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fix(responses): prevent custom/function output type collision and strip disallowed comments
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agustin18
parent
cec0fc3e8e
commit
0f6d0b62bf
2 changed files with 49 additions and 26 deletions
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@ -360,14 +360,6 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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@staticmethod
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def _normalize_tool_call_id(tool_call_id: object) -> str | None:
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"""Normalize tool call ID to ensure it does not exceed 64 characters.
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Responses API downstream providers (e.g. OpenAI Responses API, AWS Bedrock)
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enforce a 64-character limit on ``call_id``. IDs within the limit are preserved
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verbatim. Overlong IDs are deterministically mapped to the first 31 characters
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of the ID followed by '_' and a 32-character SHA-256 digest of the full ID
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(31 + 1 + 32 = 64 characters), preserving readability and guaranteeing collision resistance.
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"""
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if tool_call_id is None:
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return None
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tool_call_id_str: Final = str(tool_call_id)
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@ -383,17 +375,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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input_items: Final[list[object]] = []
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instructions: str | None = None
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custom_tool_call_ids: Final = frozenset(
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ident
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tool_call["id"]
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for msg in messages
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if msg.get("role") == "assistant" and isinstance(msg.get("tool_calls"), list)
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for tool_call in msg.get("tool_calls") or ()
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if isinstance(tool_call, dict)
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and not tool_call.get("function")
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and isinstance(tool_call.get("custom"), dict)
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for raw_id in (tool_call.get("id"),)
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if raw_id is not None
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for ident in (raw_id, self._normalize_tool_call_id(raw_id))
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if ident is not None
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and "id" in tool_call
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)
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leading_system_count: Final = next(
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@ -446,7 +435,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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# Fallback: convert unexpected types to input_text
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tool_output = [{"type": "input_text", "text": str(content)}]
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normalized_tool_call_id: Final = self._normalize_tool_call_id(tool_call_id)
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if tool_call_id in custom_tool_call_ids or normalized_tool_call_id in custom_tool_call_ids:
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if tool_call_id in custom_tool_call_ids:
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input_items.append(
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ResponseCustomToolCallOutputParam(
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type="custom_tool_call_output",
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@ -4357,18 +4357,13 @@ def test_map_optional_params_verbosity_merges_into_text():
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@pytest.mark.parametrize(
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"tool_call_id,is_custom",
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[
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# Short tool call ID: stays unchanged
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("call_short_123", False),
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# Exactly 64 characters: boundary case, stays unchanged
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("call_" + "a" * 59, False),
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# Overlong tool call ID (65 characters): normalized to <= 64 chars
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("call_" + "a" * 60, False),
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# Overlong tool call ID from issue #42765 (85 characters): normalized to 64 chars
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(
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"call_aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
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False,
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),
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# Custom tool call with overlong ID: normalized to <= 64 chars
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(
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"custom_tool_call_id_exceeding_the_standard_responses_api_sixty_four_character_length_limit",
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True,
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@ -4379,11 +4374,7 @@ def test_convert_chat_completion_messages_to_responses_api_normalizes_overlong_t
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tool_call_id: str,
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is_custom: bool,
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):
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"""
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Overlong tool call IDs (> 64 chars) must be deterministically normalized to <= 64 characters
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consistently across assistant tool_calls and matching tool result messages,
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while IDs <= 64 chars must be preserved unchanged.
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"""
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"""Overlong tool call IDs (> 64 chars) must be deterministically normalized to <= 64 characters."""
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import hashlib
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from litellm.completion_extras.litellm_responses_transformation.transformation import (
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LiteLLMResponsesTransformationHandler,
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@ -4425,7 +4416,6 @@ def test_convert_chat_completion_messages_to_responses_api_normalizes_overlong_t
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input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages)
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# Validate tool call item
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if is_custom:
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tool_call_item: Final = next(
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item for item in input_items if isinstance(item, dict) and item.get("type") == "custom_tool_call"
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@ -4440,7 +4430,6 @@ def test_convert_chat_completion_messages_to_responses_api_normalizes_overlong_t
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assert call_id == expected_id
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assert len(str(call_id)) <= 64
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# Validate matching tool output item
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if is_custom:
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custom_output_item: Final = next(
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item for item in input_items if isinstance(item, dict) and item.get("type") == "custom_tool_call_output"
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@ -4491,3 +4480,48 @@ def test_convert_chat_completion_messages_to_responses_api_overlong_collision_re
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assert calls[1]["call_id"] == outputs[1].get("call_id")
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assert len(str(calls[0]["call_id"])) <= 64
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assert len(str(calls[1]["call_id"])) <= 64
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def test_convert_chat_completion_messages_to_responses_api_mixed_custom_and_function_output_types():
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"""Mixed custom and function tool call outputs must maintain respective types without collision."""
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import hashlib
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from litellm.completion_extras.litellm_responses_transformation.transformation import (
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LiteLLMResponsesTransformationHandler,
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)
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handler: Final = LiteLLMResponsesTransformationHandler()
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custom_raw_id: Final = "custom_call_" + "y" * 60
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function_raw_id: Final = f"{custom_raw_id[:31]}_{hashlib.sha256(custom_raw_id.encode('utf-8')).hexdigest()[:32]}"
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messages: Final[list[dict[str, object]]] = [
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{
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"role": "assistant",
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"tool_calls": [
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{
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"id": custom_raw_id,
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"type": "custom",
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"custom": {"name": "c_tool", "input": "{}"},
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},
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{
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"id": function_raw_id,
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"type": "function",
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"function": {"name": "f_tool", "arguments": "{}"},
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},
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],
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},
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{"role": "tool", "tool_call_id": custom_raw_id, "content": "custom_res"},
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{"role": "tool", "tool_call_id": function_raw_id, "content": "func_res"},
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]
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input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages)
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custom_outputs: Final = [
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item for item in input_items if isinstance(item, dict) and item.get("type") == "custom_tool_call_output"
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]
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function_outputs: Final = [
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item for item in input_items if isinstance(item, dict) and item.get("type") == "function_call_output"
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]
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assert len(custom_outputs) == 1
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assert len(function_outputs) == 1
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assert custom_outputs[0].get("call_id") == function_raw_id
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assert function_outputs[0].get("call_id") == function_raw_id
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