama 4 Scout and Llama 4 Maverick, the first mixture of experts (MoE) models released by Meta, provide frontier multimodal performance, high speeds, low cost, and industry leading context length. With the introduction of these latest offerings from Meta, IBM now supports a total of 13 Meta models in the expansive library of \\u00a0foundation models available in watsonx.ai. 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b/tests/integration/responses/recordings/fb36a435632d5de6e5decf333a7e38e1c629713bfdcc2536d569a776a4091791.json new file mode 100644 index 000000000..a2cac6d79 --- /dev/null +++ b/tests/integration/responses/recordings/fb36a435632d5de6e5decf333a7e38e1c629713bfdcc2536d569a776a4091791.json @@ -0,0 +1,894 @@ +{ + "test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[client_with_models-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]", + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "How many experts does the Llama 4 Maverick model have?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_zS2WxgXWetjnlPt2MzH9Asrc", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_zS2WxgXWetjnlPt2MzH9Asrc", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-5217982280, score: 2.57802841833685, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5217982280', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5217982280|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n" + } + ] + } + ], + "stream": true, + "tools": [ + { + "type": "function", + "function": { + "name": "knowledge_search", + "description": "Search for information in a database.", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The query to search for. 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b/tests/integration/responses/test_extra_body_shields.py index 3dedb287a..eb41cc150 100644 --- a/tests/integration/responses/test_extra_body_shields.py +++ b/tests/integration/responses/test_extra_body_shields.py @@ -16,6 +16,7 @@ import pytest from llama_stack_client import APIStatusError +@pytest.mark.xfail(reason="Shields are not yet implemented inside responses") def test_shields_via_extra_body(compat_client, text_model_id): """Test that shields parameter is received by the server and raises NotImplementedError.""" diff --git a/tests/integration/responses/test_file_search.py b/tests/integration/responses/test_file_search.py index ba7775a0b..3fc0f001e 100644 --- a/tests/integration/responses/test_file_search.py +++ b/tests/integration/responses/test_file_search.py @@ -47,12 +47,14 @@ def test_response_text_format(compat_client, text_model_id, text_format): @pytest.fixture -def vector_store_with_filtered_files(compat_client, text_model_id, tmp_path_factory): - """Create a vector store with multiple files that have different attributes for filtering tests.""" +def vector_store_with_filtered_files(compat_client, embedding_model_id, embedding_dimension, tmp_path_factory): + # """Create a vector store with multiple files that have different attributes for filtering tests.""" if isinstance(compat_client, LlamaStackAsLibraryClient): - pytest.skip("Responses API file search is not yet supported in library client.") + pytest.skip("upload_file() is not yet supported in library client somehow?") - vector_store = new_vector_store(compat_client, "test_vector_store_with_filters") + vector_store = new_vector_store( + compat_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension + ) tmp_path = tmp_path_factory.mktemp("filter_test_files") # Create multiple files with different attributes diff --git a/tests/integration/responses/test_tool_responses.py b/tests/integration/responses/test_tool_responses.py index 5d6899fa6..2cff4d27d 100644 --- a/tests/integration/responses/test_tool_responses.py +++ b/tests/integration/responses/test_tool_responses.py @@ -46,11 +46,13 @@ def test_response_non_streaming_web_search(compat_client, text_model_id, case): @pytest.mark.parametrize("case", file_search_test_cases) -def test_response_non_streaming_file_search(compat_client, text_model_id, tmp_path, case): +def test_response_non_streaming_file_search( + compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case +): if isinstance(compat_client, LlamaStackAsLibraryClient): pytest.skip("Responses API file search is not yet supported in library client.") - vector_store = new_vector_store(compat_client, "test_vector_store") + vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension) if case.file_content: file_name = "test_response_non_streaming_file_search.txt" @@ -101,11 +103,13 @@ def test_response_non_streaming_file_search(compat_client, text_model_id, tmp_pa assert case.expected.lower() in response.output_text.lower().strip() -def test_response_non_streaming_file_search_empty_vector_store(compat_client, text_model_id): +def test_response_non_streaming_file_search_empty_vector_store( + compat_client, text_model_id, embedding_model_id, embedding_dimension +): if isinstance(compat_client, LlamaStackAsLibraryClient): pytest.skip("Responses API file search is not yet supported in library client.") - vector_store = new_vector_store(compat_client, "test_vector_store") + vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension) # Create the response request, which should query our vector store response = compat_client.responses.create( @@ -127,12 +131,14 @@ def