mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 18:00:36 +00:00
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 ```
This commit is contained in:
Ashwin Bharambe
GitHub
parent
26fd5dbd34
commit
f50ce11a3b
284 changed files with 296191 additions and 631 deletions
6
.github/workflows/integration-tests.yml
vendored
6
.github/workflows/integration-tests.yml
vendored
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@ -54,14 +54,14 @@ jobs:
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# Define (setup, suite) pairs - they are always matched and cannot be independent
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# Weekly schedule (Sun 1 AM): vllm+base
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# Input test-setup=ollama-vision: ollama-vision+vision
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# Default (including test-setup=ollama): both ollama+base and ollama-vision+vision
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# Default (including test-setup=ollama): ollama+base, ollama-vision+vision, gpt+responses
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config: >-
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${{
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github.event.schedule == '1 0 * * 0'
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&& fromJSON('[{"setup": "vllm", "suite": "base"}]')
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|| github.event.inputs.test-setup == 'ollama-vision'
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&& fromJSON('[{"setup": "ollama-vision", "suite": "vision"}]')
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|| fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}]')
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|| fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}, {"setup": "gpt", "suite": "responses"}]')
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}}
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steps:
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@ -79,6 +79,8 @@ jobs:
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- name: Run tests
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uses: ./.github/actions/run-and-record-tests
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env:
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OPENAI_API_KEY: dummy
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with:
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stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }}
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setup: ${{ matrix.config.setup }}
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@ -61,6 +61,9 @@ jobs:
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- name: Run and record tests
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uses: ./.github/actions/run-and-record-tests
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env:
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# Set OPENAI_API_KEY if using gpt setup
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OPENAI_API_KEY: ${{ inputs.test-setup == 'gpt' && secrets.OPENAI_API_KEY || '' }}
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with:
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stack-config: 'server:ci-tests' # recording must be done with server since more tests are run
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setup: ${{ inputs.test-setup || 'ollama' }}
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@ -68,7 +68,9 @@ recordings/
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Direct API calls with no recording or replay:
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```python
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with inference_recording(mode=InferenceMode.LIVE):
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from llama_stack.testing.api_recorder import api_recording, APIRecordingMode
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with api_recording(mode=APIRecordingMode.LIVE):
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response = await client.chat.completions.create(...)
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```
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@ -79,7 +81,7 @@ Use for initial development and debugging against real APIs.
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Captures API interactions while passing through real responses:
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```python
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with inference_recording(mode=InferenceMode.RECORD, storage_dir="./recordings"):
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with api_recording(mode=APIRecordingMode.RECORD, storage_dir="./recordings"):
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response = await client.chat.completions.create(...)
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# Real API call made, response captured AND returned
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```
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@ -96,7 +98,7 @@ The recording process:
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Returns stored responses instead of making API calls:
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```python
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with inference_recording(mode=InferenceMode.REPLAY, storage_dir="./recordings"):
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with api_recording(mode=APIRecordingMode.REPLAY, storage_dir="./recordings"):
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response = await client.chat.completions.create(...)
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# No API call made, cached response returned instantly
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```
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42
llama_stack/core/id_generation.py
Normal file
42
llama_stack/core/id_generation.py
Normal file
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@ -0,0 +1,42 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from collections.abc import Callable
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IdFactory = Callable[[], str]
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IdOverride = Callable[[str, IdFactory], str]
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_id_override: IdOverride | None = None
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def generate_object_id(kind: str, factory: IdFactory) -> str:
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"""Generate an identifier for the given kind using the provided factory.
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Allows tests to override ID generation deterministically by installing an
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override callback via :func:`set_id_override`.
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"""
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override = _id_override
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if override is not None:
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return override(kind, factory)
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return factory()
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def set_id_override(override: IdOverride) -> IdOverride | None:
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"""Install an override used to generate deterministic identifiers."""
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global _id_override
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previous = _id_override
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_id_override = override
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return previous
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def reset_id_override(previous: IdOverride | None) -> None:
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"""Restore the previous override returned by :func:`set_id_override`."""
