mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-12 12:06:04 +00:00
more involved id mappings
This commit is contained in:
Ashwin Bharambe
parent
ecea7ccdb8
commit
39aa17f975
15 changed files with 16151 additions and 4 deletions
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@ -9,6 +9,7 @@ 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 Generator
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from contextlib import contextmanager
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from enum import StrEnum
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@ -134,6 +135,194 @@ def _normalize_file_ids(obj: Any) -> Any:
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return obj
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def _get_current_test_id_mappings() -> dict[str, str]:
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"""Get the current test's ID normalization mappings.
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Returns a dict mapping normalized_id -> original_id (e.g., "file-1" -> "file-abc123...")
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This is the inverse of what's stored in _id_normalizers.
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"""
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global _id_normalizers
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test_id = _test_context.get()
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if not test_id or test_id not in _id_normalizers:
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return {}
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# Invert the mapping: normalized_id -> original_id
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result = {}
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for id_type, mappings in _id_normalizers[test_id].items():
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for original_id, normalized_id in mappings.items():
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result[normalized_id] = original_id
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return result
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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 _denormalize_response(obj: Any, recorded_mapping: dict[str, str], current_mapping: dict[str, str]) -> Any:
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"""Replace recorded IDs with current runtime IDs in a response object.
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Args:
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obj: The response object to denormalize
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recorded_mapping: normalized_id -> recorded_original_id (from recording file)
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current_mapping: normalized_id -> current_original_id (from current runtime)
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Returns:
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The response with all recorded IDs replaced with current IDs
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"""
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import re
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# Build reverse mapping: recorded_original_id -> current_original_id
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# via the normalized intermediary
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id_translation = {}
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for normalized_id, recorded_id in recorded_mapping.items():
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if normalized_id in current_mapping:
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current_id = current_mapping[normalized_id]
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id_translation[recorded_id] = current_id
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if isinstance(obj, dict):
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result = {}
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for k, v in obj.items():
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# Translate document_id fields
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if k == "document_id" and isinstance(v, str):
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result[k] = id_translation.get(v, v)
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# Translate vector database/store IDs
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elif k in ("vector_db_id", "vector_store_id", "bank_id") and isinstance(v, str):
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# Replace any recorded IDs in the value
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translated = v
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for recorded_id, current_id in id_translation.items():
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translated = translated.replace(recorded_id, current_id)
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result[k] = translated
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else:
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result[k] = _denormalize_response(v, recorded_mapping, current_mapping)
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return result
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elif isinstance(obj, list):
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return [_denormalize_response(item, recorded_mapping, current_mapping) for item in obj]
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elif hasattr(obj, "model_dump"):
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# Handle Pydantic/BaseModel instances by denormalizing their dict form
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data = obj.model_dump(mode="python")
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denormalized = _denormalize_response(data, recorded_mapping, current_mapping)
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cls = obj.__class__
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try:
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return cls.model_validate(denormalized)
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except Exception:
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try:
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return cls.model_construct(**denormalized)
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except Exception:
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return denormalized
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elif isinstance(obj, str):
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# Replace file-<uuid> patterns in strings (like in text content and citations)
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translated = obj
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for recorded_id, current_id in id_translation.items():
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# Handle both bare file IDs and citation format <|file-...|>
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translated = translated.replace(recorded_id, current_id)
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return translated
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else:
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return obj
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def normalize_inference_request(method: str, url: str, headers: dict[str, Any], body: dict[str, Any]) -> str:
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"""Create a normalized hash of the request for consistent matching.
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@ -438,6 +627,7 @@ class ResponseStorage:
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"test_id": _test_context.get(),
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"request": request,
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"response": serialized_response,
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"id_normalization_mapping": _get_current_test_id_mappings(),
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},
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f,
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indent=2,
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@ -569,7 +759,7 @@ async def _patched_tool_invoke_method(
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original_method, provider_name: str, self, tool_name: str, kwargs: dict[str, Any]
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):
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"""Patched version of tool runtime invoke_tool method for recording/replay."""
