mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-12 20:12:33 +00:00
a lot of simplification finally. all works
This commit is contained in:
Ashwin Bharambe
parent
b47bf340db
commit
00fd27be1f
39 changed files with 16027 additions and 1969 deletions
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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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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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@ -9,7 +9,6 @@ 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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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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@ -35,12 +34,10 @@ _original_methods: dict[str, Any] = {}
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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
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from llama_stack.core.testing_context import get_test_context
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# update the "finish_reason" field, since its type definition is wrong (no None is accepted)
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CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None
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CompletionChoice.model_rebuild()
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@ -60,6 +57,7 @@ _ID_KIND_PREFIXES: dict[str, str] = {
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"file": "file-",
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"vector_store": "vs_",
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"vector_store_file_batch": "batch_",
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"tool_call": "call_",
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}
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@ -68,176 +66,32 @@ def _allocate_test_scoped_id(kind: str) -> str | None:
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global _id_counters
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test_id = _test_context.get()
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test_id = get_test_context()
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prefix = _ID_KIND_PREFIXES.get(kind)
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if prefix is None:
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return None
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key = test_id or "__global__"
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if not test_id:
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raise ValueError(f"Test ID is required for {kind} ID allocation")
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key = test_id
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if key not in _id_counters:
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_id_counters[key] = {}
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counter = _id_counters[key].get(kind, 0) + 1
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# each test should get a contiguous block of IDs otherwise we will get
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# collisions between tests inside other systems (like file storage) which
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# expect IDs to be unique
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test_hash = hashlib.sha256(test_id.encode()).hexdigest()
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test_hash_int = int(test_hash, 16)
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counter = test_hash_int % 1000000000000
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counter = _id_counters[key].get(kind, counter) + 1
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_id_counters[key][kind] = counter
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return f"{prefix}{counter}"
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class _IdCanonicalizer:
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PATTERN = re.compile(r"(file-[A-Za-z0-9_-]+|vs_[A-Za-z0-9_-]+|batch_[A-Za-z0-9_-]+)")
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def __init__(self) -> None:
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self._mappings: dict[str, dict[str, str]] = {kind: {} for kind in _ID_KIND_PREFIXES}
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self._counters: dict[str, int] = dict.fromkeys(_ID_KIND_PREFIXES, 0)
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def canonicalize(self, obj: Any) -> Any:
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if isinstance(obj, dict):
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return {k: self._canonicalize_value(k, v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [self.canonicalize(item) for item in obj]
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if isinstance(obj, str):
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return self._canonicalize_string(obj)
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return obj
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def _canonicalize_value(self, key: str, value: Any) -> Any:
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if key in {"vector_db_id", "vector_store_id", "bank_id"} and isinstance(value, str):
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return self._canonicalize_string(value)
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if key == "document_id" and isinstance(value, str) and value.startswith("file-"):
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return self._canonicalize_string(value)
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return self.canonicalize(value)
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def _canonicalize_string(self, value: str) -> str:
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def replace(match: re.Match[str]) -> str:
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token = match.group(0)
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if token.startswith("file-"):
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return self._mapped_value("file", token)
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if token.startswith("vs_"):
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return self._mapped_value("vector_store", token)
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if token.startswith("batch_"):
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return self._mapped_value("vector_store_file_batch", token)
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return token
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return self.PATTERN.sub(replace, value)
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def _mapped_value(self, kind: str, original: str) -> str:
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mapping = self._mappings[kind]
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if original not in mapping:
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self._counters[kind] += 1
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mapping[original] = f"{_ID_KIND_PREFIXES[kind]}{self._counters[kind]}"
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return mapping[original]
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def _canonicalize_for_hashing(obj: Any) -> Any:
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canonicalizer = _IdCanonicalizer()
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return canonicalizer.canonicalize(obj)
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def _chunk_text_content(chunk: Any) -> tuple[str | None, bool]:
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"""Return (content, has_structured_fields) for OpenAI chat completion chunks."""