test_response_non_streaming_file_search_empty_vector_store(compat_client, te assert response.output_text -def test_response_sequential_file_search(compat_client, text_model_id, tmp_path): +def test_response_sequential_file_search( + compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path +): """Test file search with sequential responses using previous_response_id.""" if isinstance(compat_client, LlamaStackAsLibraryClient): pytest.skip("Responses API file search is not yet supported in library client.") - vector_store = new_vector_store(compat_client, "test_vector_store") + vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension) # Create a test file with content file_content = "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture." diff --git a/tests/integration/suites.py b/tests/integration/suites.py index e82e766e3..bc252bb08 100644 --- a/tests/integration/suites.py +++ b/tests/integration/suites.py @@ -39,7 +39,7 @@ class Setup(BaseModel): name: str description: str - defaults: dict[str, str] = Field(default_factory=dict) + defaults: dict[str, str | int] = Field(default_factory=dict) env: dict[str, str] = Field(default_factory=dict) @@ -88,6 +88,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = { defaults={ "text_model": "openai/gpt-4o", "embedding_model": "openai/text-embedding-3-small", + "embedding_dimension": 1536, }, ), "tgi": Setup( diff --git a/tests/unit/distribution/test_api_recordings.py b/tests/unit/distribution/test_api_recordings.py new file mode 100644 index 000000000..2208ac506 --- /dev/null +++ b/tests/unit/distribution/test_api_recordings.py @@ -0,0 +1,318 @@ +# 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 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.api_recorder import ( + APIRecordingMode, + ResponseStorage, + api_recording, + normalize_inference_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_inference_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_inference_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_inference_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_inference_request( + "POST", + "http://test/v1/chat/completions", + {}, + {"messages": [{"role": "user", "content": "Hello world\n\n"}]}, + ) + hash2 = normalize_inference_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_inference_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7000001}) + hash4 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7}) + assert hash3 == hash4 # Small float precision differences should normalize to the same hash + + # String-embedded decimals with excessive precision should also normalize. + body_with_precise_scores = { + "messages": [ + { + "role": "tool", + "content": "score: 0.7472640164649847", + } + ] + } + body_with_precise_scores_variation = { + "messages": [ + { + "role": "tool", + "content": "score: 0.74726414959878", + } + ] + } + hash5 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, body_with_precise_scores) + hash6 = normalize_inference_request( + "POST", "http://test/v1/chat/completions", {}, body_with_precise_scores_variation + ) + assert hash5 == hash6 + + body_with_close_scores = { + "messages": [ + { + "role": "tool", + "content": "score: 0.662477492560699", + } + ] + } + body_with_close_scores_variation = { + "messages": [ + { + "role": "tool", + "content": "score: 0.6624775971970099", + } + ] + } + hash7 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, body_with_close_scores) + hash8 = normalize_inference_request( + "POST", "http://test/v1/chat/completions", {}, body_with_close_scores_variation + ) + assert hash7 == hash8 + + 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 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 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 api_recording(mode=APIRecordingMode.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) + assert storage._get_test_dir().exists() + + 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 api_recording(mode=APIRecordingMode.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 api_recording(mode=APIRecordingMode.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 api_recording(mode=APIRecordingMode.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 api_recording(mode=APIRecordingMode.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 api_recording(mode=APIRecordingMode.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 api_recording(mode=APIRecordingMode.LIVE, storage_dir="foo"): + 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." diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py deleted file mode 100644 index cb6b92837..000000000 --- a/tests/unit/distribution/test_inference_recordings.py +++ /dev/null @@ -1,382 +0,0 @@ -# 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 tempfile -from pathlib import Path -from unittest.mock import AsyncMock, Mock, patch - -import pytest -from openai import NOT_GIVEN, AsyncOpenAI -from openai.types.model import Model as OpenAIModel - -# Import the real Pydantic response types instead of using Mocks -from llama_stack.apis.inference import ( - OpenAIAssistantMessageParam, - OpenAIChatCompletion, - OpenAIChoice, - OpenAICompletion, - 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 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 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.""" - temp_storage_dir = temp_storage_dir / "test_recording_mode" - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) - - # Verify the response was returned correctly - assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." - client.chat.completions._post.assert_called_once() - - # Verify recording was stored - storage = ResponseStorage(temp_storage_dir) - dir = storage._get_test_dir() - assert dir.exists() - - async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response): - """Test that replay mode returns stored responses without making real calls.""" - temp_storage_dir = temp_storage_dir / "test_replay_mode" - # First, record a response - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) - client.chat.completions._post.assert_called_once() - - # Now test replay mode - should not call the original method - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - - 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 - client.chat.completions._post.assert_not_called() - - async def test_replay_mode_models(self, temp_storage_dir): - """Test that replay mode returns stored responses without making real model listing calls.""" - - async def _async_iterator(models): - for model in models: - yield model - - models = [ - OpenAIModel(id="foo", created=1, object="model", owned_by="test"), - OpenAIModel(id="bar", created=2, object="model", owned_by="test"), - ] - - expected_ids = {m.id for m in models} - - temp_storage_dir = temp_storage_dir / "test_replay_mode_models" - - # baseline - mock works without recording - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.models._get_api_list = Mock(return_value=_async_iterator(models)) - assert {m.id async for m in client.models.list()} == expected_ids - client.models._get_api_list.assert_called_once() - - # record the call - with inference_recording(mode=InferenceMode.RECORD, storage_dir=temp_storage_dir): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.models._get_api_list = Mock(return_value=_async_iterator(models)) - assert {m.id async for m in client.models.list()} == expected_ids - client.models._get_api_list.assert_called_once() - - # replay the call - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=temp_storage_dir): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.models._get_api_list = Mock(return_value=_async_iterator(models)) - assert {m.id async for m in client.models.list()} == expected_ids - client.models._get_api_list.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.""" - - # baseline - mock works without recording - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - response = await client.embeddings.create( - model=real_embeddings_response.model, - input=["Hello world", "Test embedding"], - encoding_format=NOT_GIVEN, - ) - assert len(response.data) == 2 - assert response.data[0].embedding == [0.1, 0.2, 0.3] - client.embeddings._post.assert_called_once() - - temp_storage_dir = temp_storage_dir / "test_embeddings_recording" - # Record - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - - response = await client.embeddings.create( - model=real_embeddings_response.model, - input=["Hello world", "Test embedding"], - encoding_format=NOT_GIVEN, - dimensions=NOT_GIVEN, - user=NOT_GIVEN, - ) - - assert len(response.data) == 2 - - # Replay - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - - response = await client.embeddings.create( - model=real_embeddings_response.model, - 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 - client.embeddings._post.assert_not_called() - - async def test_completions_recording(self, temp_storage_dir): - real_completions_response = OpenAICompletion( - id="test_completion", - object="text_completion", - created=1234567890, - model="llama3.2:3b", - choices=[ - { - "text": "Hello! I'm doing well, thank you for asking.", - "index": 0, - "logprobs": None, - "finish_reason": "stop", - } - ], - ) - - temp_storage_dir = temp_storage_dir / "test_completions_recording" - - # baseline - mock works without recording - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.completions._post = AsyncMock(return_value=real_completions_response) - response = await client.completions.create( - model=real_completions_response.model, - prompt="Hello, how are you?", - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) - assert response.choices[0].text == real_completions_response.choices[0].text - client.completions._post.assert_called_once() - - # Record - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.completions._post = AsyncMock(return_value=real_completions_response) - - response = await client.completions.create( - model=real_completions_response.model, - prompt="Hello, how are you?", - temperature=0.7, - max_tokens=50, - user=NOT_GIVEN, - ) - - assert response.choices[0].text == real_completions_response.choices[0].text - client.completions._post.assert_called_once() - - # Replay - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") - client.completions._post = AsyncMock(return_value=real_completions_response) - response = await client.completions.create( - model=real_completions_response.model, - prompt="Hello, how are you?", - temperature=0.7, - max_tokens=50, - ) - assert response.choices[0].text == real_completions_response.choices[0].text - client.completions._post.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, storage_dir="foo"): - 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."