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global _current_mode, _current_storage
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global _current_mode, _current_storage, _id_normalizers
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if _current_mode == APIRecordingMode.LIVE or _current_storage is None:
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# Normal operation
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@ -618,7 +808,7 @@ async def _patched_tool_invoke_method(
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async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
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global _current_mode, _current_storage
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global _current_mode, _current_storage, _id_normalizers
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mode = _current_mode
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storage = _current_storage
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@ -671,6 +861,48 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
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if recording:
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response_body = recording["response"]["body"]
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if recording["response"].get("is_streaming", False) and isinstance(response_body, list):
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response_body = _coalesce_streaming_chunks(response_body)
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recording["response"]["body"] = response_body
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# Denormalize the response: replace recorded IDs with current runtime IDs
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recorded_mapping = recording.get("id_normalization_mapping", {})
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current_mapping = _get_current_test_id_mappings()
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if recorded_mapping or current_mapping:
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import sys
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print(f"\n=== DENORM DEBUG ===", file=sys.stderr)
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print(f"Recorded mapping: {recorded_mapping}", file=sys.stderr)
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print(f"Current mapping: {current_mapping}", file=sys.stderr)
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print(f"_id_normalizers before: {_id_normalizers.get(_test_context.get(), {})}", file=sys.stderr)
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response_body = _denormalize_response(response_body, recorded_mapping, current_mapping)
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# Update _id_normalizers to register the current IDs with their normalized values
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# This ensures that if these IDs appear in future requests, they get the same
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# normalized value as they had in the recording
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test_id = _test_context.get()
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if test_id and recorded_mapping:
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for normalized_id, recorded_id in recorded_mapping.items():
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if normalized_id in current_mapping:
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current_id = current_mapping[normalized_id]
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# Extract ID type from normalized_id (e.g., "file-1" -> "file")
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id_type = normalized_id.rsplit("-", 1)[0]
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# Ensure structures exist
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if test_id not in _id_normalizers:
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_id_normalizers[test_id] = {}
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if id_type not in _id_normalizers[test_id]:
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_id_normalizers[test_id][id_type] = {}
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# Register: current_id -> normalized_id
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_id_normalizers[test_id][id_type][current_id] = normalized_id
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print(f"Registered {current_id} -> {normalized_id}", file=sys.stderr)
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print(f"_id_normalizers after: {_id_normalizers.get(_test_context.get(), {})}", file=sys.stderr)
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print(f"=== END DENORM DEBUG ===\n", file=sys.stderr)
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if recording["response"].get("is_streaming", False):
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async def replay_stream():
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@ -714,9 +946,11 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
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if is_streaming:
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# For streaming responses, we need to collect all chunks immediately before yielding
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# This ensures the recording is saved even if the generator isn't fully consumed
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chunks = []
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raw_chunks: list[Any] = []
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async for chunk in response:
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chunks.append(chunk)
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raw_chunks.append(chunk)
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chunks = _coalesce_streaming_chunks(raw_chunks)
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# Store the recording immediately
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response_data = {"body": chunks, "is_streaming": True}
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File diff suppressed because it is too large
Load diff
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@ -0,0 +1,629 @@
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{
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"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]",
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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": "How many experts does the Llama 4 Maverick model have?"
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}
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],
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"stream": true,
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"description": "Search for information in a database.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query to search for. Can be a natural language sentence or keywords."
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}
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},
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"required": [
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"query"
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]
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}
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}
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}
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]
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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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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-0f79646fcf8a",
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"choices": [
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{
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"delta": {
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"content": null,
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"function_call": null,
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"refusal": null,
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"role": "assistant",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_aPe1vS1v5bIwPgl789D5bfmW",
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"function": {
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"arguments": "",
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"name": "knowledge_search"
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},
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"type": "function"
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}
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]
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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_f64f290af2",
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"usage": null,
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"obfuscation": "b"
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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-0f79646fcf8a",
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"choices": [
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{
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"delta": {
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"content": null,
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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": [
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{
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"index": 0,
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"id": null,
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"function": {
|
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"arguments": "{\"",
|
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"name": null
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},
|
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"type": null
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}
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]
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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",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": ""
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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-0f79646fcf8a",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "query",
|
||||
"name": null
|
||||
},
|
||||
"type": 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_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "XcH6k6sgVs7b6E"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0f79646fcf8a",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "\":\"",
|
||||
"name": null
|
||||
},
|
||||
"type": 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_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "5JbtLEKFxBMFGe"
|
||||
}
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Loading…
Add table
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