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fix(responses): normalize overlong tool call IDs in chat-to-responses bridge
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agustin18
parent
7db8fc81f9
commit
038b826d95
2 changed files with 207 additions and 4 deletions
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@ -2,6 +2,7 @@
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Handler for transforming /chat/completions api requests to litellm.responses requests
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"""
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import hashlib
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import json
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import os
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from collections.abc import AsyncIterator, Callable, Iterable, Iterator, Mapping, Sequence
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@ -796,6 +797,16 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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# Unknown or unsupported type
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return None, index
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@staticmethod
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def _normalize_tool_call_id(tool_call_id: str | None) -> str | None:
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if tool_call_id is None:
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return None
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if len(tool_call_id) <= 64:
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return tool_call_id
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prefix: Final = tool_call_id[:31]
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digest: Final = hashlib.sha256(tool_call_id.encode("utf-8")).hexdigest()[:32]
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return f"{prefix}_{digest}"
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def convert_chat_completion_messages_to_responses_api(
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self,
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messages: list["AllMessageValues"],
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@ -836,6 +847,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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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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and "id" in tool_call
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)
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leading_system_count: Final = next(
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@ -893,7 +905,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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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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call_id=tool_call_id,
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call_id=(
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self._normalize_tool_call_id(f"{tool_call_id}") or ""
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if tool_call_id is not None
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else ""
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),
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output=content if isinstance(content, str) else tool_output,
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)
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)
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@ -901,7 +917,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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input_items.append(
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FunctionCallOutput(
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type="function_call_output",
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call_id=tool_call_id,
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call_id=(
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self._normalize_tool_call_id(f"{tool_call_id}") if tool_call_id is not None else None
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),
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output=tool_output,
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)
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)
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@ -923,7 +941,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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if function:
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input_tool_call: dict[str, object] = {
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"type": "function_call",
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"call_id": tool_call["id"],
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"call_id": (
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self._normalize_tool_call_id(f"{tool_call['id']}")
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if "id" in tool_call and tool_call["id"] is not None
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else None
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),
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}
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if "name" in function:
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input_tool_call["name"] = function["name"]
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@ -938,7 +960,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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input_items.append(
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ResponseCustomToolCallParam(
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type="custom_tool_call",
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call_id=tool_call["id"],
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call_id=(
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self._normalize_tool_call_id(f"{tool_call['id']}") or ""
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if "id" in tool_call and tool_call["id"] is not None
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else ""
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),
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name=custom.get("name", ""),
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input=custom.get("input", ""),
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)
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@ -6060,3 +6060,180 @@ def test_stream_structured_output_drops_text_after_first_message_item(
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content: Final = "".join(chunk.choices[0].delta.content or "" for chunk in chunks)
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assert content == expected_content
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assert chunks[-1].choices[0].finish_reason == "stop"
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@pytest.mark.parametrize(
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"tool_call_id,is_custom",
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[
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(None, False),
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("call_short_123", False),
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("call_" + "a" * 59, False),
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("call_" + "a" * 60, False),
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(
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"call_aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
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False,
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),
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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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),
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],
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)
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def test_convert_chat_completion_messages_to_responses_api_normalizes_overlong_tool_call_ids(
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tool_call_id: str | None,
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is_custom: bool,
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):
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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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expected_id: Final = (
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None
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if tool_call_id is None
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else tool_call_id
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if len(tool_call_id) <= 64
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else f"{tool_call_id[:31]}_{hashlib.sha256(tool_call_id.encode('utf-8')).hexdigest()[:32]}"
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)
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handler: Final = LiteLLMResponsesTransformationHandler()
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assistant_tool_call: Final[dict[str, object]] = (
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{
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"id": tool_call_id,
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"type": "custom",
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"custom": {"name": "example_custom_tool", "input": "{}"},
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}
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if is_custom
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else {
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"id": tool_call_id,
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"type": "function",
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"function": {"name": "example_tool", "arguments": "{}"},
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}
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)
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messages: Final[list[dict[str, object]]] = [
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{"role": "user", "content": "Run tool"},
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{
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"role": "assistant",
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"tool_calls": [assistant_tool_call],
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},
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{
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": "tool execution result",
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},
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]
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input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages)
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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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)
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call_id: Final = tool_call_item.get("call_id")
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else:
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func_tool_call_item: Final = next(
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item for item in input_items if isinstance(item, dict) and item.get("type") == "function_call"
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)
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call_id = func_tool_call_item.get("call_id")
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assert call_id == expected_id
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if call_id is not None:
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assert len(str(call_id)) <= 64
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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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)
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output_call_id: Final = custom_output_item.get("call_id")
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else:
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func_output_item: Final = next(
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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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output_call_id = func_output_item.get("call_id")
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assert output_call_id == expected_id
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assert output_call_id == call_id
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def test_convert_chat_completion_messages_to_responses_api_overlong_collision_resistance():
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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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id_1: Final = "call_" + "x" * 60 + "_1"
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id_2: Final = "call_" + "x" * 60 + "_2"
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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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{"id": id_1, "type": "function", "function": {"name": "f1", "arguments": "{}"}},
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{"id": id_2, "type": "function", "function": {"name": "f2", "arguments": "{}"}},
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],
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},
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{"role": "tool", "tool_call_id": id_1, "content": "res1"},
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{"role": "tool", "tool_call_id": id_2, "content": "res2"},
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]
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input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages)
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calls: Final = [item for item in input_items if isinstance(item, dict) and item.get("type") == "function_call"]
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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(calls) == 2
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assert len(outputs) == 2
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assert calls[0]["call_id"] != calls[1]["call_id"]
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assert calls[0]["call_id"] == outputs[0].get("call_id")
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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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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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