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global _id_override
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_id_override = previous
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@ -232,14 +232,25 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
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await log_request_pre_validation(request)
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test_context_token = None
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# Use context manager with both provider data and auth attributes
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with request_provider_data_context(request.headers, user):
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if os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE"):
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from llama_stack.core.testing_context import (
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TEST_CONTEXT,
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reset_test_context,
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sync_test_context_from_provider_data,
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)
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test_context_token = sync_test_context_from_provider_data()
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is_streaming = is_streaming_request(func.__name__, request, **kwargs)
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try:
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if is_streaming:
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gen = preserve_contexts_async_generator(
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sse_generator(func(**kwargs)), [CURRENT_TRACE_CONTEXT, PROVIDER_DATA_VAR]
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sse_generator(func(**kwargs)), [CURRENT_TRACE_CONTEXT, PROVIDER_DATA_VAR, TEST_CONTEXT]
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)
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return StreamingResponse(gen, media_type="text/event-stream")
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else:
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@ -258,6 +269,9 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
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else:
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logger.error(f"Error executing endpoint {route=} {method=}: {str(e)}")
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raise translate_exception(e) from e
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finally:
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if test_context_token is not None:
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reset_test_context(test_context_token)
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sig = inspect.signature(func)
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@ -316,13 +316,13 @@ class Stack:
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# asked for in the run config.
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async def initialize(self):
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if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ:
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from llama_stack.testing.inference_recorder import setup_inference_recording
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from llama_stack.testing.api_recorder import setup_api_recording
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global TEST_RECORDING_CONTEXT
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TEST_RECORDING_CONTEXT = setup_inference_recording()
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TEST_RECORDING_CONTEXT = setup_api_recording()
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if TEST_RECORDING_CONTEXT:
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TEST_RECORDING_CONTEXT.__enter__()
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logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")
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logger.info(f"API recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")
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dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name)
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policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else []
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@ -381,7 +381,7 @@ class Stack:
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try:
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TEST_RECORDING_CONTEXT.__exit__(None, None, None)
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except Exception as e:
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logger.error(f"Error during inference recording cleanup: {e}")
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logger.error(f"Error during API recording cleanup: {e}")
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global REGISTRY_REFRESH_TASK
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if REGISTRY_REFRESH_TASK:
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44
llama_stack/core/testing_context.py
Normal file
44
llama_stack/core/testing_context.py
Normal file
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@ -0,0 +1,44 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import os
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from contextvars import ContextVar
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from llama_stack.core.request_headers import PROVIDER_DATA_VAR
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TEST_CONTEXT: ContextVar[str | None] = ContextVar("llama_stack_test_context", default=None)
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def get_test_context() -> str | None:
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return TEST_CONTEXT.get()
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def set_test_context(value: str | None):
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return TEST_CONTEXT.set(value)
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def reset_test_context(token) -> None:
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TEST_CONTEXT.reset(token)
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def sync_test_context_from_provider_data():
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"""Sync test context from provider data when running in server test mode."""
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if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ:
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return None
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stack_config_type = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client")
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if stack_config_type != "server":
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return None
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try:
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provider_data = PROVIDER_DATA_VAR.get()
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except LookupError:
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provider_data = None
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if provider_data and "__test_id" in provider_data:
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return TEST_CONTEXT.set(provider_data["__test_id"])
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return None
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@ -108,7 +108,7 @@ class OpenAIResponsesImpl:
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# Use stored messages directly and convert only new input
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message_adapter = TypeAdapter(list[OpenAIMessageParam])
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messages = message_adapter.validate_python(previous_response.messages)
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new_messages = await convert_response_input_to_chat_messages(input)
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new_messages = await convert_response_input_to_chat_messages(input, previous_messages=messages)
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messages.extend(new_messages)
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else:
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# Backward compatibility: reconstruct from inputs
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@ -103,9 +103,13 @@ async def convert_response_content_to_chat_content(
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async def convert_response_input_to_chat_messages(
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input: str | list[OpenAIResponseInput],
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previous_messages: list[OpenAIMessageParam] | None = None,
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) -> list[OpenAIMessageParam]:
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"""
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Convert the input from an OpenAI Response API request into OpenAI Chat Completion messages.
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:param input: The input to convert
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:param previous_messages: Optional previous messages to check for function_call references
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"""
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messages: list[OpenAIMessageParam] = []
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if isinstance(input, list):
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@ -169,7 +173,31 @@ async def convert_response_input_to_chat_messages(
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raise ValueError(
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f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
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)
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# Skip user messages that duplicate the last user message in previous_messages
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# This handles cases where input includes context for function_call_outputs
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if previous_messages and input_item.role == "user":
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last_user_msg = None
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for msg in reversed(previous_messages):
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if isinstance(msg, OpenAIUserMessageParam):
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last_user_msg = msg
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break
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if last_user_msg:
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last_user_content = getattr(last_user_msg, "content", None)
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if last_user_content == content:
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continue # Skip duplicate user message
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messages.append(message_type(content=content))
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if len(tool_call_results):
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# Check if unpaired function_call_outputs reference function_calls from previous messages
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if previous_messages:
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previous_call_ids = _extract_tool_call_ids(previous_messages)
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for call_id in list(tool_call_results.keys()):
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if call_id in previous_call_ids:
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# Valid: this output references a call from previous messages
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# Add the tool message
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messages.append(tool_call_results[call_id])
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del tool_call_results[call_id]
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# If still have unpaired outputs, error
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if len(tool_call_results):
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raise ValueError(
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f"Received function_call_output(s) with call_id(s) {tool_call_results.keys()}, but no corresponding function_call"
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@ -179,6 +207,19 @@ async def convert_response_input_to_chat_messages(
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return messages
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def _extract_tool_call_ids(messages: list[OpenAIMessageParam]) -> set[str]:
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"""Extract all tool_call IDs from messages."""