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choices = getattr(chunk, "choices", None)
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if not choices:
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return None, False
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delta = choices[0].delta
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content = getattr(delta, "content", None)
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if not content:
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return None, False
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has_structured = bool(getattr(delta, "tool_calls", None) or getattr(delta, "function_call", None))
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return content, has_structured
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def _chunk_with_content(chunk: Any, content: str) -> Any:
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"""Return a copy of the chunk with delta.content replaced by the provided string."""
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choices = getattr(chunk, "choices", None)
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if not choices:
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return chunk
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updated_choices = []
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for choice in choices:
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delta = choice.delta
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if getattr(delta, "content", None) is not None:
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new_delta = delta.model_copy(update={"content": content})
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updated_choices.append(choice.model_copy(update={"delta": new_delta}))
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else:
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updated_choices.append(choice)
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return chunk.model_copy(update={"choices": updated_choices})
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def _ends_with_partial_identifier(text: str) -> bool:
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"""Return True if text ends in an incomplete file identifier."""
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match = re.search(r"(?:<\|)?file-[A-Za-z0-9_-]*$", text)
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if not match:
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return False
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token = match.group()
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enclosed = token.startswith("<|")
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if enclosed and not token.endswith("|>"):
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return True
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if enclosed:
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core = token[2:-2] if token.endswith("|>") else token[2:]
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else:
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core = token
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suffix = core[len("file-") :]
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if len(suffix) < 16:
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return True
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if not re.fullmatch(r"[A-Za-z0-9_-]+", suffix):
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return True
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return False
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def _has_safe_boundary(text: str) -> bool:
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if not text:
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return False
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last_char = text[-1]
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if last_char.isspace():
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return True
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return last_char in ".,?!;:)]}>\"'"
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def _coalesce_streaming_chunks(chunks: list[Any]) -> list[Any]:
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"""Merge adjacent text chunks to avoid breaking identifiers across boundaries."""
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result: list[Any] = []
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pending_chunk: Any | None = None
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pending_content = ""
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for chunk in chunks:
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content, has_structured = _chunk_text_content(chunk)
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if content is None or has_structured:
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if pending_chunk is not None:
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result.append(_chunk_with_content(pending_chunk, pending_content))
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pending_chunk = None
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pending_content = ""
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result.append(chunk)
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continue
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if pending_chunk is None:
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pending_chunk = chunk
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pending_content = content
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else:
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pending_content += content
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if (not _ends_with_partial_identifier(pending_content)) and _has_safe_boundary(pending_content):
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result.append(_chunk_with_content(pending_chunk, pending_content))
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pending_chunk = None
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pending_content = ""
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if pending_chunk is not None:
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result.append(_chunk_with_content(pending_chunk, pending_content))
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return result
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def _deterministic_id_override(kind: str, factory: Callable[[], str]) -> str:
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deterministic_id = _allocate_test_scoped_id(kind)
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if deterministic_id is not None:
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@ -262,12 +116,12 @@ def normalize_inference_request(method: str, url: str, headers: dict[str, Any],
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normalized: dict[str, Any] = {
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"method": method.upper(),
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"endpoint": parsed.path,
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"body": _canonicalize_for_hashing(body),
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"body": body,
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}
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# Include test_id for isolation, except for shared infrastructure endpoints
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if parsed.path not in ("/api/tags", "/v1/models"):
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normalized["test_id"] = _test_context.get()
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normalized["test_id"] = get_test_context()
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# Create hash - sort_keys=True ensures deterministic ordering
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normalized_json = json.dumps(normalized, sort_keys=True)
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@ -279,7 +133,7 @@ def normalize_tool_request(provider_name: str, tool_name: str, kwargs: dict[str,
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normalized = {
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"provider": provider_name,
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"tool_name": tool_name,
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"kwargs": _canonicalize_for_hashing(kwargs),
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"kwargs": kwargs,
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}
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# Create hash - sort_keys=True ensures deterministic ordering
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@ -287,33 +141,6 @@ def normalize_tool_request(provider_name: str, tool_name: str, kwargs: dict[str,
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return hashlib.sha256(normalized_json.encode()).hexdigest()
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def _sync_test_context_from_provider_data():
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"""In server mode, sync test ID from provider_data to _test_context.