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call_ids = set()
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for msg in messages:
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if isinstance(msg, OpenAIAssistantMessageParam):
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tool_calls = getattr(msg, "tool_calls", None)
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if tool_calls:
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for tool_call in tool_calls:
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# tool_call is a Pydantic model, use attribute access
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call_ids.add(tool_call.id)
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return call_ids
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async def convert_response_text_to_chat_response_format(
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text: OpenAIResponseText,
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) -> OpenAIResponseFormatParam:
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@ -22,6 +22,7 @@ from llama_stack.apis.files import (
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OpenAIFilePurpose,
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)
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from llama_stack.core.datatypes import AccessRule
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from llama_stack.core.id_generation import generate_object_id
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from llama_stack.log import get_logger
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from llama_stack.providers.utils.files.form_data import parse_expires_after
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from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
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@ -65,7 +66,7 @@ class LocalfsFilesImpl(Files):
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def _generate_file_id(self) -> str:
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"""Generate a unique file ID for OpenAI API."""
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return f"file-{uuid.uuid4().hex}"
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return generate_object_id("file", lambda: f"file-{uuid.uuid4().hex}")
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def _get_file_path(self, file_id: str) -> Path:
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"""Get the filesystem path for a file ID."""
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@ -95,7 +96,9 @@ class LocalfsFilesImpl(Files):
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raise RuntimeError("Files provider not initialized")
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if expires_after is not None:
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raise NotImplementedError("File expiration is not supported by this provider")
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logger.warning(
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f"File expiration is not supported by this provider, ignoring expires_after: {expires_after}"
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)
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file_id = self._generate_file_id()
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file_path = self._get_file_path(file_id)
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@ -23,6 +23,7 @@ from llama_stack.apis.files import (
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OpenAIFilePurpose,
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)
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from llama_stack.core.datatypes import AccessRule
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from llama_stack.core.id_generation import generate_object_id
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from llama_stack.providers.utils.files.form_data import parse_expires_after
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from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
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from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
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@ -198,7 +199,7 @@ class S3FilesImpl(Files):
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purpose: Annotated[OpenAIFilePurpose, Form()],
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expires_after: Annotated[ExpiresAfter | None, Depends(parse_expires_after)] = None,
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) -> OpenAIFileObject:
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file_id = f"file-{uuid.uuid4().hex}"
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file_id = generate_object_id("file", lambda: f"file-{uuid.uuid4().hex}")
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filename = getattr(file, "filename", None) or "uploaded_file"
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@ -40,6 +40,7 @@ from llama_stack.apis.vector_io import (
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VectorStoreSearchResponse,
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VectorStoreSearchResponsePage,
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)
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from llama_stack.core.id_generation import generate_object_id
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from llama_stack.log import get_logger
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from llama_stack.providers.utils.kvstore.api import KVStore
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from llama_stack.providers.utils.memory.vector_store import (
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@ -352,7 +353,7 @@ class OpenAIVectorStoreMixin(ABC):
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"""Creates a vector store."""
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created_at = int(time.time())
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# Derive the canonical vector_db_id (allow override, else generate)
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vector_db_id = provider_vector_db_id or f"vs_{uuid.uuid4()}"
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vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
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if provider_id is None:
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raise ValueError("Provider ID is required")
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@ -986,7 +987,7 @@ class OpenAIVectorStoreMixin(ABC):
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chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
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created_at = int(time.time())
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batch_id = f"batch_{uuid.uuid4()}"
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batch_id = generate_object_id("vector_store_file_batch", lambda: f"batch_{uuid.uuid4()}")
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# File batches expire after 7 days
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expires_at = created_at + (7 * 24 * 60 * 60)
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|
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@ -9,7 +9,8 @@ from __future__ import annotations # for forward references
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import hashlib
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import json
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import os
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from collections.abc import Generator
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import re
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from collections.abc import Callable, Generator
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from contextlib import contextmanager
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from enum import StrEnum
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from pathlib import Path
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@ -17,6 +18,7 @@ from typing import Any, Literal, cast
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from openai import NOT_GIVEN, OpenAI
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from llama_stack.core.id_generation import reset_id_override, set_id_override
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from llama_stack.log import get_logger
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logger = get_logger(__name__, category="testing")
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|
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@ -29,13 +31,14 @@ _current_mode: str | None = None
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_current_storage: ResponseStorage | None = None
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_original_methods: dict[str, Any] = {}
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# Per-test deterministic ID counters (test_id -> id_kind -> counter)
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_id_counters: dict[str, dict[str, int]] = {}
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# Test context uses ContextVar since it changes per-test and needs async isolation
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from contextvars import ContextVar
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_test_context: ContextVar[str | None] = ContextVar("_test_context", default=None)
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||||
|
||||
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)
|
||||
|
||||
# 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"]
|
||||
|
||||
|
||||
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):
|
||||
|
|
@ -464,22 +538,62 @@ 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()
|
||||
|
||||
try:
|
||||
# Get base URL based on client type
|
||||
if client_type == "openai":
|
||||
base_url = str(self._client.base_url)
|
||||
|
|
@ -503,11 +617,11 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
|
|||
headers = {}
|
||||
body = kwargs
|
||||
|
||||
request_hash = normalize_request(method, url, headers, body)
|
||||
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 == InferenceMode.REPLAY or mode == InferenceMode.RECORD_IF_MISSING:
|
||||
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)
|
||||
|
|
@ -527,7 +641,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
|
|||
return replay_stream()
|
||||
else:
|
||||
return response_body
|
||||
elif mode == InferenceMode.REPLAY:
|
||||
elif mode == APIRecordingMode.REPLAY:
|
||||
# REPLAY mode requires recording to exist
|
||||
raise RuntimeError(
|
||||
f"No recorded response found for request hash: {request_hash}\n"
|
||||
|
|
@ -536,7 +650,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
|
|||
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 mode == APIRecordingMode.RECORD or (mode == APIRecordingMode.RECORD_IF_MISSING and not recording):
|
||||
if endpoint == "/v1/models":
|
||||
response = original_method(self, *args, **kwargs)
|
||||
else:
|
||||
|
|
@ -561,7 +675,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
|
|||
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 = []
|
||||
chunks: list[Any] = []
|
||||
async for chunk in response:
|
||||
chunks.append(chunk)
|
||||
|
||||
|
|
@ -582,13 +696,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
|
|||
|
||||
else:
|
||||
raise AssertionError(f"Invalid mode: {mode}")
|
||||
finally:
|
||||
if test_context_token:
|
||||
_test_context.reset(test_context_token)
|
||||
|
||||
|
||||
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
|
||||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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... ({e})")
|
||||
pass
|
||||
if server_thread.is_alive():
|
||||
# Give the server a moment to bind to the port
|
||||
time.sleep(0.1)
|
||||
logger.debug(f"MCP server is ready on port {port}")
|
||||
break
|
||||
time.sleep(0.05)
|
||||
else:
|
||||
# If we exit the loop due to timeout
|
||||
logger.error(f"MCP server failed to start within {timeout} seconds on port {port}")
|
||||
logger.error(f"Thread alive: {server_thread.is_alive()}")
|
||||
if server_thread.is_alive():
|
||||
logger.error("Server thread is still running but not responding to HTTP requests")
|
||||
logger.error(f"MCP server thread failed to start within {timeout} seconds on port {port}")
|
||||
|
||||
try:
|
||||
yield {"server_url": server_url}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,107 @@
|
|||
{
|
||||
"test_id": null,
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Which planet has rings around it with a name starting with letter S?"
|
||||
}
|
||||
],
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-224f7e7bd332",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "QgpggKqlsUi"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-224f7e7bd332",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "The planet with rings around it that starts with the letter S is Saturn.",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "HYFma3xA0U"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-224f7e7bd332",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "GqfSkdB"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -0,0 +1,107 @@
|
|||
{
|
||||
"test_id": null,
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Which planet do humans live on?"