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This ensures that storage operations (which read from _test_context) work correctly
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in server mode where the test ID arrives via HTTP header → provider_data.
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Returns a token to reset _test_context, or None if no sync was needed.
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"""
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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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from llama_stack.core.request_headers import PROVIDER_DATA_VAR
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provider_data = PROVIDER_DATA_VAR.get()
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if provider_data and "__test_id" in provider_data:
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test_id = provider_data["__test_id"]
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return _test_context.set(test_id)
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except ImportError:
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pass
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return None
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def patch_httpx_for_test_id():
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"""Patch client _prepare_request methods to inject test ID into provider data header.
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@ -335,13 +162,12 @@ def patch_httpx_for_test_id():
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def patched_prepare_request(self, request):
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# Call original first (it's a sync method that returns None)
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# Determine which original to call based on client type
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if "llama_stack_client" in self.__class__.__module__:
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_original_methods["llama_stack_client_prepare_request"](self, request)
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_original_methods["openai_prepare_request"](self, request)
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_original_methods["llama_stack_client_prepare_request"](self, request)
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_original_methods["openai_prepare_request"](self, request)
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# Only inject test ID in server mode
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stack_config_type = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client")
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test_id = _test_context.get()
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test_id = get_test_context()
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if stack_config_type == "server" and test_id:
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provider_data_header = request.headers.get("X-LlamaStack-Provider-Data")
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@ -482,8 +308,6 @@ class ResponseStorage:
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def __init__(self, base_dir: Path):
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self.base_dir = base_dir
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# Don't create responses_dir here - determine it per-test at runtime
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self._legacy_index: dict[str, Path] = {}
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self._scanned_dirs: set[Path] = set()
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def _get_test_dir(self) -> Path:
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"""Get the recordings directory in the test file's parent directory.
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@ -491,7 +315,7 @@ class ResponseStorage:
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For test at "tests/integration/inference/test_foo.py::test_bar",
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returns "tests/integration/inference/recordings/".
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"""
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test_id = _test_context.get()
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test_id = get_test_context()
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if test_id:
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# Extract the directory path from the test nodeid
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# e.g., "tests/integration/inference/test_basic.py::test_foo[params]"
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@ -506,7 +330,7 @@ class ResponseStorage:
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# Fallback for non-test contexts
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return self.base_dir / "recordings"
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def _ensure_directories(self):
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def _ensure_directory(self):
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"""Ensure test-specific directories exist."""
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test_dir = self._get_test_dir()
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test_dir.mkdir(parents=True, exist_ok=True)
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@ -514,7 +338,7 @@ class ResponseStorage:
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def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]):
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"""Store a request/response pair."""
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responses_dir = self._ensure_directories()
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responses_dir = self._ensure_directory()
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# Use FULL hash (not truncated)
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response_file = f"{request_hash}.json"
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@ -543,7 +367,7 @@ class ResponseStorage:
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with open(response_path, "w") as f:
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json.dump(
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{
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"test_id": _test_context.get(),
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"test_id": get_test_context(),
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"request": request,
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"response": serialized_response,
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"id_normalization_mapping": {},
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|
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@ -554,8 +378,6 @@ class ResponseStorage:
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f.write("\n")
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f.flush()
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self._legacy_index[request_hash] = response_path
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def find_recording(self, request_hash: str) -> dict[str, Any] | None:
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"""Find a recorded response by request hash.