|
||||
}
|
||||
],
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-a44164820534",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_1827dd0c55",
|
||||
"usage": null,
|
||||
"obfuscation": "Uk6yP9DR13H"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-a44164820534",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "Humans live on Earth.",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_1827dd0c55",
|
||||
"usage": null,
|
||||
"obfuscation": "6RKlE6NFLf"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-a44164820534",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_1827dd0c55",
|
||||
"usage": null,
|
||||
"obfuscation": "IhlSKzm"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -0,0 +1,124 @@
|
|||
{
|
||||
"test_id": null,
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "what teams are playing in this image?"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": "https://upload.wikimedia.org/wikipedia/commons/3/3b/LeBron_James_Layup_%28Cleveland_vs_Brooklyn_2018%29.jpg",
|
||||
"detail": "auto"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-a6ad8748dce1",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_cbf1785567",
|
||||
"usage": null,
|
||||
"obfuscation": "XomWZpEB3cK"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-a6ad8748dce1",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "The teams playing in the image are the Cleveland Cavaliers and the Brooklyn Nets.",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_cbf1785567",
|
||||
"usage": null,
|
||||
"obfuscation": "w3SqawrKwS"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-a6ad8748dce1",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
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"id": "gpt-4.1-mini",
|
||||
"created": 1744318173,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-4.1-nano-2025-04-14",
|
||||
"created": 1744321025,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-4.1-nano",
|
||||
"created": 1744321707,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-image-1",
|
||||
"created": 1745517030,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-4o-realtime-preview-2025-06-03",
|
||||
"created": 1748907838,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-4o-audio-preview-2025-06-03",
|
||||
"created": 1748908498,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-chat-latest",
|
||||
"created": 1754073306,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-2025-08-07",
|
||||
"created": 1754075360,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5",
|
||||
"created": 1754425777,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-mini-2025-08-07",
|
||||
"created": 1754425867,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-mini",
|
||||
"created": 1754425928,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-nano-2025-08-07",
|
||||
"created": 1754426303,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-nano",
|
||||
"created": 1754426384,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-audio-2025-08-28",
|
||||
"created": 1756256146,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-realtime",
|
||||
"created": 1756271701,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-realtime-2025-08-28",
|
||||
"created": 1756271773,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-audio",
|
||||
"created": 1756339249,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-codex",
|
||||
"created": 1757527818,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-image-1-mini",
|
||||
"created": 1758845821,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-pro-2025-10-06",
|
||||
"created": 1759469707,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-5-pro",
|
||||
"created": 1759469822,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-audio-mini",
|
||||
"created": 1759512027,
|
||||
"object": "model",
|
||||
"owned_by": "system"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "gpt-3.5-turbo-16k",
|
||||
"created": 1683758102,
|
||||
"object": "model",
|
||||
"owned_by": "openai-internal"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "tts-1",
|
||||
"created": 1681940951,
|
||||
"object": "model",
|
||||
"owned_by": "openai-internal"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "whisper-1",
|
||||
"created": 1677532384,
|
||||
"object": "model",
|
||||
"owned_by": "openai-internal"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "text-embedding-ada-002",
|
||||
"created": 1671217299,
|
||||
"object": "model",
|
||||
"owned_by": "openai-internal"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": false
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -6,6 +6,7 @@
|
|||
import inspect
|
||||
import itertools
|
||||
import os
|
||||
import tempfile
|
||||
import textwrap
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
|
@ -14,6 +15,7 @@ import pytest
|
|||
from dotenv import load_dotenv
|
||||
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.testing.api_recorder import patch_httpx_for_test_id
|
||||
|
||||
from .suites import SETUP_DEFINITIONS, SUITE_DEFINITIONS
|
||||
|
||||
|
|
@ -35,6 +37,10 @@ def pytest_sessionstart(session):
|
|||
if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ:
|
||||
os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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 = openai_client.vector_stores.create(name=name)
|
||||
# OpenAI SDK client uses extra_body for non-standard parameters
|
||||
from openai import OpenAI
|
||||
|
||||
if isinstance(openai_client, OpenAI):
|
||||
# OpenAI SDK client - use extra_body
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name,
|
||||
extra_body={"embedding_model": embedding_model, "embedding_dimension": embedding_dimension},
|
||||
)
|
||||
else:
|
||||
# LlamaStack client - direct parameter
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name, embedding_model=embedding_model, embedding_dimension=embedding_dimension
|
||||
)
|
||||
|
||||
return vector_store
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,943 @@
|
|||
{
|
||||
"test_id": "tests/integration/responses/test_basic_responses.py::test_response_streaming_basic[openai_client-txt=openai/gpt-4o-saturn]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Which planet has rings around it with a name starting with letter S?"
|
||||
}
|
||||
],
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-007c4711219f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
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File diff suppressed because it is too large
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File diff suppressed because it is too large
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||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "bWmmqTA"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {
|
||||
"file-1": "file-d7cee10212814cfcb75cc091eee11688"
|
||||
}
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show more
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Add table
Add a link
Reference in a new issue