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@ -579,52 +401,6 @@ class ResponseStorage:
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if fallback_path.exists():
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return _recording_from_file(fallback_path)
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return self._find_in_legacy_index(request_hash, [test_dir, fallback_dir])
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def _find_in_legacy_index(self, request_hash: str, directories: list[Path]) -> dict[str, Any] | None:
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for directory in directories:
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if not directory.exists() or directory in self._scanned_dirs:
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continue
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for path in directory.glob("*.json"):
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try:
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with open(path) as f:
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data = json.load(f)
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||||
except Exception:
|
||||
continue
|
||||
|
||||
request = data.get("request")
|
||||
if not request:
|
||||
continue
|
||||
|
||||
body = request.get("body")
|
||||
canonical_body = _canonicalize_for_hashing(body) if isinstance(body, dict | list) else body
|
||||
|
||||
token = None
|
||||
test_id = data.get("test_id")
|
||||
if test_id:
|
||||
token = _test_context.set(test_id)
|
||||
|
||||
try:
|
||||
legacy_hash = normalize_inference_request(
|
||||
request.get("method", ""),
|
||||
request.get("url", ""),
|
||||
request.get("headers", {}),
|
||||
canonical_body,
|
||||
)
|
||||
finally:
|
||||
if token is not None:
|
||||
_test_context.reset(token)
|
||||
|
||||
if legacy_hash not in self._legacy_index:
|
||||
self._legacy_index[legacy_hash] = path
|
||||
|
||||
self._scanned_dirs.add(directory)
|
||||
|
||||
legacy_path = self._legacy_index.get(request_hash)
|
||||
if legacy_path and legacy_path.exists():
|
||||
return _recording_from_file(legacy_path)
|
||||
|
||||
return None
|
||||
|
||||
def _model_list_responses(self, request_hash: str) -> list[dict[str, Any]]:
|
||||
|
|
@ -740,46 +516,38 @@ async def _patched_tool_invoke_method(
|
|||
# Normal operation
|
||||
return await original_method(self, tool_name, 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()
|
||||
request_hash = normalize_tool_request(provider_name, tool_name, kwargs)
|
||||
|
||||
try:
|
||||
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.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)
|
||||
|
||||
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}
|
||||
|
||||
request_data = {
|
||||
"test_id": _test_context.get(),
|
||||
"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
|
||||
|
||||
# Store the recording
|
||||
_current_storage.store_recording(request_hash, request_data, response_data)
|
||||
return result
|
||||
|
||||
else:
|
||||
raise AssertionError(f"Invalid mode: {_current_mode}")
|
||||
finally:
|
||||
# Reset test context if we set it in server mode
|
||||
if test_context_token is not None:
|
||||
_test_context.reset(test_context_token)
|
||||
else:
|
||||
raise AssertionError(f"Invalid mode: {_current_mode}")
|
||||
|
||||
|
||||
async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
|
||||
|
|
@ -794,120 +562,108 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
|
|||
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_inference_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 == 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:
|
||||
recording = storage.find_recording(request_hash)
|
||||
|
||||
if recording:
|
||||
response_body = recording["response"]["body"]
|
||||
|
||||
if recording["response"].get("is_streaming", False) and isinstance(response_body, list):
|
||||
response_body = _coalesce_streaming_chunks(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 == 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:
|
||||
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
|
||||
raw_chunks: list[Any] = []
|
||||
async for chunk in response:
|
||||
raw_chunks.append(chunk)
|
||||
|
||||
chunks = _coalesce_streaming_chunks(raw_chunks)
|
||||
|
||||
# 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():
|
||||
|
|
|
|||
|
|
@ -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,
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"index": 0,
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"logprobs": null
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}
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],
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"created": 0,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"service_tier": "default",
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"system_fingerprint": "fp_1827dd0c55",
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"usage": null,
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"obfuscation": "Uk6yP9DR13H"
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}
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},
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-a44164820534",
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"choices": [
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{
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"delta": {
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"content": "Humans live on Earth.",
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"function_call": null,
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"refusal": null,
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"role": null,
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"tool_calls": null
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},
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"finish_reason": null,
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"index": 0,
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"logprobs": null
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}
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],
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"created": 0,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"service_tier": "default",
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"system_fingerprint": "fp_1827dd0c55",
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"usage": null,
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"obfuscation": "6RKlE6NFLf"
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}
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},
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-a44164820534",
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"choices": [
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{
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"delta": {
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"refusal": null,
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"role": null,
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"tool_calls": null
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},
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"finish_reason": "stop",
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"index": 0,
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"logprobs": null
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}
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],
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"created": 0,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"service_tier": "default",
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"system_fingerprint": "fp_1827dd0c55",
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"usage": null,
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"obfuscation": "IhlSKzm"
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}
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}
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],
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"is_streaming": true
|
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},
|
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"id_normalization_mapping": {}
|
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}
|
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|
|
@ -0,0 +1,124 @@
|
|||
{
|
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"test_id": null,
|
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"request": {
|
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
|
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"headers": {},
|
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"body": {
|
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"model": "gpt-4o",
|
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"messages": [
|
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{
|
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"role": "user",
|
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"content": [
|
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{
|
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"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,
|
||||
"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_cbf1785567",
|
||||
"usage": null,
|
||||
"obfuscation": "kkQyIuu"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -849,5 +849,6 @@
|
|||
}
|
||||
],
|
||||
"is_streaming": false
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -59,15 +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.api_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):
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -0,0 +1,372 @@
|
|||
{
|
||||
"test_id": "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",
|
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"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: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-690481987689', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-690481987689|>)\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"
|
||||
}
|
||||
]
|
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},
|
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{
|
||||
"role": "assistant",
|
||||
"content": "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture <|file-690481987689|>."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Can you tell me more about the architecture?"
|
||||
},
|
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{
|
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"role": "assistant",
|
||||
"content": "",
|
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"tool_calls": [
|
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{
|
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"index": 0,
|
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"id": "call_UqyVZyvMh30eQuiKg7lJIUhQ",
|
||||
"type": "function",
|
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"function": {
|
||||
"name": "knowledge_search",
|
||||
"arguments": "{\"query\":\"Llama 4 Maverick model architecture\"}"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
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{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_UqyVZyvMh30eQuiKg7lJIUhQ",
|
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"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
|
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},
|
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{
|
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"type": "text",
|
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"text": "[1] document_id: file-690481987689, score: 1.9327567816402336, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-690481987689', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-690481987689|>)\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"
|
||||
},
|
||||
{
|
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"type": "text",
|
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"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model architecture\". 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"
|
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}
|
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]
|
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}
|
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],
|
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"stream": true,
|
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"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. Can be a natural language sentence or keywords."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"query"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
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},
|
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"endpoint": "/v1/chat/completions",
|
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"model": "gpt-4o"
|
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},
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"response": {
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"body": [
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"refusal": null,
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|
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"choices": [
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"delta": {
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"content": "The Llama ",
|
||||
"function_call": null,
|
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"refusal": null,
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|
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"tool_calls": null
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}
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],
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"created": 0,
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"object": "chat.completion.chunk",
|
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|
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"system_fingerprint": "fp_f64f290af2",
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|
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||||
{
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"text": "[1] document_id: file-862946202236, score: 0.7656461893973538, attributes: {'region': 'eu', 'category': 'marketing', 'date': 1672531200.0, 'filename': 'eu_marketing_q1.txt', 'document_id': 'file-862946202236', 'token_count': 17.0, 'metadata_token_count': 32.0} (cite as <|file-862946202236|>)\nEuropean advertising campaign results for Q1 2023. Strong growth in EU markets.\n"
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File diff suppressed because it is too large
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Loading…
Add table
Add a link
Reference in a new issue